Abstract

RadiologicalTechnology

SCHEDULE
Session 1 : 11 : 00 ~ 12 : 00
Session chair: Prof. Yoshie Kodera
1. Effect of reconstruction filter on image in dental cone beam computed tomography
Yuuki Houno
2. Characteristics of image in dual-energy computed tomography(CT) imaging
Dae Hong Kim
3. Verification of lateral biological dose distribution with microdosimetric kinetic modelin carbon-ion radiotherapy
Mikoto Tamura
4. The imparct of secondary particles on in-beam PET imaging for dose verification incarbon ion therapy
Su Jung An

Session 2 : 13 : 30 ~Session chair: Prof. Hee Joung Kim
5. Evaluation of radiation dose to organs and tissues in infant X-ray CT
Takanori Ishii
6. Digital breast tomosynthesis and digital mammography- A comparison of figures ofmerit for various average glandular doses
Ye Sle Kim
7. Reduction of patient dose in digital mammography: Simulation of low-dose image
from routine-dose
Yuki Saito- 15 minutes rest –Session chair: Prof. Haruo Isoda
8. Optimization of the angle between detector modules in a dual-head cardiac SPECT
Woo Jin Jo
9. Accuracy of 3D cine phase contrast MR flow measurement for common carotid
arteries
Ikki Tozawa
10. A high resolution SPECT system with a photon counting detector: Monte Carlosimulation studies
Young Jin Lee

EFFECT OF RECONSTRUCTION FILTER ON IMAGE IN DENTAL CONE BEAMCOMPUTED TOMOGRAPHY
Y. Houno1), T. Hishikawa2), K. Goto3), Y. Kodera1)
1) Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
2) Department of Periodontology, School of Dentistry, Aichi Gakuin University, Nagoya, Japan
3) Department of Radiological Technology, Aichi Gakuin University Dental Hospital, Nagoya, Japan

Introduction
Recently, dentistry diagnoses have been performed using
panoramic radiography or dental X-rays, but cone-beam CT(CBCT), which can obtain more information, has been
gaining attention. CBCT has high spatial resolution, but it isdifficult to describe that teeth and alveolar bones separately
because the reconstructed image is blurred. It is important toknow the condition of alveolar bones in diagnosis ofperiodontal disease or simulation of implant placement.
In CBCT, spatial resolution is influenced by machine
characteristics such as detector spacing, focal spot size, andoverall geometry. In addition, reconstruction parameters suchas the voxel size and reconstruction filter influence thespatial resolution. However, users cannot change the machine
characteristics. Thus, we examined the use of areconstruction filter to provide a sharp image.
Materials and Methods
1. Exposure of phantom
We exposed a head, copper wire (0.28 mm diameter), andwater phantoms with the Alphard “3030,” which is a conebeam CT manufactured by Asahi Rentgen (Fig.1). The
exposure conditions are shown in table 1. The Alphard“3030” has four exposure modes for CT, and each mode has
a different field of view [D-mode: 51 mm × 51 mm (voxel
size: 0.1 mm); I-mode: 102 mm × 102 mm (voxel size: 0.2mm); P-mode: 153 mm × 153 mm (voxel size: 0.3 mm); Cmode: 200 mm × 179 mm (voxel size: 0.39 mm)]. In thisstudy, we selected the I-mode to obtain raw data andreconstructed two dimensional (2D) images.
We then created a three dimensional (3D) image from thereconstructed 2D images in INTAGE Realia, which is free
software and a DICOM 3D viewer.

Table 1 Exposure conditions
Parameter
Tube voltage
Tube current
Exposed time
Focus - rotation center distanceSource - image distanceMatrix of flat panel detector
Pixel pitch
Focus size
Number of view
Cone angle (Head side)
Cone angle (Foot side)

Value
80 kV
5 mA
17 sec
650 mm1010 mm
768×768
0.39 mm
0.6 mm510
4.2 degree
5.9 degree

2. Reconstruction in OSCaRFirst, we reconstructed the raw data with OSCaR[1] (opensource cone-beam reconstructor), which is a software package
written in Matlab code and can be used to easily change areconstruction filter. This software uses the FDK (Feldkamp)
algorithm, which is one of the most widely referenced
algorithms and a popular approximation for CBCT
reconstruction. This algorithm is based on the filtered backprojection method.
We could not load OSCaR with the raw data at the originalsize, so we trimmed the image by imageJ and reconstructed itwith a new reconstruction filter developed in this study. Thisfilter is shown in Fig.2 as a New Filter.
We then created a 3D image, as mentioned in previous
section.

Fig.1 Experimental system

Fig.2 Reconstruction filter

3. Measurement of sharpness and granularity
For the wire and water phantom image, we calculated themodulation transfer function (MTF) and Wiener spectrum(WS)[2]. The MTF, which is calculated from the point spreadfunction, is the established method for characterizing the
spatial response of an image. In this study, we used a wire
method (Fig.3). The WS is a valuable tool for assessing thenoise power of an image in the spatial frequency domain andis a useful index. In this study, we calculated it by virtual slit
method (Fig.4).
We calculated images reconstructed both by Alphard“3030” and by our method.

Fig.7 MTF

Fig.3 Wire method

Fig.4 Virtual slit method

Results and Discussion
1. Comparison of 2D images
Figure 5a shows the image reconstructed by Alphard“3030,” and Fig.5b shows by our method. In 2D, the image
reconstructed with our method clearly shows the pulp cavity
and structure of alveolar bones, but it is slightly little noisy.

Fig. 5 Comparison of 2D images

Fig.8 WS

2. Comparison of 3D images
Figure 6a shows the image reconstructed by Alphard“3030,” and Fig.6b shows by our method. In 3D, the image
reconstructed with our method shows a gritty surface, but it
can describe the area between the teeth.

Conclusions
We were able to obtain a sharp image in this study anddetermined that the reconstruction filter and the image
sharpness and granularity are strongly related. However, ahigher spatial resolution leads to an increased noise level.
In the future, we will develop a method that will maintainthe sharpness and yet be noiseless. Furthermore, we will try to
reduce metal artifacts and have our method evaluated bydentists.

Fig. 6 Comparison of 3D images
3. Measurement of sharpness and granularity
The MTF and WS measurement results are shown in
Figs.7 and 8. Our method improved the image sharpness but
also increased the image noise. We thought that because weused a reconstruction filter which emphasized high frequency.
These results fit with a comparison of the 2D/3D images.

References[1]Rezvani N, et al. (2010) An Open Source ConeBeam CT
Reconstruction Tool for Imaging Research. American
Association of Physicists in Medicine[2]Ichikawa K, Muramatsu Y. (2009) Measurement of X-ray
CT image. Ohmsha: 29-118
Author address

E-Mail: houno.yuuki@f.mbox.nagoya-u.ac.jp

CHARACTERISTICS OF IMAGE IN D UAL-ENERGY COMPUTED TOMOGRAPHY(CT)IMAGING
D.H. Kim1), H. J. Kim1), C.L. Lee1), P.H. Jeon1), Y.J. Lee1), Y.S. Kim1)
1) Department of Radiological Sciences, Yonsei University, Wonju, Korea
Introduction
Dual-energy computed tomography (DECT) is
commonly used to obtain information such as tissue
segmentation, plaque segmentation in angiography, and lungperfusion [1, 2]. As the dual-energy (DE) scan includes lowand high-energy images, the image quality and the dose
dependence of the phantom size need to be compared tothose in single-energy (SE) image. Furthermore, DE images
must optimize the image quality within recommended dose
limitations. Hence, The purpose of this study was to estimate
the image quality of head and abdomen phantoms with SE
and DE mixed methods. The results from SE and DE CT
scans were compared under routine clinical exposure
conditions. The scan protocols for tube current such as mA
was alternated withing the recommended limited dose levels
while the other scan protocols were fixed in SE and DE scans.
The dose ranges were extended within the recommended
dose levels for the head and the abdomen phantom.
Materials and Methods
Data Acquisition: The image quality and the radiationdose of SE and DE scans in linearly mixed method were
evaluated for head and abdomen phantoms. The equation
reported by Yu et al. for obtaining the leneraly-mixed image
is as follows:
I  wL I L  wH I H
where IL and IH denote the low- and high-energy scan images,
respectively, wL and wH are the weighting factors for the lowand high-energy scan images. wL + wH = 1. wL is given by
CL ( H2 , s   H2 , b )wL CL ( H2 , s   H2 , b )  CH ( L2, s   L2, b )where CL and CH are the contrasts between the signal and thenoise for low- and high-energy cans, σL,s and σL,b are thenoised expressed as standard deviations at the signal and thebackground images in low-energy scans, and σH,s and σH,b are
the noises at the signal and the background in high-energyscans, respectively.
With head and abdomen phantom, we used x-ray tube
voltages of 80 and 140 kV to obtain results similar to thoseof DE CT scans. The SE scans, typically 120 kV, for thehead and the abdomen phantoms were performed with scan
protocols based on the International ElectrotechnicalCommission (IEC).
CTDIw: The CTDIw values of SE and DE scan were
measured for head (16 cm diameters) and abdomen (32 cm
diameter) Polymethylmethacrylate (PMMA) phantom. The
CTDIw can be obtained from the CTDI100. The CTDI100 was
measured using bothe head and abdomen phantoms with nine
holes and a pencil ion chamber for the CT (Radical
Corporation, 20X6-3CT, CA, USA). The values of CTDI100
were calculated with the following equation:
CTDI 100 M  Chamberlen gth (cm)  F  tp  cf
n T

where M is the measured value in Roentgen, F is the
exposure-to-dose converstion factor (0.78 cGy/R in acryl), tp
is the correction factor for both temperature and pressure, cf
is the chamber calibration factor, n is the number of slices,
and T is the slice thickness (cm). The dose measurements

were estimated at the center and four peripheral locations (3o’clock, 6 o’clock, 9 o’clock, 12 o’clock). The CTDIw can beobtained by using a weighted CTDI100 from the center andprepheral locations and the following the equation:
1
2
CTDI w  CTDI C  CTDI P
3
3

where CTDIc is the CTDI100 measured in the center, and CTDIp
is the mean of the CTDI100 values measured at the 3 o’clock, 6o’clock, 9 o’clock, and 12 o’clock positions.
Image Evaluation: We chose the image quality metric as thecontrast-to-noise ratio (CNR) for the quantitative method for thehead and the abdomen phantoms. The CNR is calculated as
CNR | S s  Sb |

 s2   b2
where Ss and Sb are the signal intensities of the signal andbackground, respectively, and σs and σb are the noise values forthe signal and background in Region of Interset (ROI).

Results
Estimation of CNR and CTDIw in SE and DE scans: The
CTDIw
was
withinEuropeanCommission(EC)
recommendataion, 60 mGy for Head and 35 mGy for abdomen,
respectively (Table 1).
Table 1 Diagnostic reference levels for CT examination of
adults (European Commission, 1999)
Diagnostic reference level
Examination
CTDIw (mGy)
DLP (mGy cm)
Head
60
1050
Abdomen
35
780
The maximum values of the CNR for SE and DE scans
were 115.50 and 133.45 in similar dose regions for the headphantom, respectively in this work. The minimum values of
CNR for SE and DE scans were 107.53 and 119.49, respectively.
In the abdomen phantom anlysis, the maximum values of the
CNR SE and DE scans were 41.83 and 54.58 in similar dose
regions, respectively. The minimum values of the CNR values
for SE and DE scans were 39.73 and 50.44, respectively.

Figure 1. Comparisons of CNR values in head phantoms between
single-energy (SE) and dual-energy (DE) scans at one-half theradiation dose of SE scans.

One-half of the doses for SE scans were chosen for DE
scans to validate dose reduction, as described in Fig. 1 and 2. In
the SE scnas for the head phantom, the CNR values were
107.53, 123,58, and 129.26 at 32.85 mGy, 47.98 mGy, and56.25 mGy, respectively. And the CNR values for the DE scans
were 92.48, 108.70, and 119.49 at 16.05 mGy, 23.92 mGy, and27.80 mGy, respectively. In the abdomen image evaluation, theCNR values for the SE scans were 41.32, 41.78, 44.80, and44.47 at 17.32 mGy, 20.65 mGy, 25.67 mGy, and 28.85 mGy,
respectively. The CNR values for DE scans were 36.93,
42.83, 46.81, and 50.44 at 8.36 mGy, 10.48 mGy, 12.67 mGy,and 14.41 mGy, respectively.

Figure 2. Comparisons of CNR values in abdomen phantoms
between SE and DE scans at one-half the radiation dose of SE
scans.

Figures 3 and 4 show the dose evaluations between SE
and DE scans at similar CNRs. The doses between SE andDE scnas were evaluated when the image qualities weresimilar. With the head phantom, the doses in the SE scans
were 32.85, 38.70, and 54.19 mGy at CNR values of 107.53,
120.98, and 126.82, respectively. The doses in the DE scans
were 23.92, 27.80, and 32.05 mGy at CNR values of 108.70,
119.49, and 127.42, respectively. For the abdomen phantom,
the doses were 19.37, 29.39, and 31.94 mGy at CNR valuesof 41.83, 47.59, and 49.34 for SE scans, and the dose for DE
scnas were 10.48, 12.67, and 14.41 mGy at CNR values of
42.83, 46.81, and 50.44, respectively.

Figure 3. The dose evaluations at similar dose points for SE andDE scans in head phantom.

42.83, and the radiation doses were 19.37 and 10.48 mGy,
respectively.
Discussion
In this study, CNR values heavily depended on the phantom
size. The CNR values for the DE scans were higher than these
for the SE scans for both head and abdomen phantoms insimilar dose regions. The CNR improvement of DE images was
remarkable, with maxima of 18.50 and 30.48 % for the head andthe abdomen phantoms, respectively, relative to SE images.
These results contributed to the dose reduction observed using
DE CT.
When comparing the dose reduction effect, Figures 1 and 2
demonstrate that about one-half the dose could be used for thehead and the abdomen regions when applying DE scans. The SE
scan was outperformed in terms of CNR values for headphantoms at every dose comparison. The CNR values were
16.27, 13.69, and 8.18 % higher for head phantoms when using
SE scans. The CNR results for the head phantom indicate thatthe dose reduction effect was approximately 50% for similar
CNR values. The CNR difference between SE and DE scansdecreased when the current was increased. Relative to the SE
scan, the DE demonstrated higher CNR values than the SE scan,
except for one point for the abdomen phantoms at one-half thedose. As a point of comparison between the 32.85 mGy (SE)and the 16.05 mGy (DE) scans, the CNR of the SE scan was
11.89% higher whereas the CNR values for DE scans were 2.45,
4.29, and 11.84% higher. For the abdomen study, the CNRvalues for SE scans using abdomen phantoms were 10.62 %higher than those of DE scans at 17.32 vs. 8.36 at one-half theradiation dose of the SE scans. With increasing radiation dose,
the CNR values of the DE scan increased from 2.51% to13.42%. Thus, a DE scan of the abdomen can be used with onehalf of the radiation dose for a similar SE scan.
In Figures 3 and 4, the dose evaluations at similar CNR
points are presented using SE and DE for head and abdomen
phantoms. Greater dose increases were required for single
energy CTs to obtain CNRs similar to the DE CTs for both head
and abdomen phantoms. These results also included the dose
reduction aspect of DE CT.
We estimated the image quality and dose between SE andDE scans in routine exposure conditions seen in clinical practice.
Figures 3 and 4 show the evaluation for two scans in which thedose was reduced up to 40.86 and 45.90% for the head and theabdomen phantoms, respectively.
Conclusions
The current study suggests that DE scans can contribute to a
reduced radiation dose while maintaining image quality byusing the only current alternation. The DE scan achieved ahigher CNR with one-half the radiation dose of a single-energy
scna of the abdomen.

Figure 4. The dose estimations at similar dose regions for SE andDE scans in abdomen phantom.

Estimation of CNR and CTDIw in clinical radiation
exposure conditions: In SE scans, the routine radiation
exposure condition for the current on the head ranged fromapproximately 300 to 360 mAs at Yonsei University Wonju
Christian Hospital. The CNR value was 126.82 for this rangeof currents for SE scans, The CNR of 127.42 in DE CT scans
is comparable to the CNR value for the SE scans for headphantoms; the associated doses for SE and DE scans were
54.19 and 32.05 mGy, respectively. The current for theabdomen imaging ranged from 250 to 300 mAs. The CNRvlaues for SE and DE scans within this range were 41.83 andReferences[1]Bazalova M, Carrier JF, Beaulieu L, Verhaegen F. Tissue
segmentation in Monte Carlo treatment planning: asimulation study using dual-energy CT images. Radiotherand Oncol. 2008;86(1):93-98.
[2]Boll DT, Merkle EM, Paulson EK. Mirza RA, Fleiter TR.
Calcified vascular plaque specimens: assessment withcardiacdual-energy
multidetectorCTinanthropomorphically moving heart phantom. Radiol.
2008;249:119-126.
Author address
E-Mail: goldcollar011@yonsei.ac.kr

VERIFICATION OF LATERALATERAL BIOLOGICAL DOSE DISTRIBUTIONISTRIBUTION WITH
MICRODOSIMETRIC KINETICK
MODEL IN CARBON-ION RADIOTHERAPYADIOTHERAPY
M.Tamura1), Y.Iwamoto1), T.Rachi1), K.Ota2), A.Hemmi3), T.Shimozato4), Y.Obata5), H.Oguchi1), M.Komori1)
1) Department of Radiological Sciencies,
Sciencies Graduate School of Medicine,, Nagoya University, Nagoya
Nagoya, Japan
2) Okazaki City Hospital,
Hospital Okazaki, Japan, 3) Komaki City Hospital, Komaki, Japan4) Iwata City Hospital, Iwata, Japan, 5) Ichinomiya Municipal Hospital, Ichinomiya, Japan

Results
Figure 2 shows the comparison of the calculated results withthe measurements about the depth absorbed dose distribution
and the depth RBE10 distribution where the field size wascontrolled to 10 cm × 10 cm with MLC.
2.5

1.5

(a)

Measurement
Geant4

1.0

0.0

0.0
0

20

40

60

80

100

120

Measurement (TEPC)
Geant4 (Small TEPC)

0.5

140

160

180

0

200

20

40

60

80

100

120

140

160

180

200

Depth [mm]Depth [mm]Fig. 2 The depth absorbed dose distribution (a) and thedepth RBE10 distribution (b) by measurement and Monte
Carlo calculation with Geant4.
Figure 3 shows the comparison
mparison of the calculations with themeasurements about the lateral absorbed dose distributions
distributions. In
Figure 2 and 3, those
hose data show the Geant4 calculations weregood agreement with the measurements
measurements, which indicated that
the Geant4 calculations reproduced the experimental conditions.
Relative absorbed dose

Relative absorbed dose

1.2

(a)

1.0
0.8
0.6
0.4

Measurement
Geant4

0.2
0.0
0

10

20

30

40

50

60

70

Relative absorbed dose

(c)

1.0
0.8
0.6
0.4

Measurement
Geant4

0.2
0.0
0

10

20

30

40

50

Off-axis distance [mm]

(b)

1.0
0.8
0.6
0.4

Measurement
Geant4

0.2
0.0
0

10

20

30

40

50

60

70

Off-axis distance [mm]Off-axis distance [mm]1.2

The RBE was calculated with the lineal energy spectra
measured using tissue equivarent proportionalroportional counter (TEPC).
The sensitive volume of the commercially available TEPC
(LET-1/2,
1/2, Far West Technology, Goleta CA, USA) was asphere (diameter: 12.7 mm) filled with a propane-based
propanetissueequivalent gas (54.6 % C3H8, 40.16 % CO2, and 5.26 % N2 inmass), which was covered with
ith a spherical wall of A-150
Atissue-equivalentequivalent plastic (thickness: 1.27 mm). The gaspressure was 4.4 kPa to simulate the energy imparted to aspherical tissue of 1.0 μm diameter. However, the diameter of
the TEPC is too large as clinical dosimeter. Therefore, the

1.5

0.5

1.2

Fig. 1 Schematic view of the horizontal beam port of
HIMAC at NIRS for carbon-ion
ion radiotherapy.

(b)

2.0

1.0

RBE10

Materials and Methods
The Monte Carlo calculation code of Geant4 simulated ahorizontal port at the Heavy Ion Medical Accelerator in Chiba(HIMAC) of National Institute of Radiological Sciences(NIRS) in Japan. Figure 1 shows the schematic view of thebeam port. The projectile particle was 12C with a kinetic
energy of 290 MeV/n, and the 60 mm SOBP was formed by abar ridge filter made of aluminum. The field size of the broad
beam was formed into 10 cmφ using wobbler method and thiswas controlled to each 10 cm × 10 cm and 5 cm × 5 cm with amultileaf collimator (MLC).

size of the small TEPC which was simulated in Geant4 was ahalf of TEPC (diameter: 6.35 mm) in order to improve position
resolution. The tissue-equivalentequivalent gas pressure was 8.8 kPa
which was double gas pressure of TEPC to simulate a sphericaltissue of 1.0 μm.
m. The wall thickness of the small TEPC was
0.914 mm because the effective wall thickness of the small
TEPC was equall to that of the TEPC. The lineal energy spectraof the lateral direction for the beam line were simulated at threedepths of the proximal (depth = 87 mm)mm), middle (depth = 117mm), and distal (depth = 143 mm) parts of SOBP, respectly.
The lateral RBE10 distributions were calculated with themodified MKM using the simulated spectra. The lateral
biological dose distributions were calculated from those lateral
RBE distributions and the lateral absorbed dose distributions.
The statistical errors of calculation
calculations were within 3 %.

Relative absorbed dose

Introduction
In a carbon-ion
ion radiotherapy, it is essential to evaluate not
only the absorbed dose but also the biological dose because
relation between the survival curve and the dose changes
drastically due to the kinetic energy of particles. The
biological dose is defined
fined as the absorbed dose multiplied by
the relative biological effectiveness (RBE). The
microdosimetric kinetic model
odel (MKM) was useful for
calculating the survival curves of human salivary gland (HSG)
tumor cells in response to treatment with mono-energetic
mono
heavy-ionion beams. The modified MKM which considered overover
kill effect was shown to be calculate the RBE even mixedmixed
beam radiation such as spread-out Bragg peakeak (SOBP)(
carbonion beam without particle identification by Y. Kase et al.
al[1][2]. The biologicall depth dose distribution calculated by themodified MKM has been known well, however, there are notsufficient reports about the lateral biological dose distribution.
Furthermore, the lateral RBE distributions were regarded as
constant in the carbon-ion
ion treatment planning system.
system The
purpose of this study is verification of the lateral biologicaldose distribution using modified MKM.

60

70

Fig
Fig. 3 The results of the lateralabsorbed dose distributions at
the proximal (a)(a), the middle(b), and the distal (c) parts of
SOBP by measurement andcalculation with Geant4 where
the field size was controlled to
10 cm × 10 cm with MLC.

Figure 4 and 5 show the lateral biological dose
distributions and the lateral RBE10 distributions at theproximal, middle, and distal parts of SOBP, where the fieldsize were controlled to 10 cm × 10 cm and 5 cm × 5 cm with
MLC respectively.
In Figure 4, the lateral RBE10 distributions were almost
constant in the flattened area, and built down in the penumbra
region. Also, the flattened area of the lateral RBE10
distribution was decreasing with depth increasing. Therefore,
the lateral biological dose distributions were similar to thelateral absorbed dose distributions in the flattened area
however the lateral biological dose distributions were smaller
than the lateral absorbed dose distributions in the penumbra
region.
In Figure 5, the lateral RBE10 distributions where the fieldsize was controlled to 5 cm × 5 cm with MLC were same withthe one where the field size was controlled to 10 cm × 10 cm
within the error range. The lateral biological dose distributions
were also similar to the one where the field size of 10 cm × 10
cm.
1.2

0.8
0.6
0.4

Absorbed Dose
Biological Dose

0.2

(b)

1.0

Relative dose

Relative dose

1.2

(a)

1.0

0.8
0.6
0.4

Absorbed Dose
Biological Dose

0.2

0.0

0.0
0

10

20

30

40

50

60

70

0

10

Off-axis distance [mm]

(c)

1.0

30

40

50

60

70

(d)

2.4
2.2
2.0

0.8

RBE10

Relative dose20

Off-axis distance [mm]

1.2

0.6
0.4

Absorbed Dose
Biological Dose

0.2

10

20

30

40

1.6
1.4

87 mm
117 mm
143 mm

1.2
1.0

0.0
0

1.8

50

60

0.8

70

0

Off-axis distance [mm]

10

20

30

40

50

60

70

Off-axis distance [mm]Fig. 4 The results of the lateral absorbed dose andbiological dose distributions at the proximal (a), the middle(b), and the distal (c) parts of SOBP and the lateral RBE
distributions at three depths of SOBP (d) where the fieldsize was controlled to 10 cm × 10 cm with MLC. The
vertical axis of absorbed dose and biological dose wasnormalized with the value at the beam line (Off-Axis
Distance = 0 mm).
1.2

1.2

(a)

0.8
0.6
0.4

Absorbed Dose
Biological Dose

0.2

(b)

1.0

Relative dose

Relative dose

1.0

0.8
0.6
0.4

Absorbed Dose
Biological Dose

0.2

0.0

0.0
0

10

20

30

40

0

Off-axis distance [mm]
1.2

30

2.6

40

(d)

2.4
2.2
2.0

0.8

RBE10

Relative dose20

Off-axis distance [mm]

(c)

1.0

10

0.6

1.8
1.6
1.4

0.4

Absorbed Dose
Biological Dose

0.2

87 mm
117 mm
143 mm

1.2
1.0
0.8

0.0
0

10

20

30

Off-axis distance [mm]

40

0

10

20

30

40

Off-axis distance [mm]Fig. 5 The results of the lateral absorbed dose andbiological dose distributions at the proximal (a), the middle(b), and the distal (c) parts of SOBP and the lateral RBE
distributions at three depths of SOBP (d) where the fieldsize was controlled to 5 cm × 5 cm with MLC. The verticalaxis of absorbed dose and biological dose was normalizedwith the value at the beam line (Off-Axis Distance = 0 mm).

Discussion
The lateral RBE10 distributions were different for each depth
because of the contributions of the energy deposit for the
primary particles and the fragment particles. The contributionsof the energy deposit for primary particles were investigatedconsidering multiple coulomb scattering. Because the carbonions stopped at the proximal part of SOBP penetrate thick partof the bar ridge filter, those particles have large angle scattering
at upstream. On the other hand, the carbon-ions stopped at the
distal of the SOBP penetrate thin part of the bar ridge filter,
those particles rectilinear the irradiation field approximately.
Moreover, the fragment particles from the upstream scatteringsuch as hydrogen, helium, lithium, beryllium, and boron, havelarge angle, in the penumbra region and outside of theirradiation field, the contributions of the energy deposit for
those particles were larger than in the irradiation field [3]. That
tendency was more notable in the distal part of SOBP.
Therefore, the lateral biological dose distributions at theproximal part of SOBP were similar to the lateral absorbed dose
distributions. The lateral biological dose distributions at the
distal part of SOBP were sharper than the lateral absorbed dose
distribution.
The RBE10 at the beam line where the field size wascontrolled to 5 cm × 5 cm with MLC was larger than the one
where the field size was controlled to 10 cm × 10 cm. It wasconsidered that where the field size was large, more fragment
particles from off-center region were injected into the beam line.
Because they have relatively small lineal energy, the RBE10
decreased. That field size dependencies of radiation quality atthe beam line were almost same as the results reported by H.
Nose et al. [2]. However, the lateral RBE10 distributions were
almost same according to the field size. It was considered thatthe species of the injected particles had the same ratio of thelateral distributions relatively to the particles injected into thebeam line. Moreover, the lateral biological dose distributions
were different for each field size. It was considered that thelateral RBE10 distributions were almost same for each field size
however the lateral absorbed dose distributions were different.
Conclusions
The lateral biological dose distributions were similar to thelateral absorbed dose distributions in the flattened area.
However, in the penumbra region, the lateral biological dose
distributions were sharper than the lateral absorbed distributions.
Furthermore, that was more notable with depth increasing.
Therefore, the field sizes of the biological dose were smaller
than the one of the absorbed dose with depth increasing. If thebiological effect wasn’t taken into account, the lateral dose
distributions based on the absorbed dose were overestimated inpenumbra region.
References[1]Kase Y, Kanai T, Sakama M, Himukai Y, Nose H, Matsufuji
N. Microdisimetric Approach to NIRS-defined Biological
Dose Measurement for Carbon-ion Treatment Beam. J.
Radiat. Res. 2011;52:59-68.
[2]Nose H, Kase Y, Matsufuji N, Kanai T. Field size effect ofradiation quality in carbon therapy using passive method.
Med. Phys. 2009;36 (3):870-875.
[3]Kusano Y, Kanai T, Kase Y, Matsufuji N, Komori M,
Kanematsu N, Ito A, Uchida H. Dose contributions fromlarge-angle scattered particles in therapeutic carbon beams
Med. Phys. 2007;34 (1): 193-198.
Author address

E-Mail: tamura.mikoto@a.mbox.nagoya-u.ac.jp

THE IMPARCT OF SECONDARY PARTICLES ON IN-BEAM PET IMAGING FOR DOSEVERIFICATION IN CARBON ION THERAPYSu Jung An1),2), Yong Hyun Chung1),2)
1) Department of Radiological Science, College of Health Science, Yonsei University, Wonju, Republic of Korea
2) Institute of Health Science, Yonsei University, Wonju, Republic of Korea

Introduction
We are developing the C-shaped in-beam PET scanner fordose verification of the carbon therapy. The C-shaped
scanner has a ring diameter of 30 cm and an axial field ofview of 10 cm for brain imaging. In this design, two detector
modules on the incident beam side are removed to avoid
blockage of the carbon beam by the detector. During the inbeam PET monitoring of carbon beam delivered by the heavy
ion synchrotron, typically, the in-spill data are discarded
from the image reconstruction to prevent the degradation ofimage quality induced by the secondary particles and prompt
gamma-rays.
In order to determine the application possibility of the use ofdata acquired during the beam extraction for PET imaging,
we simulated the spatial distributions and yields of thesecondary particles and prompt gamma-rays using GATE,
and analyzed their influence on the C-shaped PET
performances.
Materials and Methods
We simulated carbon beam irradiation on a PMMA
(C5H8O2) phantom with 20 cm diameter and 20 cm heightusing GATE Version 6.1. The carbon beam was delivered
horizontally to the phantom, as shown in Fig. 1. 170 AMeV
mono-energetic carbon beam was selected and the intensityof the carbon beam was 1 × 108 particles per second (pps).

The C-shaped scanner consists of 14 detector modules, andeach module is composed of an LYSO array of 13 × 13
elements with a pixel size of 4 × 4 × 20 mm3 and four round
PMTs 25.4 mm in diameter. (Fig. 2)

Figure. 2. The simulated PET scanner configuration inwhich the scanner consists of 14 detector modules.
The PET images were obtained based on these assumptions.
The carbon beam has a tunable time structure given
by a repetition of 2 seconds spill length and 3 seconds pause.
Total therapy time is 2 minutes.
The PET images are acquired during the total therapy
time.
The relative the number of coincidences is calculated.

Figure 1. The simulation of carbon beam irradiation

Results and conclusion
The spatial emission distributions and yields of thesecondary particles and prompt gamma were calculated. Fig. 3
shows the simulated flux of secondary particles and prompt
gamma for each virtual plane. The major secondary particles
produced during carbon beam treatment are neutrons, protons,
deuterons and tritons and the dominating contribution to total
yield is from neutrons. The secondary particles are mostly
emitted toward the direction of the incident carbon beam while
the prompt gamma-rays are emitted in all directions with auniform distribution.

To estimate the spatial emission distributions and yieldsof the secondary particles and prompt gamma, the five virtual
planes which store the phase-space files were positioned at30.1 cm away from the center of the phantom (Fig. 1). The
size of virtual plane is 6 × 6 cm2, and the spacing between
planes is 22.5 degree.
To analyze the influence of secondary particles andprompt gamma on PET coincidence image, we obtained the
PET images using C-shaped in-beam PET scanner during
beam pause and beam extraction, respectively. The simulated
C-shaped PET scanner has a ring diameter of 30.2 cm and anaxial field of view of 5 cm for brain imaging. To avoid
blockage of the carbon beam by the detector modules, thescanner removed two modules on the incident beam side.

Figure 3. The flux of secondary particles and prompt
gamma for each virtual plane.

Table 1. The relative number of coincidences
Effecting radiation

Coincidence

Positron emitter only

100

Positron emitter + neutron

74.51

Positron emitter + deuteron

92.62

Positron emitter + triton

94.42

Positron emitter + gamma

85.53

Table. 1 illustrates the relative number of coincidences
showing the influence of the secondary particles and prompt
gamma on PET coincidence count during beam extraction.
The sensitivity acquired during beam extraction was
significantly lower than expected due to random and multiple
coincidences by neutrons. To improve image quality byminimizing the effect of neutron, we suggested the use of apartial-ring scanner during beam extraction in which two
detector modules on the beam line are inactivated from Cshaped scanner. By using the partial-ring data acquisition
during beam extraction, the sensitivity and image quality are
significantly improved. This paper provides the basis for a
new data acquisition concept to utilize the C-shaped scanner
during the beam pause and the partial-ring scanner during thebeam extraction for improvement of image quality.
Author address

E-Mail: crystal8374@yonsei.ac.kr

EVALUATION OF RADIATION DOSE
TO ORGANS AND TISSUES IN INFANT X-RAY CT
T. Ishii1), T. Haba1), S. Kondo1), D. Hayashi1), H. Numamoto1), T. Aoyama1), S. Koyama1)
1) Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan

Introduction
X-ray Computed Tomography (CT) examination is avaluable diagnostic technique, and the immediate benefit to
the individual patient can be substantial. Evaluations ofradiation dose to patients have been absolutely important,because the relatively high radiation doses from x-ray CT
compared with other plain radiography have raised healthconcerns [1]. In particular for infant and children who are
more sensitive to radiation than adult, there is the potential
increase in future cancer risks from radiation exposure. In
this study, the objective was to evaluate absorbed dose fororgans of infant undergoing x-ray CT scans by using Monte
Carlo simulation. Additionally, organ doses for infant in xray CT were measured by in-phantom dosimetry system [2],and compared with the doses calculated by the Monte Carlosimulation.
Materials and Methods
1. Comparison of doses between measurement andsimulation
In a whole body X-ray CT scan, organ doses measured in
an infant anthropomorphic phantom were compared withorgan doses calculated by using Monte Carlo simulation. The
Monte Carlo simulation code used for the calculation was
Electron Gamma Shower Ver.5 (EGS5). Dose measurements
were performed with an in-phantom dosimetry system andnon-helical X-ray CT unit TCT-300 (Toshiba Medical
Systems, Tochigi, JAPAN). The in-phantom dosimetry
system was comprised of an infant anthropomorphic
phantom ATOM 703(CIRS, Norfolk, VA) composed of lung,bone, and soft tissue, photodiode sensors, and a data analysis
program. Photodiode sensor devices were implanted in thecentroid of each organs and tissues. Organ and tissue doses
were obtained from calculations by the analysis program
based on output voltage resulting from the photodiodesensors. The number of the photodiode sensor device is 24,
and Fig.1 shows correspondence relation between the devices
and the organs and tissues.

In the simulation, X-ray CT scanning geometry was same as
the measurements. A voxelized mathematical phantom (voxel
phantom) was developed based on the X-ray CT images of theinfant anthropomorphic phantom. The voxel phantom
containing photodiode sensor regions was incorporated into thesimulation. The sensor regional doses were calculated as eachcorresponding organ. These calculated organ doses werecompared with measurements, and the Monte Carlo simulation
was verified.
2. Calculation of average dose in the organs installed in theanthropomorphic phantom
The organ doses in the voxel phantom were obtained by theabove simulation method, however, these doses were evaluatedin each small region in the centroid of the organs. Each organ
has some volume, and the organs which have the volume basedon a Japanese standard were incorporated in the voxel phantom.
Fig.2 shows an image of the voxel phantom. The organs andtissues labels were assigned correctly to the value of each voxel
in reference to the weights of the organs in normal Japanese [3].
The absorbed energy doses for entire target organs were
evaluated. The calculations and measurements were carried out
for head CT and thoracoabdominal CT.

Fig.2 The infant voxelized mathematical phantom.
Image of the form (left) and incorporated organs andtissues (right).

Fig.1 Anthropomorphic phantom of infant. The numberof photodiode x-ray sensors implanted in phantom andeach position of tissues and organs.

Results and discussion
Fig.3 shows comparison of the absorbed doses in eachphotodiode sensors positions between the measurement andthe simulation undergoing X-ray CT examination. The
measured dose and the calculated dose in each position in thephantom were agreed within average error ±8.0%. The
maximum difference channel was at the ch 20, and thedifference was about 14%. The error of dose estimation
depends on the dosimetry system in the measurement andstatistical error associated with calculated photon number inthe simulation. In the dosimetry system, a measure cause of
the error was attributed to conversion from detector output toabsorbed dose. This error was estimated within about ±6.0%
by prior experimental research. In the calculation, statisticalerror was within ±7.0%. In the incorporation of the organs
and tissues in the voxel phantom, size and location were
important factors.

(a)

(b)

Fig.3 Comparison of the absorbed energy doses
between measurement and simulation in points.
Fig.4(a) shows comparison of the absorbed doses inorgans and tissue between measurement and simulation
undergoing a head CT scan. The calculated organ doses
based on the average doses for entire target organs and themeasured organ doses based on the absorbed doses in eachcentroid points of organs and tissues were agreed withinaverage difference of ±16%. The maximum difference
organs was bone marrow.
This error was due to adifference inlocation of the evaluation point. In thesimulation, bone marrow was distributed over the wholeinside of the bones of 1mm the voxel phantom. In themeasurement, the dose for bone marrow was evaluated bythree photodiode sensor devices implanted in the brain andthe salivary gland.
Fig.4(b) shows comparison of the absorbed doses inorgans and tissues between measurement and simulation
undergoing a thoracoabdominal CT scans. Within the scan
range, the calculated average doses in the whole organs andthe measured point dose were agreed within average
difference of ±15.0%. In the organs such as thyroid and testis
located near the both ends of scan range, the calculated doses
were higher than measured doses. In the calculation, theorgan was partially irradiated in the end of the scan area. In
the measurement, however, the photodiode sensors for theseorgans were located out of scan range, the absorbed doses forthese organs.

Fig.4 Comparison of the organ doses between
measurement and simulation in (a) head(b) thoracoabdominal CT scans.
Conclusions
For measurements, some organ doses are evaluated by theabsorbed energy in each center points of tissues and organs
which is small points. Using Monte Carlo simulation, organ
doses for infant were calculated and evaluated based on
absorbed energy doses for entire target organs. In this study, we
reaffirmed utility of the dosimetry systems and computer
simulation in evaluation for radiation doses.
References[1] International Commission on Radiological Protection, “The2007 Recommendations of the International Commission on
Radiological Protection,” ICRP publication 103, Annals of
the ICRP 37 (2007).
[2] T. Aoyama, S. Koyama, C. Kawaura, “An in-phantom
dosimetry system using pin silicon photodiode radiation
sensors for measuring organ doses in x-ray CT and otherdiagnostic radiology,” Med. Phys. 29, 1504-1510, (2009).
[3] G. Tanaka, Y. Nakahara, Y. Nakazima, “Japanese Reference
Man 1988-IV –Studies on the Weight and Size of Internal
Organs of Normal Japanese-,” JRS. 49(3), 344-364, (1989)
Author address
E-Mail: ishii.takanori@a.mbox.nagoya-u.ac.jp

DIGITAL BREAST TOMOSYNTHESIS AND DIGITAL MAMMOGRAPHY –A COMPARISON OF FIGURES OF MERIT FOR VARIOUS AVERAGE GLANDULAR
DOSES
Y.S.Kim1), H.S.Park1), S.J.Park1), J.G.Choi2), Y.W.Choi2) and H.J.Kim*1)
1) Department of Radiological Science and Research Institute of Health Science, Yonsei University, Wonju, Korea
2) Pioneering Medical-Physics Research Center, Kora Electrotechnology Research Institute (KERI), Ansan, Korea

Introduction
Breast cancer is a disease in which malignant cancer inthe tissues of the breast. One in eight women get breast
cancer in their lives and breast cancer is the second leading
cause of women cancer death [1]. Digital mammography(DM) has been credited as one of the major reasons for the
early detection to decrease in breast cancer mortality in thelast 20 years. However, DM is still far from perfect forseveral limitations [2]. Most patients can be uncomfortableand occasionally painful due to the breast compression
during the screening mammography and the two dimensional
projection can degrade the lesion conspicuity due to theoverlapping suspicious lesion and fibroglandular tissue. The
digital breast tomosynthesis (DBT) system can provide 3D
information with a limited number of cone-beam projections
and therefore reduce the obscuring effect of breast tissue
overlap in screening mammography for the improved marginvisibility and early detection of breast cancer [3]. In addition
to the reduction of structure noise contribution, DBT system
makes depth localization possible and requires the smaller
dynamic range for each reconstructed slice .
The aim of the study is to compare the performance of
DBT with DM system. We acquired a CC view from DM
system and reconstructed images from DBT system withbreast tissue equivalent phantom designed for three
dimensional breast imaging. The average glandular dose used
for image acquisition was from 1 mGy to 4 mGy. The figureof merit (FOM) and measurement accuracy of lesion size
were evaluated and discussed in this study.
Materials and Methods
1. Image acquisition system
The mammography system used in this study wasdescribed in figure 1 (a) (Brestige, MEDI-FUTURE, Korea).
The detector type is TFT-based direct conversion full-fieldflat-panel digital detector and the x-ray absorption material isamorphous selenium (a-Se). The DBT was performed on a
prototype unit (KERI DBT system, KERI, Korea) shown infigure 1 (b). The system developed by KERI (Korea
electrotechnology research institute) has not been yet
commercially available in Korea and its clinical applicationis under preparation. The DBT prototype is equipped with an
CsI phosphor/CMOS flat panel detector (2923MAM, Dexela
Ltd., UK) optimized for very low exposures with rapid
readout. Images were acquired from 15 different angular
position using a prototype DBT system and one projection
from 0° with a DM system. The reconstructed algorithm used
is based on separable paraboidal surrogates and is related tothe maximum-likelihood expectation-maximization (MLEM) algorithm. The reconstructed slice thickness is 1 mm
and the reconstructed image matrix is 4025 ×2006. Five
iterations were used for all reconstruction.

(a)

(b)

Figure 1. Digital mammography system (a) and prototype digitalbreast tomosynthesis system(b) description

2. Average glandular dose (AGD)
For conventional DM, the average glandular dose (AGD) isestimated using
D=K∙g∙c∙s(1)where K is the incident air kerma at the surface of breast
phantom and factor g, c and s are conversion factors tabulatedby Dance et al[4].
Average glandular dose proposed for DBT was calculatedusing a formalism, which is a simple extension of the UK,
European and IAEA protocols for dosimetry in conventional
DM introduced by D. Dance et al (2011) [5]. The formalism
D_T=K_T∙g∙c∙s∙T(2)is proposed. For a 3D breast dose, D_T gives the average
glandular dose for total projections acquired at total angularspan. The incident air kerma KT is measured with the sum ofmAs for the examination in the straight through position which
projection angle is 0°. The factor T gives ‘tomo’ factor fordifferent scan ranges against breast thickness [5].
3. Image analysis
The traditional Figure of Merit (FOM) used in diagnostic
radiology to optimize the signal detectability used previously byother authors is given by
FOM=CNR2/AGD(3)In above equation, the denominator is the average
glandular dose, which is directly proportional to the patientstochastic radiation risk. The value of FOM is independent of
the number of photons, and relates solely to differences in theradiation quality. Therefore, the optimum imaging condition isfound at exposure conditions where FOM is at its maximum.
Results
To compare the results quantitatively, the contrast and
CNR were calculated with the in-focus plane from DBT
reconstruction image and CC images from DM system.

Seen in figure 2 are plots of FOM as a function of breast
thickness (cm) for DBT and DM. The results of mass asshown in figure 2 (a) exhibit the highest FOM acquired with
1 mGy of AGD by DM system and the DBT system provides
second highest FOM with 1 mGy of AGD. However, as
increasing the breast thickness, the results of DM systemdecrease sharply. In result of 5 cm of breast phantom, overall
FOM of DBT system is superior to that of DM system.
Figure 2 (b) shows the FOM of micro-calcification acquiredwith each volume of breast. As noticeably high CNR of DBT
influences on the results of FOM, the FOM of microcalcification acquired with DBT system is superior to that
acquired with DM system and the highest FOM is exhibitedwith DBT system with 1 mGy of AGD.

FOM (figure of merit) of mass

2.5

1 mGy DBT
2 mGy DBT
3 mGy DBT
4 mGy DBT

2.0

1 mGy DM
2 mGy DM
3 mGy DM
4 mGy DM

1.5

1.0

0.5

0.0
2

3

4

5

breast thickness (cm)(a)

details with DBT system and figure 4 (b) shows mass details
acquired with DM system.

(a)

(b)
Figure 4.Mass details acquired with DM(a) and DBT (b)Discussion
FOM provides the optimal exposure condition and imaging
system as a function of breast thickness, that is to say, highquality image in low glandular dose derives high FOM result.
As shown in figure 5, for thin breast (e.g. 2 cm of breast
phantom), DM system with 1 mGy of AGD provides the highest
FOM of mass result due to the reduction of overlapping normal
breast tissue and lesion. The FOM of mass with DM systemdecreases sharply as increasing breast thickness due to theincrease in the effects of anatomical noise as shown in CNR
result. As compared with DM, DBT system acquires slices at
each depth and this reduces the influence of anatomical noise.
This study, with further studies and limitations,demonstrates the potential of DBT system for early detection ofbreast cancer and contributes to the development of DBT
system and clinical application.

100

1 mGy DBT
2 mGy DBT
3 mGy DBT
4 mGy DBT

FOM (figure of merit) of MC80

1 mGy DM
2 mGy DM
3 mGy DM
4 mGy DM

60

40

20

0
2

3

4

5

breast thickness (cm)(b)
Figure 2. The traditional FOM measured for different thicknessof breass: (a) mass and (b) micro-calcification

Figure 3 shows the magnification for the microcalcification details acquired with DM (a) and DBT system(b). The number of micro-calcification observed is same withboth systems and evident superiority in visibility ofcalcification is not demonstrated.

(a)

(b)
Figure 3.MC details acquired with DM(a) and DBT (b)On the other hand, better conspicuity of mass is clear
observed in the image acquired with DBT system as shownin figure 4. Figure 4 (a) shows the magnification for the mass

Conclusions
In this study we compared the performance of DM and DBT
system with different AGD and breast thickness. Although the
DM showed higher FOM of mass with thin breast due to the
reduced anatomical noise, increasing breast thickness causes thesuperiority of FOM with DBT system over DM system. For the
micro-calcification detection, the DBT system showedsignificantly higher FOM and led more accurate measurementof the micro-calcification size than DM system. Moreover, thevisibility of lesions, especially for low contrast of mass, in thickbreast was better with DBT system. In conclusions, these results
indicated that DBT system plays an important role in early
detection of breast cancer especially for dense breast.
References[1]Christina MS, Ehsan S, Robert SS, Moustafa Z, Joseph YL.
Toward Quantification of Breast Tomosynthesis Imaging.
Proc. of SPIE. 2008; 6913: 69134N.
[2]J Zhou, B Zhao, W Zhao. A computer simulation platform
for the optimization of a breast tomosynthesis system. Med.
Phys. 2007;34(3):1098-1109.
[3]JM Park, EA Franken, M Garg, LL Fajardo, LT Niklason.
Breast Tomosynthesis:Present Considerations and Future
Applications. RadioGraphics. 2007;27:S231-S240.
[4]DR Dance, CL Skinner, KC Young, JR Beckett, CJ Kotre.
Additional factors for the estimation of mean glandular
breast dose using the UK mammography dosimetry protocol.
Phy. Med. Biol. 2000;45:3225-3240[5]DR Dance, KC Young, RE van Engen. Estimation of mean
glandular dose for breast tomosynthesis: factors for use with
UK, European and IAEA breast dosimetry protocols. Phys.
Med. Biol. 2011;56:453-471
Author address

E-Mail: yeskim@yonsei.ac.kr

REDUCTION OF PATIENT DOSE IN DIGITAL MAMMOGRAPHY:SIMULATION OF LOW-DOSE IMAGE FROM ROUTINE-DOSE
Yuki Saito1), Maki Sakai2), Naotoshi Fujita3), Yoshie Kodera1)
1) Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
2) Department of Radiological Technology, Nagoya Daini Red Cross Hospital, Nagoya, Japan
3) Department of Radiological Technology, Nagoya University Hospital, Nagoya, Japan
Introduction
To reduce patients’ exposure to radiation during digital
mammography, it is preferable to acquire images using aminimum dose. Recently, owing to the developments of Xray detectors, the exposure dose required for acquiring a
digital image has been re-examined. It is necessary to assess
several low-dose images in order to establish a diagnosis.
However, it is clinically undesirable to expose a patient toseveral exposure conditions for determining an optimal
image. Therefore, it is necessary to generate a low-dose
image through simulation.
The purpose of this study is to simulate a low-dose image
from the image generated using a routine-dose. The noise in
an image varies because the detected photons pass throughmaterials consisting of different substances and having
varying thicknesses. Hence, it is necessary to add a different
noise to each pixel value in the image. The aim of this study
is the addition of a different noise for each pixel byconsidering the resolution of the X-ray detector.
Materials and Methods
1. Materials
This study used the digital mammography system withdifferent detectors. It is computed radiography (CR) systemand flat panel detector (FPD) system. The CR reader used
was a REGIUS V stage, Model 190. The CR plate used was
CP1M200 with columnar crystal photostimulable phosphor.
Pixel pitch of CR reader is 43.75 μm. These were
manufactured by Konica Minolta MG. And, FPD system is
Hologic Selenia with an amorphous selenium detector withpixel pitch of 70 μm.
2. Simulation process

Figure 1 shows a flow-chart of the simulation process. The
noise image that accounts for the pixel value of the low-dose
image is generated from the relationship between the pixel
value and the standard deviation (SD), which serves as the noise
index. The noise is calculated for each pixel value, based on thevariation in detected photons of a routine-dose image. The noiseis blurred by the resolution of the X-ray detector in a spatial
frequency domain. The blurred noise image is added to theroutine-dose image to generate the simulation image.
3. Generation of additional noiseFig.2 Experimental setup
Images of the acrylic steps were obtained to examine therelationship between the pixel value and the noise. The
experimental setup is shown in Fig. 2. It included 10 acrylic
steps spaced at 1 mm intervals and four acrylic plates spaced at
1 cm intervals. By employing both acrylic steps and acrylic
plates, images with up to 50 acrylic steps placed at 1mm
intervals were obtained. Ten different exposure doses were
established through digital mammography. Next, the images
were generated under 500 different exposure conditions. We
measured the pixel value under each exposure condition andcalculated the SD as the noise index. Under all exposure
conditions, the tube voltage was 28 kV and the target/filter was
Mo/Mo.
4. Take into account detector resolution
The noise made was approximated to the Gaussian noise, andvariations in dosage resulted in changes in SD. The simulationnoise became identical to the value of the Wiener spectrum(WS) ranging from a low frequency to a high frequency.
However, the actual shape of the WS changed on the basis of
the modulation transfer function (MTF) of the imaging systemin equation (1).

WS (u ) 

Fig.1 Flow-chart of the simulation process 2 MTF 2 (u )n(1)Thus, a spatial frequency filter was designed using the MTF.
The shape of the WS was corrected using the following filters.
The first filter was designed based on the presampled MTF, thesecond was designed based on the digital MTF containing
aliasing, and the third was designed based on the measured
value of the WS.

Results
1. Relationship between the pixel value and the noise
We determined relationship between the pixel value andthe SD of the acrylic steps. It can be seen rerationship of theCR system of that the SD decreases as the pixel value
increases. On the other hand, it can be seen from relationshipof the FPD system that the SD increases as the pixel value
increases. But relationship of the CR system is non-linear
imaging system, and has a logarithmic amplifier. Then,
relationship of FPD system transformed to the non-linear
date when the SD decreases as the pixel value increases. The
difference between the pixel values of the routine-dose andlow-dose images freely fluctuates with the mAs value of the
low-dose image to be simulated. And SD was calculated foreach pixel.
2. Comparison of phantom images
The 50 mAs image was used as the routine-dose image,and the low-dose image of 10 mAs by both systems was
obtained through simulation. We simulated the images using
a tissue-equivalent mammography phantom of CIRS Model
012A. Figure 3 shows the actual low-dose image of 10 mAs
by CR system, Fig. 4 shows the image simulated using thefilter based on the WS of CR system, Fig. 5 shows theimages actual low-dose image of 10 mAs by FPD system,
and Fig. 6 shows the image simulated using the filter basedon the digital MTF of FPD system. The image simulatedusing the filter based on the WS is similar to the actual lowdose image by both systems. The noise suitable for thegeneration of the low-dose image could be easily transferredto the routine-dose image. The image simulated using thepresampled MTF filter is very different from the actual lowdose image by CR systems. And, we consider the image
simulated using the digital MTF filter was closer to an actual
low-dose image compared to the image simulated using thepresampled MTF filter of CR systems. But the image
simulated using the digital MTF filter of FPD system was
similar to an actual low-dose image.

Fig.3 The images actual low-dose image of 10 mAs
by CR system

Fig.5 The images actual low-dose image of 10 mAs
by FPD system

Fig.6 The image simulated using the filter based on
the digital MTF of FPD system
Discussion
The image simulated using the presampled MTF filter is lesssimilar to the actual low-dose image by CR system. Since the
MTF value is high, the CR plate used in this case wasdeveloped for mammography and influences the aliasing in theimage. Therefore, the image simulated using the digital MTF
filter is close to an actual low-dose image. In addition, whenusing the digital MTF filter, it is better to divide noise sources.
This is because certain noise is influenced by the resolution of
the X-ray detector (e.g., quantum noise) while certain othernoise is not (e.g., structure noise). In addition, the dominantnoise component depends on the amount of photons absorbedby the X-ray detector. We believe that the simulation becomeseven more precise when the additional noise is also divided into
noise sources.
Conclusions
By using the proposed method, we were able to obtain asimulated low-dose image from an image generated by aroutine-dose. This method is expected to facilitate a lowexposure condition for generating an image of sufficient quality
and lead to reduced dose exposure to patients.
References[1] M. Båth, M. Håkansson, A. Tingberg, L G Månsson,
Method of simulating dose reduction for digital radiographic
systems, Radiation Protection Dosimetry, 2005;114; 253-259.
[2] W J.H. Veldkamp, L J.M. Kroft, J Pieter A. V Delft, J
Geleijns, A Technique for Simulating the Effect of Dose
Reduction on Image Quality in Digital Chest Radiography,
Journal of Digital Imaging, 2009;22-2; 114-125.
Author address

Fig.4 The image simulated using the filter based on
the WS of CR system

E-Mail: saitoh.yuuki@e.mbox.nagoya-u.ac.jp

Optimization of the angle between detector modules in a dual-head cardiac SPECT
Woo Jin Jo1,2), Yong Hyun Chung1,2)
1) Department of Radiological Science, College of Health Science, Yonsei University, Wonju, Korea
2) Institute of Health Science, Yonsei University, Wonju, Republic of Korea

Introduction
In the early days of SPECT imaging, it was performed byrotating a single-head Anger camera to obtain projection datafrom multiple angles. With advances in instrumentation andsoftware, alternative designs have been developed andcommercialized such as the use of multi-head systems thatallow simultaneous acquisition of multiple-projection views[1-2]. Multi-head cameras can be constructed using a varietyof configurations, with 2 or 3 heads angled at 90°, 120°, or
180°. This set-up allows for improved image quality by
reducing the image acquisition time or increasing the total
counts compared with a single-head camera [3]. Since theheart is located in the left side of the thorax, gamma rays arehighly attenuated in the posterior projections from left
posterior oblique (LPO) to right anterior oblique (RAO)
angles [4]. To minimize tissue attenuation, projections areusually obtained using 180° acquisition that extends from the
LPO to the RAO view. Therefore, current standard protocol
for myocardial perfusion SPECT is 180° acquisition with a
90° dual-head system [5]. The optimal angular displacement
between detector heads in dual-head SPECT remains
controversial, however, because it depends on both the sizeof the patient and the detector. It is necessary to explore therelationship between image quality and the angle between
detector heads to minimize the distance between the heart
and the detector head.
In this study, the effect of the angle between detector
heads in small-animal dual-head SPECT on cardiac image
quality was investigated through Monte Carlo simulations.
Prior to these simulations, we validated the GATE simulator
experimentally.
Materials and Methods
Small animal SPECT system for simulator validation
study: The detector used in the simulator validation wascomposed of a 50 x 50 x 6 mm3 monolithic NaI(Tl) crystal
coupled to a position-sensitive photomultiplier tube
(Hamamatsu, H8500), a parallel-hole collimator, the frontend
electronics, a data acquisition system, and a motorized
rotating stage as shown in Figure 1(a). The crystal and
PSPMT were mounted in the metal housing with leadshielding against background radiation. The lead parallelhole collimator was 35 mm thick and consisted of a 1.5 mm
diameter hexagonal hole with 0.2 mm septa. The 64-channel
anode signals of PSPMT were routed to the resistive anger
logic circuit (Vertilon, SIB064-1018) to generate four
positioning outputs. These outputs connected to a Vertilon
PhotoniQ IQSP418 multichannel data acquisition system via
four coaxial cables.
Experimental measurements were carried out using a 28
µCi 57Co point source on the rotation stage. The distance
between the center of rotation and the collimator surface was
20 mm as shown in Figure 1(b). SPECT data acquisition timewas 8 minutes per projection, and the data were collected at
2° increments over 180° in step-and-shoot mode. Imaging
was performed using a 20% energy window centered on the
122 keV photopeak of 57Co. The filtered back projection witha Hanning filter was used to reconstruct tomographic image.

System sensitivity was measured and reconstructed spatial
resolution was calculated using Gaussian fit on the count profile
from the point source.

Figure 1. SPECT system developed for simulator verification. This module was composed of
H8500 PSPMT, NaI(Tl) scintillation crystal, a parallel hole collimator, and an object rotatingstage (a). The distance between the center of rotation and the collimator was 20 mm (b).

The simulation was performed using GATE version 3.1.2
based on the GEANT4 toolkit. The detector was modeledaccording to geometrical characteristics as described above. In
order to reduce the computation time, two detector heads were
simulated and the activity of the source was set to 5 mCi.
Projection data for 90 views was acquired over a 180° step-andshoot acquisition, with a 1 sec acquisition per projection.
Effect of the angle between scanner heads on cardiac image
quality: To characterize the relationship between image quality
and the angle between scanner heads, dual head SPECT was
simulated by varying the angle between heads as 70°, 80°, 90°,
100°, and 110°. Each detector head consists of a 50 x 50 x 6
mm3 NaI(Tl) optically coupled to a Hamamatsu H8500 PSPMTand a parallel-hole collimator as described previously. The
distance between the collimator surface and the center ofrotation were set to 20, 20, 20, 25, and 31.5 mm for 70°, 80°,90°, 100°, and 110°, respectively, using a 40 mm field of view(FOV) as shown in Figure 2.

Figure 2. Simulation geometry of the dual-head SPECT systems with an angle between thedetector heads of 70° (a), 80° (b), 90° (c), 100° (d), and 110° (e).

The mouse myocardium phantom was modeled as a cylinder
with an outer diameter of 10 mm, wall thickness of 2.7 mm, andheight of 5 mm in a water-filled cylindrical phantom 4 cm indiameter and 3 cm in height. The myocardium phantom wasfilled with water and 1.5 mCi 99mTc-99, and the projections
were obtained using 180° acquisition that extended from the
LPO to the RAO view. Projection data from 110, 100, 90, 100
and 110 views were acquired in 2° increments over 180° for theangles of 70°, 80°, 90°, 100°, and 110°, respectively, between

heads. The tomographic image was reconstructed using afiltered back projection algorithm, and its quality wasevaluated by measuring the myocardial wall to cavity ratio.
Results
Simulator validation study: Figure 3 shows the simulated
and measured SPECT images obtained using a point sourceat the center of rotation. Spatial resolution defined as a full
width at half maximum (FWHM) of the point source profile,and sensitivity defined as the ratio between source activity
and the total number of events were calculated as shown in
Table 1. There were no significant differences between
simulated and measured results. The relative error of thespatial resolution and sensitivity were 5.7% and 8.4%,
respectively.
Table 1. Simulated and measured spatial resolution and sensitivity
Measurement

Simulation

Relative error

FWHM (mm)

3.7

3.5

5.7%

Sensitivity(cps/MBq)

167

181

8.4%

Figure 3. Simulated and measured point source image. Computer simulated (a) andexperimental results (b).

Effecto of the angle between heads on cardiac image
quality: Figure 4 shows the SPECT images of the mousemyocardium phantom obtained by varying the angle between
heads and their horizontal profiles. The x-axis represents the
pixel size (0.25 mm per pixel) and the y-values werenormalized to the maximum pixel value. When the angle
between heads exceeded 90°, images of the myocardium
were blurred by the increased distance between the phantom
and the detector head. The myocardial wall and cavity weremost clearly distinguished at an angle between heads of 80°.
In order to find the best angle between heads, the angulardisplacement was subdivided and additional simulations
were performed at 76° and 84°. The peak to valley ratio
representing the wall to cavity ratio was measured for 18
angular directions with 10° intervals, and the mean andstandard deviation were calculated.

Figure 4. Myocardium phantom images and their profiles acquired by varying the angle
between detector heads.

As shown in figure 5, the myocardial wall to cavity ratio
was measured as 2.18, 3.97, 4.47, 3.64, 2.97, 2.78 and 1.69 forangles between heads of 70°, 76°, 80°, 84°, 90°, 100° and 110°,respectively. An 80° arrangement of the detector heads in thissystem generated the best wall to cavity ratio.
Discussion
In this paper, we presented validation results for use of the
GATE simulator based comparisons of simulated versus
experimental measurements acquired using a small-animal
SPECT system. Spatial resolution and sensitivity were
evaluated using a 57Co source, and overall results were within
10% accuracy between the simulated and acquired SPECT
images.
Dual-head cardiac SPECT for small animal imaging was
simulated using a validated GATE model to characterize theimaging performance as a function of the angle between the two
detector heads. Consequently, an angular displacement of 80°
provided the highest quality myocardial image in the simulation.
This is the result of the cardiac geometry shifting to one side.
This leads to a change in source-to-detector distance, which
affects scatter and attenuation. As a result, image quality
characteristics were varied depending on the angle between
heads.
In this study, the surfaces of both heads were close enoughto each other to only consider the effects of the angle between
heads. In the actual dual head SPECT system, however, if both
camera heads are at an angle of exactly 90° to each other, partof the patient lies outside of the field of view. For this reason,
one manufacturer provides SPECT acquisitions with heads at a
76° angle to each other. [6]In order to obtain meaningful 180° information using thedual head SPECT system, the number of projections was varied
according to detector angle. For example, in 70° and 110° angle
geometry, 110 projections were acquired, in 80° and 100° angle
geometry, 100 projections were acquired, and in 90° angle
geometry, 90 projections were acquired. As a result, dataacquisition time was increased in 70°, 80°, 100° and 110° angle
geometry compared with 90° angle geometry. This increase inacquisition time was not significant (approximately 10% of thetotal acquisition time).
Conclusions
The dual heads at an 80° geometric angle produce thehighest quality myocardial images in our small animal SPECT
system. This is the optimal angle between detector heads thatmaximizes image quality in cardiac SPECT. Since the optimum
angle geometry also depends on patient body type, preparing a
protocol for patients with a particular body habitus will be
helpful in acquiring optimum myocardial SPECT images
without additional hardware.
References[1]Jonathan M. Links, Eur J Nucl Med. 20, 440 (1993)[2]Links JM, Frank TL, Engdahl JC et al, Eur J Nucl Med. 6,548 (1995)[3]Travin MI, Semin Nucl Med. 3, 182 (2011)[4]Einsner RL, Nowak DJ, Pettigrew R et al, J Nucl Med. 11.
1717 (1986)[5]Tracy L. Faber, J nucl Cardiol. 3, 292 (1994)[6]Jerrold T, Bush berg, “The Essential Physics of medical
imaging”, p712
Author address

E-Mail: wincom@yonsei.ac.kr
Figure 5. The mean and standard deviation of myocardial wall and cavity ratio for the angle
between heads of 70°, 76°, 80°, 84°, 90°, 100° and 110°.

ACCURACY OF 3D CINE PHASE CONTRAST MR FLOW MEASUREMENTFOR COMMON CAROTID ARTERIES
Ikki Tozawa1, Haruo Isoda1, Atsushi Fukuyama1, Kento Morita2, Katsuya Maruyama3, Yoshiaki Komori3,
Toshiyasu Shimizu4, Takashi Kosugi4, Yasuo Takehara5, Shinji Naganawa6
1
2

Department of Radiological Sciences, Nagoya University Graduate School of Medicine
Department of Radiological Technology, Nagoya University School of Health Sciences
3
Siemens Japan K.K.
4
Renaissance of Technology Corporation
5
Department of Radiology, Hamamatsu University School of Medicine
6
Department of Radiology, Nagoya University Graduate School of Medicine

Introduction
Hemodynamics plays a very important role in theinitiation and progression of vascular diseases [1]. Therefore, it
is important to evaluate the blood flow in the vessel accurately.
Doppler ultrasound is a common method for the evaluation of
carotid arterial hemodynamics, however it has low operator
reproducibility and its coverage of the sample volume within
the vessel is limited. Hemodynamic analysis by twodimensional phase contrast MR imaging (2D cine PC MRI) has
also been used. Recently, 3D cine PC MRI [2], which provides
us with temporal three directional and three dimentional flow
information, has been used for evaluation of hemodynamics ofthe carotid artery. This promising imaging technique has notbeen fully utilized in clinical practice as its accuracy has notbeen adequately verified.
The purpose of this study was to investigate the effects ofspatial resolution on the accuracy of 2D and 3D cine PC MR
flow measurement with the aid of a carotid arterial phantom,
and to evaluate of the accuracy of 3D cine PC MRI of thecommon carotid artery (CCA) of healthy volunteers using theresults of existing 2D cine PC MRI as a gold standard.
Materials and Methods
Our flow phantom was an acrylic tube with a 6 mm innerdiameter mimicking a CCA. We ran a 40 % glycerin solution
with a steady flow (30 cm/s) using a pump (Nemo pump
NHL29PUA; Heishin Ltd., Kobe, Japan). The flow rate wasmeasured with a digital Coriolis flowmeter (FD-SF1: Keyence
Corporation, Osaka, Japan; 2% error in the range of 200ml/min
to 500ml/min). We performed 2D and 3D cine PC MRI withprospective pseudo ECG gating using 3T MR system(MAGNETOM Verio 3T; SIEMENS, Erlangen, Germany)with neck matrix coil. We set the spatial resolutions at 0.63 to
2.5 mm for 2D cine PC MRI and 0.67 to 2.0 mm for 3D cine
PC MRI (Table 1) and investigated their effects on theaccuracy of the two imaging techniques.
We also performed 2D and 3D cine PC MRI using
prospective ECG gating (Table 1) for the CCAs of two healthy
volunteers, from whom we got informed consent. The center ofthe imaging slices or slab were placed 2cm below the apex of
the carotid bifurcation as determined by MR angiography [3].
We calculated temporal averaged spatial mean andmaximum velocities using 2D and 3D cine PC MR data set
with the aid of flow visualization and analysis software (Flova;
R'Tech, Hamamatsu, Japan). In the phantom study, we
compared the results of 2D and 3D cine PC MR flow
measurements with the measured value of the flow meter. In
the volunteer study, we evaluated accuracy of 3D cine PC MRI
of four CCAs of two healthy volunteers using the results of 2D
cine PC MRI as a gold standard.

Table 1. Imaging Parameters
2D cine PC
3D cine PC
Voxel size (mm3)0.63*-2.5
0.67*-2.0××
0.63*-2.5
0.67*-2.0
××
5.0
0.67*-2.0
VENC (cm/sec)
150
150
TR/TE(msec)
51.7/8.86
39.76/5.23
Bandwidth (Hz/pixel)
260
476
Flip angle (degree)
15
15
Number of phases
4 , 15*-16*
16*-20*, 18-25
TR, Repetion Time ; TE, Echo Time ; VENC, Velocity
Encoding; * shows a parameter used for healthy volunteers.
Results
In the CCA phantom study, mean velocity based on
measured value provided by the Coriolis flowmeter was 29.82
cm/sec, and the theoretical value of maximum velocity
calculated by the Hagen-Poiseuille law [4] was 59.64 cm/sec;these values were considered to be the reference standard.
Spatial resolution had relatively little effect on the accuracy of
2D and 3D cine PC MRI (Fig. 1). When we set the spatial
resolution to around 0.65 mm (approximately 10% of the inside
diameter), the measured maximum velocity approached thereference standard (Fig. 2). Errors of 2D and 3D cine PC MRI
were 6.2% and 5.4% respectively and errors of mean velocities
were 0.5% and 1.8% respectively.
In the healthy volunteer study, errors of temporal
averaged spatial mean velocity and maximum velocity of 3D
cine PC MRI were 8.8% and 8.1% respectively using theresults of 2D cine PC MRI as a gold standard. In addition,
temporal velocity changes during cardiac cycle obtained by 3D
cine PC MRI were similar to those of 2D cine PC MRI （Fig. 3,
4）.

Fig. 1. Mean velocities in a 6mm inner diameter tube
obtained by 2D and 3D cine PC MRI with different spatial
resolution

Fig. 2. Maximum velocities in a 6mm inner diameter tube
obtained by 2D and 3D cine PC MRI with different spatial
resolutions

vessel, because the vessel boundary voxels contain both thesignal from vessel lumen and from the surrounding tissue,
resulting in a partial volume effect [5] . It was thought that the
influence of partial volume effect was relatively small in ourstudy because the inside diameter of our phantom was 6mm.
Our phantom showed laminar flow with the largest
velocity at the center of the tube. The velocity progressively
decreased nearer to the wall. Maximum velocities at the center
obtained by 2D and 3D cine PC MRI with high spatial
resolutions approached the reference standards. A large voxel
at the center of the tube was thought to include surrounding
lower velocities and showed lower velocities because of partial
volume effect due to low spatial resolutions.
In the volunteer study, 3D cine PC MRI temporal
averaged spatial mean and maximum velocity were accurate.
Temporal velocity changes during cardiac cycle obtained bythis imaging technique were similar to those of 2D cine PC
MRI.
Conclusions
In our study, temporal averaged spatial mean andmaximum velocities of 3D cine PC MRI with a spatial
resolution of around 0.65 mm were within 10 % of thereference standards and temporal velocity changes during
cardiac cycle obtained by this imaging technique were similar
to those of 2D cine PC MRI. Our 3D cine PC MRI technique
may be a viable diagnostic tool for carotid arterial disease.

Fig. 3. Temporal changes of spatial mean velocities
during cardiac cycle of the cross section 2cm below theapex of the carotid bifurcation for four common carotid
arteries of two healthy volunteers obtained by 2D and 3D
cine PC MRI. Error bars expresses standard deviations.

Fig. 4. Temporal changes of spatial maximum velocities
during cardiac cycle of the cross section 2cm below theapex of the carotid bifurcation for four common carotid
arteries of two healthy volunteers obtained by 2D and 3D
cine PC MRI. Error bars expresses standard deviations.
Discussion
In this study, we investigated the optimal spatial
resolutions of cine PC MRI for the CCA phantom, andperformed and compared those two imaging technique for
CCAs of healthy volunteers.
Spatial resolution had little effect on the mean velocities
in the phantom study. The mean velocity in a small tube (3mm inner diameter) is expected to be lower than that in a large

References[1] Malek AM, Alper SL, Izumo S. Hemodynamic shear
stress and its role in atherosclerosis. JAMA.1999;282(21):2035-42.
[2] Markl M, Chan FP, Alley MT, Wedding KL, Draney
MT, Elkins CJ, Parker DW,Wicker R, Taylor CA,
Herfkens RJ, Pelc NJ. Time-resolved three-dimensional
phase-contrast MRI. J Magn Reson Imaging. 2003;17(4):499-506.
[3] Sui B, Gao P, Lin Y, Gao B, Liu L, An J. Assessment ofwall shear stress in the common carotid artery of healthy
subjects using 3.0-tesla magnetic resonance. Acta Radiol.
2008;49(4):442-9.
[4] Liu, H.-C. and Fried, J. R., Breakthrough of lysozyme
through an affinity membrane of cellulose-cibacron blue.
AIChE Journal.1994: 40(1):40-9.
[5] Hofman MB, Visser FC, van Rossum AC, Vink QM,
Sprenger M, Westerhof N. In vivo
validation ofmagnetic resonance blood volume flow measurementswith limited spatial resolution in small vessels. Magn
Reson Med. 1995;33(6):778-84.
[6] Sui B, Gao P, Lin Y, Gao B, Liu L, An J. Blood flow
pattern and wall shear stress in the internal carotid
arteries of healthy subjects. Acta Radiol. 2008;49 (7):806-14.
[7] Stalder AF, Russe MF, Frydrychowicz A, Bock J,
Hennig J, Markl M. Quantitative 2D and 3D phase
contrast MRI: optimized analysis of blood flow andvessel wall parameters. Magn Reson Med. 2008;60(5):1218-31.
Author address
E-Mail: tozawa.ikki@e.mbox.nagoya-u.ac.jp

A HIGH RESOLUTION SPECT SYSTEM WITH A PHOTON COUNTING DETECTOR:MONTE CARLO SIMULATION STUDIES
Y. J. Lee1), H. J. Ryu1), S. J. Park1), D. H. Kim1), Y. S. Kim1), H. J. Kim1)
1) Department of Radiological Science and Research Institute of Health Science, Yonsei Universitiy, Wonju, Korea

Introduction
Recently, new semiconductor materials such as cadmium
telluride (CdTe) (density = 5.85 g/cm3) and cadmium zinctelluride (CZT) (density = 5.76 g/cm3) have becomecommercially available and are considered as the mostpromising semiconductor material for clinical imaging
systems [1-2]. Especially, the signal from photons wascollected individually for each pixel, and thus the intrinsic
resolution of the detector is almost the same as the size of a
pixel [3-7]. However a disadvantage of this detector was aloss in sensitivity due to the small pixel on the detector. By
using the pixelated parallel-hole collimator, we may be able
to improve the sensitivity and the spatial resolution. In thisstudy, we simulated the SPECT system using the photon
counting detector based on CdTe and CZT, and evaluated theperformance of these systems.
The purpose of this paper was to evaluate high resolution
SPECT system with semiconductor detector, which had verysmall pixels such as 0.35 × 0.35 mm2, using pixelatedparallel-hole collimator. For that purpose, we evaluated thesensitivity, the spatial resolution and the contrast resolution
with Geant4 Application for Tomographic Emission (GATE)
simulation.

Figure 1. Schematic diagrams of the pixelated parallel-holecollimator
Evaluation of performance: In evaluating the performance of
this system, we estimated both sensitivity and spatial resolution.
We measured a 99mTc point source with an activity of 1 MBq(placed in air) using a scan time of 900 seconds. The distancesof the point source from the collimator were 2, 4, 6, 8 and 10
cm. For the measurement of the contrast resolution, the hot-rod
phantom was designed using GATE simulation (Figure 2). The
phantom was filled with water and consisted of and six areas
with different diameters from 0.5 to 2.1 mm. The thickness ofthe phantom was 10 mm. In order to evaluate the improvementof the contrast resolution, we defined as follows:

Materials and Methods
Simulation set-up: To evaluate the image performance ofour proposed system we performed simulations with GATE
version 6 in this study. The proposed system used the CdTe
and CZT detector with small pixel. The size of pixel was
0.35 × 0.35 mm2 such as PID 350 CdTe detector (Ajat Oy
Ltd., Finland). Also it consists of 1 mm thickness 44.8 ×
44.8 mm2 detector size and 128 × 128 pixel arrays. In dataacquisition, the number of views was 90 over 360°, and thedata acquisition time was 10 second/view. Image
reconstruction was carried out with the filtered backprojection (FBP) method. However, for clarity and to ensure
acceptable image quality, charge sharing was not simulated.
We designed pixelated parallel–hole collimator, which has
the same hole size to the pixel size. The parallel-holecollimator efficiency (εparallel-hole) and resolution (Rparallel-hole)
are the extrinsic parameters for the nuclear medicine detector
and influence image performance. Equations for εparallel-holeand Rparallel-hole of a parallel-hole collimator are given as
follows [8]: parallelhole 1d 2 d2()
4 leffective (d  t ) 2

R parallel  hole  dl effective  bl effective(1)

Contrast resolution =m p  mv
mp

 100 (%)

(3)

where mp is the mean value of four peaks of a profile, and mv isthe mean value of three valleys of a profile.

)(2)

The shape of the collimator hole was square, and the sizeof the hole was 0.3 × 0.3 mm2. The thickness of the septum
was 0.05 mm and septal height was 20 mm (Figure 1).

Figure 2. Hot-rod phantom diagram for evaluating the contrast
resolution and overall image performance.

Results
The obtained sensitivity and spatial resolution wereshown in Table 1 and 2, respectively.
Table 1 Measured the sensitivity (counts/sec/kBq) basedon CdTe and CZT detector
Source to collimator
distance (cm)Sensitivity (counts/sec/kBq)
CdTe detector

CZT detector

20.00460

0.0044340.004540.0044560.00458

0.0044280.00458

0.00446100.004540.00443

Table 2 Measured the spatial resolution (mm, FWHM)
based on CdTe and CZT detector
Source to collimator
distance (cm)Spatial resolution (mm, FWHM)
CdTe detector

CZT detector

2

0.73

0.69

4

1.01

0.98

6

1.30

1.25

8

1.55

1.54

10

1.88

1.85

Figure 3 and 4 show reconstructed images of the hot-rod
phantom using CdTe and CZT detector, respectively. The
results showed that the contrast resolution of CdTe and CZT
detector was 90.7% and 90.3%, respectively. The results
reflected the improvement of the sensitivity and the spatial
resolution.

Figure 3. Reconstructed hot-rod phantom images using
the CdTe detetor : source to collimator distance (a) 2 cm,(b) 4 cm, (c) 6 cm, (d) 8 cm, and (e) 10 cm.

Figure 4. Reconstructed hot-rod phantom images using the
CZT detetor : source to collimator distance (a) 2 cm, (b) 4
cm, (c) 6 cm, (d) 8 cm, and (e) 10 cm.
Discussion
We simulated a high resolution SPECT system with a CdTe
and CZT detector and pixelated parallel–hole collimator. If weapply these detectors to a pixelated parallel-hole collimator, we
can acquire excellent spatial resolution since the intrinsic
resolution was equal to the pixel size. In these reconstructed
images, we could resolve 0.85 mm diameter when hot-rod
phantom located at 2 cm from the collimator. This spatial
resolution is similar to the spatial resolution of the pinhole
collimator. Also the results of simulations showed that SPECT
images measured with a CdTe and CZT detector had highcontrast resolution and superb sensitivity in small pixel
condition.
Conclusions
In conclusion, our results demonstrated that we establishedthe high resolution SPECT system with the pixelated parallel–hole collimator.
References[1] Verger L, Gentet M C, Gerfault L et al. Performance andPerspectives of a CdZnTe-Based Gamma Camera for
Medical Imaging. IEEE Trans Nucl Sci. 2004;51(6):31113117.
[2] Fritz S G, Shikhaliev P M. CZT detectors used in different
irradiation geometries: Simulations and experimental results.
Med Phys. 2009;36(4):1098-1108.
[3] Takahashi T, Watanabe S. Recent Progress in CdTe and
CdZnTe detector. IEEE Trans Nucl Sci. 2001;48(4):950-959.
[4] Scheiber C, Giakos G C. Medical applications of CdTe and
CdZnTe detectors. Nucl Instr Methods Phy Res A.
2001;458:12-25.
[5] Wen L, Yanfeng D, Brian D et al. Impact of Temperature
Variation on the Energy Resolution of 3D Position Sensitive
CZT Gamma-Ray Spectrometers. IEEE Nucl Sci Con Rec.
2007;N27-6:1809-1815.
[6] Ogawa K, Ohumura N, Iida H et al. Development of an
ultra-high resolution SPECT system with a CdTe
semiconductor detector. Ann Nucl Med. 2009;23:763-770.
[7] Ogawa K, Muraishi M. Feasibility Study on an Ultra-HighResolution SPECT With CdTe Detectors. IEEE Trans Nucl
Sci. 2010;57(1):17-24.
[8] Wieczorek H, Goedicke A. Analytical Model for SPECT
Detector Concepts. IEEE Trans Nucl Sci. 2006;53(3):11021112.
Author address

E-Mail: gloriousguy@yonsei.ac.kr