308

ORIGINAL
Correlation and Characteristics of Intravoxel Incoherent
Motion and Arterial Spin Labeling Techniques Versus
Multiple Parameters Obtained on Dynamic Susceptibility
Contrast Perfusion MRI for Brain Tumors
Enkh-Amgalan Dolgorsuren1, MD, Masafumi Harada1, MD, Ph.D., Yuki Kanazawa2, Ph.D., Takashi Abe1,
MD, Ph.D., Maki Otomo1, MD, Yuki Matsumoto1, MD, Yoshifumi Mizobuchi3, MD, Ph.D., and Kohhei Nakajima3, MD,Ph.D.
Department of Radiology and Radiation Oncology, Tokushima University, Tokushima, Japan, 2Institute of Biomedical Sciences, Tokushima
University, Tokushima, Japan, 3Department of Neurosurgery, Tokushima University, Tokushima, Japan
1

Abstract : Purpose : To compare data on brain tumors derived from intravoxel incoherent motion (IVIM) and arterial spin labeling (ASL) imaging with multiple parameters obtained on dynamic susceptibility contrast (DSC)
perfusion MRI and to clarify the characteristics of IVIM and ASL perfusion data from the viewpoint of cerebral
blood flow (CBF) analysis. Methods : ASL-CBF and IVIM techniques as well as DSC examination were performedin 24 patients with brain tumors. The IVIM data were analyzed with the two models. The relative blood flow(rBF), relative blood volume (rBV) corrected relative blood volume (crBV), mean transit time (MTT), and leakage
coefficient (K2) were obtained from the DSC MRI data. Results : The ASL-CBF had the same tendency as the perfusion parameters derived from the DSC data, but the permeability from the vessels had less of an effect on the
ASL-CBF. The diffusion coefficient of the fast component on IVIM contained more information on permeability
than the f value. Conclusion : ASL-CBF is more suitable for the evaluation of perfusion in brain tumors than IVIM
parameters. ASL-CBF and IVIM techniques should be carefully selected and the biological significance of eachparameter should be understood for the correct comprehension of the pathological status of brain tumors. J.
Med. Invest. 66 : 308-313, August, 2019
Keywords : Intravoxel incoherent motion, brain tumor, arterial spin labeling, dynamic susceptibility contrast perfusion

INTRODUCTION
Perfusion plays a vital role in brain function and is an essential part of brain metabolism. Perfusion imaging is useful forevaluating tumor blood supply, particularly for the differential
diagnosis of tumor stages (1-3) and in prognosis prediction (4)and treatment strategy (5) planning. In addition, perfusion canreflect the microvascular proliferation or angiogenesis of thetumor (1, 6).
Three different methods have been reported to evaluate perfusion on MRI. The most standard method in the clinical setting
is dynamic susceptibility contrast (DSC), which is based oncontinuous time-series measurement after injection of contrast
medium. Another is the arterial spin labeling (ASL) technique,which uses radiofrequency pulses to detect blood flow. The final
technique is the intra-voxel incoherent motion (IVIM) method,which uses several different strengths of motion-probing gradients, called b-values.
DSC is commonly used to evaluate and differentiate typesof brain tumors (7) and is a fundamental technique for brain
tumor follow-up (8-10). The DSC method provides many parameters related to perfusion, not only relative blood flow (rBF) andrelative blood volume (rBV) in the cerebrum, but also the meantransit time (MTT) and leakage coefficient (K2), which reflect
Received for publication June 18, 2019 ; accepted June 27, 2019.
Address correspondence and reprint requests to Enkh-Amgalan
Dolgorsuren, Department of Radiology and Radiation Oncology, Institute of Biomedical Sciences, The University of Tokushima Graduate
School, 3-18-15, Kuramoto-cho, Tokushima city, Tokushima 7708509, Japan and Fax : +81-88-633-7468.

physiological conditions (7) such as angiogenesis and vasculardensity (8-12).
Blood flow in the carotid arteries is used as an endogenous
tracer for perfusion imaging in the ASL technique, which is themain method applied in non-enhanced perfusion MRI (6, 13).
ASL is an advanced and useful technique for assessing intracranial masses and enables differentiation of brain tumor recurrence from its necrosis. Thus, ASL plays a role in monitoring theoutcome and progress of tumor treatment (14).
The IVIM procedure concurrently determines perfusion anddiffusion parameters of tumors (3, 15-20) without the need forcontrast medium (3, 9). This method has major advantages because it is safe, repeatable, quantitative, and independent of arterial input function (3, 16, 19). Although IVIM is used to assesshead and neck tumors (5, 19), breast tumors, and other kinds oftumors and diseases (8, 18), its characteristics and differencesfrom other perfusion MRI techniques remain unknown.
The purpose of this study was to compare the IVIM and ASL
techniques with various DSC perfusion parameters and to clarify the characteristics and differences of IVIM and ASL perfusion data from those derived by the DSC method.

MATERIALS AND METHODSThe ethics committee of Tokushima University Hospital
approved this research, and informed consent was obtained
from all patients prior to their enrollment. From September
2015 to March 2017, MRI examinations including ASL, IVIM,
and DSC methods were conducted in 24 patients (15 men and 9
women ; age, 22–93 years ; mean age, 59 ± 34 years) at Tokushima

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The Journal of Medical Investigation Vol. 66 August 2019
University Hospital. Of the 24 patients, 13 underwent pathological confirmation. There were 4 cases of glioblastoma, 3 of malignant lymphoma, 3 of oligodendroglioma, and 1 each of anaplastic
astrocytoma, pilocytic astrocytoma, and diffuse astrocytoma.
The remaining 11 patients were clinically diagnosed without
pathological examination as having 7 metastatic brain tumors, 2
malignant lymphomas, and 2 diffuse astrocytomas.

MR IMAGING PROTOCOL
Imaging was performed using a 3-T scanner (Discovery
750 ; GE Healthcare, Chicago, IL) with a standard 8-channelhead coil. All MRI examinations included T2-weighted axial
IVIM, ASL, and DSC perfusion MRI.
For the ASL method, the pseudo-continuous labeling technique was used with a 3D spiral fast spin-echo sequence encompassing the whole brain. The settings applied were as
follows : 512 sampling points on 8 spirals ; field of view (FOV),
24 cm ; repetition time, 4635 ms ; echo time, 10.5 ms ; numberof excitations (NEX), 2 ; reconstructed matrix, 64 × 64 ; labeling
duration, 1650 ms ; post-labeling delay, 1525 ms ; slice thickness,
4 mm ; and number of slices, 36. The total acquisition time was 3
min 15 s. Quantitation of ASL imaging was conducted according
to the literature (21) and an ASL-cerebral blood flow (ASL-CBF)
map was generated.
DSC MRI was acquired using gradient-echo echo planar
imaging as follows : FOV, 24 cm ; repetition time, 1990 ms ; echotime, 30 ms ; reconstructed matrix, 128 × 128 ; NEX, 1 ; flip
angle, 90° ; slice thickness, 4 mm ; number of slices, 20 ; andtotal acquisition time, 100 s.
We used a standard diffusion-weighted spin-echo echo planar imaging as the IVIM sequence, with 11 different b-values(10, 20, 30, 40, 60, 80, 100, 200, 400, 800, and 1000 s/mm2) inthree orthogonal directions. The IVIM images were obtained asfollows : FOV, 24 cm ; repetition time, 4000 ms ; echo time, 58.3
ms ; slice thickness, 5 mm ; number of slices, 25 ; NEX, 1 ; andtotal scan time, 2 min 20 s.

DATA ANALYSIS
IVIM, ASL, and DSC MRI images were examined with Vitrea version 7 (Canon Medical Systems Ltd., Otawara, Japan)
based on the Bayesian fitting method. DSC data were obtainedusing an automated arterial input function (7), which was used fordeconvolution of the measured tissue tracer concentration-time
curve.
MTT maps are described as the ratio between the rBF andrBV, which is called the tissue response function. The rBF map
is the ratio between the rBV and MTT, which is the concentration–time curve. Using the numeric integration of cerebral bloodflow values, DSC-rBF maps were created (7,10). The time topeak (TTP) was calculated on DSC MRI and is the time fromthe injection of the contrast medium to the peak of the signal increase ; it reaches its highest concentration in specific areas of interest (22). The leakage or permeability rate, which is useful for
DSC tumor imaging, is described by the K2 (11). DSC corrected
relative blood volume (crBV) was calculated by the compensation
function depending on K2 values.
The IVIM data were obtained using two different models.
(a) The two-component model is expressed by :S/S0 = fexp[-bD*] + (1-f) exp[-bD]
Where D* is the diffusion coefficient of the fast component, D
is the diffusion coefficient of the slow component, f is the ratioof the fast component, b is the b-value, S is the mean diffusion309

signal intensity, and S0 is the mean signal intensity. This value
is described as the theory of the water movement, and each map
reports different information (23).
(b) The stretched model, which generates DDC and alpha (α), as
non-Gaussian diffusion indices, expressed by :S/S0 = exp {-(bc-b0) DDC} α
Where DDC is the distributed diffusion coefficient and alpha is
the heterogeneity index (0 ≤ α ≤ 1).
The regions of interest (ROIs) were drawn in the regions oftumors by avoiding hemorrhagic, necrotic, and non-parenchymal
areas using T2-weighted and contrast-enhanced T1-weighted
imaging (Fig. 1). The ROIs were copied to the ASL, IVIM, and
DSC perfusion MRI parameter maps. Circular ROIs with a diameter of 7 mm were additionally measured on normal-appearing white matter (NAWM). The mean values of the ROIs in thetumor were normalized by the ratio divided by the mean valuesof the ROIs in NAWM.

Fig 1. Oligodendroglioma. 1. Tumor region of interest (ROI).
2. Normal appearing white matter.

STATISTICAL ANALYSIS
All statistical analyses were performed with GraphPad Prism
7.00 (GraphPad Software, Inc., San Diego, CA). Pearson’s correlation was used to assess the relationship among IVIM parameters and ASL and DSC perfusion parameters. A p-value of lessthan 0.05 was considered to be significant.

RESULTSThe representative maps obtained in this study are shown in
Figures 2–4. Figure 2 shows the parameter maps derived from
ASL, IVIM, and DSC imaging of a patient with glioblastoma.
The ASL-CBF map showed high perfusion in the tumor (Fig.
2B), the D map showed inhomogeneous hyperintensity in thetumor (Fig. 2C), the D* map clearly demarcated the tumor borders as an enhancement image (Fig. 2D), and the f map clearlyhighlighted an area with high perfusion in the anterior tumor
margin (Fig. 2E). High perfusion was detected in the center of
the tumor in the rBF (Fig. 2G), rBV (Fig. 2H) and crBV (Fig. 2I)
maps. The K2 showed high value in the area of the tumor tissue

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E. Dolgorsuren, et al. Perfusion MRI for brain tumors(Fig. 2J).
Figure 3 shows various images of a patient with oligodendroglioma. Low perfusion was found in the ASL-CBF map (Fig.
3B), D* (Fig. 3D), and f (Fig. 3E) maps. rBF (Fig. 3G) and rBV(Fig. 3H) maps showed almost the same tendency as the low
perfusion.
Figure 4 shows a patient with malignant lymphoma. The
ASL-CBF map (Fig. 4B) revealed the same level in the lymphoma as in the normal white matter. The D* (Fig. 4D), rBF(Fig. 4G), rBV (Fig. 4H), and crBV (Fig. 4I) maps did not show
increased index values in the tumor. The f map showed a moderately high value in the center of the tumor (Fig. 4E).
The results of a comparison of the multiple parameters derived from the IVIM, ASL, and DSC methods are summarizedin Table 1. The ASL-CBF map showed a stronger correlation
with the DSC-rBF and crBV than the D* and f values on IVIM.
ASL was correlated with the f map on IVIM (r = 0.414, p < 0.05),
rBF (r = 0.667, p < 0.001), rBV (r = 0.584, p < 0.01), and crBV
(r = 0.662, p < 0.01), but not with the K2. The D* was correlatedwith the rBV (r = 0.604, p < 0.01), crBV (r = 0.494, p < 0.05),

and K2 (r = 0.581, p < 0.01) and the f was correlated with therCBF (r = 0.44, p < 0.05) and rBV (r = 0.435, p < 0.05), but was
only slightly correlated with the K2. The D value was strongly
correlated with the ADC (r = 0.992, p < 0.0001) and sigma(r = 0.768, p < 0.0001) but these parameters were not correlatedwith any DSC parameters. The alpha on IVIM was negatively
correlated with the ASL-CBF map (r = –0.446, p < 0.05) and thef on IVIM (r = –0.606, p < 0.01). The ASL-CBF map showed amoderate correlation with the TTP on DSC (r = 0.535, p < 0.05),
but the correlation between the ASL-CBF and K2 was weak.
The results of various perfusion parameters compared among
high-grade glioma, low-grade glioma, and malignant lymphoma
are summarized in Table 2. The mean values of high-grade
glioma derived from the ASL-CBF, f, rBF, rBV, and crBV maps
were slightly higher than those of low-grade glioma, but there
were no significant differences. The mean values of malignant
lymphoma derived from the ASL-CBF, rBF, rBV, and crBV maps
were lower than those of high-grade glioma, but the differences
between the two groups were not significant.

Fig 2. Male, aged 77 years with glioblastoma. T2 weighted image (A), Arterial spin labeling (B), D map (C), D*map (D),f map (E), ADC map (F), rBF (G), rBV (H), crBV (I), K2 (J).

Fig 3. Female, aged 67 years with oligodendroglioma. T2 weighted image (A), Arterial spin labeling (B), D map (C),D*map (D), f map (E), ADC map (F), rBF (G), rBV (H), crBV (I), K2 (J).

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The Journal of Medical Investigation Vol. 66 August 2019

Fig 4. Female, aged 40 years with malignant lymphoma. T2 weighted image (A), Arterial spin labeling (B), D map (C),D*map (D), f map (E), ADC map (F), rBF (G), rBV (H), crBV (I), K2(J).

Table 1. Correlation analysis : DSC and ASL or IVIM
ASL-CBF

D

D*

f

ADC

alfa

sigma

ASL-CBF--0.1390.361

0.414*-0.071-0.446*-0.045rBF

0.667***

0.015

0.382

0.44*

0.147

-0.507*

0.244

rBV

0.584**

0.084

0.604**

0.435*

0.249-0.371

0.391

crBV

0.662**

0.018

0.494*

0.289

0.111

-0.2280.19

MTT-0.332

0.154

0.257

0.0007

0.92

0.157

0.253

Tmax

-0.419

0.122

-0.33

-0.115

0.14

0.169

0.202

TTP

-0.535*-0.004-0.205-0.125

0.022

0.244

0.203

K2

0.344

0.153

0.581**

0.344

0.26

-0.4

0.373

tMIP

0.589**

0.034

0.523*

0.28

0.129

-0.26

0.228

Pearson`s rank correlation (no.of cases = 24) **** p < 0.0001 ; *** p < 0.001 ; ** p < 0.01 ; *p < 0.05.
ASL-CBF -arterial spin labeling, D -diffusion coefficient of slow, D* -diffusion coefficient of fast component, f -ratio of the fastcomponent, ADC -apparent diffusion coefficient, rBF -relative cerebral blood flow, rBV -relative cerebral volume, crBV -relative
cerebral blood corrected, MTT -mean transit time, Tmax -time to max, TTP -time to peak, K2 -leakage coefficient

Table 2. Comparison of brain tumors
HGG

LGG

Lymphoma

ASL-CBF

1.22 ± 13.6

1.04 ± 5.41

1.09 ± 8.93

f

2.26 ± 0.12

1.66 ± 0.35

2.56 ± 0.13

D*

0.66 ± 15.8

0.67 ± 12.36

0.80 ± 18.3

rBF

1.92 ± 11.83

1.86 ± 12.15

1.24 ± 6.16

rBV

2.18 ± 2.78

2.04 ± 1.16

1.58 ± 1.20

crBV

2.48 ± 1.00

2.28 ± 0.87

1.58 ± 0.59

MTT

1.16 ± 5.90

0.99 ± 3.03

1.06 ± 3.68

K2

2.09 ± 304.01

3.22 ± 142.02

1.72 ± 77.46

The numeric value is determined by the mean value. (mean value± standard deviation).
ASL-CBF -arterial spin labeling, D* -diffusion coefficient of fastcomponent, f -ratio of the fast component, rBF -relative cerebral
blood flow, rBV -relative cerebral volume, crBV -relative cerebral
blood corrected, MTT -mean transit time, K2 -leakage coefficient,
HGG -High-grade glioma, LGG -Low-grade glioma.

DISCUSSION
The DSC technique is the most common method for perfusion
MRI and is based on multiple theories of arterial input function(24). DSC is often used for brain tumors and can usually identify
leakage of the contrast medium in a tumor (25). The K2 parameter can usually be used to reflect permeability, and leakage
correction for rBV can be conducted (11, 26) However, the contrast agent is harmful to patients with kidney disease due tothe risk of renal failure. Because ASL and IVIM do not require
contrast medium, these methods can be used as alternative DSC
perfusion MRI techniques for patients with a contraindication tocontrast medium. Furthermore, the IVIM method can generate
diffusion parameters in addition to visualizing perfusion (5, 6).
In this study, the ASL-rCBF was more strongly correlatedwith the rBF and crBV on DSC than any other parameter derived from IVIM. Previous studies indicated the highest correlation between ASL and the DSC-rCBF, as well as between ASL
and the DSC-rBV, and that these correlations can be affected by
the blood flow rate (27). In addition, in this study, we found that

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the ASL-rCBF was slightly correlated with the K2 and strongly
correlated with the crBV than the non-corrected rBV. Moreover,
the ASL-rCBF showed a moderate correlation with the TTP on
DSC. These findings suggest that the ASL-rCBF was less influenced by permeability but included information on the transit
time (6, 28, 29).
The D* and f values on IVIM included some perfusion information that correlated with the DSC-rBV, although thecorrelation was weaker than with the ASL-CBF. According to aprevious study, the f is moderately correlated with the DSC-rBV(8). Similarly, another study (9) reported a moderate positive
correlation among the f, DSC-rBF, and rBV. Our results showedthat the f, DSC-rCBF, and DSC-rBV were identically correlatedwith each other. Based on these results, the f value can be used
to estimate the vascularity of gliomas and may be correlatedwith the DSC-rBV on DSC imaging (1, 8, 17). Previous studies
suggested that the f value can differentiate high- and low-grade
gliomas (1, 8, 23), although we failed to find a significant difference in the f value according to glioma grade, probably due to thesmall number of subjects.
Our study showed that the permeability reflected by K2 hadmore influence on the D* than the f and ASL-CBF. The D* value
is considered to contain more information on permeability due topathological changes in tumors than the f (3, 6), which cannot be
obtained on ASL perfusion data.
The correlation analysis also showed that the alpha valueon the stretched model was correlated with the ASL-rCBF andrBF. This finding was considered to indicate that the ASL-CBF
and rBF include information on the heterogeneity of blood flow,which deviates from the Gaussian distribution. The extent of the
non-Gaussian distribution of blood flow may influence the valuesof the ASL-CBF and rBF. The D and DDC were not correlatedwith any perfusion parameters derived from DSC, but the alphavalue analyzed by the stretched model was moderately correlated with the DSC-rBF. The D and DDC are independent in termsof perfusion information, but the non-Gaussian effect of IVIM issomewhat related to the rBF (16, 30).
All of the three methods have their own features. DSC MRI
is mainly used in clinics for evaluating brain tumor. However,
because gadolinium may cause side effects in some people, ASLCBF can be applicable for patients of contraindication of contrast
medium. On the other hand, IVIM imaging simultaneously
shows additional information concerning with blood flow such aspermeability and diffusion parameters.
This study has some important limitations. We included asmall number of patients in our study and half of their tumors
had not been pathologically identified. This may be why wefailed to find significant differences between high- and low-grade
glioma.
In conclusion, the ASL-CBF better reflects tissue perfusion
than any other IVIM parameters and is possibly not influenced
by permeability. The D* may contain more information on permeability than the f value. The ASL-CBF and DSC-rBF areslightly correlated with the non-Gaussian extent on IVIM. The
differences in these characteristics are important to interpret
various perfusion data using different techniques.

CONFLICT OF INTEREST
This study received partial economical support from Bayer
Pharmaceutical company.

ACKNOWLEDGEMENTS
We appreciate the cooperation of the Department of Neurosurgery managed by Prof. Yasushi Takagi. This study was supported by a Grant-in-Aid for research from the National Center of
Neurology and Psychiatry and cooperated with GE Healthcare
Japan.

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