bluuebunny/crossref_metadata_2025 huggingface dataset:
WE‐E‐18A‐04: Precision In‐Vivo Dosimetry Using Optically Stimulated Luminescence Dosimeters and a Pulsed‐Stimulating Dose Reader
Authors: Chen Q , Herrick A , Hoke S , Burns J
Published: July 2014

Purpose:

A new readout technology based on pulsed optically stimulating luminescence is introduced (microSTARii, Landauer, Inc, Glenwood, IL60425). This investigation searches for approaches that maximizes the dosimetry accuracy in clinical applications.

Methods:

The sensitivity of each optically stimulated luminescence dosimeter (OSLD) was initially characterized by exposing it to a given radiation beam. After readout, the luminescence signal stored in the OSLD was erased by exposing its sensing area to a 21W white LED light for 24 hours. A set of OSLDs with consistent sensitivities was selected to calibrate the dose reader. Higher order nonlinear curves were also derived from the calibration readings. OSLDs with cumulative doses below 15 Gy were reused. Before an in‐vivo dosimetry, the OSLD luminescence signal was erased with the white LED light.

Results:

For a set of 68 manufacturer‐screened OSLDs, the measured sensitivities vary in a range of 17.3%. A sub‐set of the OSLDs with sensitivities within ±1% was selected for the reader calibration. Three OSLDs in a group were exposed to a given radiation. Nine groups were exposed to radiation doses ranging from 0 to 13 Gy. Additional verifications demonstrated that the reader uncertainty is about 3%. With an external calibration function derived by fitting the OSLD readings to a 3rd‐order polynomial, the dosimetry uncertainty dropped to 0.5%. The dose‐luminescence response curves of individual OSLDs were characterized. All curves converge within 1% after the sensitivity correction. With all uncertainties considered, the systematic uncertainty is about 2%. Additional tests emulating in‐vivo dosimetry by exposing the OSLDs under different radiation sources confirmed the claim.

Conclusion:

The sensitivity of individual OSLD should be characterized initially. A 3rd‐order polynomial function is a more accurate representation of the dose‐luminescence response curve. The dosimetry uncertainty specified by the manufacturer is 4%. Following the proposed approach, it can be controlled to 2%.
WE‐G‐18A‐05: Cone‐Beam CT Reconstruction with Deformed Prior Image
Authors: Zhang H , Ouyang L , Huang J , Ma J , Chen W , Wang J
Published: July 2014

Purpose:

Prior image can be incorporated into image reconstruction process to improve the quality of on‐treatment cone‐beam CT (CBCT) from sparseview or low‐dose projections. However, the deformation between the prior image and on‐treatment CBCT are not considered in current prior image based reconstructions (e.g., prior image constrained compressed sensing (PICCS)). The purpose of this work is to develop a deformed‐prior‐imagebased‐ reconstruction strategy (DPIR) to address the mismatch problem between the prior image and target image.

Methods:

The deformed prior image is obtained by a projection based registration approach. Specifically, the deformation vector fields (DVF) used to deform the prior image is estimated through matching the forward projection of the prior image and the measured on‐treatment projection. The deformed prior image is then used as the prior image in the standard PICCS algorithm. Simulation studies on the XCAT phantom was conducted to evaluate the performance of the projection based registration procedure and the proposed DPIR strategy.

Results:

The deformed prior image matches the geometry of on‐treatment CBCT closer as compared to the original prior image. Using the deformed prior image, the quality of the image reconstructed by DPIR from few‐view projection data is greatly improved as compared to the standard PICCS algorithm. The relative image reconstruction error is reduced to 11.13% in the proposed DPIR from 17.57% in the original PICCS.

Conclusion:

The proposed DPIR approach can solve the mismatch problem between the prior image and target image, which overcomes the limitation of the original PICCS algorithm for CBCT reconstruction from sparse‐view or low‐dose projections.
WE‐G‐18C‐02: Estimation of Optimal B‐Value Set for Obtaining Apparent Diffusion Coefficient Free From Perfusion in Non‐Small Cell Lung Cancer
Authors: Karki K , Hugo G , Ford J , Olsen K , Saraiya S , Groves R , Weiss E
Published: July 2014

Purpose:

Diffusion‐weighted MRI (DW‐MRI) is increasingly being investigated for radiotherapy planning and response assessment. Selection of a limited number of b‐values in DW‐MRI is important to keep geometrical variations low and imaging time short. We investigated various b‐value sets to determine an optimal set for obtaining monoexponential apparent diffusion coefficient (ADC) close to perfusion‐insensitive intravoxel incoherent motion (IVIM) model ADC (ADCIVIM) in nonsmall cell lung cancer.

Methods:

Seven patients had 27 DW‐MRI scans before and during radiotherapy in a 1.5T scanner. Respiratory triggering was applied to the echo‐planar DW‐MRI with TR=4500ms approximately, TE=74ms, pixel size=1.98×1.98mm2, slice thickness=4–6mm and 7 axial slices. Diffusion gradients were applied to all three axes producing traceweighted images with eight b‐values of 0–1000μs/μm2. Monoexponential model ADC values using various b‐value sets were compared to ADCIVIM using all b‐values. To compare the relative noise in ADC maps, intra‐scan coefficient of variation (CV) of active tumor volumes was computed.

Results:

ADCIVIM, perfusion coefficient and perfusion fraction for tumor volumes were in the range of 880‐1622 μm2/s, 8119‐33834 μm2/s and 0.104–0.349, respectively. ADC values using sets of 250, 800 and 1000; 250, 650 and 1000; and 250–1000μs/μm2 only were not significantly different from ADCIVIM(p>0.05, paired t‐test). Error in ADC values for 0–1000, 50–1000, 100–1000, 250–1000, 500–1000, and three b‐value sets‐ 250, 500 and 1000; 250, 650 and 1000; and 250, 800 and 1000μs/μm2 were 15.0, 9.4, 5.6, 1.4, 11.7, 3.7, 2.0 and 0.2% relative to the reference‐standard ADCIVIM, respectively. Mean intrascan CV was 20.2, 20.9, 21.9, 24.9, 32.6, 25.8, 25.4 and 24.8%, respectively, whereas that for ADCIVIM was 23.3%.

Conclusion:

ADC values of two 3 b‐value sets (250, 650 and 1000; and 250, 800 and 1000μs/μm2) approached ADCIVIM, with relative noise comparable to that of ADCIVIM. These sets may be used to obtain perfusion‐insensitive ADC values in lung tumors.

E. Weiss: Funding through Varian Medical Systems and Philips Oncology Systems, UpToDate royalties.

G. Hugo: NIH R01CA166119, P01 CA116602, NHMRC Project Grant.
WE‐G‐18C‐03: Effects of Breathing Irregularity On Tumor Motion Measurement of 4D‐MRI: A Comparison Between Cine and Sequential Image Acquisition Modes
Authors: Liu Y , Yin F , Rhee D , Cai J
Published: July 2014

Purpose:

We have recently developed a cine‐mode T2*/T1‐weighted 4DMRI technique, and a sequential‐mode T2‐weighted 4D‐MRI for imaging respiratory motion . The latter has better tumor‐contrast due to its T2‐ weighting. This study aims at investigating which 4D‐MRI image acquisition mode, cine or sequential, provides more accurate measurement of respiratory motion.

Methods:

A 4D Digital Extended Cardiac‐Torso (XCAT) human phantom with a hypothesized tumor was used to simulate image acquisition and 4D‐MRI reconstruction. Tumor was set to move continuously with a given breathing signal. Its trajectories were measured from both sequential‐ and cine‐mode 4D‐MRI. Comparisons of measurements with the average tumor trajectories calculated from the input profile as references were conducted. Absolute tumor motion amplitude differences (D) were determined. A total of 500 simulated respiratory profiles with a wide range of irregularity were used to investigate the relationship between D and Ir. Statistical analysis of D for breathing profiles of 20 real cancer patients were conducted with a sign rank test regarding two modes. Lastly, we investigated the possibility of further improving motion measurement accuracy in sequential‐mode 4D‐MRI by removing data points of high irregularity. An irregularity filter was applied to the same 20 patients. Sign rank tests for D of sequential‐mode with filter and cine‐mode were performed.

Results:

D increased faster for cine‐mode (D=0.37*Ir) than sequential‐mode (D=0.16*Ir) as irregularity increased. For 20 cancer patients, D was 0.12cm and 0.10cm for cine‐ and sequentialmodes on average; p‐value was 0.0228. Removing highly irregular data points increased accuracy of tumor trajectory for sequential‐mode 4D‐MRI, D was decreased by 10%. P‐value was 0.0065.

Conclusion:

Tumor motion measurement is more accurate and less susceptible to breathing irregularity in sequential‐mode 4D‐MRI than that in cine‐mode 4D‐MRI; its accuracy could be further improved in sequential‐mode 4D‐MRI by selectively removing data points of high irregularity.

NIH (1R21CA165384‐01A1)
WE‐G‐18C‐04: Comparison of Image Registration Strategies to Improve DCE‐MRI Uptake Curves in Esophageal Cancer
Authors: Heethuis S , van Rossum P , Lips I , van Vulpen M , Meijer G , van Lier A
Published: July 2014

Purpose:

Neoadjuvant chemoradiotherapy (nCRT) is often used to treat resectable esophageal cancer. In 29% of the patients, nCRT results in a pathologic complete reponse (pCR). pCR is an important prognostic factor and surgery can potentially be omitted in this patient group. We will study the potential of DCE‐MRI for accurate response assessment in those patients. In preparation to this study, motion compensation by retrospective image registration strategies of DCE‐MRI time series was investigated to enable correct pharmacokinetic analysis required for response assessment.

Methods:

Five patients with biopsy‐proven esophageal cancer received a DCE‐MRI scan (62 time‐frames, 3 s/frame) prior to neoadjuvant therapy; contrast was injected after the 10th time‐frame. Three different registration strategies were reviewed; registration to: (1) 1st time‐frame, (2) 41th timeframe, and (3) previous time‐frame; rigid or affine transformations were allowed (using Elastix toolbox). We chose these time‐points as they occur well before (1) or after (2) contrast uptake. In strategy (3), the registration process is thought to be less susceptible to image intensity variations caused by contrast uptake. The effectiveness of the registration strategies was measured with SSIM (structure component of Structural SIMilarity, 1 = perfect match) metric within the volume encompassing the tumor. The 41th frame was chosen as reference image.

Results:

The average SSIM over all time‐frames and patients was: pre‐registration: 0.67, post‐registration rigid to 1st/41th/previous frame: 0.68/0.70/0.66, affine: 0.68/0.69/0.66. For rigid registration to frame 41, the minimum and maximum SSIM change per time‐frame ranged from −0.09 to 0.14.

Conclusion:

In this analysis, rigid registration to a time‐frame after contrast‐enhancement led to the best retrospective motion compensation of DCE‐MRI in esophageal cancer measured by SSIM. After verification in a larger patient group, this correction will be used prior to semi‐quantitative analysis and pharmacokinetic modeling to assess response to neoadjuvant therapy.
WE‐G‐18C‐05: Characterization of Cross‐Vendor, Cross‐Field Strength MR Image Intensity Variations
Authors: Paulson E , Prah D
Published: July 2014

Purpose:

Variations in MR image intensity and image intensity nonuniformity (IINU) can challenge the accuracy of intensity‐based image segmentation and registration algorithms commonly applied in radiotherapy. The goal of this work was to characterize MR image intensity variations across scanner vendors and field strengths commonly used in radiotherapy.

Methods:

ACR‐MRI phantom images were acquired at 1.5T and 3.0T on GE (450w and 750, 23.1), Siemens (Espree and Verio, VB17B), and Philips (Ingenia, 4.1.3) scanners using commercial spin‐echo sequences with matched parameters (TE/TR: 20/500 ms, rBW: 62.5 kHz, TH/skip: 5/5mm). Two radiofrequency (RF) coil combinations were used for each scanner: body coil alone, and combined body and phased‐array head coils. Vendorspecific B1‐ corrections (PURE/Pre‐Scan Normalize/CLEAR) were applied in all head coil cases. Images were transferred offline, corrected for IINU using the MNI N3 algorithm, and normalized. Coefficients of variation (CV=σ/μ) and peak image uniformity (PIU = 1−(Smax−Smin)/(Smax+Smin)) estimates were calculated for one homogeneous phantom slice. Kruskal‐Wallis and Wilcoxon matched‐pairs tests compared mean MR signal intensities and differences between original and N3 image CV and PIU.

Results:

Wide variations in both MR image intensity and IINU were observed across scanner vendors, field strengths, and RF coil configurations. Applying the MNI N3 correction for IINU resulted in significant improvements in both CV and PIU (p=0.0115, p=0.0235). However, wide variations in overall image intensity persisted, requiring image normalization to improve consistency across vendors, field strengths, and RF coils. These results indicate that B1‐ correction routines alone may be insufficient in compensating for IINU and image scaling, warranting additional corrections prior to use of MR images in radiotherapy.

Conclusions:

MR image intensities and IINU vary as a function of scanner vendor, field strength, and RF coil configuration. A two‐step strategy consisting of MNI N3 correction followed by normalization was required to improve MR image consistency.

Funding provided by Advancing a Healthier Wisconsin.
WE‐G‐18C‐07: Accelerated Water/fat Separation in MRI for Radiotherapy Planning Using Multi‐Band Imaging Techniques
Authors: Crijns S , Stemkens B , Sbrizzi A , Lagendijk J , van den Berg C , Andreychenko A
Published: July 2014

Purpose:

Dixon sequences are used to characterize disease processes, obtain good fat or water separation in cases where fat suppression fails and to obtain pseudo‐CT datasets. Dixon's method uses at least two images acquired with different echo times and thus requires prolonged acquisition times. To overcome associated problems (e.g., for DCE/cine‐MRI), we propose to use a method for water/fat separation based on spectrally selective RF pulses.

Methods:

Two alternating RF pulses were used, that imposes a fat selective phase cycling over the phase encoding lines, which results in a spatial shift for fat in the reconstructed image, identical to that in CAIPIRINHA. Associated aliasing artefacts were resolved using the encoding power of a multi‐element receiver array, analogous to SENSE. In vivo measurements were performed on a 1.5T clinical MR‐scanner in a healthy volunteer's legs, using a four channel receiver coil. Gradient echo images were acquired with TE/TR = 2.3/4.7ms, flip angle 20°, FOV 45×22.5cm2, matrix 480×216, slice thickness 5mm. Dixon images were acquired with TE,1/TE,2/TR=2.2/4.6/7ms. All image reconstructions were done in Matlab using the ReconFrame toolbox (Gyrotools, Zurich, CH).

Results:

RF pulse alternation yields a fat image offset from the water image. Hence the water and fat images fold over, which is resolved using in‐plane SENSE reconstruction. Using the proposed technique, we achieved excellent water/fat separation comparable to Dixon images, while acquiring images at only one echo time.

Conclusion:

The proposed technique yields both inphase water and fat images at arbitrary echo times and requires only one measurement, thereby shortening the acquisition time by a factor 2. In future work the technique may be extended to a multi‐band water/fat separation sequence that is able to achieve single point water/fat separation in multiple slices at once and hence yields higher speed‐up factors.
TH‐A‐19A‐02: Expanding TOPAS Towards Biological Modeling
Authors: Schuemann J , Giantsoudi D , Rinaldi I , Perl J , Faddegon B , Paganetti H
Published: July 2014

Purpose:

To expand Monte Carlo simulations of TOPAS to directly calculate radiobiological effects using various biological models.

Methods:

We use the TOPAS toolkit version beta9 and expand the provided scoring system to directly include scorers for relative biological effect (RBE) modeling. A scorer is a class to record a given physical property in the Monte Carlo simulations. We developed a scorer for the dose averaged linear energy transfer (LETd). This scorer was used to compare RBE weighted dose distributions assuming a constant RBE of 1.1 with the RBE weighted dose using a variable RBE based on a model using LETd and dose as input. Additionally, the local effect model (LEM) was implemented in TOPAS to estimate RBE weighted dose distributions. Survival curves for the LEM were simulated for a monoenergetic proton beams impinging on phantoms and for full patient treatment plans.

Results:

We have implemented radiobiological effect models in TOPAS, providing users with the possibility to directly obtain parameters related to radiobiological dose and use those as additional information to assess the quality of treatment plans. We applied the scorers to clinical patient treatments and phantom simulations. We find that for the proton therapy plans investigated the regions with the highest LETd are generally outside of the target volume, coinciding with the distal edge of treatment fields. LETd‐based variable RBE weighted dose models predict higher doses at the distal edge of over 10% of the prescribed dose than when assuming a constant RBE.

Conclusion:

By implementing radiobiological effect models into a Monte Carlo simulation package we combine accurate dose calculation with biological modeling. TOPAS thereby provides distributions of multiple physical quantities such as the dose and LETd, and estimates of biological parameters (RBE, survival) thus allowing direct comparison of biological effective dose predictions between different models.
TH‐A‐19A‐05: Modeling Physics Properties and Biologic Effects Induced by Proton and Helium Ions
Authors: Taleei R , Titt U , Peeler C , Guan F , Mirkovic D , Grosshans D , Mohan R
Published: July 2014

Purpose:

Currently, proton and carbon ions are used for cancer treatment. More recently, other light ions including helium ions have shown interesting physical and biological properties. The purpose of this work is to study the biological and physical properties of helium ions (He‐3) in comparison to protons.

Methods:

Monte Carlo simulations with FLUKA, GEANT4 and MCNPX were used to calculate proton and He‐3 dose distributions in water phantoms. The energy spectra of proton and He‐3 beams were calculated with high resolution for use in biological models. The repair‐misrepairfixation (RMF) model was subsequently used to calculate the RBE.

Results:

The proton Bragg curve calculations show good agreement between the three general purpose Monte Carlo codes. In contrast, the He‐3 Bragg curve calculations show disagreement (for the magnitude of the Bragg peak) between FLUKA and the other two Monte Carlo codes. The differences in the magnitude of the Bragg peak are mainly due to the discrepancy in the secondary fragmentation cross sections used by the codes. The RBE for V79 cell lines is about 0.96 and 0.98 at the entrance of proton and He‐3 ions depth dose respectively. The RBE increases to 1.06 and 1.59 at the Bragg peak of proton and He‐3 ions. The results demonstrated that LET, microdosimetric parameters (such as dose‐mean lineal energy) and RBE are nearly constant along the plateau region of Bragg curve, while all parameters increase within the Bragg peak and at the distal edge for both proton and He‐3 ions.

Conclusion:

The Monte Carlo codes should revise the fragmentation cross sections to more accurately simulate the physical properties of He‐3 ions. The increase in RBE for He‐3 ions is higher than for proton beams at the Bragg peak.
TH‐A‐19A‐07: The Effect of Particle Tracking Step Size Limit On Monte Carlo‐ Computed LET Spectrum of Therapeutic Proton Beams
Authors: Guan F , Bronk L , Kerr M , Titt U , Taleei R , Mirkovic D , Zhu X , Grosshans D , Mohan R
Published: July 2014

Purpose:

To investigate the effect of charged particle tracking step size limit in the determination of the LET spectrum of therapeutic proton beams using Monte Carlo simulations.

Methods:

The LET spectra at different depths in a water phantom from a 79.7 MeV spot‐scanning proton beam were calculated using Geant4. Five different tracking step limits 0.5 mm, 0.1 mm, 0.05 mm, 0.01 mm and 1 μm were adopted. The field size was set to 10×10 cm2 on the isocenter plane. A 40×40×6 cm3 water phantom was modelled as the irradiation target. The voxel size was set to 1×1×0.5 mm3 to obtain high resolution results. The LET spectra were scored ranging from 0.01 keV/μm to 104keV/μm in the logarithm scale. In addition, the proton energy spectra at different depths were also scored.

Results:

The LET spectra calculated using different step size limits were compared at four depths along the Bragg curve. At any depths, the spread of the LET spectra increases with the decrease of step size limit. In the dose buildup region (z = 1.9 cm) and in the region proximal to the Bragg peak (z = 3.95 cm), the frequency mean LET does not vary with decreasing step size limit. At Bragg peak (z = 4.75 cm) and in the distal edge (z = 4.85 cm), frequency mean LET decreases with decreasing step size limit. The energy spectrum at any specified depths does not vary with the step size limit.

Conclusion:

The calculated LET has a spectral distribution rather than a single value at any depths along the Bragg curve and the spread of the computed spectrum depends on the tracking step limit. Incorporating the LET spectrum distribution into the robust IMPT optimization plan may provide more accurate biological dose distribution than using the dose‐ or fluence‐averaged LET.

NIH Program Project Grant P01CA021239.
TH‐A‐19A‐10: Fast Four Dimensional Monte Carlo Dose Computations for Proton Therapy of Lung Cancer
Authors: Mirkovic D , Yepes P , Titt U , Mohan R
Published: July 2014

Purpose:

To develop and validate a fast and accurate four dimensional (4D) Monte Carlo (MC) dose computation system for proton therapy of lung cancer and other thoracic and abdominal malignancies in which the delivered dose distributions can be affected by respiratory motion of the patient.

Methods:

A 4D computer tomography (CT) scan for a lung cancer patient treated with protons in our clinic was used to create a time dependent patient model using our in‐house, MCNPX‐based Monte Carlo system (“MC2”). The beam line configurations for two passively scattered proton beams used in the actual treatment were extracted from the clinical treatment plan and a set of input files was created automatically using MC2. A full MC simulation of the beam line was computed using MCNPX and a set of phase space files for each beam was collected at the distal surface of the range compensator. The particles from these phase space files were transported through the 10 voxelized patient models corresponding to the 10 phases of the breathing cycle in the 4DCT, using MCNPX and an accelerated (fast) MC code called “FDC”, developed by us and which is based on the track repeating algorithm. The accuracy of the fast algorithm was assessed by comparing the two time dependent dose distributions.

Results:

The error of less than 1% in 100% of the voxels in all phases of the breathing cycle was achieved using this method with a speedup of more than 1000 times.

Conclusion:

The proposed method, which uses full MC to simulate the beam line and the accelerated MC code FDC for the time consuming particle transport inside the complex, time dependent, geometry of the patient shows excellent accuracy together with an extraordinary speed.
TH‐A‐BRF‐01: A Probabilistic Bayesian Approach to Derive Electron Density From MRI for Radiation Therapy Treatment Planning
Authors: Gudur M , Hara W , Wang L , Xing L , Li R
Published: July 2014

Purpose:

MRI significantly improves the accuracy and reliability of target delineation for patient simulation and treatment planning in radiation therapy, due to its superior soft tissue contrast as compared to CT. An MRI based simulation will reduce cost and simplify clinical workflow with zero ionizing radiation. However, MRI lacks the key electron density information. The purpose of this work is to develop a reliable method to derive electron density from MRI.

Methods:

We adopt a probabilistic Bayesian approach for electron density mapping based on T1‐weighted head MRI. For each voxel, we compute conditional probability of electron densities given its: (1) T1 intensity and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of test patient and atlas. Intensity and geometry information are combined into a unifying posterior probability density function whose mean gives the electron density. Mean absolute HU error between the estimated and true CT, as well as ROC's for bone detection (HU>200) were calculated for 8 patients. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water).

Results:

The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 132, compared with 139 for deformable registration (p=10−3), 371 for the intensity approach (p=10−5) and 282 without density correction (p=2×10−4). For 90% sensitivity in bone detection, the proposed method had a specificity of 85% and that for deformable registration, intensity and without density correction are 80%, 24% and 10% respectively.

Conclusion:

The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI‐based treatment planning.
TH‐A‐BRF‐04: Intra‐Fraction Motion Characterization for Early Stage Rectal Cancer Using Cine‐MRI
Authors: Kleijnen J , Asselen B , Burbach M , Intven M , Reerink O , Philippens M , Lagendijk J , Raaymakers B
Published: July 2014

Purpose:

To investigate the intra‐fraction motion in patients with early stage rectal cancer using cine‐MRI.

Methods:

Sixteen patient diagnosed with early stage rectal cancer underwent 1.5 T MR imaging prior to each treatment fraction of their short course radiotherapy (n=76). During each scan session, three 2D sagittal cine‐MRIs were performed: at the beginning (Start), after 9:30 minutes (Mid), and after 18 minutes (End). Each cine‐MRI has a duration of one minute at 2Hz temporal resolution, resulting in a total of 3:48 hours of cine‐MRI. Additionally, standard T2‐weighted (T2w) imaging was performed. Clinical target volume (CTV) an tumor (GTV) were delineated on the T2w scan and transferred to the first time‐point of each cine‐MRI scan. Within each cine‐MRI, the first frame was registered to the remaining frames of the scan, using a non‐rigid B‐spline registration. To investigate potential drifts, a similar registration was performed between the first frame of the Start and End scans. To evaluate the motion, the distances by which the edge pixels of the delineations move in anterior‐posterior (AP) and cranial‐caudal (CC) direction, were determined using the deformation field of the registrations. The distance which incorporated 95% of these edge pixels (dist95%) was determined within each cine‐MRI, and between Start‐ End scans, respectively.

Results:

Within a cine‐MRI, we observed an average dist95% for the CTV of 1.3mm/1.5mm (SD=0.7mm/0.6mm) and for the GTV of 1.2mm/1.5mm (SD=0.8mm/0.9mm), in respectively AP/CC. For the CTV motion between the Start and End scan, an average dist95% of 5.5mm/5.3mm (SD=3.1mm/2.5mm) was found, in respectively AP/CC. For the GTV motion, an average dist95% of 3.6mm/3.9mm (SD=2.2mm/2.5mm) was found in AP/CC, respectively.

Conclusion:

Although intra‐fraction motion within a one minute cine‐MRI is limited, substantial intra‐fraction motion was observed within the 18 minute time period between the Start and End cine‐MRI.
TH‐A‐BRF‐08: Deformable Registration of MRI and CT Images for MRI‐Guided Radiation Therapy
Authors: Zhong H , Wen N , Gordon J , Movsas B , Chetty I
Published: July 2014

Purpose:

To evaluate the quality of a commercially available MRI‐CT image registration algorithm and then develop a method to improve the performance of this algorithm for MRI‐guided prostate radiotherapy.

Methods:

Prostate contours were delineated on ten pairs of MRI and CT images using Eclipse. Each pair of MRI and CT images was registered with an intensity‐based B‐spline algorithm implemented in Velocity. A rectangular prism that contains the prostate volume was partitioned into a tetrahedral mesh which was aligned to the CT image. A finite element method (FEM) was developed on the mesh with the boundary constraints assigned from the Velocity generated displacement vector field (DVF). The resultant FEM displacements were used to adjust the Velocity DVF within the prism. Point correspondences between the CT and MR images identified within the prism could be used as additional boundary constraints to enforce the model deformation. The FEM deformation field is smooth in the interior of the prism, and equal to the Velocity displacements at the boundary of the prism. To evaluate the Velocity and FEM registration results, three criteria were used: prostate volume conservation and center consistence under contour mapping, and unbalanced energy of their deformation maps.

Results:

With the DVFs generated by the Velocity and FEM simulations, the prostate contours were warped from MRI to CT images. With the Velocity DVFs, the prostate volumes changed 10.2% on average, in contrast to 1.8% induced by the FEM DVFs. The average of the center deviations was 0.36 and 0.27 cm, and the unbalance energy was 2.65 and 0.38 mJ/cc3 for the Velocity and FEM registrations, respectively.

Conclusion:

The adaptive FEM method developed can be used to reduce the error of the MIbased registration algorithm implemented in Velocity in the prostate region, and consequently may help improve the quality of MRI‐guided radiation therapy.
TH‐A‐BRF‐11: Image Intensity Non‐Uniformities Between MRI Simulation and Diagnostic MRI
Authors: Paulson E
Published: July 2014

Purpose:

MRI simulation for MRI‐based radiotherapy demands that patients be setup in treatment position, which frequently involves use of alternative radiofrequency (RF) coil configurations to accommodate immobilized patients. However, alternative RF coil geometries may exacerbate image intensity non‐uniformities (IINU) beyond those observed in diagnostic MRI, which may challenge image segmentation and registration accuracy as well as confound studies assessing radiotherapy response when MR simulation images are used as baselines for evaluation. The goal of this work was to determine whether differences in IINU exist between MR simulation and diagnostic MR images.

Methods:

ACR‐MRI phantom images were acquired at 3T using a spin‐echo sequence (TE/TR:20/500ms, rBW:62.5kHz, TH/skip:5/5mm). MR simulation images were obtained by wrapping two flexible phased‐array RF coils around the phantom. Diagnostic MR images were obtained by placing the phantom into a commercial phased‐array head coil. Pre‐scan normalization was enabled in both cases. Images were transferred offline and corrected for IINU using the MNI N3 algorithm. Coefficients of variation (CV=σ/μ) were calculated for each slice. Wilcoxon matched‐pairs and Mann‐Whitney tests compared CV values between original and N3 images and between MR simulation and diagnostic MR images.

Results:

Significant differences in CV were detected between original and N3 images in both MRI simulation and diagnostic MRI groups (p=0.010, p=0.010). In addition, significant differences in CV were detected between original MR simulation and original and N3 diagnostic MR images (p=0.0256, p=0.0016). However, no significant differences in CV were detected between N3 MR simulation images and original or N3 diagnostic MR images, demonstrating the importance of correcting MR simulation images beyond pre‐scan normalization prior to use in radiotherapy.

Conclusions:

Alternative RF coil configurations used in MRI simulation can Result in significant IINU differences compared to diagnostic MR images. The MNI N3 algorithm reduced MR simulation IINU to levels observed in diagnostic MR images. Funding provided by Advancing a Healthier Wisconsin.
TH‐A‐BRF‐12: Assessment of 4D‐MRI for Robust Motion and Volume Characterization
Authors: Glide‐Hurst C , Kim J , Hu Y , Wen N , Mutic S , Chetty I
Published: July 2014

Purpose:

Precise radiation therapy for abdominal lesions is complicated by respiratory motion and poor soft tissue contrast from 4DCT whereas 4DMRI provides superior tissue discrimination. We evaluated a novel 4D‐MRI algorithm for MR‐SIM motion management.

Methods:

Respiratory‐triggered, T2‐weighted single‐shot Turbo Spin Echo 4D‐MRI was evaluated for open high‐field 1.0T MR‐SIM. A programmable platform pulled objects on a trolley ∼2 cm superior‐inferior (S‐I) for “regular” (sinusoidal, (1‐cos2), 3‐5 second periods) and “irregular” breathing patterns (exaggerated (1‐cos2) and patient curves), while a respiratory waveform was generated via a pressure sensor device. Coronal 4D‐MRIs (2–12;10 phases, TE/TR/α = 35−61/6100 ms/90°, voxel=1×1×4 mm3) were acquired for 54 unique phantom cases. Abdominal 4D−MRIs were evaluated for 5 healthy volunteers and 1 liver cancer patient (6–10 phases, TE/TR/α = 30−96/4500−6100 ms/90°, voxel=1×1×5–10 mm3) on an IRB‐approved protocol. Duty cycle, scan time, and excursion were evaluated between phase acquisitions and compared to coronal cine‐MRI (∼1 frame/sec). Maximum intensity projections (MIPs) were analyzed.

Results:

In phantom, average duty cycle was 42.6 ± 11.4% (range: 23.6–69.1%). Regular, periodic breathing (sinusoidal, (1‐cos2)) yielded higher duty cycles than irregular (48.5% and 35.9%, respectively, p<0.001) and fast periods had higher duty cycles than slow (50.4% for 3s and 39.4% for 5s, p<0.001). ∼4‐fold acquisition time increase was measured for 10‐phase versus 2‐phase. MIP renderings revealed that SI object extent was underestimated a maximum of 4% (3mm) and 8% (6mm) for cine and 2‐phase 4D‐MRI, respectively, with respect to 10‐phases. However, this was waveform dependent. A highly irregular breathing volunteer yielded lowest duty cycle (23%) and longest 10‐phase scan time (∼14 minutes), although 6‐phase acquisition for a liver cancer patient was reasonable (50% and 7.4 minutes, respectively).

Conclusion:

4D‐MRI offers potential for excursion characterization, although results suggest the use of adequate phases is important. Further application and clinical validation are warranted.

Research supported in part by a grant from Philips HealthCare (Best, Netherlands) and an Internal Mentored Grant from Henry Ford Health System.
TH‐A‐18C‐04: Ultrafast Cone‐Beam CT Scatter Correction with GPU‐Based Monte Carlo Simulation
Authors: Xu Y , Bai T , Yan H , Ouyang L , Wang J , Pompos A , Zhou L , Jiang S , Jia X
Published: July 2014

Purpose:

Scatter artifacts severely degrade image quality of cone‐beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU‐based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstructions within 30 seconds.

Methods:

The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter‐corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down‐sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC scatter noise caused by low photon numbers. The method is validated on head‐and‐neck cases with simulated and clinical data.

Results:

We have studied impacts of photo histories, volume down sampling factors on the accuracy of scatter estimation. The Fourier analysis was conducted to show that scatter images calculated at 31 angles are sufficient to restore those at all angles with <0.1% error. For the simulated case with a resolution of 512×512×100, we simulated 10M photons per angle. The total computation time is 23.77 seconds on a Nvidia GTX Titan GPU. The scatter‐induced shading/cupping artifacts are substantially reduced, and the average HU error of a region‐of‐interest is reduced from 75.9 to 19.0 HU. Similar results were found for a real patient case.

Conclusion:

A practical ultrafast MC‐based CBCT scatter correction scheme is developed. The whole process of scatter correction and reconstruction is accomplished within 30 seconds.

This study is supported in part by NIH (1R01CA154747‐01), The Core Technology Research in Strategic Emerging Industry, Guangdong, China (2011A081402003).
TH‐A‐18C‐08: Design of a Small Animal Contrast Enhanced Dual Energy CT
Authors: Martin R , Li B , Pan T
Published: July 2014

Purpose:

Dual energy CT has a variety of uses in a small animal setting including quantification and enhanced visualization of contrast agent. This study aims to determine the best energy combinations for contrast enhanced DECT on the XRAD 225Cx (Precision x‐ray), a small animal IGRT system with a nominal energy range of 20 – 225 kVp. Focus was placed on material density accuracy and low contrast detectability.

Methods:

Simulations of single energy scans of an object containing concentrations of iodine varying from 0.5 to 50 mg/ml were performed using the simulation package ImaSim. Energy spectra from 50 – 220 kVp were calculated using the same software. For approximate Poisson noise modeling, mAs were chosen such that 30% of the total 10cGy dose was assigned to the low energy scan. A calibration involving projections of objects containing different thicknesses of iodine (0–0.5 mm) and water (0–50 mm) was performed for each energy and fit to a cubic equation as the calibration curve for each energy pair.

Results:

Contrast to noise ratios of the iodine material images and accuracies in iodine density measurements were measured. Gradual improvements in each metric were seen with increasing high energy. Larger improvements in CNR were observed for decreasing the low energy. Errors in iodine density were generally close to 5% for concentrations of iodine above 3 mg/ml but increased to around 15% for 50 kVp, likely due to its proximity to the discontinuity caused by the k‐edge of iodine.

Conclusion:

Based on these simulations, the best energy combination for detecting low concentrations of iodine using a projection space calibration procedure is 50/200 kVp. However, if accuracy is most important 80/220 kVp is ideal, with 60/220 kVp being a good compromise to achieve both goals. Future work is necessary to verify these conclusions with physical data.
TH‐A‐9A‐01: Active Optical Flow Model: Predicting Voxel‐Level Dose Prediction in Spine SBRT
Authors: Liu J , Wu Q.J. , Yin F , Kirkpatrick J , Cabrera A , Ge Y
Published: July 2014

Purpose:

To predict voxel‐level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT.

Methods:

We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel‐level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model.

Results:

The DVHs predicted by the AOFM‐based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively.

Conclusion:

The AOFM is effective in predicting voxel‐level dose distribution for spine SBRT.

Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.
TH‐A‐9A‐03: Dosimetric Effect of Rotational Errors for Lung Stereotactic Body Radiotherapy
Authors: Lee J , Park J , Kim J , Kim H , Kim H , Ye S
Published: July 2014

Purpose:

To evaluate the dosimetric effects on target volume and organs at risk (OARs) due to roll rotational errors in treatment setup of stereotactic body radiation therapy (SBRT) for lung cancer.

Methods:

There were a total of 23 volumetric modulated arc therapy (VMAT) plans for lung SBRT examined in this retrospective study. Each CT image of VMAT plans was intentionally rotated by ±1°, ±2°, and ±3° to simulate roll rotational setup errors. The axis of rotation was set at the center of T‐spine. The target volume and OARs in the rotated CT images were re‐defined by deformable registration of original contours. The dose distributions on each set of rotated images were re‐calculated to cover the planning target volume (PTV) with the prescription dose before and after the couch translational correction. The dose‐volumetric changes of PTVs and spinal cords were analyzed.

Results:

The differences in D95% of PTVs by −3°, −2°, −1°, 1°, 2°, and 3° roll rotations before the couch translational correction were on average −11.3±11.4%, −5.46±7.24%, −1.11±1.38% −3.34±3.97%, −9.64±10.3%, and −16.3±14.7%, respectively. After the couch translational correction, those values were −0.195±0.544%, −0.159±0.391%, −0.188±0.262%, −0.310±0.270%, −0.407±0.331%, and −0.433±0.401%, respectively. The maximum dose difference of spinal cord among the 23 plans even after the couch translational correction was 25.9% at −3° rotation.

Conclusions:

Roll rotational setup errors in lung SBRT significantly influenced the coverage of target volume using VMAT technique. This could be in part compensated by the translational couch correction. However, in spite of the translational correction, the delivered doses to the spinal cord could be more than the calculated doses. Therefore if rotational setup errors exist during lung SBRT using VMAT technique, the rotational correction would rather be considered to prevent over‐irradiation of normal tissues than the translational correction.
TH‐C‐12A‐11: Target Correlation of a 3D Surface Surrogate for Left Breast Irradiation Using the Respiratory‐Gated Deep‐Inspiration Breath‐Hold Technique
Authors: Rong Y , Walston S
Published: July 2014

Purpose:

To evaluate the use of 3D optical surface imaging as a new surrogate for respiratory motion gated deep‐inspiration breath‐hold (DIBH) technique for left breast cancer patients.

Methods:

Patients with left‐sided breast cancer after lumpectomy or mastectomy were selected as candidates for DIBH technique for their external beam radiation therapy. Treatment plans were created on both free breathing (FB) and DIBH CTs to determine whether DIBH was beneficial in reducing heart doses. The Real‐time Position Management (RPM) system was used to acquire patient's breathing trace during DIBH CT acquisition and treatment delivery. The reference 3D surface models from FB and DIBH CTs were generated and transferred to the “AlignRT” system for patient positioning and real‐time treatment monitoring. MV Cine images were acquired for each beam as quality assurance for intra‐fractional position verification. The chest wall excursions measured on these images were used to define the actual target position during treatment, and to investigate the accuracy and reproducibility of RPM and AlignRT.

Results:

Reduction in heart dose can be achieved for left‐sided breast patients using DIBH. Results showed that RPM has poor correlation with target position, as determined by the MV Cine imaging. This indicates that RPM may not be an adequate surrogate in defining the breath‐hold level when used alone. Alternatively, the AlignRT surface imaging demonstrated a better correlation with the actual CW excursion during DIBH. Both the vertical and magnitude real‐time deltas (RTDs) reported by AlignRT can be used as the gating parameter, with a recommend threshold of ±3 mm and 5 mm, respectively.

Conclusion:

3D optical surface imaging serves as a superior target surrogate for the left breast treatment when compared to RPM. Working together with the realtime MV Cine imaging, they ensure accurate patient setup and dose delivery, while minimizing the imaging dose to patients.
TH‐E‐BRE‐01: A 3D Solver of Linear Boltzmann Transport Equation Based On a New Angular Discretization Method with Positivity for Photon Dose Calculation Benchmarked with Geant4
Authors: Hong X , Gao H
Published: July 2014

Purpose:

The Linear Boltzmann Transport Equation (LBTE) solved through statistical Monte Carlo (MC) method provides the accurate dose calculation in radiotherapy. This work is to investigate the alternative way for accurately solving LBTE using deterministic numerical method due to its possible advantage in computational speed from MC.

Methods:

Instead of using traditional spherical harmonics to approximate angular scattering kernel, our deterministic numerical method directly computes angular scattering weights, based on a new angular discretization method that utilizes linear finite element method on the local triangulation of unit angular sphere. As a Result, our angular discretization method has the unique advantage in positivity, i.e., to maintain all scattering weights nonnegative all the time, which is physically correct. Moreover, our method is local in angular space, and therefore handles the anisotropic scattering well, such as the forward‐peaking scattering. To be compatible with image‐guided radiotherapy, the spatial variables are discretized on the structured grid with the standard diamond scheme. After discretization, the improved sourceiteration method is utilized for solving the linear system without saving the linear system to memory. The accuracy of our 3D solver is validated using analytic solutions and benchmarked with Geant4, a popular MC solver.

Results:

The differences between Geant4 solutions and our solutions were less than 1.5% for various testing cases that mimic the practical cases. More details are available in the supporting document.

Conclusion:

We have developed a 3D LBTE solver based on a new angular discretization method that guarantees the positivity of scattering weights for physical correctness, and it has been benchmarked with Geant4 for photon dose calculation.