bluuebunny/crossref_metadata_2025_split huggingface dataset:
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).