X Jia

WE-FG-207B-03: Multi-Energy CT Reconstruction with Spatial Spectral Nonlocal Means Regularization

PURPOSEnMulti-energy computed tomography (MECT) is an emerging application in medical imaging due to its ability of material differentiation and potential for molecular imaging. In MECT, image correlations at different spatial and channels exist. It is desirable to incorporate these correlations in reconstruction to improve image quality. For this purpose, this study proposes a MECT reconstruction technique that employes spatial spectral non-local means (ssNLM) regularization.nnnMETHODSnWe consider a kVp-switching scanning method in which source energy is rapidly switched during data acquisition. For each energy channel, this yields projection data acquired at a number of angles, whereas projection angles among channels are different. We formulate the reconstruction task as an optimziation problem. A least square term enfores data fidelity. A ssNLM term is used as regularization to encourage similarities among image patches at different spatial locations and channels. When comparing image patches at different channels, intensity difference were corrected by a transformation estimated via histogram equalization during the reconstruction process.nnnRESULTSnWe tested our method in a simulation study with a NCAT phantom and an experimental study with a Gammex phantom. For comparison purpose, we also performed reconstructions using conjugate-gradient least square (CGLS) method and conventional NLM method that only considers spatial correlation in an image. ssNLM is able to better suppress streak artifacts. The streaks are along different projection directions in images at different channels. ssNLM discourages this dissimilarity and hence removes them. True image structures are preserved in this process. Measurements in regions of interests yield 1.1 to 3.2 and 1.5 to 1.8 times higher contrast to noise ratio than the NLM approach. Improvements over CGLS is even more profound due to lack of regularization in the CGLS method and hence amplified noise.nnnCONCLUSIONnThe proposed ssNLM method for kVp-switching MECT reconstruction can achieve high quality MECT images.

SU-C-BRC-06: OpenCL-Based Cross-Platform Monte Carlo Simulation Package for Carbon Ion Therapy

PURPOSEnMonte Carlo (MC) simulation is considered to be the most accurate method for calculation of absorbed dose and fundamental physical quantities related to biological effects in carbon ion therapy. Its long computation time impedes clinical and research applications. We have developed an MC package, goCMC, on parallel processing platforms, aiming at achieving accurate and efficient simulations for carbon therapy.nnnMETHODSngoCMC was developed under OpenCL framework. It supported transport simulation in voxelized geometry with kinetic energy up to 450 MeV/u. Class II condensed history algorithm was employed for charged particle transport with stopping power computed via Bethe-Bloch equation. Secondary electrons were not transported with their energy locally deposited. Energy straggling and multiple scattering were modeled. Production of secondary charged particles from nuclear interactions was implemented based on cross section and yield data from Geant4. They were transported via the condensed history scheme. goCMC supported scoring various quantities of interest e.g. physical dose, particle fluence, spectrum, linear energy transfer, and positron emitting nuclei.nnnRESULTSngoCMC has been benchmarked against Geant4 with different phantoms and beam energies. For 100 MeV/u, 250 MeV/u and 400 MeV/u beams impinging to a water phantom, range difference was 0.03 mm, 0.20 mm and 0.53 mm, and mean dose difference was 0.47%, 0.72% and 0.79%, respectively. goCMC can run on various computing devices. Depending on the beam energy and voxel size, it took 20∼100 seconds to simulate 107 carbons on an AMD Radeon GPU card. The corresponding CPU time for Geant4 with the same setup was 60∼100 hours.nnnCONCLUSIONnWe have developed an OpenCL-based cross-platform carbon MC simulation package, goCMC. Its accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon therapy.

SU-E-T-499: Initial Developments of An OpenCL-Based Cross-Platform Monte Carlo Dose Engine for Carbon Ion Therapy

Purpose Dose calculation is of critical importance for carbon ion therapy. Monte Carlo (MC) simulation is considered to be the most accurate method for calculation of absorbed dose and of all the more fundamental physical quantities related to biological effects. The long computation time, however, limits its routine clinical applications. We have recently started developing a fast MC package, gCMC for carbon therapy on a parallel processing platform, e.g. GPU, aiming at achieving sufficient efficiency to enable MC in clinically important tasks. This abstract reports our progress. Methods gCMC was developed in OpenCL environment. Our initial developments focused on water material. gCMC supported carbon ion transport in the energy range of 1–450 MeV/u. A Class II condensed history algorithm was implemented for charged particle transport simulations with stopping power computed via Bethe-Bloch equation. Energy straggling and multiple scattering were modeled. Total cross section of nuclear interaction was extracted from Geant4. At present, nuclear interaction events were sampled but transports of secondary particles were not included. Results We tested cases with a homogeneous water phantom and a pencil carbon ion beam with energy of 200–400 MeV/u. When only electro-magnetic channel was included, dose/fluence difference between gCMC and Geant4 results averaged within 10% isodose line was <0.5% of the maximum dose/fluence. After enabling nuclear interactions without transporting secondary particles, dose and fluence agreed with the corresponding results computed by Geant4 with <1% difference. Due to the support for multiple platforms of OpenCL, gCMC was executable on NVidia and AMD GPUs, and Intel CPUs. It took ∼50 sec to transport 107 200MeV/u source carbon ions on an NVidia Titan GPU card. Conclusion Preliminary studies have demonstrated the accuracy and efficiency of gCMC. With further developments in near future, gCMC will potentially achieve clinically acceptable fast and accurate MC simulations for carbon ion therapy.

MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring

PURPOSEnTreatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method.nnnMETHODSnThe simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a ready signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time.nnnRESULTSnAn IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time.nnnCONCLUSIONnBy embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

PURPOSEnTo develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency.nnnMETHODSnA 3D dictionary containing 256 small volumes (atoms) of 3×3×3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied.nnnRESULTSnCompared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness.nnnCONCLUSIONn3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.

SU-D-206-01: Employing a Novel Consensus Optimization Strategy to Achieve Iterative Cone Beam CT Reconstruction On a Multi-GPU Platform

PURPOSEnWhile compressed sensing-based cone-beam CT (CBCT) iterative reconstruction techniques have demonstrated tremendous capability of reconstructing high-quality images from undersampled noisy data, its long computation time still hinders wide application in routine clinic. The purpose of this study is to develop a reconstruction framework that employs modern consensus optimization techniques to achieve CBCT reconstruction on a multi-GPU platform for improved computational efficiency.nnnMETHODSnTotal projection data were evenly distributed to multiple GPUs. Each GPU performed reconstruction using its own projection data with a conventional total variation regularization approach to ensure image quality. In addition, the solutions from GPUs were subject to a consistency constraint that they should be identical. We solved the optimization problem with all the constraints considered rigorously using an alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework was implemented using OpenCL on a platform with two Nvidia GTX590 GPU cards, each with two GPUs. We studied the performance of our method and demonstrated its advantages through a simulation case with a NCAT phantom and an experimental case with a Catphan phantom.nnnRESULTnCompared with the CBCT images reconstructed using conventional FDK method with full projection datasets, our proposed method achieved comparable image quality with about one third projection numbers. The computation time on the multi-GPU platform was ∼55 s and ∼ 35 s in the two cases respectively, achieving a speedup factor of ∼ 3.0 compared with single GPU reconstruction.nnnCONCLUSIONnWe have developed a consensus ADMM-based CBCT reconstruction method which enabled performing reconstruction on a multi-GPU platform. The achieved efficiency made this method clinically attractive.

TU-AB-BRC-11: Moving a GPU-OpenCL-Based Monte Carlo (MC) Dose Engine Towards Routine Clinical Use: Automatic Beam Commissioning and Efficient Source Sampling

PURPOSEnWe have previously developed a GPU-OpenCL-based MC dose engine named goMC with built-in analytical linac beam model. To move goMC towards routine clinical use, we have developed an automatic beam-commissioning method, and an efficient source sampling strategy to facilitate dose calculations for real treatment plans.nnnMETHODSnOur commissioning method is to automatically adjust the relative weights among the sub-sources, through an optimization process minimizing the discrepancies between calculated dose and measurements. Six models built for Varian Truebeam linac photon beams (6MV, 10MV, 15MV, 18MV, 6MVFFF, 10MVFFF) were commissioned using measurement data acquired at our institution. To facilitate dose calculations for real treatment plans, we employed inverse sampling method to efficiently incorporate MLC leaf-sequencing into source sampling. Specifically, instead of sampling source particles control-point by control-point and rejecting the particles blocked by MLC, we assigned a control-point index to each sampled source particle, according to MLC leaf-open duration of each control-point at the pixel where the particle intersects the iso-center plane.nnnRESULTSnOur auto-commissioning method decreased distance-to-agreement (DTA) of depth dose at build-up regions by 36.2% averagely, making it within 1mm. Lateral profiles were better matched for all beams, with biggest improvement found at 15MV for which root-mean-square difference was reduced from 1.44% to 0.50%. Maximum differences of output factors were reduced to less than 0.7% for all beams, with largest decrease being from1.70% to 0.37% found at 10FFF. Our new sampling strategy was tested on a Head&Neck VMAT patient case. Achieving clinically acceptable accuracy, the new strategy could reduce the required history number by a factor of ∼2.8 given a statistical uncertainty level and hence achieve a similar speed-up factor.nnnCONCLUSIONnOur studies have demonstrated the feasibility and effectiveness of our auto-commissioning approach and new efficient source sampling strategy, implying the potential of our GPU-based MC dose engine goMC for routine clinical use.

TH-CD-207A-12: Impacts of Inter- and Intra-Fractional Organ Motion for High-Risk Prostate Cancer Stereotactic Body Radiation Therapy

PURPOSEnWe are conducting a clinical trial on stereotactic body radiation therapy (SBRT) for high-risk prostate cancer. Doses to three targets, prostate, intra-prostatic lesion, and pelvic lymph node (PLN) region, are escalated to three different levels via simultaneous integrated boost technique. Inter-/intra-fractional organ motions deteriorate planned dose distribution. This study aims at developing a dose reconstruction system to comprehensively understand the impacts of organ motion in our clinical trial.nnnMETHODSnA 4D dose reconstruction system has been developed for this study. Using a GPU-based Monte-Carlo dose engine and delivery log file, the system is able to reconstruct dose on static or dynamic anatomy. For prostate and intra-prostatic targets, intra-fractional motion is the main concern. Motion trajectory acquired from Calypso in previously treated SBRT patients were used to perform 4D dose reconstructions. For pelvic target, inter-fractional motion is one concern. Eight patients, each with four cone beam CTs, were used to derive fractional motion. The delivered dose was reconstructed on the deformed anatomy. Dosimetric parameters for delivered dose distributions of the three targets were extracted and compared with planned levels.nnnRESULTSnFor prostate intra-fractional motion, the mean 3D motion amplitude during beam delivery ranged from 1.5mm to 5.0mm and the average among all patients was 2.61mm. Inter-fractional motion for the PLN target was more significant. The average amplitude among patients was 4mm with the largest amplitude up to 9.6mm. The D95% deviation from planned level for prostate PTVs and GTVs are on average less than<0.1% and this deviation for intra-prostatic lesion PTVs and GTVs were more prominent. The dose at PLN was significantly affected with D95 % reduced by up to 44%.nnnCONCLUSIONnIntra-/inter-fractional organ motion is a concern for high-risk prostate SBRT, particularly for the PLN target. Our dose reconstruction approach can also serve as the basis to guide treatment adaptation.

WE-AB-207A-06: Progressive Dose Control for Cone Beam CT with Deformation Assisted Temporal Nonlocal Means Method

PURPOSEnTo reduce cone beam CT (CBCT) imaging dose, we previously proposed a progressive dose control (PDC) scheme to employ temporal correlation between CBCT images at different fractions for image quality enhancement. A temporal non-local means (TNLM) method was developed to enhance quality of a new low-dose CBCT using existing high-quality CBCT. To enhance a voxel value, the TNLM method searches for similar voxels in a window. Due to patient deformation among the two CBCTs, a large searching window was required, reducing image quality and computational efficiency. This abstract proposes a deformation-assisted TNLM (DA-TNLM) method to solve this problem.nnnMETHODSnFor a low-dose CBCT to be enhanced using a high-quality CBCT, we first performed deformable image registration between the low-dose CBCT and the high-quality CBCT to approximately establish voxel correspondence between the two. A searching window for a voxel was then set based on the deformation vector field. Specifically, the search window for each voxel was shifted by the deformation vector. A TNLM step was then applied using only voxels within this determined window to correct image intensity at the low-dose CBCT.nnnRESULTSnWe have tested the proposed scheme on simulated CIRS phantom data and real patient data. The CITS phantom was scanned on Varian onboard imaging CBCT system with coach shifting and dose reducing for each time. The real patient data was acquired in four fractions with dose reduced from standard CBCT dose to 12.5% of standard dose. It was found that the DA-TNLM method can reduce total dose by over 75% on average in the first four fractions.nnnCONCLUSIONnWe have developed a PDC scheme which can enhance the quality of image scanned at low dose using a DA-TNLM method. Tests in phantom and patient studies demonstrated promising results.

WE‐AB‐207B‐07: Dose Cloud: Generating “Big Data” for Radiation Therapy Treatment Plan Optimization Research

PURPOSEnTo provide a tool to generate large sets of realistic virtual patient geometries and beamlet doses for treatment optimization research. This tool enables countless studies exploring the fundamental interplay between patient geometry, objective functions, weight selections, and achievable dose distributions for various algorithms and modalities.nnnMETHODSnGenerating realistic virtual patient geometries requires a small set of real patient data. We developed a normalized patient shape model (PSM) which captures organ and target contours in a correspondence-preserving manner. Using PSM-processed data, we perform principal component analysis (PCA) to extract major modes of variation from the population. These PCA modes can be shared without exposing patient information. The modes are re-combined with different weights to produce sets of realistic virtual patient contours. Because virtual patients lack imaging information, we developed a shape-based dose calculation (SBD) relying on the assumption that the region inside the body contour is water. SBD utilizes a 2D fluence-convolved scatter kernel, derived from Monte Carlo simulations, and can compute both full dose for a given set of fluence maps, or produce a dose matrix (dose per fluence pixel) for many modalities. Combining the shape model with SBD provides the data needed for treatment plan optimization research.nnnRESULTSnWe used PSM to capture organ and target contours for 96 prostate cases, extracted the first 20 PCA modes, and generated 2048 virtual patient shapes by randomly sampling mode scores. Nearly half of the shapes were thrown out for failing anatomical checks, the remaining 1124 were used in computing dose matrices via SBD and a standard 7-beam protocol. As a proof of concept, and to generate data for later study, we performed fluence map optimization emphasizing PTV coverage.nnnCONCLUSIONSnWe successfully developed and tested a tool for creating customizable sets of virtual patients suitable for large-scale radiation therapy optimization research.