bluuebunny/crossref_metadata_2025 huggingface dataset:
WE‐E‐17A‐08: Prediction of Response to Neoadjuvant Chemotherapy Using a Mechanically Coupled Reaction‐Diffusion Model
Authors: Weis J , Miga M , Li X , Arlinghaus L , Chakravarthy A , Abramson V , Farley J , Yankeelov T
Published: July 2014

Purpose:

To develop a clinically‐relevant patient‐specific modeling framework for oncology that is amenable to readily available clinical imaging data and yet retains the most salient features of response prediction. We use a mechanically coupled mathematical model of tumor growth that is initialized and constrained by MRI data early in the course of therapy, to guide the determination of model parameters and predict the response of breast cancers to neoadjuvant chemotherapy (NAC).

Methods:

We adopt a patient‐scale spatiotemporal tumor growth modeling framework and apply patient‐specific predictive modeling, constrained by quantitative imaging data, to a group of 26 patients exhibiting a varying degree of response to NAC. Dynamic contrast enhanced MRI, diffusion weighted MRI, and anatomical T1‐weighted MRI volumes were acquired prior to beginning NAC, after one cycle of NAC, and at the conclusion of NAC. Tumor response is parameterized using data from before and after the first cycle of therapy, and the model is driven forward in time to predict tumor burden at the conclusion of therapy. Model reconstructed parameters and predictions are retrospectively assessed for prognostic value in predicting patients that eventually respond or do not respond to NAC.

Results:

Using our mechanics‐coupled modeling approach, we are able to discriminate, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with an area under the receiver operator characteristic curve of 0.81, sensitivity of 90%, and specificity of 56%.

Conclusion:

We show the potential for model‐predictions at the conclusion of therapy for use as a prognostic indicator of response to therapy. This work provides considerable promise for predictive modeling centered on integrating quantitative in vivo imaging data with biomechanical models of tumor growth.

National Institutes of Health NCI 1U01CA142565, NCI U01CA174706, NCI R25CA092043, NCI 1P50 098131, NCI P30CA68485, NCI R01CA138599, NINDS R01NS049251. The Vanderbilt initiative in Surgery and Engineering Pilot Award Program and the Whitaker Foundation.
WE‐G‐18A‐07: Clinical Evaluation of Normalized Metal Artifact Reduction in KVCT Using MVCT Prior Images (MVCT‐NMAR) Technique in Radiotherapy
Authors: Paudel M , MacKenzie M , Fallone B , Rathee S
Published: July 2014

Purpose:

To evaluate the metal artifacts in diagnostic kVCT images of patients that are corrected using a normalized metal artifact reduction method with MVCT prior images, MVCT‐NMAR.

Methods:

An MVCTNMAR algorithm was developed and applied to five patients: three with bilateral hip prostheses, one with unilateral hip prosthesis and one with dental fillings. The corrected images were evaluated for visualization of tissue structures and their interfaces, and for radiotherapy dose calculations. They were also compared against the corresponding images corrected by a commercial metal artifact reduction technique, O‐MAR, on a Phillips™ CT scanner.

Results:

The use of MVCT images for correcting kVCT images in the MVCT‐NMAR technique greatly reduces metal artifacts, avoids secondary artifacts, and makes patient images more useful for correct dose calculation in radiotherapy. These improvements are significant over the commercial correction method, provided the MVCT and kVCT images are correctly registered. The remaining and the secondary artifacts (soft tissue blurring, eroded bones, false bones or air pockets, CT number cupping within the metal) present in O‐MAR corrected images are removed in the MVCT‐NMAR corrected images. Large dose reduction is possible outside the planning target volume (e.g., 59.2 Gy in comparison to 52.5 Gy in pubic bone) when these MVCT‐NMAR corrected images are used in TomoTherapy™ treatment plans, as the corrected images no longer require directional blocks for prostate plans in order to avoid the image artifact regions.

Conclusion:

The use of MVCT‐NMAR corrected images in radiotherapy treatment planning could improve the treatment plan quality for cancer patients with metallic implants.

Moti Raj Paudel is supported by the Vanier Canada Graduate Scholarship, the Endowed Graduate Scholarship in Oncology and the Dissertation Fellowship at the University of Alberta. The authors acknowledge the CIHR operating grant number MOP 53254.
WE‐G‐18A‐08: Axial Cone Beam DBPF Reconstruction with Three‐Dimensional Weighting and Butterfly Filtering
Authors: Tang S , Wang W , Tang X
Published: July 2014

Purpose:

With the major benefit in dealing with data truncation for ROI reconstruction, the algorithm of differentiated backprojection followed by Hilbert filtering (DBPF) is originally derived for image reconstruction from parallel‐ or fan‐beam data. To extend its application for axial CB scan, we proposed the integration of the DBPF algorithm with 3‐D weighting. In this work, we further propose the incorporation of Butterfly filtering into the 3‐D weighted axial CB‐DBPF algorithm and conduct an evaluation to verify its performance.

Methods:

Given an axial scan, tomographic images are reconstructed by the DBPF algorithm with 3‐D weighting, in which streak artifacts exist along the direction of Hilbert filtering. Recognizing this orientation‐specific behavior, a pair of orthogonal Butterfly filtering is applied on the reconstructed images with the horizontal and vertical Hilbert filtering correspondingly. In addition, the Butterfly filtering can also be utilized for streak artifact suppression in the scenarios wherein only partial scan data with an angular range as small as 270° are available.

Results:

Preliminary data show that, with the correspondingly applied Butterfly filtering, the streak artifacts existing in the images reconstructed by the 3‐D weighted DBPF algorithm can be suppressed to an unnoticeable level. Moreover, the Butterfly filtering also works at the scenarios of partial scan, though the 3‐D weighting scheme may have to be dropped because of no sufficient projection data are available.

Conclusion:

As an algorithmic step, the incorporation of Butterfly filtering enables the DBPF algorithm for CB image reconstruction from data acquired along either a full or partial axial scan.
WE‐G‐18C‐01: Novel Method for Geometrically Undistorted B0 Inhomogeneity Field Map Estimation and Image Correction
Authors: Matakos A , Balter J , Cao Y
Published: July 2014

Purpose:

To develop and evaluate a method to estimate undistorted B0 inhomogeneity field maps of MRI and to correct susceptibility‐induced geometrical and intensity distortions in clinical images in order to support precision RT planning.

Methods:

The proposed method consisted of two major steps. First, undistorted B0 inhomogeneity field maps and images were estimated from k‐space data of a 3D dual gradient‐echo (GRE) acquisition using a novel joint reconstruction algorithm. After linearizing the cost function that includes two spatial regularizations, the algorithm iteratively estimated the undistorted field maps and images. Second, clinical images acquired with a low readout‐bandwidth in the same imaging session, e.g., 180 Hz/pixel in clinical brain T1‐weighted images, were corrected for geometrical and intensity distortions by using the undistorted field map. The method was evaluated through simulation, and studies of a geometric phantom with air‐water interfaces and human brain on a 3T scanner.

Results:

Simulation showed that our method reduced field map errors (RMS and maximum error) in the undistorted field maps ten‐fold compared to the distorted ones, and geometric and intensity artifacts in the jointly reconstructed images. In the phantom study, the 2‐mm distortion near the air‐water interface was fully corrected using the undistorted field map, but only partially corrected using the distorted field map with approximately 1‐mm residual distortion. In the patient study, there was >2 mm distortion near the sinus in the distorted field map. The geometrically accurate field map obtained by our method resulted in improved correction of geometry and intensity in both GRE and clinical T1‐weighted images at the air/tissue interface.

Conclusion:

Our method can correct geometrical distortion in clinical images, create a geometrical accurate image template, or provide a means for quality assurance of individual patient images, to enable MRI as a primary modality in precision RT planning.

Work supported by: NIH R01 EB016079
WE‐G‐18C‐06: Is Diaphragm Motion a Good Surrogate for Liver Tumor Motion?
Authors: Yang J , Cai J , Wang H , Zheng C , Czito B , Bashir M , Palta M , Yin F
Published: July 2014

Purpose:

To investigate whether diaphragm motion is a good surrogate for liver tumor motion by comparing their motion trajectories obtained from cine‐MRI.

Methods:

Fourteen patients with hepatocellular carcinoma (10/14) or liver metastases (4/14) undergoing radiation therapy were included in this study. All patients underwent single‐slice 2D cine‐MRI simulations across the center of the tumor in three orthogonal planes. Tumor and diaphragm motion trajectories in the superior‐inferior (SI), anteriorposterior (AP), and medial‐lateral (ML) directions were obtained using the normalized cross‐correlation based tracking technique. Agreement between tumor and diaphragm motions was assessed by calculating the phase difference percentage (PDP), intra‐class correlation coefficient (ICC), Bland‐Altman analysis (Diffs) and paired t‐test. The distance (D) between tumor and tracked diaphragm area was analyzed to understand its impact on the correlation between tumor and diaphragm motions.

Results:

Of all patients, the means (±standard deviations) of PDP were 7.1 (±1.1)%, 4.5 (±0.5)% and 17.5 (±4.5)% in the SI, AP and ML directions, respectively. The means of ICC were 0.98 (±0.02), 0.97 (±0.02), and 0.08 (±0.06) in the SI, AP and ML directions, respectively. The Diffs were 2.8 (±1.4) mm, 2.4 (±1.1) mm, and 2.2 (±0.5) mm in the SI, AP and ML directions, respectively. The p‐values derived from the paired t‐test were < 0.02 in SI and AP directions, whereas were > 0.58 in ML direction primarily due to the small motion in ML direction. Tumor and diaphragmatic motion had high concordance when the distance between the tumor and tracked diaphragm areas was small.

Conclusion:

Preliminary results showed that liver tumor motion had good correlations with diaphragm motion in the SI and AP directions, indicating diaphragm motion in the SI and AP directions could potentially be a reliable surrogate for liver tumor motion.

NIH (1R21CA165384‐01A1), Golfers Against Cancer (GAC) Foundation, The China Scholarship Council (CSC)
WE‐G‐18C‐08: Real Time Tumor Imaging Using a Novel Dynamic Keyhole MRI Reconstruction Technique
Authors: Lee D , Greer P , Pollock S , Whelan B , Kim T , Keall P
Published: July 2014

Purpose:

To test the hypothesis that the novel Dynamic Keyhole MRI reconstruction technique can accelerate image acquisition whilst maintaining high image quality for lung cancer patients.

Methods:

18 MRI datasets from 5 lung cancer patients were acquired using a 3T MRI scanner. These datasets were retrospectively reconstructed using (A) The novel Dynamic Keyhole technique, (B) The conventional keyhole technique and (C) the conventional zero filling technique. The dynamic keyhole technique in MRI refers to techniques in which previously acquired k‐space data is used to supplement under sampled data obtained in real time. The novel Dynamic Keyhole technique utilizes a previously acquired a library of kspace datasets in conjunction with central k‐space datasets acquired in realtime. A simultaneously acquired respiratory signal is utilized to sort, match and combine the two k‐space streams with respect to respiratory displacement. Reconstruction performance was quantified by (1) comparing the keyhole size (which corresponds to imaging speed) required to achieve the same image quality, and (2) maintaining a constant keyhole size across the three reconstruction methods to compare the resulting image quality to the ground truth image.

Results:

(1) The dynamic keyhole method required a mean keyhole size which was 48% smaller than the conventional keyhole technique and 60% smaller than the zero filling technique to achieve the same image quality. This directly corresponds to faster imaging. (2) When a constant keyhole size was utilized, the Dynamic Keyhole technique resulted in the smallest difference of the tumor region compared to the ground truth.

Conclusion:

The dynamic keyhole is a simple and adaptable technique for clinical applications requiring real‐time imaging and tumor monitoring such as MRI guided radiotherapy. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by a factor of five compared with full k‐space methods.
WE‐G‐18C‐09: Separating Perfusion and Diffusion Components From Diffusion Weighted MRI of Rectum Tumors Based On Intravoxel Incoherent Motion (IVIM) Analysis
Authors: Tyagi N , Wengler K , Mazaheri Y , Hunt M , Deasy J , Gollub M
Published: July 2014

Purpose:

Pseudodiffusion arises from the microcirculation of blood in the randomly oriented capillary network and contributes to the signal decay acquired using a multi‐b value diffusion weighted (DW)‐MRI sequence. This effect is more significant at low b‐values and should be properly accounted for in apparent diffusion coefficient (ADC) calculations. The purpose of this study was to separate perfusion and diffusion component based on a biexponential and a segmented monoexponential model using IVIM analysis

Methods.

The signal attenuation is modeled as S(b) = S0[(1−f)exp(−bD) + fexp(−bD*)]. Fitting the biexponetial decay leads to the quantification of D, the true diffusion coefficient, D*, the pseudodiffusion coefficient, and f, the perfusion fraction. A nonlinear least squares fit and two segmented monoexponential models were used to derive the values for D, D*,‘and f. In the segmented approach b = 200 s/mm2 was used as the cut‐off value for calculation of D. DW‐MRI's of a rectum cancer patient were acquired before chemotherapy, before radiation therapy (RT), and 4 weeks into RT and were investigated as an example case.

Results:

Mean ADC for the tumor drawn on the DWI cases was 0.93, 1.0 and 1.13 10−3×mm2/s before chemotherapy, before RT and 4 weeks into RT. The mean (D.10−3 × mm2/s, D* 10−3 × mm2/s, and f %) based on biexponential fit was (0.67, 18.6, and 27.2%), (0.72, 17.7, and 28.9%) and (0.83,15.1, and 30.7%) at these time points. The mean (D, D* f) based on segmented fit was (0.72, 10.5, and 12.1%), (0.72, 8.2, and 17.4%) and (.82, 8.1, 16.5%)

Conclusion:

ADC values are typically higher than true diffusion coefficients. For tumors with significant perfusion effect, ADC should be analyzed at higher b‐values or separated from the perfusion component. Biexponential fit overestimates the perfusion fraction because of increased sensitivity to noise at low b‐values.
WE‐G‐9A‐01: Radiation Oncology Outcomes Informatics
Authors: Mayo C , Miller R , Sloan J , Wu Q , Howell R
Published: July 2014

The construction of databases and support software to enable routine and systematic aggregation, analysis and reporting of patient outcomes data is emerging as an important area. “How have results for our patients been affected by the improvements we have made in our practice and in the technologies we use?” To answer this type of fundamental question about the overall pattern of efficacy observed, it is necessary to systematically gather and analyze data on all patients treated within a clinic. Clinical trials answer, in great depth and detail, questions about outcomes for the subsets of patients enrolled in a given trial. However, routine aggregation and analysis of key treatment parameter data and outcomes information for all patients is necessary to recognize emergent patterns that would be of interest from a public health or practice perspective and could better inform design of clinical trials or the evolution of best practice principals. To address these questions, Radiation Oncology outcomes databases need to be constructed to enable combination essential data from a broad group of data types including: diagnosis and staging, dose volume histogram metrics, patient reported outcomes, toxicity metrics, performance status, treatment plan parameters, demographics, DICOM data and demographics. Developing viable solutions to automate aggregation and analysis of this data requires multidisciplinary efforts to define nomenclatures, modify clinical processes and develop software and database tools requires detailed understanding of both clinical and technical issues. This session will cover the developing area of Radiation Oncology Outcomes Informatics.

Learning Objectives:

Audience will be able to speak to the technical requirements (software, database, web services) which must be considered in designing an outcomes database.

Audience will be able to understand the content and the role of patient reported outcomes as compared to traditional toxicity measures.

Audience will be understand approaches, clinical process changes, consensus building efforts and standardizations which must be addressed to succeed in a multi‐disciplinary effort to aggregate data for all patients.

Audience will be able to discuss technical and process issues related to pooling data among institutions in the context of collaborative studies among the presenting institutions.
TH‐A‐18A‐01: Innovation in Clinical Breast Imaging
Authors: Liu B , Yang K , Yaffe M , Chen J
Published: July 2014

Several novel modalities have been or are on the verge of being introduced into the breast imaging clinic. These include tomosynthesis imaging, dedicated breast CT, contrast‐enhanced digital mammography, and automated breast ultrasound, all of which are covered in this course.

Tomosynthesis and dedicated breast CT address the problem of tissue superimposition that limits mammography screening performance, by improved or full resolution of the 3D breast morphology. Contrast‐enhanced digital mammography provides functional information that allows for visualization of tumor angiogenesis. 3D breast ultrasound has high sensitivity for tumor detection in dense breasts, but the imaging exam was traditionally performed by radiologists. In automated breast ultrasound, the scan is performed in an automated fashion, making for a more practical imaging tool, that is now used as an adjunct to digital mammography in breast cancer screening.

This course will provide medical physicists with an in‐depth understanding of the imaging physics of each of these four novel imaging techniques, as well as the rationale and implementation of QC procedures. Further, basic clinical applications and work flow issues will be discussed.

Learning Objectives:

To be able to describe the underlying physical and physiological principles of each imaging technique, and to understand the corresponding imaging acquisition process.

To be able to describe the critical system components and their performance requirements.

To understand the rationale and implementation of quality control procedures, as well as regulatory requirements for systems with FDA approval.

To learn about clinical applications and understand risks and benefits/strength and weakness of each modality in terms of clinical breast imaging.
TH‐A‐19A‐01: An Open Source Software for Proton Treatment Planning in Heterogeneous Medium
Authors: Desplanques M , Wang K , Phillips J , Gueorguiev G , Baroni G , Sharp G
Published: July 2014

Purpose:

Due to its success in Radiation Oncology during the last decade, interest in proton therapy is on the rise. Unfortunately, despite the global enthusiasm in the field, there is presently no free, multiplatform and customizable Treatment Planning System (TPS) providing proton dose distributions in heterogenous medium. This restricts substantially the progress of clinical research for groups without access to a commercial Proton TPS. The latest implementation of our pencil beam dose calculation algorithm for proton beams within the 3D Slicer open‐source environment fulfills all the conditions described above.

Methods:

The core dose calculation algorithm is based on the Hong algorithm (1), which was upgraded with the Kanematsu theory describing the evolution of the lateral scattering of proton beamlets in heterogeneous medium. This algorithm deals with both mono‐energetic beams and Spread Out Bragg Peak (SOBP). In order to be user‐friendly, we provide a graphical user interface implemented with the Qt libraries, and visualization with the 3D Slicer medical image analysis software. Two different pencil beam algorithms were developed, and the clinical proton beam line at our facility was modeled.

Results:

The dose distributions provided by our algorithms were compared to dose distributions coming from both commercialized XiO TPS and literature (dose measurements, GEANT4 and MCNPx) and turned out to be in a good agreement, with maximum dose discrepancies of 5% in homogeneous phantoms and 10% in heterogeneous phantoms. The algorithm of SOBP creation from an optimized weigthing of mono‐energetic beams results in flat SOBP.

Conclusion:

We hope that our efforts in implementing this new, open‐source proton TPS will help the research groups to have a free access to a useful, reliable proton dose calculation software.(1) L. Hong et al., A pencil beam algorithm for proton dose calculations, Phys. Med. Biol. 41 (1996) 1305–1330.

This project is paid for by NCI/MGH Federal share as a research grant.
TH‐A‐19A‐03: Impact of Proton Dose Calculation Method On Delivered Dose to Lung Tumors: Experiments in Thorax Phantom and Planning Study in Patient Cohort
Authors: Grassberger C , Daartz J , Dowdell S , Ruggieri T , Sharp G , Paganetti H
Published: July 2014

Purpose:

Evaluate Monte Carlo (MC) dose calculation and the prediction of the treatment planning system (TPS) in a lung phantom and compare them in a cohort of 20 lung patients treated with protons.

Methods:

A 2‐dimensional array of ionization chambers was used to evaluate the dose across the target in a lung phantom. 20 lung cancer patients on clinical trials were re‐simulated using a validated Monte Carlo toolkit (TOPAS) and compared to the TPS.

Results:

MC increases dose calculation accuracy in lung compared to the clinical TPS significantly and predicts the dose to the target in the phantom within ±2%: the average difference between measured and predicted dose in a plane through the center of the target is 5.6% for the TPS and 1.6% for MC. MC recalculations in patients show a mean dose to the clinical target volume on average 3.4% lower than the TPS, exceeding 5% for small fields. The lower dose correlates significantly with aperture size and the distance of the tumor to the chest wall (Spearman's p=0.0002/0.004). For large tumors MC also predicts consistently higher V5 and V10 to the normal lung, due to a wider lateral penumbra, which was also observed experimentally. Critical structures located distal to the target can show large deviations, though this effect is very patient‐specific.

Conclusion:

Advanced dose calculation techniques, such as MC, would improve treatment quality in proton therapy for lung cancer by avoiding systematic overestimation of target dose and underestimation of dose to normal lung. This would increase the accuracy of the relationships between dose and effect, concerning tumor control as well as normal tissue toxicity. As the role of proton therapy in the treatment of lung cancer continues to be evaluated in clinical trials, this is of ever‐increasing importance.

This work was supported by National Cancer Institute Grant R01CA111590.
TH‐A‐19A‐04: Latent Uncertainties and Performance of a GPU‐Implemented Pre‐Calculated Track Monte Carlo Method
Authors: Renaud M , Roberge D , Seuntjens J
Published: July 2014

Purpose:

Assessing the performance and uncertainty of a pre‐calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre‐generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre‐generated track bank size can be selected for a desired dose calculation uncertainty.

Methods:

Particle tracks were pre‐generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implemented on the CUDA programming framework. GPU‐PMC dose distributions were compared to benchmark dose distributions simulated using general‐purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories.

Results:

GPU‐PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance‐to‐agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively.

Conclusion:

The GPU‐PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU‐PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams.

This work was supported in part by FRSQ‐MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP‐211360).
TH‐A‐19A‐08: Intel Xeon Phi Implementation of a Fast Multi‐Purpose Monte Carlo Simulation for Proton Therapy
Authors: Souris K , Lee J , Sterpin E
Published: July 2014

Purpose:

Recent studies have demonstrated the capability of graphics processing units (GPUs) to compute dose distributions using Monte Carlo (MC) methods within clinical time constraints. However, GPUs have a rigid vectorial architecture that favors the implementation of simplified particle transport algorithms, adapted to specific tasks. Our new, fast, and multipurpose MC code, named MCsquare, runs on Intel Xeon Phi coprocessors. This technology offers 60 independent cores, and therefore more flexibility to implement fast and yet generic MC functionalities, such as prompt gamma simulations.

Methods:

MCsquare implements several models and hence allows users to make their own tradeoff between speed and accuracy. A 200 MeV proton beam is simulated in a heterogeneous phantom using Geant4 and two configurations of MCsquare. The first one is the most conservative and accurate. The method of fictitious interactions handles the interfaces and secondary charged particles emitted in nuclear interactions are fully simulated. The second, faster configuration simplifies interface crossings and simulates only secondary protons after nuclear interaction events. Integral depth‐dose and transversal profiles are compared to those of Geant4. Moreover, the production profile of prompt gammas is compared to PENH results.

Results:

Integral depth dose and transversal profiles computed by MCsquare and Geant4 are within 3%. The production of secondaries from nuclear interactions is slightly inaccurate at interfaces for the fastest configuration of MCsquare but this is unlikely to have any clinical impact. The computation time varies between 90 seconds for the most conservative settings to merely 59 seconds in the fastest configuration. Finally prompt gamma profiles are also in very good agreement with PENH results.

Conclusion:

Our new, fast, and multi‐purpose Monte Carlo code simulates prompt gammas and calculates dose distributions in less than a minute, which complies with clinical time constraints. It has been successfully validated with Geant4.

This work has been financialy supported by InVivoIGT, a public/private partnership between UCL and IBA.
TH‐A‐19A‐09: Towards Sub‐Second Proton Dose Calculation On GPU
Authors: da Silva J
Published: July 2014

Purpose:

To achieve sub‐second dose calculation for clinically relevant proton therapy treatment plans. Rapid dose calculation is a key component of adaptive radiotherapy, necessary to take advantage of the better dose conformity offered by hadron therapy.

Methods:

To speed up proton dose calculation, the pencil beam algorithm (PBA; clinical standard) was parallelised and implemented to run on a graphics processing unit (GPU). The implementation constitutes the first PBA to run all steps on GPU, and each part of the algorithm was carefully adapted for efficiency. Monte Carlo (MC) simulations obtained using Fluka of individual beams of energies representative of the clinical range impinging on simple geometries were used to tune the PBA. For benchmarking, a typical skull base case with a spot scanning plan consisting of a total of 8872 spots divided between two beam directions of 49 energy layers each was provided by CNAO (Pavia, Italy). The calculations were carried out on an Nvidia Geforce GTX680 desktop GPU with 1536 cores running at 1006 MHz.

Results:

The PBA reproduced within ±3% of maximum dose results obtained from MC simulations for a range of pencil beams impinging on a water tank. Additional analysis of more complex slab geometries is currently under way to fine‐tune the algorithm. Full calculation of the clinical test case took 0.9 seconds in total, with the majority of the time spent in the kernel superposition step.

Conclusion:

The PBA lends itself well to implementation on many‐core systems such as GPUs. Using the presented implementation and current hardware, sub‐second dose calculation for a clinical proton therapy plan was achieved, opening the door for adaptive treatment. The successful parallelisation of all steps of the calculation indicates that further speedups can be expected with new hardware, brightening the prospects for real‐time dose calculation.

This work was funded by ENTERVISION, European Commission FP7 grant 264552.
TH‐A‐19A‐11: Validation of GPU‐Based Monte Carlo Code (gPMC) Versus Fully Implemented Monte Carlo Code (TOPAS) for Proton Radiation Therapy: Clinical Cases Study
Authors: Giantsoudi D , Schuemann J , Dowdell S , Jia X , Jiang S , Paganetti H
Published: July 2014

Purpose:

For proton radiation therapy, Monte Carlo simulation (MCS) methods are recognized as the gold‐standard dose calculation approach. Although previously unrealistic due to limitations in available computing power, GPU‐based applications allow MCS of proton treatment fields to be performed in routine clinical use, on time scales comparable to that of conventional pencil‐beam algorithms. This study focuses on validating the results of our GPU‐based code (gPMC) versus fully implemented proton therapy based MCS code (TOPAS) for clinical patient cases.

Methods:

Two treatment sites were selected to provide clinical cases for this study: head‐and‐neck cases due to anatomical geometrical complexity (air cavities and density heterogeneities), making dose calculation very challenging, and prostate cases due to higher proton energies used and close proximity of the treatment target to sensitive organs at risk. Both gPMC and TOPAS methods were used to calculate 3‐dimensional dose distributions for all patients in this study. Comparisons were performed based on target coverage indices (mean dose, V90 and D90) and gamma index distributions for 2% of the prescription dose and 2mm.

Results:

For seven out of eight studied cases, mean target dose, V90 and D90 differed less than 2% between TOPAS and gPMC dose distributions. Gamma index analysis for all prostate patients resulted in passing rate of more than 99% of voxels in the target. Four out of five head‐neck‐cases showed passing rate of gamma index for the target of more than 99%, the fifth having a gamma index passing rate of 93%.

Conclusion:

Our current work showed excellent agreement between our GPU‐based MCS code and fully implemented proton therapy based MC code for a group of dosimetrically challenging patient cases.
TH‐A‐BRE‐01: The Status of Intensity Modulated Proton and Ion Therapy
Authors: Dong L , Zhu X , Unkelbach J , Schulte R
Published: July 2014

IMRT with photons has become a radiation therapy standard of care for many cancer treatment sites. The situation is quite different with intensity modulated particle (protons and ion) radiation therapy (IMPT). With the rapid development of beam scanning techniques and many of the newer proton facilities exclusively offering active beam scanning as their radiation delivery technique, it is timely to give an update on the status and challenges of IMPT. The leading principle in IMPT is to aim at the target from several, not necessarily coplanar, directions with multiple pencil beams that are modulated in their intensity and adjusted in their energy such that a desired dose distribution or, more generally, a desired bio‐effective dose distribution is achieved. Different from low‐LET photons, the varying relative biological effectiveness (RBE) along the beam path adds an additional dimension to the treatment planning process and will require biophysical modeling at least for carbon ion therapy. IMPT involves computationally challenging tasks, yet it needs to be very fast in order to be clinically relevant. To make IMPT computationally tractable, robust and efficient optimization methods are required. Lastly, IMPT planning is very sensitive to accurate knowledge of relative stopping and scattering powers of the intervening tissues as well as intra‐ and inter‐fraction motion. Robust planning methods are being developed in order to obtain IMPT plans that are less sensitive against such uncertainties. This therapy symposium will present an update on the current status and emerging developments of IMPT from the medical physics perspective.

Learning Objectives:

Become familiar with current delivery techniques for IMPT and their limitations.

Understand the basics of dose calculational algorithms and commissioning of IMPT.

Learn how to assess the accuracy of planning and delivery of IMPT treatments.

Get an overview of currently used and emerging optimization techniques.

Learn about the pro or contra arguments for using IMPT for moving targets.
TH‐A‐BRF‐02: BEST IN PHYSICS (JOINT IMAGING‐THERAPY) – Modeling Tumor Evolution for Adaptive Radiation Therapy
Authors: Liu Y , Chan TCY , Lee CG , Cho YB , Islam MK
Published: July 2014

Purpose:

To develop mathematical models of tumor geometry changes under radiotherapy that may support future adaptive paradigms.

Methods:

A total of 29 cervical patients were scanned using MRI, once for planning and weekly thereafter for treatment monitoring. Using the tumor volumes contoured by a radiologist, three mathematical models were investigated based on the assumption of a stochastic process of tumor evolution. The “weekly MRI” model predicts tumor geometry for the following week from the last two consecutive MRI scans, based on the voxel transition probability. The other two models use only the first pair of consecutive MRI scans, and the transition probabilities were estimated via tumor type classified from the entire data set. The classification is based on either measuring the tumor volume (the “weekly volume” model), or implementing an auxiliary “Markov chain” model. These models were compared to a constant volume approach that represents the current clinical practice, using various model parameters; e.g., the threshold probability β converts the probability map into a tumor shape (larger threshold implies smaller tumor). Model performance was measured using volume conformity index (VCI), i.e., the union of the actual target and modeled target volume squared divided by product of these two volumes.

Results:

The “weekly MRI” model outperforms the constant volume model by 26% on average, and by 103% for the worst 10% of cases in terms of VCI under a wide range of β. The “weekly volume” and “Markov chain” models outperform the constant volume model by 20% and 16% on average, respectively. They also perform better than the “weekly MRI” model when β is large.

Conclusion:

It has been demonstrated that mathematical models can be developed to predict tumor geometry changes for cervical cancer undergoing radiotherapy. The models can potentially support adaptive radiotherapy paradigm by reducing normal tissue dose.

This research was supported in part by the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF) and the MITACS Accelerate Internship Program.
TH‐A‐17A‐01: Innovation in PET Instrumentation and Applications
Authors: Casey M , Miyaoka R , Shao Y
Published: July 2014

Innovation in PET instrumentation has led to the new millennium revolutionary imaging applications for diagnosis, therapeutic guidance, and development of new molecular imaging probes, etc. However, after several decades innovations, will the advances of PET technology and applications continue with the same trend and pace? What will be the next big thing beyond the PET/CT, PET/MRI, and Time‐of‐flight PET? How will the PET instrumentation and imaging performance be further improved by novel detector research and advanced imaging system development? Or will the development of new algorithms and methodologies extend the limit of current instrumentation and leapfrog the imaging quality and quantification for practical applications?

The objective of this session is to present an overview of current status and advances in the PET instrumentation and applications with speakers from leading academic institutes and a major medical imaging company. Presenting with both academic research projects and commercial technology developments, this session will provide a glimpse of some latest advances and challenges in the field, such as using semiconductor photon‐sensor based PET detectors to improve performance and enable new applications, as well as the technology trend that may lead to the next breakthrough in PET imaging for clinical and preclinical applications. Both imaging and image‐guided therapy subjects will be discussed.

Learning Objectives:

Describe the latest innovations in PET instrumentation and applications

Understand the driven force behind the PET instrumentation innovation and development

Learn the trend of PET technology development for applications
TH‐A‐18C‐01: Design Optimization of Segmented Scintillators for Megavoltage Cone‐ Beam CT
Authors: Liu L , Antonuk L , El‐Mohri Y , Zhao Q , Jiang H
Published: July 2014

Purpose:

Active matrix flat‐panel imagers incorporating thick, segmented scintillators for megavoltage cone‐beam CT (MV CBCT) imaging have demonstrated strong potential for facilitating soft‐tissue visualization at low, clinically practical doses. In order to identify scintillator design parameters that optimize performance for this purpose, a modeling technique which includes both radiation and optical effects and which lends itself to computationally practical implementation has been developed and explored.

Methods:

A hybrid modeling technique, based on Monte Carlo event‐by‐event simulation of radiation transport and separate determination of optical effects, was devised as an alternative to computationally prohibitive event‐by‐ event simulations of both radiation and optical transport. The technique was validated against empirical results from a previously reported 1.13 cm thick, 1.016 mm element‐to‐element pitch BGO scintillator prototype. Using this technique, the contrast‐to‐noise ratio (CNR) and spatial resolution performance of numerous scintillator designs, with thicknesses ranging from 0.5 to 6 cm and pitches ranging from 0.508 to 1.524 mm, were examined.

Results:

CNR and spatial resolution performance for the various scintillator designs demonstrate complex behavior as scintillator thickness and pitch are varied – exhibiting a clear trade‐off between these two imaging metrics up to a thickness of ˜3 cm. Based on these results, an optimization map highlighting those regions of design that provide a balance between these metrics was created. The map indicates that, for a given set of optical parameters, scintillator thickness and pitch can be judiciously chosen to maximize performance without resorting to thicker, more costly scintillators.

Conclusion:

Modeling radiation and optical effects in thick, segmented scintillators through use of a hybrid modeling technique provides a practical way to gain insight as to how to optimize the performance of such devices for radiotherapy imaging. Assisted by such modeling, the development of practical designs should greatly facilitate low‐dose, soft tissue visualization through MV CBCT imaging.

This project was supported in part by NIH grant R01 CA051397.
TH‐A‐18C‐02: An Electrostatic Model for Assessment of Joint Space Morphology in Cone‐Beam CT
Authors: Cao Q , Thawait G , Gang G , Zbijewski W , Riegel T , Demehri S , Siewerdsen J
Published: July 2014

Purpose:

High‐resolution cone‐beam CT (CBCT) of the extremities presents a potentially valuable basis for image‐based biomarkers of arthritis, trauma, and risk of injury. We present a new method for 3D joint space analysis that exploits the high isotropic spatial resolution of CBCT and is sensitive to small changes in disease‐related morphology.

Methods:

The approach uses an “electrostatic” model in which joint surfaces (e.g., distal femur and proximal tibia) are labeled as charge densities between which the electric field is solved by approximation to the Laplace equation. The method yields a unique solution determined by the field lines across the “capacitor” and is hypothesized to be more sensitive than conventional (Sharp) scores and immune to degeneracies that limit simple distance‐along‐axis or closest‐point analysis. The algorithm was validated in CBCT phantom images and applied in two clinical scenarios: osteoarthritis (OA, change in loadbearing tibiofemoral joint space); and assessment of injury risk (correlation of 3D joint space to tibial slope).

Results:

Joint space maps computed from the electrostatic model were accurate to within the voxel size (0.26 mm). The method highlighted subtle regions of morphological change that would likely be missed by conventional scalar metrics. Regions of subtle cartilage erosion were well quantified, and the method confidently discriminated OA and non‐OA cohorts. 3D joint space maps correlated well with tibial slope and provide a new basis for principal component analysis of loadbearing injury risk. Runtime was less than 5 min (235×235×121 voxel subvolume in Matlab).

Conclusion:

A new method for joint space assessment was reported as a possible image‐based biomarker of subtle articular change. The algorithm yields accurate quantitation of the joint in a manner that is robust against operator and patient setup variation. The method shows promising initial results in ongoing trials of CBCT in osteoarthritis, rheumatoid arthritis, and injury risk assessment.

Research supported by R01 and R21 grants from the National Institutes of Health, academic‐industry partnership with Carestream Health, and a grant from the US Army Natick Soldier Research, Development & Engineering Center.
TH‐A‐18C‐03: Noise Correlation in CBCT Projection Data and Its Application for Noise Reduction in Low‐Dose CBCT
Authors: ZHANG H , Ouyang L , Huang J , Ma J , Chen W , Wang J
Published: July 2014

Purpose:

To study the noise correlation properties of cone‐beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics‐based projection restoration algorithm for noise reduction in low‐dose CBCT.

Methods:

In this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam on‐board CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 mAs to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least‐squares (PWLS) criterion for noise reduction of low‐dose CBCT.

Results:

The analyses of the repeated measurements show that noise correlation coefficients are non‐zero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first‐ and second‐ order neighbors are about 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS‐Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS‐Dia) at the matched resolution.

Conclusion:

Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics‐based projection restoration algorithm for low‐dose CBCT.
TH‐A‐18C‐09: Ultra‐Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma
Authors: Sisniega A , Zbijewski W , Stayman J , Yorkston J , Aygun N , Koliatsos V , Siewerdsen J
Published: July 2014

Purpose:

Application of cone‐beam CT (CBCT) to low‐contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high‐quality CBCT imaging of acute brain injury.

Methods:

The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC‐GPU), with baseline simulation speed of ˜1e7 photons/sec. MC‐GPU is accelerated by a novel, GPU‐optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced for additional speed‐up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source‐detector distance 700 mm, source‐axis distance 480 mm).

Results:

For a fixed run‐time of ˜1 sec/projection, GPU‐optimized VR reduces the noise in MC‐GPU scatter estimates by a factor of 4. For scatter correction, MC‐GPU with VR is executed with 4‐fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run‐time per scan, and de‐noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast‐tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction.

Conclusion:

Acceleration of MC‐GPU achieved through GPU‐optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications.

Research grant from Carestream Health. JY is an employee of Carestream Health.