Dosimetric comparison of multiple vs single isocenter technique for linear accelerator-based stereotactic radiosurgery: The Importance of the six degree couch.

PURPOSE: Single isocenter technique (SIT) for linear accelerator-based stereotactic radiosurgery (SRS) is feasible. However, SIT introduces the potential for rotational error which can lead to geographical miss. Additional planning treatment volume (PTV) margin is required when using SIT. With the six degrees of freedom (6DoF) couch, rotational error can be minimized. We sought to evaluate the effect of the 6DoF couch on the dosimetry of patients with multiple brain metastases treated with SIT. MATERIALS AND METHODS: Ten consecutive patients treated with SRS to ≥3 metastases were identified. Original treatments had MIT plans (MITP). The lesions were replanned using SIT. Lesions 5-10 cm from isocenter had an additional 1mm of margin. Patients were replanned with these additional margins to account for inability to correct rotational error (SITPM). Multiple dosimetric variables and time metrics were evaluated. Dosimetry planning time (DPT) and patient treatment time (PTT) were evaluated. Statistics were calculated using the Wilcoxon signed-rank test. RESULTS: A total of 73 brain metastases receiving SRS, to a median of 6 lesions per patient, were identified. MITPs treated 73 lesions with 63 isocenters. On average, MITPs had a 19.2% higher brain V12 than SITPs (P = 0.017). For creation of SITPM, 30 lesions required 1 mm of additional margin, while none required 2 mm of margin. This increased V12 by 47.8% on average per patient (P = 0.008) from SITP to SITPM. DPT was 5.5 hours for SITP, while median for MITP was 12.5 hours (P = 0.005) PTT was 30 minutes for SITP, while median for MITP was 144 minutes (P = 0.005). CONCLUSIONS: SITPs are comparable to MITPs if rotational error can be corrected with the use of a 6DoF couch. Increasing margin to account for rotational error leads to a nearly 50% increase in V12, which could result in higher rates of radiation necrosis. Time savings are significant using SIT.

Clinical assessment of geometric distortion for a 0.35T MR-guided radiotherapy system.

PURPOSE: To estimate the overall spatial distortion on clinical patient images for a 0.35 T MR-guided radiotherapy system. METHODS: Ten patients with head-and-neck cancer underwent CT and MR simulations with identical immobilization. The MR images underwent the standard systematic distortion correction post-processing. The images were rigidly registered and landmark-based analysis was performed by an anatomical expert. Distortion was quantified using Euclidean distance between each landmark pair and tagged by tissue interface: bone-tissue, soft tissue, or air-tissue. For baseline comparisons, an anthropomorphic phantom was imaged and analyzed. RESULTS: The average spatial discrepancy between CT and MR landmarks was 1.15 ± 1.14 mm for the phantom and 1.46 ± 1.78 mm for patients. The error histogram peaked at 0-1 mm. 66% of the discrepancies were <2 mm and 51% <1 mm. In the patient data, statistically significant differences (p-values < 0.0001) were found between the different tissue interfaces with averages of 0.88 ± 1.24 mm, 2.01 ± 2.20 mm, and 1.41 ± 1.56 mm for the air/tissue, bone/tissue, and soft tissue, respectively. The distortion generally correlated with the in-plane radial distance from the image center along the longitudinal axis of the MR. CONCLUSION: Spatial distortion remains in the MR images after systematic distortion corrections. Although the average errors were relatively small, large distortions observed at bone/tissue interfaces emphasize the need for quantitative methods for assessing and correcting patient-specific spatial distortions.

Residual setup errors in cranial stereotactic radiosurgery without six degree of freedom robotic couch: Frameless versus rigid immobilization systems.

PURPOSE AND OBJECTIVES: This IRB-approved study was to compare the residual inter-fractional setup errors and intra-fractional motion of patients treated with cranial stereotactic radiosurgery without a 6 degree of freedom (DoF) couch. We evaluated both frameless non-invasive vacuum-suction immobilization (Aktina PinPoint) and TALON rigid screw immobilization. MATERIALS AND METHODS: Twenty consecutive patients treated by Varian TrueBeam STX or Tomotherapy were selected for data collection. The dose and number of fractions received by each patient ranged from 18 Gy in 1 fraction (SRS) to 25 Gy in 5 fractions (SRT). Twelve patients were immobilized using PinPoint, a frameless suction system (Aktina Medical, New York) and eight patients were immobilized using the TALON rigid screw system. Customized head cushions were used for all patients. Six Atkina patients received pre- and post-treatment cone-beam CT (CBCT) to evaluate the intra-fractional motion of the Aktina system. The intra-fractional motion with the TALON rigid screw system has been reported to be negligible and was not repeated in this study. All patients received pre-treatment CBCT or megavoltage CT (MVCT) to assess inter-fractional setup accuracy. Shifts to the final treatment position were determined based on matching bony anatomy in the pre-treatment setup CT and the planning CT. Setup CT and planning CT were registered retrospectively based on bony anatomy using image registration software to quantify rotational and translational errors. RESULTS: For the frameless Aktina system, mean and standard deviation of the intra-fractional motion were -0.5 ± 0.7 mm (lateral), 0.1 ± 0.9 mm (vertical), -0.5 ± 0.6 mm (longitudinal), -0.04 ± 0.18°(pitch), -0.1 ± 0.23°(yaw), and -0.03 ± 0.17°(roll) indicating negligible intra-fractional motion. Inter-fractional rotation errors were -0.10 ± 0.25° (pitch), -0.08 ± 0.16° (yaw), and -0.20 ± 0.41° (roll) for TALON rigid screw immobilization versus 0.20 ± 0.69° (pitch), 0.34 ± 0.56° (yaw), 0.35 ± 0.82° (roll) for frameless vacuum-suction immobilization showing that the rigid immobilization setup is more reproducible than the frameless immobilization. Without rotational correction by a 6 DoF couch, residual registration error exists and increases with distance from the image fusion center. In a 3D vector space, a tumor located 5 cm from the center of image fusion would require a 0.9 mm margin with the TALON system and a 2.1 mm margin with Aktina. CONCLUSIONS: With image-guided radiotherapy, translational setup errors can be corrected by image registration between pre-treatment setup CT and planning CT. However, rotational errors cannot be accounted for without a 6 DoF couch. Our study showed that the frameless Aktina immobilization system provided negligible intra-fractional motion. The inter-fractional rotation setup error using Aktina was larger than rigid immobilization with the TALON system. To treat a single lesion far from the center of image registration or for multiple lesions in a single plan, additional margin may be needed to account for the uncorrectable rotational setup errors.

Multishot diffusion-prepared magnitude-stabilized balanced steady-state free precession sequence for distortion-free diffusion imaging.

PURPOSE: To develop and evaluate a multishot diffusion-prepared (DP) magnitude-stabilized balanced steady-state free precession (bSSFP) diffusion imaging sequence with improved geometric fidelity. METHODS: A signal spoiler (magnitude stabilizer; MS) was implemented in a DP-bSSFP diffusion sequence. Effects of magnitude stabilizers with respect to phase errors were simulated using Bloch simulation. Phantom study was conducted to compare the apparent diffusion coefficient (ADC) accuracy and geometric reliability, quantified using target registration error (TRE), with diffusion-weighted single-shot echo-planar imaging (DW-ssEPI). Six volunteers were recruited. DW-ssEPI, DP-bSSFP with and without ECG triggering, and DP-MS-bSSFP with and without ECG triggering were acquired 10 times with b = 500 s/mm2 in a single-shot manner to evaluate magnitude variability. Diffusion trace images and diffusion tensor images were acquired using a 4-shot DP-MS-bSSFP. RESULTS: Simulation showed that the DP-MS-bSSFP approach is insensitive to phase errors. The DP-MS-bSSFP approach had satisfactory ADC accuracy on the phantom with <5% difference with DW-ssEPI. The mean/max TRE for DW-ssEPI was 2.31/4.29 mm and was 0.51/1.20 mm for DP-MS-bSSFP. In the repeated single-shot study, DP-bSSFP without ECG triggering had severe signal void artifacts and exhibited a nonrepeatable pattern, which can be partially mitigated by ECG triggering. Adding the MS provided stable signal magnitude across all repetitions. High-quality ADC maps and color-coded fractional anisotropy maps were generated using the 4-shot DP-MS-bSSFP. The mean/max TRE was 2.89/10.80 mm for DW-ssEPI and 0.59/1.69 mm for DP-MS-bSSFP. Good agreements of white matter ADC, cerebrospinal fluid ADC, and white matter fractional anisotropy value were observed between DP-MS-bSSFP and DW-ssEPI. CONCLUSION: The proposed DP-MS-bSSFP approach provided high-quality diffusion-weighted and diffusion-tensor images with minimal geometric distortion.

Analytic Intermodel Consistent Modeling of Volumetric Human Lung Dynamics.

Human lung undergoes breathing-induced deformation in the form of inhalation and exhalation. Modeling the dynamics is numerically complicated by the lack of information on lung elastic behavior and fluid-structure interactions between air and the tissue. A mathematical method is developed to integrate deformation results from a deformable image registration (DIR) and physics-based modeling approaches in order to represent consistent volumetric lung dynamics. The computational fluid dynamics (CFD) simulation assumes the lung is a poro-elastic medium with spatially distributed elastic property. Simulation is performed on a 3D lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of a human subject. The heterogeneous Young's modulus (YM) is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The deformation obtained from the CFD is then coupled with the displacement obtained from the 4D lung DIR by means of the Tikhonov regularization (TR) algorithm. The numerical results include 4DCT registration, CFD, and optimal displacement data which collectively provide consistent estimate of the volumetric lung dynamics. The fusion method is validated by comparing the optimal displacement with the results obtained from the 4DCT registration.

Multi-scale, multi-modal image integration for image-guided clinical interventions in the head and neck anatomy.

The aim of this paper is to enable model guided multi-scale and multi-modal image integration for the head and neck anatomy. The image modality used for this purpose includes multi-pose Magnetic Resonance Imaging (MRI), Mega Voltage CT, and hand-held Optical Coherence Tomography. A biomechanical model that incorporates subject-specific young's modulus and shear modulus properties is developed from multi-pose MRI, positioned in the treatment setup using Mega Voltage CT (MVCT), and actuated using multiple kinect surface cameras to mimic patient postures during Optical Coherence Microscopy (OCM) imaging. Two different 3D tracking mechanisms were employed for aligning the patient surface and the probe position to the MRI data. The results show the accuracy of the two tracking algorithms and the 3D head and neck deformation representing the multiple poses, the subject will take during the OCM imaging.

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy.

Background and purpose: Currently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity. Materials and methods: Algorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting. Results: ARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions. Conclusion: The high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.

Magnetic Resonance Imaging-Guided vs Computed Tomography-Guided Stereotactic Body Radiotherapy for Prostate Cancer: The MIRAGE Randomized Clinical Trial.

Importance: Magnetic resonance imaging (MRI) guidance offers multiple theoretical advantages in the context of stereotactic body radiotherapy (SBRT) for prostate cancer. However, to our knowledge, these advantages have yet to be demonstrated in a randomized clinical trial. Objective: To determine whether aggressive margin reduction with MRI guidance significantly reduces acute grade 2 or greater genitourinary (GU) toxic effects after prostate SBRT compared with computed tomography (CT) guidance. Design, Setting, and Participants: This phase 3 randomized clinical trial (MRI-Guided Stereotactic Body Radiotherapy for Prostate Cancer [MIRAGE]) enrolled men aged 18 years or older who were receiving SBRT for clinically localized prostate adenocarcinoma at a single center between May 5, 2020, and October 1, 2021. Data were analyzed from January 15, 2021, through May 15, 2022. All patients had 3 months or more of follow-up. Interventions: Patients were randomized 1:1 to SBRT with CT guidance (control arm) or MRI guidance. Planning margins of 4 mm (CT arm) and 2 mm (MRI arm) were used to deliver 40 Gy in 5 fractions. Main Outcomes and Measures: The primary end point was the incidence of acute (≤90 days after SBRT) grade 2 or greater GU toxic effects (using Common Terminology Criteria for Adverse Events, version 4.03 [CTCAE v4.03]). Secondary outcomes included CTCAE v4.03-based gastrointestinal toxic effects and International Prostate Symptom Score (IPSS)-based and Expanded Prostate Cancer Index Composite-26 (EPIC-26)-based outcomes. Results: Between May 2020 and October 2021, 156 patients were randomized: 77 to CT (median age, 71 years [IQR, 67-77 years]) and 79 to MRI (median age, 71 years [IQR, 68-75 years]). A prespecified interim futility analysis conducted after 100 patients reached 90 or more days after SBRT was performed October 1, 2021, with the sample size reestimated to 154 patients. Thus, the trial was closed to accrual early. The incidence of acute grade 2 or greater GU toxic effects was significantly lower with MRI vs CT guidance (24.4% [95% CI, 15.4%-35.4%] vs 43.4% [95% CI, 32.1%-55.3%]; P = .01), as was the incidence of acute grade 2 or greater gastrointestinal toxic effects (0.0% [95% CI, 0.0%-4.6%] vs 10.5% [95% CI, 4.7%-19.7%]; P = .003). Magnetic resonance imaging guidance was associated with a significantly smaller percentage of patients with a 15-point or greater increase in IPSS at 1 month (6.8% [5 of 72] vs 19.4% [14 of 74]; P = .01) and a significantly reduced percentage of patients with a clinically significant (≥12-point) decrease in EPIC-26 bowel scores (25.0% [17 of 68] vs 50.0% [34 of 68]; P = .001) at 1 month. Conclusions and Relevance: In this randomized clinical trial, compared with CT-guidance, MRI-guided SBRT significantly reduced both moderate acute physician-scored toxic effects and decrements in patient-reported quality of life. Longer-term follow-up will confirm whether these notable benefits persist. Trial Registration: ClinicalTrials.gov Identifier: NCT04384770.

Development and interinstitutional validation of an automatic vertebral-body misalignment error detector for cone-beam CT-guided radiotherapy.

BACKGROUND: In cone-beam computed tomography (CBCT)-guided radiotherapy, off-by-one vertebral-body misalignments are rare but serious errors that lead to wrong-site treatments. PURPOSE: An automatic error detection algorithm was developed that uses a three-branch convolutional neural network error detection model (EDM) to detect off-by-one vertebral-body misalignments using planning computed tomography (CT) images and setup CBCT images. METHODS: Algorithm training and test data consisted of planning CTs and CBCTs from 480 patients undergoing radiotherapy treatment in the thoracic and abdominal regions at two radiotherapy clinics. The clinically applied registration was used to derive true-negative (no error) data. The setup and planning images were then misaligned by one vertebral-body in both the superior and inferior directions, simulating the most likely misalignment scenarios. For each of the aligned and misaligned 3D image pairs, 2D slice pairs were automatically extracted in each anatomical plane about a point within the vertebral column. The three slice pairs obtained were then inputted to the EDM that returned a probability of vertebral misalignment. One model (EDM1 ) was trained solely on data from institution 1. EDM1 was further trained using a lower learning rate on a dataset from institution 2 to produce a fine-tuned model, EDM2 . Another model, EDM3 , was trained from scratch using a training dataset composed of data from both institutions. These three models were validated on a randomly selected and unseen dataset composed of images from both institutions, for a total of 303 image pairs. The model performances were quantified using a receiver operating characteristic analysis. Due to the rarity of vertebral-body misalignments in the clinic, a minimum threshold value yielding a specificity of at least 99% was selected. Using this threshold, the sensitivity was calculated for each model, on each institution's test set separately. RESULTS: When applied to the combined test set, EDM1 , EDM2 , and EDM3 resulted in an area under curve of 99.5%, 99.4%, and 99.5%, respectively. EDM1 achieved a sensitivity of 96% and 88% on Institution 1 and Institution 2 test set, respectively. EDM2 obtained a sensitivity of 95% on each institution's test set. EDM3 achieved a sensitivity of 95% and 88% on Institution 1 and Institution 2 test set, respectively. CONCLUSION: The proposed algorithm demonstrated accuracy in identifying off-by-one vertebral-body misalignments in CBCT-guided radiotherapy that was sufficiently high to allow for practical implementation. It was found that fine-tuning the model on a multi-facility dataset can further enhance the generalizability of the algorithm.

Setup Accuracy in Craniospinal Irradiation: Implications for Planning Treatment Volume Margins.

PURPOSE: Craniospinal irradiation (CSI) using tomotherapy has advantages over standard 3-dimensional techniques. However, there is a paucity of published data on craniospinal setup reproducibility to guide appropriate planning treatment volume (PTV) margins. We sought to evaluate the setup accuracy of patients undergoing CSI to optimize PTV margins. METHODS AND MATERIALS: We measured residual setup deviation between simulation computed tomography (CT) and daily megavoltage CT after couch shifts made by therapists after megavoltage CT-based image registration for 10 patients who completed CSI at our institution. Translational displacement values were recorded at the sella, top of T1, and top of L5 in the anteroposterior (AP) and lateral planes. Systematic and random error were calculated from displacement values. Using z score analysis, we calculated minimal PTV margins to encompass 90% of recorded fractions at each level. We evaluated whether patient characteristics predict for increased setup error using standard statistical techniques. RESULTS: The mean setup deviation in the AP plane across all treatments was 2.49, 3.40, and 3.83 mm at the sella, T1, and L5, respectively. Mean lateral setup error was 2.86, 4.02, and 5.46 mm at the sella, T1, and L5, respectively. Systematic error ranged from 0.75 to 1.01 mm at the sella, 1.09 to 1.37 mm at T1, and 1.30 to 1.50 mm at L5. Random error ranged from 1.35 to 1.41 mm at the sella, 1.48 to 1.73 mm at T1, and 2.26 to 2.37 mm at L5. The minimum margin to cover 90% of the treatments was 6.4, 8.2, and 10.5 mm at the sella, T1, and L5, respectively. There appeared to be a correlation between older age and lateral setup error in the L spine approaching statistical significance (R, 0.629; P = .052). CONCLUSIONS: Setup error increases in the caudal direction of the spine and is greater in the lateral plane compared with the AP plane. We recommend a PTV margin of 5 to 7 mm in the brain and 10 mm in the spine.

Comparative evaluation of treatment plan quality for a prototype biology-guided radiotherapy system in the treatment of nasopharyngeal carcinoma.

We aimed to compare prototype treatment plans for a new biology-guided radiotherapy (BgRT) machine in its intensity-modulated radiation therapy (IMRT) mode with those using existing IMRT delivery techniques in treatment of nasopharyngeal carcinoma (NPC). We retrospectively selected ten previous NPC patients treated in 33 fractions according to the NRG-HN001 treatment protocol. Three treatment plans were generated for each patient: a helical tomotherapy (HT) plan with a 2.5-cm jaw, a volumetric modulated arc therapy (VMAT) plan using 2 to 4 6-MV arc fields, and a prototype IMRT plan for a new BgRT system which uses a 6-MV photon beam on a ring gantry that rotates at 60 rotations per minute with a couch that moves in small incremental steps. Treatment plans were compared using dosimetric parameters to planning target volumes (PTVs) and organs at risk (OARs) as specified by the NRG-HN001 protocol. Plans for the three modalities had comparable dose coverage, mean dose, and dose heterogeneity to the primary PTV, while the prototype IMRT plans had greater dose heterogeneity to the non-primary PTVs, with the average homogeneity index ranging from 1.28 to 1.50 in the prototype plans. Six of all the 7 OAR mean dose parameters were lower with statistical significance in the prototype plans compared to the HT and VMAT plans with the other mean dose parameter being comparable, and all the 18 OAR maximum dose parameters were comparable or lower with statistical significance in the prototype plans. The average left and right parotid mean doses in the prototype plans were 10.5 Gy and 10.4 Gy lower than those in the HT plans, respectively, and were 5.1 Gy and 5.2 Gy lower than those in the VMAT plans, respectively. Compared to that with the HT and VMAT plans, the treatment time was longer with statistical significance with the prototype IMRT plans. Based on dosimetric comparison of ten NPC cases, the prototype IMRT plans achieved comparable or better critical organ sparing compared to the HT and VMAT plans for definitive NPC radiotherapy. However, there was higher dose heterogeneity to non-primary targets and longer estimated treatment time with the prototype plans.

Systematic feasibility analysis of performing elastography using reduced dose CT lung image pairs.

PURPOSE: Elastography using computer tomography (CT) is a promising methodology that can provide patient-specific regional distributions of lung biomechanical properties. The purpose of this paper is to investigate the feasibility of performing elastography using simulated lower dose CT scans. METHODS: A cohort of eight patient CT image pairs were acquired with a tube current-time product of 40 mAs for estimating baseline lung elastography results. Synthetic low mAs CT scans were generated from the baseline scans to simulate the additional noise that would be present in acquisitions at 30, 25, and 20 mAs, respectively. For the simulated low mAs scans, exhalation and inhalation datasets were registered using an in-house optical flow deformable image registration algorithm. The registered deformation vector fields (DVFs) were taken to be ground truth for the elastography process. A model-based elasticity estimation was performed for each of the reduced mAs datasets, in which the goal was to optimize the elasticity distribution that best represented their respective DVFs. The estimated elasticity and the DVF distributions of the reduced mAs scans were then compared with the baseline elasticity results for quantitative accuracy purposes. RESULTS: The DVFs for the low mAs and baseline scans differed from each other by an average of 1.41 mm, which can be attributed to the noise added by the simulated reduction in mAs. However, the elastography results using the DVFs from the reduced mAs scans were similar from the baseline results, with an average elasticity difference of 0.65, 0.71, and 0.76 kPa, respectively. This illustrates that elastography can provide equivalent results using low-dose CT scans. CONCLUSIONS: Elastography can be performed equivalently using CT image pairs acquired with as low as 20 mAs. This expands the potential applications of CT-based elastography.

Parallel beamlet dose calculation via beamlet contexts in a distributed multi-GPU framework.

PURPOSE: Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model-based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under-utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi-GPU computational nodes. METHODS: The proposed context-based transformation separates beamlet-local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed-cone convolution superposition procedure, producing the set of beamlet-specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager-worker architecture is constructed around the context-based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context-based approach compared to Monte Carlo and a reference CPU-CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet-sequential GPU-CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs. RESULTS: The dosimetric accuracy of the context-based method displays <1.35% and 2.35% average error from the existing serialized CPU-CCCS algorithm and Monte Carlo simulation for beamlet-specific PDDs in water and slab phantoms, respectively. The context-based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet-sequential GPU-CCCS method in the tested configurations. The context-based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization. CONCLUSIONS: The context-based approach demonstrates a new expectation of performance for beamlet-based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large-scale treatment planning problems - such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets - in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large-scale treatment planning tasks and clinical workflows.

Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients.

OBJECTIVE:: Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity. METHODS:: We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis. RESULTS:: The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950. CONCLUSIONS:: Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups. ADVANCES IN KNOWLEDGE:: The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.

Estimation and validation of patient-specific high-resolution lung elasticity derived from 4DCT.

PURPOSE: Lung diseases are commonly associated with changes in lung tissue's biomechanical properties. Functional imaging techniques, such as elastography, have shown great promise in measuring tissue's biomechanical properties, which could expand the utility and effectiveness of radiotherapy treatment planning. We present a novel methodology for characterizing a key biomechanical property, parenchymal elasticity, derived solely from 4DCT datasets. METHODS: Specifically, end-inhalation and end-exhalation breathing phases of the 4DCT datasets were deformably registered and the resulting displacement maps were considered to be ground-truth. A mid-exhalation image was also prepared for verification purposes. A GPU-based biomechanical model was then generated from the patient end-exhalation dataset and used as a forward model to iteratively solve for the elasticity distribution. Displacements at the surface of the lungs were applied as boundary constraints for the model-guided tissue elastography, while the inner voxels were allowed to deform according to the linear elastic forces within the biomechanical model. A convergence criteria of 10% maximum deformation was employed for the iterative process. RESULTS: The lung tissue elasticity estimation was documented for a set of 15 4DCT patient datasets. Maximum lung deformations were observed to be between 6 and 31 mm. Our results showed that, on average, 89.91 ± 4.85% convergence was observed. A validation study consisting of mid-exhalation breathing phases illustrated an accuracy of 87.13 ± 10.62%. Structural similarity, normalized cross-correlation, and mutual information were used to quantify the degree of similarity between the following image pairs: (a) the model-generated end-exhalation and ground-truth end-exhalation, and (b) model-generated mid-exhalation images and ground-truth mid-exhalation. CONCLUSIONS: Overall, the results suggest that the lung elasticity can be measured with approximately 90% convergence using routinely acquired clinical 4DCT scans, indicating the potential for a lung elastography implementation within the radiotherapy clinical workflow. The regional lung elasticity found here can lead to improved tissue sparing radiotherapy treatment plans, and more precise monitoring of treatment response.

A neural network approach for fast, automated quantification of DIR performance.

PURPOSE: A critical step in adaptive radiotherapy (ART) workflow is deformably registering the simulation CT with the daily or weekly volumetric imaging. Quantifying the deformable image registration accuracy under these circumstances is a complex task due to the lack of known ground-truth landmark correspondences between the source data and target data. Generating landmarks manually (using experts) is time-consuming, and limited by image quality and observer variability. While image similarity metrics (ISM) may be used as an alternative approach to quantify the registration error, there is a need to characterize the ISM values by developing a nonlinear cost function and translate them to physical distance measures in order to enable fast, quantitative comparison of registration performance. METHODS: In this paper, we present a proof-of-concept methodology for automated quantification of DIR performance. A nonlinear cost function was developed as a combination of ISM values and governed by the following two expectations for an accurate registration: (a) the deformed data obtained from transforming the simulation CT data with the deformation vector field (DVF) should match the target image data with near perfect similarity, and (b) the similarity between the simulation CT and deformed data should match the similarity between the simulation CT and the target image data. A deep neural network (DNN) was developed that translated the cost function values to actual physical distance measure. To train the neural network, patient-specific biomechanical models of the head-and-neck anatomy were employed. The biomechanical model anatomy was systematically deformed to represent changes in patient posture and physiological regression. Volumetric source and target images with known ground-truth deformations vector fields were then generated, representing the daily or weekly imaging data. Annotated data was then fed through a supervised machine learning process, iteratively optimizing a nonlinear model able to predict the target registration error (TRE) for given ISM values. The cost function for sub-volumes enclosing critical radiotherapy structures in the head-and-neck region were computed and compared with the ground truth TRE values. RESULTS: When examining different combinations of registration parameters for a single DIR, the neural network was able to quantify DIR error to within a single voxel for 95% of the sub-volumes examined. In addition, correlations between the neural network predicted error and the ground-truth TRE for the Planning Target Volume and the parotid contours were consistently observed to be > 0.9. For variations in posture and tumor regression for 10 different patients, patient-specific neural networks predicted the TRE to within a single voxel > 90% on average. CONCLUSIONS: The formulation presented in this paper demonstrates the ability for fast, accurate quantification of registration performance. DNN provided the necessary level of abstraction to estimate a quantified TRE from the ISM expectations described above, when sufficiently trained on annotated data. In addition, biomechanical models facilitated the DNN with the required variations in the patient posture and physiological regression. With further development and validation on clinical patient data, such networks have potential impact in patient and site-specific optimization, and stream-lining clinical registration validation.

Distortion-free diffusion MRI using an MRI-guided Tri-Cobalt 60 radiotherapy system: Sequence verification and preliminary clinical experience.

PURPOSE: Monitoring tumor response during the course of treatment and adaptively modifying treatment plan based on tumor biological feedback may represent a new paradigm for radiotherapy. Diffusion MRI has shown great promises in assessing and predicting tumor response to radiotherapy. However, the conventional diffusion-weighted single-shot echo-planar-imaging (DW-ssEPI) technique suffers from limited resolution, severe distortion, and possibly inaccurate ADC at low field strength. The purpose of this work was to develop a reliable, accurate and distortion-free diffusion MRI technique that is practicable for longitudinal tumor response evaluation and adaptive radiotherapy on a 0.35 T MRI-guided radiotherapy system. METHODS: A diffusion-prepared turbo spin echo readout (DP-TSE) sequence was developed and compared with the conventional diffusion-weighted single-shot echo-planar-imaging sequence on a 0.35 T MRI-guided radiotherapy system (ViewRay). A spatial integrity phantom was used to quantitate and compare the geometric accuracy of the two diffusion sequences for three orthogonal orientations. The apparent diffusion coefficient (ADC) accuracy was evaluated on a diffusion phantom under both 0 °C and room temperature to cover a diffusivity range between 0.40 × 10-3 and 2.10 × 10-3 mm2 /s. Ten room temperature measurements repeated on five different days were conducted to assess the ADC reproducibility of DP-TSE. Two glioblastoma (GBM) and six sarcoma patients were included to examine the in vivo feasibility. The target registration error (TRE) was calculated to quantitate the geometric accuracy where structural CT or MR images were co-registered to the diffusion images as references. ADC maps from DP-TSE and DW-ssEPI were calculated and compared. A tube phantom was placed next to patients not treated on ViewRay, and ADCs of this reference tube were also compared. RESULTS: The proposed DP-TSE passed the spatial integrity test (< 1 mm within 100 mm radius and < 2 mm within 175 mm radius) under the three orthogonal orientations. The detected errors were 0.474 ± 0.355 mm, 0.475 ± 0.287 mm, and 0.546 ± 0.336 mm in the axial, coronal, and sagittal plane. DW-ssEPI, however, failed the tests due to severe distortion and low signal intensity. Noise correction must be performed for the DW-ssEPI to avoid ADC quantitation errors, whereas it is optional for DP-TSE. At 0 °C, the two sequences provided accurate quantitation with < 3% variation with the reference. In the room temperature study, discrepancies between ADCs from DP-TSE and the reference were within 4%, but could be as high as 8% for DW-ssEPI after the noise correction. Excellent ADC reproducibility with a coefficient of variation < 5% was observed among the 10 measurements of DP-TSE, indicating desirable robustness for ADC-based tumor response assessment. In vivo TRE in DP-TSE was less than 1.6 mm overall, whereas it could be greater than 12 mm in DW-ssEPI. For GBM patients, the CSF and brain tissue ADCs from DP-TSE were within the ranges found in literature. ADC differences between the two techniques were within 8% among the six sarcoma patients. For the reference tube that had a relatively low diffusivity, the two diffusion sequences provided matched measurements. CONCLUSION: A diffusion technique with excellent geometric fidelity, accurate, and reproducible ADC measurement was demonstrated for longitudinal tumor response assessment using a low-field MRI-guided radiotherapy system.

Systematic feasibility analysis of a quantitative elasticity estimation for breast anatomy using supine/prone patient postures.

PURPOSE: Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets. The purpose of this paper is to describe a quantitative elasticity estimation technique for breast anatomy using only these supine/prone patient postures. Using biomechanical, high-resolution breast geometry obtained from CT scans, a systematic assessment was performed in order to determine the feasibility of this methodology for clinically relevant elasticity distributions. METHODS: A model-guided inverse analysis approach is presented in this paper. A graphics processing unit (GPU)-based linear elastic biomechanical model was employed as a forward model for the inverse analysis with the breast geometry in a prone position. The elasticity estimation was performed using a gradient-based iterative optimization scheme and a fast-simulated annealing (FSA) algorithm. Numerical studies were conducted to systematically analyze the feasibility of elasticity estimation. For simulating gravity-induced breast deformation, the breast geometry was anchored at its base, resembling the chest-wall/breast tissue interface. Ground-truth elasticity distributions were assigned to the model, representing tumor presence within breast tissue. Model geometry resolution was varied to estimate its influence on convergence of the system. A priori information was approximated and utilized to record the effect on time and accuracy of convergence. The role of the FSA process was also recorded. A novel error metric that combined elasticity and displacement error was used to quantify the systematic feasibility study. For the authors' purposes, convergence was set to be obtained when each voxel of tissue was within 1 mm of ground-truth deformation. RESULTS: The authors' analyses showed that a ∼97% model convergence was systematically observed with no-a priori information. Varying the model geometry resolution showed no significant accuracy improvements. The GPU-based forward model enabled the inverse analysis to be completed within 10-70 min. Using a priori information about the underlying anatomy, the computation time decreased by as much as 50%, while accuracy improved from 96.81% to 98.26%. The use of FSA was observed to allow the iterative estimation methodology to converge more precisely. CONCLUSIONS: By utilizing a forward iterative approach to solve the inverse elasticity problem, this work indicates the feasibility and potential of the fast reconstruction of breast tissue elasticity using supine/prone patient postures.

GPU-Based Parallelized Solver for Large Scale Vascular Blood Flow Modeling and Simulations.

Cardio-vascular blood flow simulations are essential in understanding the blood flow behavior during normal and disease conditions. To date, such blood flow simulations have only been done at a macro scale level due to computational limitations. In this paper, we present a GPU based large scale solver that enables modeling the flow even in the smallest arteries. A mechanical equivalent of the circuit based flow modeling system is first developed to employ the GPU computing framework. Numerical studies were employed using a set of 10 million connected vascular elements. Run-time flow analysis were performed to simulate vascular blockages, as well as arterial cut-off. Our results showed that we can achieve ~100 FPS using a GTX 680m and ~40 FPS using a Tegra K1 computing platform.

Near Real-Time Assessment of Anatomic and Dosimetric Variations for Head and Neck Radiation Therapy via Graphics Processing Unit-based Dose Deformation Framework.

PURPOSE: The purpose of this study was to systematically monitor anatomic variations and their dosimetric consequences during intensity modulated radiation therapy (IMRT) for head and neck (H&N) cancer by using a graphics processing unit (GPU)-based deformable image registration (DIR) framework. METHODS AND MATERIALS: Eleven IMRT H&N patients undergoing IMRT with daily megavoltage computed tomography (CT) and weekly kilovoltage CT (kVCT) scans were included in this analysis. Pretreatment kVCTs were automatically registered with their corresponding planning CTs through a GPU-based DIR framework. The deformation of each contoured structure in the H&N region was computed to account for nonrigid change in the patient setup. The Jacobian determinant of the planning target volumes and the surrounding critical structures were used to quantify anatomical volume changes. The actual delivered dose was calculated accounting for the organ deformation. The dose distribution uncertainties due to registration errors were estimated using a landmark-based gamma evaluation. RESULTS: Dramatic interfractional anatomic changes were observed. During the treatment course of 6 to 7 weeks, the parotid gland volumes changed up to 34.7%, and the center-of-mass displacement of the 2 parotid glands varied in the range of 0.9 to 8.8 mm. For the primary treatment volume, the cumulative minimum and mean and equivalent uniform doses assessed by the weekly kVCTs were lower than the planned doses by up to 14.9% (P=.14), 2% (P=.39), and 7.3% (P=.05), respectively. The cumulative mean doses were significantly higher than the planned dose for the left parotid (P=.03) and right parotid glands (P=.006). The computation including DIR and dose accumulation was ultrafast (∼45 seconds) with registration accuracy at the subvoxel level. CONCLUSIONS: A systematic analysis of anatomic variations in the H&N region and their dosimetric consequences is critical in improving treatment efficacy. Nearly real-time assessment of anatomic and dosimetric variations is feasible using the GPU-based DIR framework. Clinical implementation of this technology may enable timely plan adaptation and improved outcome.