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Purpose: To develop a novel multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI) technique that expands single image contrast 4D‐MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. Methods and materials: The MC‐4D‐MRI technique integrates multi‐parametric MRI fusion, 4D‐MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre‐processing, fusion algorithm, adaptation, and fused multi‐contrast MRI as output. Four‐dimensional deformation vector fields (4D‐DVF) were generated from an original T2/T1‐w 4D‐MRI by deforming end‐of‐inhalation (EOI) to nine other phase volumes via DIR. The 4D‐DVF were applied to multi‐contrast MRI to generate a spectrum of 4D‐MRI in different image contrasts. The MC‐4D‐MRI technique was evaluated in five liver tumor patients on tumor contrast‐to‐noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D‐DIR introduced errors in tumor motion range, centroid location, extent, and volume. Results: MC‐4D‐MRI consisting of 4D‐MRIs in native image contrasts (T1‐w, T2‐w, and T2/T1‐w) and synthetic image contrasts, such as tumor‐enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1‐w MRI, to ‐4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1‐w, T2‐w, and TEC MRI, respectively. Patient ITV inter‐observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1‐w 4D‐MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1‐w, T2‐w, and TEC 4D‐MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D‐MRIs and original T2/T1‐w 4D‐MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase‐averaged absolute differences between the three 4D‐MRIs and the original 4D‐MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior‐inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior‐posterior (AP) direction, respectively. In the digital phantoms, phase‐averaged absolute tumor centroid shift caused by the 4D‐DIR were at or below 0.5 mm in SI, AP, and left‐right (LR) directions. Conclusion: We developed an MC‐4D‐MRI technique capable of expanding single image contrast 4D‐MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC‐4D‐MRI. The technique might offer new perspectives on the image contrast of MRI and 4D‐MRI in MR‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

To investigate the feasibility of the log-demons deformable image registration (DIR) method to correct eddy current and field inhomogeneity distortions while preserving diffusion tensor information. Diffusion-weighted images (DWIs) are susceptible to distortions caused by eddy current and echo-planar imaging (EPI) gradients. We propose a post-acquisition correction algorithm using the log-demons DIR technique for eddy current and field inhomogeneity distortions of DWI. The new correction technique was applied to DWI acquired using a diffusion phantom and the multiple acquisitions for standardization of structural imaging validation and evaluation (MASSIVE) brain database. This method is compared to previous methods using cross-correlation, mutual information (MI). In the phantom study, the log-demons algorithm reduced eddy current and field inhomogeneity distortions while preserving diffusion tensor information when compared to affine and demon's registration techniques. Analysis of the tensor metrics using percent difference and the root mean square of the apparent diffusion coefficient and fractional anisotropy found that the log-demons algorithm outperforms the other algorithms in terms of preserving diffusion information. In the MASSIVE study, the average MI of all slices increased for both eddy current and field inhomogeneity distortion correction. The average absolute differences of all slices between corrected images with opposing gradients were also on average decreased. This work indicates that the log-demons DIR algorithm is feasible to reduce eddy current and field inhomogeneity distortions while preserving quantitative diffusion information. [ABSTRACT FROM AUTHOR]

Digital tomosynthesis (DTS) has been proposed as a fast low-dose imaging technique for image-guided radiation therapy (IGRT). However, due to the limited scanning angle, DTS reconstructed by the conventional FDK method suffers from significant distortions and poor plane-to-plane resolutions without full volumetric information, which severely limits its capability for image guidance. Although existing deep learning-based methods showed feasibilities in restoring volumetric information in DTS, they ignored the inter-patient variabilities by training the model using group patients. Consequently, the restored images still suffered from blurred and inaccurate edges. In this study, we presented a DTS enhancement method based on a patient-specific deep learning model to recover the volumetric information in DTS images. The main idea is to use the patient-specific prior knowledge to train the model to learn the patient-specific correlation between DTS and the ground truth volumetric images. To validate the performance of the proposed method, we enrolled both simulated and real on-board projections from lung cancer patient data. Results demonstrated the benefits of the proposed method: (1) qualitatively, DTS enhanced by the proposed method shows CT-like high image quality with accurate and clear edges; (2) quantitatively, the enhanced DTS has low-intensity errors and high structural similarity with respect to the ground truth CT images; (3) in the tumor localization study, compared to the ground truth CT-CBCT registration, the enhanced DTS shows 3D localization errors of ≤0.7 mm and ≤1.6 mm for studies using simulated and real projections, respectively; and (4), the DTS enhancement is nearly real-time. Overall, the proposed method is effective and efficient in enhancing DTS to make it a valuable tool for IGRT applications. [ABSTRACT FROM AUTHOR]

To achieve accurate and fast deformable image registration (DIR) for pulmonary CT, we proposed a Multi-scale DIR framework with unsupervised Joint training of Convolutional Neural Network (MJ-CNN). MJ-CNN contains three models at multi-scale levels for a coarse-to-fine DIR to avoid being trapped in a local minimum. It is trained based on image similarity and deformation vector field (DVF) smoothness, requiring no supervision of ground-truth DVF. The three models are first trained sequentially and separately for their own registration tasks, and then are trained jointly for an end-to-end optimization under the multi-scale framework. In this study, MJ-CNN was trained using public SPARE 4D-CT data. The trained MJ-CNN was then evaluated on public DIR-LAB 4D-CT dataset as well as clinical CT-to-CBCT and CBCT-to-CBCT registration. For 4D-CT inter-phase registration, MJ-CNN achieved comparable accuracy to conventional iteration optimization-based methods, and showed the smallest registration errors compared to recently published deep learning-based DIR methods, demonstrating the efficacy of the proposed multi-scale joint training scheme. Besides, MJ-CNN trained using one dataset (SPARE) could generalize to a different dataset (DIR-LAB) acquired by different scanners and imaging protocols. Furthermore, MJ-CNN trained on 4D-CTs also performed well on CT-to-CBCT and CBCT-to-CBCT registration without any re-training or fine-tuning, demonstrating MJ-CNN's robustness against applications and imaging techniques. MJ-CNN took about 1.4 s for DVF estimation and required no manual-tuning of parameters during the evaluation. MJ-CNN is able to perform accurate DIR for pulmonary CT with nearly real-time speed, making it very applicable for clinical tasks. [ABSTRACT FROM AUTHOR]

Purpose: To determine clinic-specific linear accelerator quality assurance (QA) TG-142 test frequencies, to maximize physicist time efficiency and patient treatment quality.Methods and Materials: A novel quantitative approach to failure mode and effect analysis is proposed. Nine linear accelerator-years of QA records provided data on failure occurrence rates. The severity of test failure was modeled by introducing corresponding errors into head and neck intensity modulated radiation therapy treatment plans. The relative risk of daily linear accelerator QA was calculated as a function of frequency of test performance.Results: Although the failure severity was greatest for daily imaging QA (imaging vs treatment isocenter and imaging positioning/repositioning), the failure occurrence rate was greatest for output and laser testing. The composite ranking results suggest that performing output and lasers tests daily, imaging versus treatment isocenter and imaging positioning/repositioning tests weekly, and optical distance indicator and jaws versus light field tests biweekly would be acceptable for non-stereotactic radiosurgery/stereotactic body radiation therapy linear accelerators.Conclusions: Failure mode and effect analysis is a useful tool to determine the relative importance of QA tests from TG-142. Because there are practical time limitations on how many QA tests can be performed, this analysis highlights which tests are the most important and suggests the frequency of testing based on each test's risk priority number. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. Methods: Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images. Results: Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89±0.03, and single cycle sorting, DSC = 0.83±0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images. Conclusions: In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors' preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management. [ABSTRACT FROM AUTHOR]

Purpose: Current approaches using deformable vector field (DVF) for motion-compensated 4D-cone beam CT (CBCT) reconstruction typically utilize an isotropically smoothed DVF between different respiration phases. Such isotropically smoothed DVF does not work well if sliding motion exists between neighboring organs. This study investigated an anisotropic motion modeling scheme by extracting organ boundary local motions (e.g., sliding) and incorporated them into 4D-CBCT reconstruction to optimize the motion modeling and reconstruction methods. Methods: Initially, a modified simultaneous algebraic reconstruction technique (mSART) was applied to reconstruct high quality reference phase CBCT using all phase projections. The initial DVFs were precalculated and subsequently updated to achieve the optimized solution. During the DVF update, sliding motion estimation was performed by matching the measured projections to the forward projection of the deformed reference phase CBCT. In this process, each moving organ boundary was first segmented. The normal vectors of the boundary DVF were then extracted and incorporated for further DVF optimization. The regularization term in the objective function adaptively regularizes the DVF by (1) isotopically smoothing the DVF within each organ; (2) smoothing the DVF at boundary along the normal direction; and (3) leaving the tangent direction of boundary DVF unsmoothed (i.e., allowing for sliding motion). A nonlinear conjugate gradient optimizer was used. The algorithm was validated on a digital cubic tube phantom with sliding motion, nonuniform rotational B-spline based cardiac-torso (NCAT) phantom, and two anonymized patient data. The relative reconstruction error (RE), the motion trajectory's root mean square error (RMSE) together with its maximum error (MaxE), and the Dice coefficient of the lung boundary were calculated to evaluate the algorithm performance. Results: For the cubic tube and NCAT phantom tests, the REs are 10.2% and 7.4% with sliding motion compensation, compared to 13.4% and 8.9% without sliding modeling. The motion trajectory's RMSE and MaxE for NCAT phantom tests are 0.5 and 0.8 mm with sliding motion constraint compared to 3.5 and 7.3 mm without sliding motion modeling. The Dice coefficients for both NCAT phantom and the patients show a consistent trend that sliding motion constraint achieves better similarity for segmented lung boundary compared with the ground truth or patient reference. Conclusions: A sliding motion-compensated 4D-CBCT reconstruction and the motion modeling scheme was developed. Both phantom and patient study demonstrated the improved accuracy and motion modeling accuracy in reconstructed 4D-CBCT. [ABSTRACT FROM AUTHOR]

Purpose: The authors have recently developed a cine-mode T2*/T1-weighted 4D-MRI technique and a sequential-mode T2-weighted 4D-MRI technique for imaging respiratory motion. This study aims at investigating which 4D-MRI image acquisition mode, cine or sequential, provides more accurate measurement of organ motion during respiration. Methods: A 4D digital extended cardiac-torso (XCAT) human phantom with a hypothesized tumor was used to simulate the image acquisition and the 4D-MRI reconstruction. The respiratory motion was controlled by the given breathing signal profiles. The tumor was manipulated to move continuously with the surrounding tissue. The motion trajectories were measured from both sequential- and cine-mode 4D-MRI images. The measured trajectories were compared with the average trajectory calculated from the input profiles, which was used as references. The error in 4D-MRI tumor motion trajectory (E) was determined. In addition, the corresponding respiratory motion amplitudes of all the selected 2D images for 4D reconstruction were recorded. Each of the amplitude was compared with the amplitude of its associated bin on the average breathing curve. The mean differences from the average breathing curve across all slice positions (D) were calculated. A total of 500 simulated respiratory profiles with a wide range of irregularity (Ir) were used to investigate the relationship between D and Ir. Furthermore, statistical analysis of E and D using XCAT controlled by 20 cancer patients' breathing profiles was conducted. Wilcoxon Signed Rank test was conducted to compare two modes. Results: D increased faster for cine-mode (D = 1.17×Ir+0.23) than sequential-mode (D = 0.47 ×Ir+0.23) as irregularity increased. For the XCAT study using 20 cancer patients' breathing profiles, the median E values were significantly different: 0.12 and 0.10 cm for cine- and sequential-modes, respectively, with a p-value of 0.02. The median D values were significantly different: 0.47 and 0.24 cm for cine- and sequential-modes, respectively, with a p-value < 0.001. Conclusions: Respiratory motion measurement may be more accurate and less susceptible to breathing irregularity in sequential-mode 4D-MRI than that in cine-mode 4D-MRI. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer treatment is susceptible to treatment errors caused by interfractional anatomical and respirational variations of the patient. On-board treatment dose verification is especially critical for the lung stereotactic body radiation therapy due to its high fractional dose. This study investigates the feasibility of using cone-beam (CB)CT images estimated by a motion modeling and free-form deformation (MM-FD) technique for on-board dose verification. Methods: Both digital and physical phantom studies were performed. Various interfractional variations featuring patient motion pattern change, tumor size change, and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the planning CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses), and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the "gold-standard" on-board images (gold-standard doses). The absolute deviations of minimum dose (?Dmin), maximum dose (?Dmax), and mean dose (?Dmean), and the absolute deviations of prescription dose coverage (?V100%) were evaluated for the planning target volume (PTV). In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MM-FD in the physical phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. Results: Compared with the planned doses and the FDK doses, the MM-FD doses matched much better with the gold-standard doses. For the digital phantom study, the average (± standard deviation) ?Dmin, ?Dmax, ?Dmean, and ?V100% (values normalized by the prescription dose or the total PTV) between the planned and the gold-standard PTV doses were 32.9% (±28.6%), 3.0% (±2.9%), 3.8% (±4.0%), and 15.4% (±12.4%), respectively. The corresponding values of FDK PTV doses were 1.6% (±1.9%), 1.2% (±0.6%), 2.2% (±0.8%), and 17.4% (±15.3%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.3% (±0.2%), 0.9% (±0.6%), 0.6% (±0.4%), and 1.0% (±0.8%), respectively. Similarly, for the physical phantom study, the average ?Dmin, ?Dmax, ?Dmean, and ?V100% of planned PTV doses were 38.1% (±30.8%), 3.5% (±5.1%), 3.0% (±2.6%), and 8.8% (±8.0%), respectively. The corresponding values of FDK PTV doses were 5.8% (±4.5%), 1.6% (±1.6%), 2.0% (±0.9%), and 9.3% (±10.5%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (±0.8%), 0.8% (±1.0%), 0.5% (±0.4%), and 0.8% (±0.8%), respectively. For the 4D dose accumulation study, the average (± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.3% (±2.7%). The average gamma index (3%/3 mm) between the accumulated doses and the radiochromic film measured doses was 94.5% (±2.5%). Conclusions: MM-FD estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

Background and purpose A technique has been previously reported to estimate high-quality 4D-CBCT using prior information and limited-angle projections. This study is to investigate its clinical feasibility through both phantom and patient studies. Materials and methods The new technique used to estimate 4D-CBCT is called MMFD-NCC. It is based on the previously reported motion modeling and free-form deformation (MMFD) method, with the introduction of normalized-cross-correlation (NCC) as a new similarity metric. The clinical feasibility of this technique was evaluated by assessing the accuracy of estimated anatomical structures in comparison to those in the ‘ground-truth’ reference 4D-CBCTs, using data obtained from a physical phantom and three lung cancer patients. Both volume percentage error (VPE) and center-of-mass error (COME) of the estimated tumor volume were used as the evaluation metrics. Results The average VPE/COME of the tumor in the prior image was 257.1%/10.1 mm for the phantom study and 55.6%/3.8 mm for the patient study. Using only orthogonal-view 30° projections, the MMFD-NCC has reduced the corresponding values to 7.7%/1.2 mm and 9.6%/1.1 mm, respectively. Conclusion The MMFD-NCC technique is able to estimate 4D-CBCT images with geometrical accuracy of the tumor within 10% VPE and 2 mm COME, which can be used to improve the localization accuracy of radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: Current four dimensional magnetic resonance imaging (4D-MRI) techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of a new strategy for 4D-MRI which is based on retrospective k-space reordering. Methods: We simulated a k-space reordered 4D-MRI on a 4D digital extended cardiac-torso (XCAT) human phantom. A 2D echo planar imaging MRI sequence [frame rate (F) = 0.448 Hz; image resolution (R) = 256 x 256; number of k-space segments (NKS) = 4] with sequential image acquisition mode was assumed for the simulation. Image quality of the simulated "4D-MRI" acquired from the XCAT phantom was qualitatively evaluated, and tumor motion trajectories were compared to input signals. In particular, mean absolute amplitude differences (D) and cross correlation coefficients (CC) were calculated. Furthermore, to evaluate the data sufficient condition for the new 4D-MRI technique, a comprehensive simulation study was performed using 30 cancer patients' respiratory profiles to study the relationships between data completeness (Cp) and a number of impacting factors: the number of repeated scans (NR), number of slices (NS), number of respiratory phase bins (NP), NKS, F, R, and initial respiratory phase at image acquisition (P0). As a proof-of-concept, we implemented the proposed k-space reordering 4D-MRI technique on a T2-weighted fast spin echo MR sequence and tested it on a healthy volunteer. Results: The simulated 4D-MRI acquired from the XCAT phantom matched closely to the original XCAT images. Tumor motion trajectories measured from the simulated 4D-MRI matched well with input signals (D = 0.83 and 0.83 mm, and CC = 0.998 and 0.992 in superior-inferior and anterior-posterior directions, respectively). The relationship between Cp and NR was found best represented by an exponential function (CP = 100(1 - e-018NR), when NS = 30, NP = 6). At a CP value of 95%, the relative error in tumor volume was 0.66%, indicating that NR at a CP value of 95% (NR,95%) is sufficient. It was found that NR,95% is approximately linearly proportional to NP (r = 0.99), and nearly independent of all other factors. The 4D-MRI images of the healthy volunteer clearly demonstrated respiratory motion in the diaphragm region with minimal motion induced noise or aliasing. Conclusions: It is feasible to generate respiratory correlated 4D-MRI by retrospectively reordering k-space based on respiratory phase. This new technology may lead to the next generation 4D-MRI with high spatiotemporal resolution and optimal tumor contrast, holding great promises to improve the motion management in radiotherapy of mobile cancers. [ABSTRACT FROM AUTHOR]

Purpose: The authors have recently developed a novel 4D-MRI technique for imaging organ respiratory motion employing cine acquisition in the axial plane and using body area (BA) as a respiratory surrogate. A potential disadvantage associated with axial image acquisition is the space-dependent phase shift in the superior-inferior (SI) direction, i.e., different axial slice positions reach the respiratory peak at different respiratory phases. Since respiratory motion occurs mostly in the SI and anterior-posterior (AP) directions, sagittal image acquisition, which embeds motion information in these two directions, is expected to be more robust and less affected by phase-shift than axial image acquisition. This study aims to develop and evaluate a 4D-MRI technique using sagittal image acquisition. Methods: The authors evaluated axial BA and sagittal BA using both 4D-CT images (11 cancer patients) and cine MR images (6 healthy volunteers and 1 cancer patient) by comparing their corresponding space-dependent phase-shift in the SI direction (δSI SPS ) and in the lateral direction (δLAT SPS ), respectively. To evaluate sagittal BA 4D-MRI method, a motion phantom study and a digital phantom study were performed. Additionally, six patients who had cancer(s) in the liver were prospectively enrolled in this study. For each patient, multislice sagittal MR images were acquired for 4D-MRI reconstruction. 4D retrospective sorting was performed based on respiratory phases. Single-slice cine MRI was also acquired in the axial, coronal, and sagittal planes across the tumor center from which tumor motion trajectories in the SI, AP, and medial-lateral (ML) directions were extracted and used as references from comparison. All MR images were acquired in a 1.5 T scanner using a steady-state precession sequence (frame rate ~3 frames/s). Results: 4D-CT scans showed that δSI SPS was significantly greater than δLAT SPS (p-value: 0.012); the median phase-shift was 16.9% and 7.7%, respectively. Body surface motion measurement from axial and sagittal MR cines also showed δSI SPS was significantly greater than δLAT SPS . The median δSI SPS and δLAT SPS was 11.0% and 9.2% (p-value = 0.008), respectively. Tumor motion trajectories from 4D-MRI matched with those from single-slice cine MRI: the mean (±SD) absolute differences in tumor motion amplitude between the two were 1.5 ± 1.6 mm, 2.1 ± 1.9 mm, and 1.1 ± 1.0 mm in the SI, ML, and AP directions from this patient study. Conclusions: Space-dependent phase shift is less problematic for sagittal acquisition than for axial acquisition. 4D-MRI using sagittal acquisition was successfully carried out in patients with hepatic tumors. [ABSTRACT FROM AUTHOR]

Compared to other radiation therapy modalities, clinical electron beam therapy has remained practically unchanged for the past few decades even though electron beams with multiple energies are widely available on most linacs. In this paper, we present the concept of dynamic electron arc radiotherapy (DEAR), a new conformal electron therapy technique with synchronized couch motion. DEAR utilizes combination of gantry rotation, couch motion, and dose rate modulation to achieve desirable dose distributions in patient. The electron applicator is kept to minimize scatter and maintain narrow penumbra. The couch motion is synchronized with the gantry rotation to avoid collision between patient and the electron cone. In this study, we investigate the feasibility of DEAR delivery and demonstrate the potential of DEAR to improve dose distributions on simple cylindrical phantoms. DEAR was delivered on Varian's TrueBeam linac in Research Mode. In conjunction with the recorded trajectory log files, mechanical motion accuracies and dose rate modulation precision were analyzed. Experimental and calculated dose distributions were investigated for different energies (6 and 9 MeV) and cut-out sizes (1×10 cm2 and 3×10 cm2 for a 15×15 cm2 applicator). Our findings show that DEAR delivery is feasible and has the potential to deliver radiation dose with high accuracy (root mean square error, or RMSE of <0.1 MU, <0.1° gantry, and <0.1 cm couch positions) and good dose rate precision (1.6 MU min−1). Dose homogeneity within ±2% in large and curved targets can be achieved while maintaining penumbra comparable to a standard electron beam on a flat surface. Further, DEAR does not require fabrication of patient-specific shields. These benefits make DEAR a promising technique for conformal radiotherapy of superficial tumors. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of a synchronized moving grid (SMOG) system to remove scatter artifacts, improve the contrast-to-noise ratio (CNR), and reduce image lag artifacts in cone-beam CT (CBCT). Methods: The SMOG system proposed here uses a rapidly oscillating, synchronized moving grid attached to the kV source. Multiple partial projections are taken at different grid positions to form a complete projection in each gantry position, before the gantry moves to the next position during a scan. The grid has a low transmission factor, and it is used for both scatter reduction and scatter measurement for postscan scatter correction. Experimental studies using a static grid and an enlarged CATphan phantom were performed to evaluate the potential CNR enhancement for different SMOG exposure numbers (1, 2, and 4). Simulation studies were performed to evaluate the image lag correction for different exposure numbers (2, 3, and 4) and grid interspace widths in SMOG using the data from an anthropomorphic pelvis phantom scan. Imaging dose of SMOG was also estimated by measuring the imaging dose in a CIRS CT dose phantom using a static grid. Results: SMOG can enhance the CNR by 16% and 13% when increasing exposure number from 1 to 2 and from 2 to 4, respectively. This enhancement was more dramatic for larger phantoms and smaller initial exposure numbers. Simulation results indicated that SMOG could reduce the lag to less than 4.3% for 2-exposure mode and to less than 0.8% for 3-exposure mode when the grid interspace width was 1.4 cm. Increasing the number of exposures in SMOG dramatically reduced the residual lag in the image. Reducing the grid interspace width somewhat reduced the residual lag. Skin line artifacts were removed entirely in SMOG. Point dose measurement showed that imaging dose of SMOG at isocenter was similar as that of a conventional CBCT. Conclusions: Compared to our previously developed static-grid dual-rotation method, the proposed SMOG technique has the advantages of enhancing the CNR, correcting the image lag, and reducing the delivery time. Once implemented, SMOG has the potential to remove scatter and image lag artifacts, and significantly enhance CNR for CBCT using the same scanning time as conventional CBCT. [ABSTRACT FROM AUTHOR]

Abstract: The ExacTrac X-Ray 6D image-guided radiotherapy (IGRT) system will be described and its performance evaluated. The system is mainly an integration of 2 subsystems: (1) an infrared (IR)-based optical positioning system (ExacTrac) and (2) a radiographic kV x-ray imaging system (X-Ray 6D). The infrared system consists of 2 IR cameras, which are used to monitor reflective body markers placed on the patient''s skin to assist in patient initial setup, and an IR reflective reference star, which is attached to the treatment couch and can assist in couch movement with spatial resolution to better than 0.3 mm. The radiographic kV devices consist of 2 oblique x-ray imagers to obtain high-quality radiographs for patient position verification and adjustment. The position verification is made by fusing the radiographs with the simulation CT images using either 3 degree-of-freedom (3D) or 6 degree-of-freedom (6D) fusion algorithms. The position adjustment is performed using the infrared system according to the verification results. The reliability of the fusion algorithm will be described based on phantom and patient studies. The results indicated that the 6D fusion method is better compared to the 3D method if there are rotational deviations between the simulation and setup positions. Recently, the system has been augmented with the capabilities for image-guided positioning of targets in motion due to respiration and for gated treatment of those targets. The infrared markers provide a respiratory signal for tracking and gating of the treatment beam, with the x-ray system providing periodic confirmation of patient position relative to the gating window throughout the duration of the gated delivery. [Copyright &y& Elsevier]

We studied a recently proposed aggregated CT reconstruction technique which combines the complementary advantages of kilovoltage (kV) and megavoltage (MV) x-ray imaging. Various phantoms were imaged to study the effects of beam orientations and geometry of the imaging object on image quality of reconstructed CT. It was shown that the quality of aggregated CT was correlated with both kV and MV beam orientations and the degree of this correlation depended upon the geometry of the imaging object. The results indicated that the optimal orientations were those when kV beams pass through the thinner portion and MV beams pass through the thicker portion of the imaging object. A special preprocessing procedure was also developed to perform contrast conversions between kV and MV information prior to image reconstruction. The performance of two reconstruction methods, one filtered backprojection method and one iterative method, were compared. The effects of projection number, beam truncation, and contrast conversion on the CT image quality were investigated. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: An artificial intelligence (AI) guided method for parameter adjustment of inverse planning was implemented on a commercial inverse treatment planning system. For evaluation purpose, four typical clinical cases were tested and the results from both plans achieved by automated and manual methods were compared. Methods and materials: The procedure of parameter adjustment mainly consists of three major loops. Each loop is in charge of modifying parameters of one category, which is carried out by a specially customized fuzzy inference system. A physician prescribed multiple constraints for a selected volume were adopted to account for the tradeoff between prescription dose to the PTV and dose–volume constraints for critical organs. The searching process for an optimal parameter combination began with the first constraint, and proceeds to the next until a plan with acceptable dose was achieved. The initial setup of the plan parameters was the same for each case and was adjusted independently by both manual and automated methods. After the parameters of one category were updated, the intensity maps of all fields were re-optimized and the plan dose was subsequently re-calculated. When final plan arrived, the dose statistics were calculated from both plans and compared. Results: For planned target volume (PTV), the dose for 95% volume is up to 10% higher in plans using the automated method than those using the manual method. For critical organs, an average decrease of the plan dose was achieved. However, the automated method cannot improve the plan dose for some critical organs due to limitations of the inference rules currently employed. For normal tissue, there was no significant difference between plan doses achieved by either automated or manual method. Conclusion: With the application of AI-guided method, the basic parameter adjustment task can be accomplished automatically and a comparable plan dose was achieved in comparison with that achieved by the manual method. Future improvements to incorporate case-specific inference rules are essential to fully automate the inverse planning process. [Copyright &y& Elsevier]

Purpose: Cone-beam computed tomography (CBCT) images are currently used for positioning verification. However, it is yet unknown whether CBCT could be used in dose calculation for replanning in adaptive radiation therapy. This study investigates the dosimetric feasibility of CBCT-based treatment planning. Methods and Materials: Hounsfield unit (HU) values and profiles of Catphan, homogeneous/inhomogeneous phantoms, and various tissue regions of patients in CBCT images were compared to those in CT. The dosimetric consequence of the HU variation was investigated by comparing CBCT-based treatment plans to conventional CT-based plans for both phantoms and patients. Results: The maximum HU difference between CBCT and CT of Catphan was 34 HU in the Teflon. The differences in other materials were less than 10 HU. The profiles for the homogeneous phantoms in CBCT displayed reduced HU values up to 150 HU in the peripheral regions compared to those in CT. The scatter and artifacts in CBCT became severe surrounding inhomogeneous tissues with reduced HU values up to 200 HU. The MU/cGy differences were less than 1% for most phantom cases. The isodose distributions between CBCT-based and CT-based plans agreed very well. However, the discrepancy was larger when CBCT was scanned without a bowtie filter than with bowtie filter. Also, up to 3% dosimetric error was observed in the plans for the inhomogeneous phantom. In the patient studies, the discrepancies of isodose lines between CT-based and CBCT-based plans, both 3D and IMRT, were less than 2 mm. Again, larger discrepancy occurred for the lung cancer patients. Conclusion: This study demonstrated the feasibility of CBCT-based treatment planning. CBCT-based treatment plans were dosimetrically comparable to CT-based treatment plans. Dosimetric data in the inhomogeneous tissue regions should be carefully validated. [ABSTRACT FROM AUTHOR]

Purpose: To generate on-board digital tomosynthesis (DTS) and reference DTS images for three-dimensional image-guided radiation therapy (IGRT) as an alternative to conventional portal imaging or on-board cone-beam computed tomography (CBCT). Methods and Materials: Three clinical cases (prostate, head-and-neck, and liver) were selected to illustrate the capabilities of on-board DTS for IGRT. Corresponding reference DTS images were reconstructed from digitally reconstructed radiographs computed from planning CT image sets. The effect of scan angle on DTS slice thickness was examined by computing the mutual information between coincident CBCT and DTS images, as the DTS scan angle was varied from 0 degrees to 165 degrees . A breath-hold DTS acquisition strategy was implemented to remove respiratory motion artifacts. Results: Digital tomosynthesis slices appeared similar to coincident CBCT planes and yielded substantially more anatomic information than either kilovoltage or megavoltage radiographs. Breath-hold DTS acquisition improved soft-tissue visibility by suppressing respiratory motion. Conclusions: Improved bony and soft-tissue visibility in DTS images is likely to improve target localization compared with radiographic verification techniques and might allow for daily localization of a soft-tissue target. Breath-hold DTS is a potential alternative to on-board CBCT for sites prone to respiratory motion. [ABSTRACT FROM AUTHOR]

Background: Delta radiomics is a high‐throughput computational technique used to describe quantitative changes in serial, time‐series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. Purpose: The purpose of this work was to show a proof‐of‐concept of a new radiomics paradigm for sparse, time‐series imaging data, where features are extracted from a spatial‐temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). Methods: To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t=0$t = 0$ and t>0$t > 0$. These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker–Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial‐temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker–Planck estimation and simulated ground‐truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F‐FDG‐PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre‐treatment and 2‐weeks intra‐treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k‐means clustering and compared by Kaplan–Meier analyses with log‐rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. Results: Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan–Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray‐Level‐Size‐Zone‐Matrix gray‐level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). Conclusions: We developed, verified, and demonstrated the prognostic value of a novel, physics‐based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations. [ABSTRACT FROM AUTHOR]

Purpose. Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL technique is developed to resolve the memory issue, and its feasibility is demonstrated for CBCT reconstruction from sparsely sampled projection data. Methods. The novel geometry-guided deep learning (GDL) technique is composed of a GDL reconstruction module and a post-processing module. The GDL reconstruction module learns and performs projection-to-image domain transformation by replacing the traditional single fully connected layer with an array of small fully connected layers in the network architecture based on the projection geometry. The DL post-processing module further improves image quality after reconstruction. We demonstrated the feasibility and advantage of the model by comparing ground truth CBCT with CBCT images reconstructed using (1) GDL reconstruction module only, (2) GDL reconstruction module with DL post-processing module, (3) Feldkamp, Davis, and Kress (FDK) only, (4) FDK with DL post-processing module, (5) ray-tracing only, and (6) ray-tracing with DL post-processing module. The differences are quantified by peak-signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-square error (RMSE). Results. CBCT images reconstructed with GDL show improvements in quantitative scores of PSNR, SSIM, and RMSE. Reconstruction time per image for all reconstruction methods are comparable. Compared to current DL methods using large fully connected layers, the estimated memory requirement using GDL is four orders of magnitude less, making DL CBCT reconstruction feasible. Conclusion. With much lower memory requirement compared to other existing networks, the GDL technique is demonstrated to be the first DL technique that can rapidly and accurately reconstruct CBCT images from sparsely sampled data. [ABSTRACT FROM AUTHOR]

Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground‐truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. Purpose: To develop a U‐Net segmentation uncertainty quantification method based on spherical image projection of multi‐parametric MRI (MP‐MRI) in glioma segmentation. Methods: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection‐based U‐Net (SPU‐Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel‐wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU‐Net model was implemented on the basis of 369 glioma patients with MP‐MRI scans (T1, T1‐Ce, T2, and FLAIR). Three SPU‐Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU‐Net model was compared with (1) the classic U‐Net model with test‐time augmentation (TTA) and (2) linear scaling‐based U‐Net (LSU‐Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). Results: The developed SPU‐Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U‐Net. Quantitatively, the SPU‐Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U‐Net with TTA and 0.743/0.702/0.876 with the LSU‐Net (scaling factor = 2). The SPU‐Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. Conclusion: The SPU‐Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image‐related deep‐learning applications for uncertainty evaluation. [ABSTRACT FROM AUTHOR]

Purpose: To develop a spatiotemporal-constrained sorting technique for motion-robust 4 dimensional-magnetic resonance imaging.Methods and Materials: This sorting method implemented 2 new approaches for 4-dimensional imaging: (1) an optimized sparse k-space acquisition trajectory with self-gating signal derivation, and (2) a retrospective k-space sorting for reconstruction using a novel spatiotemporal-constrained strategy to minimize breathing variation-induced motion artifacts. Such sorting was regularized by a spatiotemporal index. Volumetric reconstruction was implemented iteratively with a secnd-order total generalized variation penalty. The proposed method was evaluated and compared with the conventional phase-sorting and amplitude-sorting methods in 2 studies. In a computer simulation study, 6 abdominal motion scenarios, including 2 cosine and 4 patient breathing motion patterns, were studied. Reconstruction accuracy was evaluated quantitatively in reference to the ground truth by average image relative error (IRE) in 10 phases and target Dice similarity coefficients (DSCs) in end-of-exhalation/inhalation phases. In addition, the proposed method was evaluated using a custom-made motion phantom. Reconstruction accuracy was evaluated by motion range measurement and image quality comparison in both fast and slow breathing motions.Results: In the simulation study, stitching motion artifacts in restricted images were lessened using the proposed method compared with those using the conventional methods. The average IRE and target DSC (end-of-exhalation/inhalation) were 0.031 and 0.95/0.94, respectively, suggesting better motion reconstruction accuracy than the phase-sorted method (IRE, 0.057; DSC, 0.89/0.89) and the amplitude-sorted method (IRE, 0.048; DSC, 0.91/0.88). In the phantom study, the moving target reconstructed by the proposed method demonstrated better rendering with less edge blurring. With fast breathing motion, the range measured using the proposed method was more accurate than that of the phase-sorted method and was comparable to the result of amplitude-sorted method and ground truths.Conclusions: Preliminary results suggested that the proposed sorting technique could reconstruct high-quality images and accurate motion estimation with reduced artifacts in 4 dimensional-magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi‐parametric MRI‐based glioma segmentation as a method to enhance deep learning explainability. Methods: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we implemented a novel deep learning model, Neural ODE, in which deep feature extraction was governed by an ODE parameterized by a neural network. The dynamics of (1) MR images after interactions with the deep neural network and (2) segmentation formation can thus be visualized after solving the ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate each MR image's utilization by the deep neural network toward the final segmentation results. The proposed Neural ODE model was demonstrated using 369 glioma patients with a 4‐modality multi‐parametric MRI protocol: T1, contrast‐enhanced T1 (T1‐Ce), T2, and FLAIR. Three Neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The key MRI modalities with significant utilization by deep neural networks were identified based on ACC analysis. Segmentation results by deep neural networks using only the key MRI modalities were compared to those using all four MRI modalities in terms of Dice coefficient, accuracy, sensitivity, and specificity. Results: All Neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1‐Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U‐Net results using all four MRI modalities, the Dice coefficient of ET (0.784→0.775), TC (0.760→0.758), and WT (0.841→0.837) using the key modalities only had minimal differences without significance. Accuracy, sensitivity, and specificity results demonstrated the same patterns. Conclusion: The Neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image‐related deep‐learning applications. [ABSTRACT FROM AUTHOR]

Purpose: On‐board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on‐board MRI in clinics is extremely limited. On the contrary, on‐board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on‐board kV imaging system and patient prior knowledge to generate on‐board four‐dimensional (4D)‐MRI for target localization in liver SBRT. Methods: Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRIprior) and all other phases. MRIprior was used to generate a synthetic CT at EOE phase (sCTprior). On‐board 4D MRI at each respiratory phase was considered a deformation of MRIprior. The deformation field map (DFM) was estimated by matching DRRs of the deformed sCTprior to on‐board kV projections using a motion modeling and free‐form deformation optimization algorithm. The on‐board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on‐board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. Results: In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 ± 1.31%/0.95 ± 0.01/0.88 ± 0.15 mm using orthogonal‐view 30° scan angles with 102 projections. The on‐board 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on‐board 4D MRI was generated successfully when compared to the “reference on‐board 4D MRI” for the liver patient case. Conclusions: A method was developed to generate on‐board 4D MRI using prior 4D MRI and on‐board limited kV projections. Preliminary results demonstrated the potential for MRI‐based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC. [ABSTRACT FROM AUTHOR]

Purpose: Limited‐angle intrafraction verification (LIVE) has been previously developed for four‐dimensional (4D) intrafraction target verification either during arc delivery or between three‐dimensional (3D)/IMRT beams. Preliminary studies showed that LIVE can accurately estimate the target volume using kV/MV projections acquired over orthogonal view 30° scan angles. Currently, the LIVE imaging acquisition requires slow gantry rotation and is not clinically optimized. The goal of this study is to optimize the image acquisition parameters of LIVE for different patient respiratory periods and gantry rotation speeds for the effective clinical implementation of the system. Method: Limited‐angle intrafraction verification imaging acquisition was optimized using a digital anthropomorphic phantom (XCAT) with simulated respiratory periods varying from 3 s to 6 s and gantry rotation speeds varying from 1°/s to 6°/s. LIVE scanning time was optimized by minimizing the number of respiratory cycles needed for the four‐dimensional scan, and imaging dose was optimized by minimizing the number of kV and MV projections needed for four‐dimensional estimation. The estimation accuracy was evaluated by calculating both the center‐of‐mass‐shift (COMS) and three‐dimensional volume‐percentage‐difference (VPD) between the tumor in estimated images and the ground truth images. The robustness of LIVE was evaluated with varied respiratory patterns, tumor sizes, and tumor locations in XCAT simulation. A dynamic thoracic phantom (CIRS) was used to further validate the optimized imaging schemes from XCAT study with changes of respiratory patterns, tumor sizes, and imaging scanning directions. Results: Respiratory periods, gantry rotation speeds, number of respiratory cycles scanned and number of kV/MV projections acquired were all positively correlated with the estimation accuracy of LIVE. Faster gantry rotation speed or longer respiratory period allowed less respiratory cycles to be scanned and less kV/MV projections to be acquired to estimate the target volume accurately. Regarding the scanning time minimization, for patient respiratory periods of 3–4 s, gantry rotation speeds of 1°/s, 2°/s, 3–6°/s required scanning of five, four, and three respiratory cycles, respectively. For patient respiratory periods of 5–6 s, the corresponding respiratory cycles required in the scan changed to four, three, and two cycles, respectively. Regarding the imaging dose minimization, for patient respiratory periods of 3–4 s, gantry rotation speeds of 1°/s, 2–4°/s, 5–6°/s required acquiring of 7, 5, 4 kV and MV projections, respectively. For patient respiratory periods of 5–6 s, 5 kV and 5 MV projections are sufficient for all gantry rotation speeds. The optimized LIVE system was robust against breathing pattern, tumor size and tumor location changes. In the CIRS study, the optimized LIVE system achieved the average center‐of‐mass‐shift (COMS)/volume‐percentage‐difference (VPD) of 0.3 ± 0.1 mm/7.7 ± 2.0% for the scanning time priority case, 0.2 ± 0.1 mm/6.1 ± 1.2% for the imaging dose priority case, respectively, among all gantry rotation speeds tested. LIVE was robust against different scanning directions investigated. Conclusion: The LIVE system has been preliminarily optimized for different patient respiratory periods and treatment gantry rotation speeds using digital and physical phantoms. The optimized imaging parameters, including number of respiratory cycles scanned and kV/MV projection numbers acquired, provide guidelines for optimizing the scanning time and imaging dose of the LIVE system for its future evaluations and clinical implementations through patient studies. [ABSTRACT FROM AUTHOR]

Purpose To investigate the feasibility of using structural-based principal component analysis ( PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D- CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. Methods A technique for lung 4D- CBCT reconstruction has been previously developed using a deformation field map ( DFM)-based strategy. In the previous method, each phase of the 4D- CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion model extracted by a global PCA and free-form deformation ( GMM- FD) technique, using a data fidelity constraint and deformation energy minimization. In this study, a new structural PCA method was developed to build a structural motion model ( SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4 DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation ( WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respiratory changes from planning 4D- CT to on-board volume to evaluate the method. The estimation accuracy was evaluated by the volume percent difference ( VPD)/center-of-mass-shift ( COMS) between lesions in the estimated and 'ground-truth' on-board 4D- CBCT. Different on-board projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. The method was also evaluated against three lung patients. Results The SMM- WFD method achieved substantially better accuracy than the GMM- FD method for CBCT estimation using extremely small scan angles or projections. Using orthogonal 15° scanning angles, the VPD/ COMS were 3.47 ± 2.94% and 0.23 ± 0.22 mm for SMM- WFD and 25.23 ± 19.01% and 2.58 ± 2.54 mm for GMM- FD among all eight XCAT scenarios. Compared to GMM- FD, SMM- WFD was more robust against reduction of the scanning angles down to orthogonal 10° with VPD/ COMS of 6.21 ± 5.61% and 0.39 ± 0.49 mm, and more robust against reduction of projection numbers down to only 8 projections in total for both orthogonal-view 30° and orthogonal-view 15° scan angles. SMM- WFD method was also more robust than the GMM- FD method against increasing levels of noise in the projection images. Additionally, the SMM- WFD technique provided better tumor estimation for all three lung patients compared to the GMM- FD technique. Conclusion Compared to the GMM- FD technique, the SMM- WFD technique can substantially improve the 4D- CBCT estimation accuracy using extremely small scan angles and low number of projections to provide fast low dose 4D target verification. [ABSTRACT FROM AUTHOR]

Purpose: To develop a technique to estimate onboard 4D-CBCT using prior information and limited-angle projections for potential 4D target verification of lung radiotherapy. Methods: Each phase of onboard 4D-CBCT is considered as a deformation from one selected phase (prior volume) of the planning 4D-CT. The deformation field maps (DFMs) are solved using a motion modeling and free-form deformation (MM-FD) technique. In the MM-FD technique, the DFMs are estimated using a motion model which is extracted from planning 4D-CT based on principal component analysis (PCA). The motion model parameters are optimized by matching the digitally reconstructed radiographs of the deformed volumes to the limited-angle onboard projections (data fidelity constraint). Afterward, the estimated DFMs are fine-tuned using a FD model based on data fidelity constraint and deformation energy minimization. The 4D digital extended-cardiac-torso phantom was used to evaluate the MM-FD technique. A lung patient with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume, including changes of respiration amplitude, lesion size and lesion average-position, and phase shift between lesion and body respiratory cycle. The lesions were contoured in both the estimated and 'ground-truth' onboard 4D-CBCT for comparison. 3D volume percentage-difference (VPD) and center-of-mass shift (COMS) were calculated to evaluate the estimation accuracy of three techniques: MM-FD, MM-only, and FD-only. Different onboard projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. Results: For all simulated patient and projection acquisition scenarios, the mean VPD (±S.D.)/COMS (±S.D.) between lesions in prior images and 'ground-truth' onboard images were 136.11% (±42.76%)/15.5 mm (±3.9 mm). Using orthogonal-view 15°-each scan angle, the mean VPD/COMS between the lesion in estimated and 'ground-truth' onboard images for MM-only, FD-only, and MM-FD techniques were 60.10% (±27.17%)/4.9 mm (±3.0 mm), 96.07% (±31.48%)/12.1 mm (±3.9 mm) and 11.45% (±9.37%)/1.3 mm (±1.3 mm), respectively. For orthogonal-view 30°-each scan angle, the corresponding results were 59.16% (±26.66%)/4.9 mm (±3.0 mm), 75.98% (±27.21%)/9.9 mm (±4.0 mm), and 5.22% (±2.12%)/0.5 mm (±0.4 mm). For single-view scan angles of 3°, 30°, and 60°, the results for MM-FD technique were 32.77% (±17.87%)/3.2 mm (±2.2 mm), 24.57% (±18.18%)/2.9 mm (±2.0 mm), and 10.48% (±9.50%)/1.1 mm (±1.3 mm), respectively. For projection angular-sampling-intervals of 0.6°, 1.2°, and 2.5° with the orthogonal-view 30°-each scan angle, the MM-FD technique generated similar VPD (maximum deviation 2.91%) and COMS (maximum deviation 0.6 mm), while sparser sampling yielded larger VPD/COMS. With equal number of projections, the estimation results using scattered 360° scan angle were slightly better than those using orthogonal-view 30°-each scan angle. The estimation accuracy of MM-FD technique declined as noise level increased. Conclusions: The MM-FD technique substantially improves the estimation accuracy for onboard 4D-CBCT using prior planning 4D-CT and limited-angle projections, compared to the MM-only and FD-only techniques. It can potentially be used for the inter/intrafractional 4D-localization verification. [ABSTRACT FROM AUTHOR]

Previous work demonstrated improved dosimetry of single isocenter volumetric modulated arc therapy (VMAT) of multiple intracranial targets when they are located ≤ 4 cm from isocenter because of narrower multileaf collimators (MLCs). In follow-up, we sought to determine if decreasing isocenter-target distance (d iso ) by using 2 to 3 isocenters would improve dosimetry for spatially dispersed targets. We also investigated the effect of a maximum dose constraint during VMAT optimization, and the dosimetric effect of the number of VMAT arcs used for a larger number of targets ( i.e. , 7 to 9). We identified radiosurgery cases that had multiple intracranial targets with d iso of at least 1 target > 5 cm. A single isocenter VMAT plan was created using a standardized 4-arc technique with 18 Gy per target. Each case was then replanned (1) using 2 to 3 isocenters, (2) including a maximum dose constraint per target, and in the case of 7 to 9 targets, (3) using 3 to 6 arcs. Dose evaluation included brain V 6 Gy and V 12 Gy , and conformity index (CI), gradient index (GI), and heterogeneity index (HI) per target. Two isocenters were sufficient to limit d iso to ≤ 4 cm and ≤ 5 cm for 11/15 and 13/15 cases, respectively; after replanning with 2 to 3 isocenters, d iso decreased from 5.8 ± 2.8 cm (2.3 14.9) to 2.5 ± 1.4 cm (0 5.2). All dose statistics improved on average, albeit modestly: V 6 Gy = 6.9 ± 7.1%, V 12 Gy = 0.9% ± 4.4%, CI = 2.6% ± 4.6%, GI = 0.9% ± 12.7%, and HI = 2.6% ± 5.2%; however, the number of arcs doubled and monitor units increase by nearly 2-fold. A maximum dose constraint had a negative effect on all dose indices, increasing V 12 Gy by 9.7 ± 6.9%. For ≥ 7 targets, increasing number of arcs to > 3 improved CI, V 12 Gy , and V 6 Gy . A single isocenter is likely sufficient for VMAT radiosurgery of multiple intracranial metastases. Optimal treatment plan quality is achieved when no constraint is placed on the maximum target dose; for cases with many targets at least 4 arcs are needed for optimal plan quality. [ABSTRACT FROM AUTHOR]

Purpose: The recent development of multifocal stereotactic radiosurgery (SRS) using a single isocenter volumetric modulated arc theory (VMAT) technique warrants a re-examination of the quality assurance (QA) tolerances for routine mechanical QA recommended by the American Association of Physicists in Medicine Task Group Report Number 142. Multifocal SRS can result in targets with small volumes being at a large off-axis distance from the treatment isocenter. Consequently, angular errors in the collimator, patient support assembly (PSA), or gantry could have an increased impact on target coverage. Methods: The authors performed a retrospective analysis of dose deviations caused by systematic errors in PSA, collimator, and gantry angle at the tolerance level for routine linear accelerator QA as recommended by TG-142. Dosimetric deviations from multifocal SRS plans (N = 10) were compared to traditional single target SRS using dynamic conformal arcs (N = 10). The chief dosimetric quantities used in determining clinical impact were V100% and D99% of the individual planning target volumes and V12Gy of the healthy brain. Results: Induced errors at tolerance levels showed the greatest change in multifocal SRS target coverage for collimator rotations (±1.0?) with the average changes to V100% and D99% being 5% and 6%, respectively, with maximum changes of 33% and 20%. A reduction in the induced error to half the TG-142 tolerance (±0.5?) demonstrated similar changes in coverage loss to traditional single target SRS assessed at the recommended tolerance level. The observed change in coverage for multifocal SRS was reduced for gantry errors (±1.0?) at 2% and 4.5% for V100% and D99%, respectively, with maximum changes of 18% and 12%. Minimal change in coverage was noted for errors in PSA rotation. Conclusions: This study indicates that institutions utilizing a single isocenter VMAT technique for multifocal disease should pay careful attention to the angular mechanical tolerances in designing a robust and complete QA program. [ABSTRACT FROM AUTHOR]

Purpose: To develop a radiomics filtering technique for characterizing spatial‐encoded regional pulmonary ventilation information on lung computed tomography (CT). Methods: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial‐encoded image information. Fifty‐three radiomic features were extracted within the kernel, resulting in a fourth‐order tensor object. As such, each voxel coordinate of the original lung was represented as a 53‐dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA‐SPECT) based on the Spearman correlation (ρ) analysis. Results: The radiomic feature maps GLRLM‐based Run‐Length Non‐Uniformity and GLCOM‐based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. Conclusions: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis‐generating data for future studies. [ABSTRACT FROM AUTHOR]

Purpose: To develop a framework for accurate electron Monte Carlo dose calculation. In this study, comprehensive validations of vendor provided electron beam phase space files for Varian TrueBeam Linacs against measurement data are presented. Methods:In this framework, the Monte Carlo generated phase space files were provided by the vendor and used as input to the downstream plan-specific simulations including jaws, electron applicators, and water phantom computed in the EGSnrc environment. The phase space files were generated based on open field commissioning data. A subset of electron energies of 6, 9, 12, 16, and 20 MeV and open and collimated field sizes 3 × 3, 4 × 4, 5 × 5, 6 × 6, 10 × 10, 15 × 15, 20 × 20, and 25 × 25 cm2 were evaluated. Measurements acquired with a CC13 cylindrical ionization chamber and electron diode detector and simulations from this framework were compared for a water phantom geometry. The evaluation metrics include percent depth dose, orthogonal and diagonal profiles at depths R100,R50,Rp, and Rp+ for standard and extended source-to-surface distances (SSD), as well as cone and cut-out output factors. Results:Agreement for the percent depth dose and orthogonal profiles between measurement and Monte Carlo was generally within 2% or 1 mm. The largest discrepancies were observed within depths of 5 mm from phantom surface. Differences in field size, penumbra, and flatness for the orthogonal profiles at depths R100, R50, and Rp were within 1 mm, 1 mm, and 2%, respectively. Orthogonal profiles at SSDs of 100 and 120 cm showed the same level of agreement. Cone and cut-out output factors agreed well with maximum differences within 2.5% for 6 MeV and 1% for all other energies. Cone output factors at extended SSDs of 105, 110, 115, and 120 cm exhibited similar levels of agreement. Conclusions:We have presented a Monte Carlo simulation framework for electron beam dose calculations for Varian TrueBeam Linacs. Electron beam energies of 6 to 20 MeV for open and collimated field sizes from 3 × 3 to 25 × 25 cm² were studied and results were compared to the measurement data with excellent agreement. Application of this framework can thus be used as the platform for treatment planning of dynamic electron arc radiotherapy and other advanced dynamic techniques with electron beams. [ABSTRACT FROM AUTHOR]

Background: Although four‐dimensional cone‐beam computed tomography (4D‐CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D‐CBCT scanning time while maintaining high‐quality images. Current motion‐compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling. Purpose: In this work, we aim to propose an alternative feature‐compensated method to realize the fast 4D‐CBCT with high‐quality images. Methods: We proposed a feature‐compensated deformable convolutional network (FeaCo‐DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo‐DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high‐quality images of the target phase. The proposed FeaCo‐DCN was evaluated using lung cancer patient data. Results: (1) FeaCo‐DCN generated high‐quality images with accurate and clear structures for a fast 4D‐CBCT scan; (2) 4D‐CBCT images reconstructed by FeaCo‐DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo‐DCN achieved superior performance by all metrics compared to the top‐ranked techniques in the AAPM SPARE Challenge. Conclusion: The proposed FeaCo‐DCN is effective and efficient in reconstructing 4D‐CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics. [ABSTRACT FROM AUTHOR]

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beamˈs eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments. [ABSTRACT FROM AUTHOR]

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trained with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physicianˈs single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models ( p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy ( p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models ( p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases ( p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID‐19 and non‐COVID‐19 pneumonia detection using chest x‐ray images. Methods: As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x‐ray image; thus, each feature is rendered as a 2D map in the same dimension as the x‐ray image. Based on each of the three investigated deep neural network architectures, including VGG‐16, VGG‐19, and DenseNet‐121, a pilot model was trained using x‐ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross‐correlation analysis in reference to the pilot model saliency map results. The radiomics‐boosted model was then trained based on the same deep neural network architecture using x‐ray images plus the selected RFMs as input. The proposed radiomics‐boosted design was developed using 812 chest x‐ray images with 262/288/262 COVID‐19/non‐COVID‐19 pneumonia/healthy cases, and 649/163 cases were assigned as training‐validation/independent test sets. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training‐validation set. Sensitivity, specificity, accuracy, and ROC curves together with area‐under‐the‐curve (AUC) from all three deep neural network architectures were evaluated. Results: After radiomics‐boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID‐19 and healthy individual classifications. VGG‐16 showed the largest improvement in COVID‐19 classification ROC (AUC from 0.963 to 0.993), and DenseNet‐121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging non‐COVID‐19 pneumonia classification task, radiomics‐boosted implementation of VGG‐16 (AUC from 0.918 to 0.969) and VGG‐19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet‐121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID‐19/non‐COVID‐19 pneumonia/healthy individual classifications were 0.973 (VGG‐19)/0.936 (VGG‐19)/ 0.933 (VGG‐16), respectively. Conclusions: The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID‐19/non‐COVID‐19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy. [ABSTRACT FROM AUTHOR]

Aims: To establish a framework to implement the 4D integrated extended cardiac torso (XCAT) digital phantom for 4D radiotherapy (RT) research. Materials and Methods: A computer program was developed to facilitate the characterization and implementation of the 4D XCAT phantom. The program can (1) generate 4D XCAT images with customized parameter files; (2) review 4D XCAT images; (3) generate composite images from 4D XCAT images; (4) track motion of selected region-of-interested (ROI); (5) convert XCAT raw binary images into DICOM format; (6) analyse clinically acquired 4DCT images and real-time position management (RPM) respiratory signal. Motion tracking algorithm was validated by comparing with manual method. Major characteristics of the 4D XCAT phantom were studied. Results: The comparison between motion tracking and manual measurements of lesion motion trajectory showed a small difference between them (mean difference in motion amplitude: 1.2 mm). The maximum lesion motion decreased nearly linearly (R² = 0.97) as its distance to the diaphragm (DD) increased. At any given DD, lesion motion amplitude increased nearly linearly (R 2 range: 0.89 to 0.95) as the inputted diaphragm motion increased. For a given diaphragm motion, the lesion motion is independent of the lesion size at any given DD. The 4D XCAT phantom can closely reproduce irregular breathing profile. The end-to-end test showed that clinically comparable treatment plans can be generated successfully based on 4D XCAT images. Conclusions: An integrated computer program has been developed to generate, review, analyse, process, and export the 4D XCAT images. A framework has been established to implement the 4D XCAT phantom for 4D RT research. [ABSTRACT FROM AUTHOR]

Purpose: Four-dimensional CBCT (4D-CBCT) imaging in the treatment room can provide verification of moving targets, facilitating the potential for margin reduction and consequent dose escalation. Reconstruction of 4D-CBCT images requires correlation of respiratory phase with projection acquisition, which is often achieved with external surrogate measures of respiration. However, external measures may not be a direct representation of the motion of the internal anatomy and it is therefore the aim of this work to develop a novel technique for markerless, self-sorted 4D-CBCT reconstruction. Methods: A novel 4D-CBCT reconstruction technique based on the principles of Fourier transform (FT) theory was investigated for markerless extraction of respiratory phase directly from projection data. In this FT technique, both phase information (FT-phase) and magnitude information (FT-magnitude) were separately implemented in order to discern projections corresponding to peak inspiration, which then facilitated the proceeding sort and bin processes involved in retrospective 4D image reconstruction. In order to quantitatively evaluate the accuracy of the Fourier methods, peak-inspiration projections identified each by FT-phase and FT-magnitude were compared to those manually identified by visual tracking of structures. The average phase difference as assigned by each method vs the manual technique was calculated per projection dataset. The percentage of projections that were assigned within 10% phase of each other was also computed. Both Fourier methods were tested on two phantom datasets, programmed to exhibit sinusoidal respiratory cycles of 2.0 cm in amplitude with respiratory cycle lengths of 3 and 6 s, respectively. Additionally, three sets of patient projections were studied. All of the data were previously acquired at slow-gantry speeds ranging between 0.6°/s and 0.7°/s over a 200° rotation. Ten phase bins with 10% phase windows were selected for 4D-CBCT reconstruction of one phantom and one patient case for visual and quantitative comparison. Line profiles were plotted for the 0% and 50% phase images as reconstructed by the manual technique and each of the Fourier methods. Results: As compared with the manual technique, the FT-phase method resulted in average phase differences of 1.8% for the phantom with the 3 s respiratory cycle, 3.9% for the phantom with the 6 s respiratory cycle, 2.9% for patient 1, 5.0% for patient 2, and 3.8% for patient 3. For the FT-magnitude method, these numbers were 2.1%, 4.0%, 2.9%, 5.3%, and 3.5%, respectively. The percentage of projections that were assigned within 10% phase by the FT-phase method as compared to the manual technique for the five datasets were 100.0%, 100.0%, 97.6%, 93.4%, and 94.1%, respectively, whereas for the FT-magnitude method these percentages were 98.1%, 92.3%, 98.7%, 87.3%, and 95.7%. Reconstructed 4D phase images for both the phantom and patient case were visually and quantitatively equivalent between each of the Fourier methods vs the manual technique. Conclusions: A novel technique employing the basics of Fourier transform theory was investigated and demonstrated to be feasible in achieving markerless, self-sorted 4D-CBCT reconstruction. [ABSTRACT FROM AUTHOR]

Abstract: We propose a method to quantify superior–inferior (SI) motion of a rigid target using the 3D contour from free-breathing CT (FBCT). The technique utilizes similarity between 2D contours (Jaccard Index) and a population based density function for probability of motion amplitude, and is applicable both when the static target shape is and is not known beforehand. Simulations and phantom measurements showed that motion reconstruction is often feasible, with decreasing accuracy as discrepancy is introduced between assumed and actual static shape. When no static shape is used the analysis is most robust for slow scanning speeds relative to the motion period. [Copyright &y& Elsevier]

Purpose: Localization prior to delivery of SBRT to free-breathing patients is performed by aligning the planning internal target volume (ITV) from 4DCT with an on-board free-breathing cone-beam CT (FB-CBCT) image. The FB-CBCT image is assumed to also generate an ITV that captures the full range of motion, due to the acquisition spanning multiple respiratory cycles. However, the ITV could potentially be underestimated when the ratio of time spent in inspiration versus time spent in expiration (I/E ratio) deviates from unity. Therefore, the aim of this study was to investigate the effect of variable I/E ratios on the FB ITV generated from a FB-CBCT scan. Methods: This study employed both phantom and patient imaging data. For the phantom study, five periodic respiratory cycles were simulated with different I/E ratios. Six patient respiratory cycles with variable I/E ratios were also selected. All profiles were then programmed into a motion phantom for imaging and modified to exhibit three peak-to-peak motion amplitudes (0.5, 1.0, and 2.0 cm). Each profile was imaged using two spherical targets with 1.0 and 3.0 cm diameters. 2D projections were acquired with full gantry rotation of a kiloVoltage (kV) imager mounted onto the gantry of a modern linear accelerator. CBCT images were reconstructed from 2D projections using a standard filtered back-projection reconstruction algorithm. Quantitative analyses for the phantom study included computing the change in contrast along the direction of target motion as well as determining the area (which is proportional to the target volume) inside of the contour extracted using a Canny edge detector. For the patient study, projection data that were previously acquired under an investigational 4D CBCT slow-gantry imaging protocol were used to generate both FB-CBCT and 4D CBCT images. Volumes were then manually contoured from both datasets (using the same window and level) for quantitative comparison. Results: The phantom study indicated a reduction in contrast at the inferior edge of the ITV (corresponding to inspiration) as the ratio decreased, for both simulated and patient respiratory cycles. For the simulated phantom respiratory cycles, the contrast reduction of the smallest I/E ratio was 27.6% for the largest target with the smallest amplitude and 89.7% for the smallest target with the largest amplitude. For patient respiratory cycles, these numbers were 22.3% and 94.0%, respectively. The extracted area from inside of the target contours showed a decreasing trend as the I/E ratio decreased. In the patient study, the FB-CBCT ITVs of both lung tumors studied were underestimated when compared with their corresponding 4D CBCT ITV. The underestimations found were 40.1% for the smaller tumor and 24.2% for the larger tumor. Conclusions: The ITV may be underestimated in a FB-CBCT image when a patient's respiratory pattern is characterized by a disparate length of time spent in inspiration versus expiration. Missing the full target motion information during on-board verification imaging may result in localization errors. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether the effect of organ motion can be further reduced with the application of a cardiac gating technique, together with respiratory gating. Methods and Materials: Axial and coronal images through the heart and liver were continuously scanned with fast cine magnetic resonance imaging scans at three different gating settings: (1) without respiratory and cardiac gating; (2) with respiratory gating, but without cardiac gating; and (3) with both respiratory and cardiac gating. The effect of motion for either the heart or liver was analyzed with probability maps. Results: With the application of respiratory gating only, the marginal region on the probability map was reduced by 10.0% in the axial slice and 19.8% in the coronal slice for the heart. It was reduced by 5.2% in the axial slice and 20.8% in the coronal slice for the liver. With the application of cardiac gating together with respiratory gating, the marginal region on the probability map was reduced further. The reduction was 8.0% in the axial slice and 13.6% in the coronal slice for the heart and 5.9% in the axial slice and 7.0% in the coronal slice for the liver. Conclusion: The effect of organ motion can be further reduced with the application of cardiac gating together with respiratory gating. The potential application to treatment planning merits further investigation. [Copyright &y& Elsevier]

Objective: To design a deep transfer learning framework for modeling fluence map predictions for stereotactic body radiation therapy (SBRT) of adrenal cancer and similar sites that usually have a small number of cases. Approach: We developed a transfer learning framework for adrenal SBRT planning that leverages knowledge in a pancreas SBRT planning model. Treatment plans from the two sites had different dose prescriptions and beam settings but both prioritized gastrointestinal sparing. A base framework was first trained with 100 pancreas cases. This framework consists of two convolutional neural networks (CNN), which predict individual beam doses (BD-CNN) and fluence maps (FM-CNN) sequentially for 9-beam intensity-modulated radiation therapy (IMRT) plans. Forty-five adrenal plans were split into training/validation/test sets with the ratio of 20/10/15. The base BD-CNN was re-trained with transfer learning using 5/10/15/20 adrenal training cases to produce multiple candidate adrenal BD-CNN models. The base FM-CNN was directly used for adrenal cases. The deep learning (DL) plans were evaluated by several clinically relevant dosimetric endpoints, producing a percentage score relative to the clinical plans. Main results: Transfer learning significantly reduced the number of training cases and training time needed to train such a DL framework. The adrenal transfer learning model trained with 5/10/15/20 cases achieved validation plan scores of 85.4/91.2/90.7/89.4, suggesting that model performance saturated with 10 training cases. Meanwhile, a model using all 20 adrenal training cases without transfer learning only scored 80.5. For the final test set, the 5/10/15/20-case models achieved scores of 73.5/75.3/78.9/83.3. Significance: It is feasible to use deep transfer learning to train an IMRT fluence prediction framework. This technique could adapt to different dose prescriptions and beam configurations. This framework potentially enables DL modeling for clinical sites that have a limited dataset, either due to few cases or due to rapid technology evolution. [ABSTRACT FROM AUTHOR]

The charges on this task group (TG) were as follows: (a) provide specific procedural guidelines for performing the tests recommended in TG 142; (b) provide estimate of the range of time, appropriate personnel, and qualifications necessary to complete the tests in TG 142; and (c) provide sample daily, weekly, monthly, or annual quality assurance (QA) forms. Many of the guidelines in this report are drawn from the literature and are included in the references. When literature was not available, specific test methods reflect the experiences of the TG members (e.g., a test method for door interlock is self‐evident with no literature necessary). In other cases, the technology is so new that no literature for test methods was available. Given broad clinical adaptation of volumetric modulated arc therapy (VMAT), which is not a specific topic of TG 142, several tests and criteria specific to VMAT were added. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the prognostic potential of intra‐treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence‐free survival (RFS). Materials and Methods: Sixty‐four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB‐approved study. Investigational 18F‐FDG‐PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven‐week course of therapy. Fifty‐five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan‐Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. Results: While pre‐treatment radiomics were not prognostic, intra‐treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ2 test) and RFS (HR = 7.53, 95% CI = 2.54–22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26–5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03–7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. Conclusions: Our data illustrate the prognostic value of intra‐treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow‐up times, and unsupervised clustering formalism that is less prone to hyper‐parameter tuning and over‐fitting compared to supervised learning. [ABSTRACT FROM AUTHOR]

Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique. Methods. A motion generation model was developed using bicycle-GAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which were composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients and then applied to the XCAT EOI images to generate 4D-XCAT with realistic respiratory motions. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude, and this model was developed using 300 4D-XCAT generated from 6 XCAT phantom sizes with 50 different perturbations for each size. In both patient and phantom studies, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. The generated DVF was evaluated by deformation energy. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross correlation and Spearman's correlation. Comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated. Results. Comparing the simulated and reference 4D-CTs, the maximum deviation of lung volume during respiration was 5.8%, and the Dice coefficient reached at least 0.95 for lungs. The generated DVFs presented comparable deformation energy levels. The cross correlation of DVFs achieved 0.89 ± 0.10/0.86 ± 0.12/0.95 ± 0.04 along the x/y/z direction in the testing group. The cross correlation of ventilation maps derived achieved 0.80 ± 0.05/0.67 ± 0.09/0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/0, 60 ± 0.09/0.53 ± 0.01, respectively, in the training/validation/testing groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintained the lung volumes of the original XCAT phantom (Dice = 0.95, maximum lung volume variation = 4%). The motion amplitude control models controlled the motion amplitude control error to be less than 0.5 mm. Conclusions. The results demonstrated the feasibility of synthesizing realistic controllable respiratory motion in the XCAT phantom using the proposed method. This crucial development enhances the value of XCAT phantoms for various 4D imaging and therapy studies. [ABSTRACT FROM AUTHOR]

Purpose: To develop an artificial intelligence (AI) agent for fully automated rapid head‐and‐neck intensity‐modulated radiation therapy (IMRT) plan generation without time‐consuming dose‐volume‐based inverse planning. Methods: This AI agent was trained via implementing a conditional generative adversarial network (cGAN) architecture. The generator, PyraNet, is a novel deep learning network that implements 28 classic ResNet blocks in pyramid‐like concatenations. The discriminator is a customized four‐layer DenseNet. The AI agent first generates multiple customized two‐dimensional projections at nine template beam angles from a patient's three‐dimensional computed tomography (CT) volume and structures. These projections are then stacked as four‐dimensional inputs of PyraNet, from which nine radiation fluence maps of the corresponding template beam angles are generated simultaneously. Finally, the predicted fluence maps are automatically postprocessed by Gaussian deconvolution operations and imported into a commercial treatment planning system (TPS) for plan integrity check and visualization. The AI agent was built and tested upon 231 oropharyngeal IMRT plans from a TPS plan library. 200/16/15 plans were assigned for training/validation/testing, respectively. Only the primary plans in the sequential boost regime were studied. All plans were normalized to 44 Gy prescription (2 Gy/fx). A customized Harr wavelet loss was adopted for fluence map comparison during the training of the PyraNet. For test cases, isodose distributions in AI plans and TPS plans were qualitatively evaluated for overall dose distributions. Key dosimetric metrics were compared by Wilcoxon signed‐rank tests with a significance level of 0.05. Results: All 15 AI plans were successfully generated. Isodose gradients outside of PTV in AI plans were comparable to those of the TPS plans. After PTV coverage normalization, Dmean of left parotid (DAI = 23.1 ± 2.4 Gy; DTPS = 23.1 ± 2.0 Gy), right parotid (DAI = 23.8 ± 3.0 Gy; DTPS = 23.9 ± 2.3 Gy), and oral cavity (DAI = 24.7 ± 6.0 Gy; DTPS = 23.9 ± 4.3 Gy) in the AI plans and the TPS plans were comparable without statistical significance. AI plans achieved comparable results for maximum dose at 0.01cc of brainstem (DAI = 15.0 ± 2.1 Gy; DTPS = 15.5 ± 2.7 Gy) and cord + 5mm (DAI = 27.5 ± 2.3 Gy; DTPS = 25.8 ± 1.9 Gy) without clinically relevant differences, but body Dmax results (DAI = 121.1 ± 3.9 Gy; DTPS = 109.0 ± 0.9 Gy) were higher than the TPS plan results. The AI agent needed ~3 s for predicting fluence maps of an IMRT plan. Conclusions: With rapid and fully automated execution, the developed AI agent can generate complex head‐and‐neck IMRT plans with acceptable dosimetry quality. This approach holds great potential for clinical applications in preplanning decision‐making and real‐time planning. [ABSTRACT FROM AUTHOR]

Purpose: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently.Methods and Materials: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set.Results: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process.Conclusions: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies. [ABSTRACT FROM AUTHOR]

Linac based radiosurgery to multiple metastases is commonly planned with volumetric modulated arc therapy (VMAT) as it effectively achieves high conformality to complex target arrangements. However, as the number of targets increases, VMAT can struggle to block between targets, which can lead to highly modulated and/or nonconformal multi-leaf collimator (MLC) trajectories that unnecessarily irradiation of healthy tissue. In this study we introduce, describe, and evaluate a treatment planning technique called Conformal Arc Informed VMAT (CAVMAT), which aims to reduce the dose to healthy tissue while generating highly conformal treatment plans. CAVMAT is a hybrid technique which combines the conformal MLC trajectories of dynamic conformal arcs with the MLC modulation and versatility of inverse optimization. CAVMAT has 3 main steps. First, targets are assigned to subgroups to maximize MLC blocking between targets. Second, arc weights are optimized to achieve the desired target dose, while minimizing MU variation between arcs. Third, the optimized conformal arc plan serves as the starting point for limited inverse optimization to improve dose conformity to each target. Twenty multifocal VMAT cases were replanned with CAVMAT with 20Gy applied to each target. The total volume receiving 2.5 Gy c m 3 , 6 Gy c m 3 , 12 Gy c m 3 , and 16 Gy c m 3 , conformity index, treatment delivery time, and the total MU were used to compare the VMAT and CAVMAT plans. In addition, CAVMAT was compared to a broad range of planning strategies from various institutions (108 linear accelerator based plans, 14 plans using other modalities) for a 5-target case utilized in a recent plan challenge. For the linear accelerator-based plans, a plan complexity metric based on aperture opening area and perimeter, total monitor units (MU), and MU for a given aperture opening was utilized in the plan challenge scoring algorithm to compare the submitted plans to CAVMAT. After re-planning the 20 VMAT cases, CAVMAT reduced the average V 2.5Gy c m 3 by 25.25 ± 19.23%, V 6Gy c m 3 by 13.68 ± 18.97%, V 12Gy c m 3 by 11.40 ± 19.44%, and V 16Gy c m 3 by 6.38 ± 19.11%. CAVMAT improved conformity by 3.81 ± 7.57%, while maintaining comparable target dose. MU for the CAVMAT plans increased by 24.35 ± 24.66%, leading to an increased treatment time of 2 minutes. For the plan challenge case, CAVMAT was 1 of 12 linac based plans that met all plan challenge scoring criteria. Compared to the average submitted VMAT plan, CAVMAT increased the V 10% Gy[%] of healthy tissue (Brain-PTV) by roughly 3.42%, but in doing so was able to reduce the V 25% Gy[%] by roughly 3.73%, while also reducing V 50% Gy[%] , V 75% Gy[%], and V 100% Gy[%]. The CAVMAT technique successfully eliminated insufficient MLC blocking between targets prior to the inverse optimization, leading to less complex treatment plans and improved tissue sparing. Tissue sparing, improved conformity, and decreased plan complexity at the cost of slight increase in treatment delivery time indicates CAVMAT to be a promising method to treat brain metastases. [ABSTRACT FROM AUTHOR]