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The first clinical MR-IGRT system was validated for real-time tracking and gating capabilities and shown to be reliable and accurate. Patient workflow methods were developed for efficient treatment. Periodic quality assurance tests can be efficiently performed with commercially available equipment to ensure accurate system performance.

Purpose: Severe artifacts in kilovoltage-CT simulation images caused by large metallic implants can significantly degrade the conspicuity and apparent CT Hounsfield number of targets and anatomic structures, jeopardize the confidence of anatomical segmentation, and introduce inaccuracies into the radiation therapy treatment planning process. This study evaluated the performance of the first commercial orthopedic metal artifact reduction function (O-MAR) for radiation therapy, and investigated its clinical applications in treatment planning. Methods: Both phantom and clinical data were used for the evaluation. The CIRS electron density phantom with known physical (and electron) density plugs and removable titanium implants was scanned on a Philips Brilliance Big Bore 16-slice CT simulator. The CT Hounsfield numbers of density plugs on both uncorrected and O-MAR corrected images were compared. Treatment planning accuracy was evaluated by comparing simulated dose distributions computed using the true density images, uncorrected images, and O-MAR corrected images. Ten CT image sets of patients with large hip implants were processed with the O-MAR function and evaluated by two radiation oncologists using a five-point score for overall image quality, anatomical conspicuity, and CT Hounsfield number accuracy. By utilizing the same structure contours delineated from the O-MAR corrected images, clinical IMRT treatment plans for five patients were computed on the uncorrected and O-MAR corrected images, respectively, and compared. Results: Results of the phantom study indicated that CT Hounsfield number accuracy and noise were improved on the O-MAR corrected images, especially for images with bilateral metal implants. The γ pass rates of the simulated dose distributions computed on the uncorrected and O-MAR corrected images referenced to those of the true densities were higher than 99.9% (even when using 1% and 3 mm distance-to-agreement criterion), suggesting that dose distributions were clinically identical. In all patient cases, radiation oncologists rated O-MAR corrected images as higher quality. Formerly obscured critical structures were able to be visualized. The overall image quality and the conspicuity in critical organs were significantly improved compared with the uncorrected images: overall quality score (1.35 vs 3.25, P = 0.0022); bladder (2.15 vs 3.7, P = 0.0023); prostate and seminal vesicles/vagina (1.3 vs 3.275, P = 0.0020); rectum (2.8 vs 3.9, P = 0.0021). The noise levels of the selected ROIs were reduced from 93.7 to 38.2 HU. On most cases (8/10), the average CT Hounsfield numbers of the prostate/vagina on the O-MAR corrected images were closer to the referenced value (41.2 HU, an average measured from patients without metal implants) than those on the uncorrected images. High γ pass rates of the five IMRT dose distribution pairs indicated that the dose distributions were not significantly affected by the CT image improvements. Conclusions: Overall, this study indicated that the O-MAR function can remark...

Purpose/Objectives Stereotactic body radiotherapy (SBRT) is increasingly used to treat oligometastatic or unresectable primary malignancy, although proximity of organs-at-risk (OAR) may limit delivery of sufficiently ablative dose. Magnetic resonance (MR)-based online-adaptive radiotherapy (ART) has potential to improve SBRT’s therapeutic ratio. This study characterizes potential advantages of online-adaptive MR-guided SBRT to treat oligometastatic disease of the non-liver abdomen and central thorax. Materials/Methods Ten patients treated with RT for unresectable primary or oligometastatic disease of the non-liver abdomen (n=5) or central thorax (n=5) underwent imaging throughout treatment on a clinical MR-IGRT system. SBRT plans were created based on tumor/OAR anatomy at initial CT simulation (PI) and simulated adaptive plans were created based on observed MR-image set tumor/OAR “anatomy-of-the-day” (PA). Each PA was planned under workflow constraints to simulate online-ART. Prescribed dose was 50Gy/5fractions with goal coverage of 95% PTV by 95% of the prescription, subject to hard OAR constraints. PI was applied to each MR dataset and compared to PA to evaluate changes in dose delivered to tumor/OARs, with dose escalation when possible. Results Hard OAR constraints were met for all PI based on anatomy from initial CT simulation, and all PA based on anatomy from each daily MR-image set. Application of the PI to anatomy-of-the-day caused OAR constraint violation in 19/30 cases. Adaptive planning increased PTV coverage in 21/30 cases, including 14 cases where hard OAR constraints were violated by the non-adaptive plan. For 9 PA cases, decreased PTV coverage was required to meet hard OAR constraints that would have been violated in a non-adaptive setting. Conclusions Online-adaptive MRI-guided SBRT may allow PTV dose escalation and/or simultaneous OAR sparing compared to non-adaptive SBRT. A prospective clinical trial is underway at our institution to evaluate clinical outcomes of this technique.

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Purpose The purpose of this study was to expedite the contouring process for MRI‐guided adaptive radiotherapy (MR‐IGART), a convolutional neural network (CNN) deep‐learning (DL) model is proposed to accurately segment the liver, kidneys, stomach, bowel and duodenum in 3D MR images. Methods Images and structure contours for 120 patients were collected retrospectively. Treatment sites included pancreas, liver, stomach, adrenal gland, and prostate. The proposed DL model contains a voxel‐wise label prediction CNN and a correction network which consists of two sub‐networks. The prediction CNN and sub‐networks in the correction network each includes a dense block which consists of twelve densely connected convolutional layers. The correction network was designed to improve the voxel‐wise labeling accuracy of a CNN by learning and enforcing implicit anatomical constraints in the segmentation process. Its sub‐networks learn to fix the erroneous classification of its previous network by taking as input both the original images and the softmax probability maps generated from its previous sub‐network. The parameters of each sub‐network were trained independently using piecewise training. The model was trained on 100 datasets, validated on 10 datasets and tested on the remaining 10 datasets. Dice coefficient, Hausdorff distance (HD) were calculated to evaluate the segmentation accuracy. Results The proposed DL model was able to segment the organs with good accuracy. The correction network outperformed the conditional random field (CRF), a most comparable method that is usually applied as a post‐processing step. For the 10 testing patients, the average Dice coefficients were 95.3 ± 0.73, 93.1 ± 2.22, 85.0 ± 3.75, 86.6 ± 2.69, and 65.5 ± 8.90 for liver, kidneys, stomach, bowel, and duodenum, respectively. The mean Hausdorff Distance (HD) were 5.41 ± 2.34, 6.23 ± 4.59, 6.88 ± 4.89, 5.90 ± 4.05, and 7.99 ± 6.84 mm, respectively. Manual contouring, as to correct the automatic segmentation results, was four times as fast as manual contouring from scratch. Conclusion The proposed method can automatically segment the liver, kidneys, stomach, bowel, and duodenum in 3D MR images with good accuracy. It is useful to expedite the manual contouring for MR‐IGART.

Purpose This work describes a patient-specific dosimetry quality assurance (QA) program for intensity modulated radiation therapy (IMRT) using ViewRay, the first commercial magnetic resonance imaging guided radiation therapy device. Methods and materials The program consisted of the following components: 1) one-dimensional multipoint ionization chamber measurement using a customized 15 cm3 cubic phantom, 2) two-dimensional (2D) radiographic film measurement using a 30×30×20 cm3 phantom with multiple inserted ionization chambers, 3) quasi- three-dimensional (3D) diode array (ArcCHECK) measurement with a centrally inserted ionization chamber, 4) 2D fluence verification using machine delivery log files, and 5) 3D Monte-Carlo (MC) dose reconstruction with machine delivery files and phantom CT. Results The ionization chamber measurements agreed well with treatment planning system (TPS) computed doses in all phantom geometries where the mean difference (mean ± SD) was 0.0% ± 1.3% (n=102, range, −3.0 % to 2.9%). The film measurements also showed excellent agreement with the TPS computed 2D dose distributions where the mean passing rate using 3% relative/3 mm gamma criteria was 94.6% ± 3.4% (n=30, range, 87.4% to 100%). For ArcCHECK measurements, the mean passing rate using 3% relative/3 mm gamma criteria was 98.9% ± 1.1% (n=34, range, 95.8% to 100%). 2D fluence maps with a resolution of 1×1 mm2 showed 100% passing rates for all plan deliveries (n=34). The MC reconstructed doses to the phantom agreed well with planned 3D doses where the mean passing rate using 3% absolute/3 mm gamma criteria was 99.0% ± 1.0% (n=18, range, 97.0% to100%), demonstrating the feasibility of evaluating the QA results in the patient geometry. Conclusions We have developed a dosimetry program for ViewRay’s patient-specific IMRT QA. The methodology will be useful for other ViewRay users. The QA results presented here can assist the RT community to establish appropriate tolerance and action limits for ViewRay’s IMRT QA.

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