Implementation of the DPM Monte Carlo code on a parallel architecture for treatment planning applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135058/1/mp6691a.pd

Monte-Carlo-computed dose, kerma and fluence distributions in heterogeneous slab geometries irradiated by small megavoltage photon fields

Small-field dosimetry is central to the planning and delivery of radiotherapy to patients with cancer. Small-field dosimetry is beset by complex issues, such as loss of charged-particle equilibrium (CPE), source occlusion and electron scattering effects in low-density tissues. The purpose of the present research was to elucidate the fundamental physics of small fields through the computation of absorbed dose, kerma and fluence distributions in heterogeneous media using the Monte-Carlo method. Absorbed dose and kerma were computed using the DOSRZnrc Monte-Carlo (MC) user-code for beams with square field sizes ranging from 0.25 × 0.25 to 7× 7 cm2 (for 6 MV \u27full linac\u27 geometry) and 0.25 × 0.25 to 16 × 16 cm2 (for 15 MV \u27full linac\u27 geometry). In the bone inhomogeneity the dose increases (vs. homogeneous water) for field sizes \u3c 1 × 1 cm2 at 6 MV and ≤ 3 × 3 cm2 at 15 MV and decreases (vs. homogeneous water) for field sizes ≥ 3 × 3 cm2 at 6 MV and ≥ 5 × 5 cm2 at 15 MV. In the lung inhomogeneity there is negligible decrease in dose compared to in uniform water for field sizes \u3e 5 × 5 cm2 at 6 MV and ≥ 16 × 16 cm2 at 15 MV, consistent with the Fano theorem. The near-unity value of the absorbed-dose to collision-kerma ratio, D/Kcol, at the centre of the bone and lung slabs in the heterogeneous phantom demonstrated that CPE is achieved in bone for field sizes \u3e 1 × 1 cm2 at 6 MV and \u3e 5 × 5 cm2 at 15 MV; CPE is achieved in lung at field sizes \u3e 5 × 5 cm2 at 6 MV and ≥ 16 × 16 cm2 at 15 MV. Electron-fluence perturbation factors for the 0.25 × 0.25 cm2 field were 1.231 and 1.403 for bone-to-water and 0.454 and 0.333 for lung-to-water were at 6 and 15 MV respectively. For field sizes large enough for quasi-CPE, the MC-derived dose-perturbation factors, lung-to-water, were close to unity; electron-fluence perturbation factors, lung-to-water, were ~1.0, consistent with the \u27Fano\u27 theorem. At 15 MV in the lung inhomogeneity the magnitude and also the \u27shape\u27 of the primary electron-fluence spectrum differed significantly from that in water. Beam penumbrae relative to water were narrower in the bone inhomogeneity and broader in the lung inhomogeneity for all field sizes

An anatomically realistic lung model for Monte Carlo‐based dose calculations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134911/1/mp7284.pd

Landscape of Oncology-Specific, FDA-Approved, Artificial Intelligence and Machine Learning-Enabled Medical Devices

Purpose/Objective(s): Machine learning (ML), a type of artificial intelligence (AI) technology that uses a data-driven approach for pattern recognition, has been shown by numerous research studies to be beneficial for tasks across healthcare. In this study, we aim to characterize the commercial availability of oncology-specific AI/ML applications in the clinic by performing a detailed analysis of such devices that were approved/cleared by the US Food and Drug Administration (FDA). Materials/Methods: A list of 343 AI/ML-enabled medical devices that were approved or cleared by the FDA up to June 2021 was published by the agency, and this list was used to construct the initial database for our study. The publicly available FDA approval letters for these devices were independently reviewed by two research assistants, and a device was classified as oncology-specific if its primary intended use is related to assisting the diagnosis or treatment of oncologic pathologies. For oncology-specific devices, additional details on device characteristics, FDA regulatory process, and approved indications were obtained. A basic descriptive statistical analysis was performed on the aggregated data. Results: Fifty-two (15.2%) of the 343 AI/ML-enabled medical devices were classified as oncology-specific. The growth of the oncologic-specific devices sharply rose since the mid-2010s, with 49 (94.2%) approved in 2016 or after. Fifty (96.2%) devices were cleared by the 510(k) premarket notification pathway, and, except for one class III device, the remaining 51 devices were considered as class II by the FDA. All but one device was considered Software as a Medical Device (SaMD). Thirty-six (69.2%) devices were intended for diagnostic purposes, of which 24 (66.7%), 9 (14.3%), 1 (6.3%), 1 (6.3%), and 1 (6.3%) was for the detection of breast cancer, lung cancer, prostate cancer, thyroid cancer, and bone cancer, respectively. The 16 devices intended for therapeutic purposes were all related to radiotherapy: 15 are for radiation treatment planning (all included organ auto-segmentation as the main function), and 1 is a linear accelerator equipped with AI/ML algorithms. Conclusion: Our results showed a rapid increase of oncology-specific, FDA-approved, AI/ML-enabled medical devices since 2016. Further study is needed to assess the impact made by these devices on the delivery of oncology care

Investigation of Kodak extended dose range (EDR) film for megavoltage photon beam dosimetry

We have investigated the dependence of the measured optical density on the incident beam energy, field size and depth for a new type of film, Kodak extended dose range (Kodak EDR). Film measurements have been conducted over a range of field sizes (3 × 3 cm2 to 25 × 25 cm2) and depths (dmax to 15 cm), for 6 MV and 15 MV photons within a solid water phantom, and the variation in sensitometric response (net optical density versus dose) has been reported. Kodak EDR film is found to have a linear response with dose, from 0 to 350 cGy, which is much higher than that typically seen for Kodak XV film (0–50 cGy). The variation in sensitometric response for Kodak EDR film as a function of field size and depth is observed to be similar to that of Kodak XV film; the optical density varied in the order of 2–3% for field sizes of 3 × 3 cm2 and 10 × 10 cm2 at depths of dmax, 5 cm and 15 cm in the phantom. Measurements for a 25 × 25 cm2 field size showed consistently higher optical densities at depths of dmax, 5 cm and 15 cm, relative to a 10 × 10 cm2 field size at 5 cm depth, with 4–5% differences noted at a depth of 15 cm. Fractional depth dose and profiles conducted with Kodak EDR film showed good agreement (2%/2 mm) with ion chamber measurements for all field sizes except for the 25 × 25 cm2 at depths greater than 15 cm, where differences in the order of 3–5% were observed. In addition, Kodak EDR film measurements were found to be consistent with those of Kodak XV film for all fractional depth doses and profiles. The results of this study indicate that Kodak EDR film may be a useful tool for relative dosimetry at higher dose ranges.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48973/2/m22005.pd

A Systematic Analysis of Errors in Target Localization and Treatment Delivery for Stereotactic Radiosurgery Using 2D/3D Image Registration

PURPOSE: To determine the localization uncertainties associated with 2-dimensional/3-dimensional image registration in comparison to 3-dimensional/3-dimensional image registration in 6 dimensions on a Varian Edge Linac under various imaging conditions. METHODS: The systematic errors in 6 dimensions were assessed by comparing automatic 2-dimensional/3-dimensional (kV/MV vs computed tomography) with 3-dimensional/3-dimensional (cone beam computed tomography vs computed tomography) image registrations under various conditions encountered in clinical applications. The 2-dimensional/3-dimensional image registration uncertainties for 88 patients with different treatment sites including intracranial and extracranial were evaluated by statistically analyzing 2-dimensional/3-dimensional pretreatment verification shifts of 192 fractions in stereotactic radiosurgery and stereotactic body radiotherapy. RESULTS: The systematic errors of 2-dimensional/3-dimensional image registration using kV-kV, MV-kV, and MV-MV image pairs were within 0.3 mm and 0.3° for the translational and rotational directions within a 95% confidence interval. No significant difference ( P \u3e .05) in target localization was observed with various computed tomography slice thicknesses (0.8, 1, 2, and 3 mm). Two-dimensional/3-dimensional registration had the best accuracy when pattern intensity and content filter were used. For intracranial sites, means ± standard deviations of translational errors were -0.20 ± 0.70 mm, 0.04 ± 0.50 mm, and 0.10 ± 0.40 mm for the longitudinal, lateral, and vertical directions, respectively. For extracranial sites, means ± standard deviations of translational errors were -0.04 ± 1.00 mm, 0.2 ± 1.0 mm, and 0.1 ± 1.0 mm for the longitudinal, lateral, and vertical directions, respectively. Two-dimensional/3-dimensional image registration for intracranial and extracranial sites had comparable systematic errors that were approximately 0.2 mm in the translational direction and 0.08° in the rotational direction. CONCLUSION: The standard 2-dimensional/3-dimensional image registration tool available on the Varian Edge radiosurgery device, a state-of-the-art system, is helpful for robust and accurate target positioning for image-guided stereotactic radiosurgery

Dosimetric comparison of extended dose range film with ionization measurements in water and lung equivalent heterogeneous media exposed to megavoltage photons

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135483/1/acm20025.pd

FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images

Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of prostate derived from CTs poor soft tissue contrast, and (2) the limitation of convolutional neural network based models in capturing long-range global context. Here we propose a focal transformer based image segmentation architecture to effectively and efficiently extract local visual features and global context from CT images. Furthermore, we design a main segmentation task and an auxiliary boundary-induced label regression task as regularization to simultaneously optimize segmentation results and mitigate the unclear boundary effect, particularly in unseen data set. Extensive experiments on a large data set of 400 prostate CT scans demonstrate the superior performance of our focal transformer to the competing methods on the prostate segmentation task.Comment: 13 pages, 3 figures, 2 table

Model refinement increases confidence levels and clinical agreement when commissioning a three-dimensional secondary dose calculation system

PURPOSE: Evaluate custom beam models for a second check dose calculation system using statistically verifiable passing criteria for film analysis, DVH, and 3D gamma metrics. METHODS: Custom beam models for nine linear accelerators for the Sun Nuclear Dose Calculator algorithm (SDC, Sun Nuclear) were evaluated using the AAPM-TG119 test suite (5 Intensity Modulated Radiation Therapy (IMRT) and 5 Volumetric Modulated Arc Therapy (VMAT) plans) and a set of clinical plans. Where deemed necessary, adjustments to Multileaf Collimator (MLC) parameters were made to improve results. Comparisons to the Analytic Anisotropic Algorithm (AAA), and gafchromic film measurements were performed. Confidence intervals were set to 95% per TG-119. Film gamma criteria were 3%/3 mm (conventional beams) or 3%/1 mm (Stereotactic Radiosurgery [SRS] beams). Dose distributions in solid water phantom were evaluated based on DVH metrics (e.g., D95, V20) and 3D gamma criteria (3%/3 mm or 3%/1 mm). Film passing rates, 3D gamma passing rates, and DVH metrics were reported for HD MLC machines and Millennium MLC Machines. RESULTS: For HD MLC machines, SDC gamma film agreement was 98.76% ± 2.30% (5.74% CL) for 6FFF/6srs (3%/1 mm), and 99.80% ± 0.32% (0.83% CL) for 6x (3%/3 mm). For Millennium MLC machines, film passing rates were 98.20% ± 3.14% (7.96% CL), 99.52% ± 1.14% (2.71% CL), and 99.69% ± 0.82% (1.91% CL) for 6FFF, 6x, and 10x, respectively. For SDC to AAA comparisons: HD MLC Linear Accelerators (LINACs); DVH point agreement was 0.97% ± 1.64% (4.18% CL) and 1.05% ± 2.12% (5.20% CL); 3D gamma agreement was 99.97% ± 0.14% (0.30% CL) and 100.00% ± 0.02% (0.05% CL), for 6FFF/6srs and 6x, respectively; Millennium MLC LINACs: DVH point agreement was 0.77% ± 2.40% (5.47% CL), 0.80% ± 3.40% (7.47% CL), and 0.07% ± 2.15% (4.30% CL); 3D gamma agreement was 99.97% ± 0.13% (0.29% CL), 99.97% ± 0.17% (0.36% CL), and 99.99% ± 0.06% (0.12% CL) for 6FFF, 6x, and 10x, respectively. CONCLUSION: SDC shows agreement well within TG119 CLs for film and redundant dose calculation comparisons with AAA. In some models (SRS), this was achieved using stricter criteria. TG119 plans can be used to help guide model adjustments and to establish clinical baselines for DVH and 3D gamma criteria

Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation

The Segment Anything Model (SAM) has rapidly been adopted for segmenting a wide range of natural images. However, recent studies have indicated that SAM exhibits subpar performance on 3D medical image segmentation tasks. In addition to the domain gaps between natural and medical images, disparities in the spatial arrangement between 2D and 3D images, the substantial computational burden imposed by powerful GPU servers, and the time-consuming manual prompt generation impede the extension of SAM to a broader spectrum of medical image segmentation applications. To address these challenges, in this work, we introduce a novel method, AutoSAM Adapter, designed specifically for 3D multi-organ CT-based segmentation. We employ parameter-efficient adaptation techniques in developing an automatic prompt learning paradigm to facilitate the transformation of the SAM model's capabilities to 3D medical image segmentation, eliminating the need for manually generated prompts. Furthermore, we effectively transfer the acquired knowledge of the AutoSAM Adapter to other lightweight models specifically tailored for 3D medical image analysis, achieving state-of-the-art (SOTA) performance on medical image segmentation tasks. Through extensive experimental evaluation, we demonstrate the AutoSAM Adapter as a critical foundation for effectively leveraging the emerging ability of foundation models in 2D natural image segmentation for 3D medical image segmentation.Comment: 9 pages, 4 figures, 4 table