PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction.

Introduction: Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes. Aims: To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters. Materials and Methods: Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI's Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse. Results: The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively. Conclusion: The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.

Investigation of Computed Tomography Numbers on Multiple Imaging Systems using Single and Multislice Methods.

Aim: The aim of this study is to determine the variation in Hounsfield values with single and multi-slice methods using in-house software on fan-beam computed tomography (FCT), linear accelerator (linac) cone-beam computed tomography (CBCT), and Icon-CBCT datasets acquired using Gammex and advanced electron density (AED) phantoms. Materials and Methods: The AED phantom was scanned on a Toshiba computed tomography (CT) scanner, five linac-based CBCT X-ray volumetric imaging systems, and Leksell Gamma Knife Icon. The variation between single and multi-slice methods was assessed by comparing scans acquired using Gammex and AED phantoms. The variation in Hounsfield units (HUs) between seven different clinical protocols was assessed using the AED phantom. A CIRS Model 605 Radiosurgery Head Phantom (TED) phantom was scanned on all three imaging systems to assess the target dosimetric changes due to HU variation. An in-house software was developed in MATLAB to assess the HU statistics and the trend along the longitudinal axis. Results: The FCT dataset showed a minimal variation (central slice ± 3 HU) in HU values along the long axis. A similar trend was also observed between the studied clinical protocols acquired on FCT. Variation among multiple linac CBCTs was insignificant. In the case of the water insert, a maximum HU variation of -7.23 ± 68.67 was observed for Linac 1 towards the inferior end of the phantom. All five linacs appeared to have a similar trend in terms of HU variation from the proximal to the distal end of the phantom, with a few outliers for Linac 5. Among three imaging modalities, the maximum variation was observed in gamma knife CBCTs, whereas FCT showed no appreciable deviation from the central value. In terms of dosimetric comparison, the mean dose in CT and Linac CBCT scans differed by <0.5 Gy, whereas at least a 1 Gy difference was observed between CT and gamma knife CBCT. Conclusion: This study shows a minimal variation with FCT between single, volume-based, and multislice methods, and hence the current approach of determining the CT-electron density curve based on a single-slice method would be sufficient for producing a HU calibrations curve for treatment planning. However, CBCTs acquired on linac, and in particular, gamma knife systems, show noticeable variations along the long axis, which is likely to affect the dose calculations performed on CBCTs. It is highly recommended to assess the Hounsfield values on multiple slices before using the HU curve for dose calculations.

A feasibility study on the development and use of a deep learning model to automate real-time monitoring of tumor position and assessment of interfraction fiducial marker migration in prostate radiotherapy patients.

Purpose. The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients.Methods and Materials. A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient.Results. On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum±standard deviation marker migration were found to be2.0±0.9mmand2.3±0.9mm,respectively.Conclusion. This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make evident the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.