Computer aided verification of radiotherapy treatment plans

Postprin

A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation

We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model each brain as a graph composed of distinct image regions, which is initially segmented by a graph neural network (GNN). Subsequently, the tumorous volume identified by the GNN is further refined by a simple (voxel) convolutional neural network (CNN), which produces the final segmentation. This approach captures both global brain feature interactions via the graphical representation and local image details through the use of convolutional filters. We find that the GNN component by itself can effectively identify and segment the brain tumors. The addition of the CNN further improves the median performance of the model by 2 percent across all metrics evaluated. On the validation set, our joint GNN-CNN model achieves mean Dice scores of 0.89, 0.81, 0.73 and mean Hausdorff distances (95th percentile) of 6.8, 12.6, 28.2mm on the whole tumor, core tumor, and enhancing tumor, respectively.Comment: 9 pages, 3 figures, submitted to BrainLes Workshop (MICCAI 2021) as part of BraTS2021 challeng

Graph-based risk assessment and error detection in radiation therapy

Purpose : The objective of this study was to formalize and automate quality assurance (QA) in radiation oncology. QA in radiation oncology entails a multistep verification of complex, personalized radiation plans to treat cancer involving an interdisciplinary team and high technology, multivendor software and hardware. We addressed the pretreatment physics chart review (TPCR) using methods from graph theory and constraint programming to study the effect of dependencies between variables and automatically identify logical inconsistencies and how they propagate. Materials and Methods : We used a modular approach to decompose the TPCR process into tractable units comprising subprocesses, modules and variables. Modules represent the main software entities comprised in the radiation treatment planning workflow and subprocesses group the checks to be performed by functionality. Module‐associated variables serve as inputs to the subprocesses. Relationships between variables were modeled by means of a directed graph. The detection of errors, in the form of inconsistencies, was formalized as a constraint satisfaction problem whereby checks were encoded as logical formulae. The sequence in which subprocesses are visited was described in a activity diagram. Results : The comprehensive model for the TPCR process comprised 5 modules, 19 subprocesses and 346 variables, 225 of which were distinct. Modules included ”Treatment Planning System” and ”Record and Verify System”. Subprocesses included ”Dose Prescription”, ”Documents”, ”CT Integrity”, ”Anatomical Contours”, ”Beam Configuration”, ”Dose Calculation”, ”3D Dose Distribution Quality” and ”Treatment Approval”. Variable inconsistencies, their source and propagation are determined by checking for constraint violation and through graph traversal. Impact scores, obtained through graph traversal, combined with severity scores associated with an inconsistency, allow risk assessment. Conclusions : Directed graphs combined with constraint programming hold promise for formalizing complex QA processes in radiation oncology, performing risk assessment and automating the TPCR process. Though complex, the process is tractable.PostprintPeer reviewe

Real-time analysis and display of quantitative measures to track and improve clinical workflow

PURPOSE: Radiotherapy treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary patient care team. Effective communication and real-time tracking of resources and care path activities are key for clinical efficiency and patient safety. MATERIALS AND METHODS: We designed and implemented a secure, interactive web-based dashboard for patient care path, clinical workflow, and resource utilization management. The dashboard enables visualization of resource utilization and tracks progress in a patient\u27s care path from the time of acquisition of the planning CT to the time of treatment in real-time. It integrates with the departmental electronic medical records (EMR) system without the creation and maintenance of a separate database or duplication of work by clinical staff. Performance measures of workflow were calculated. RESULTS: The dashboard implements a standardized clinical workflow and dynamically consolidates real-time information queried from multiple tables in the EMR database over the following views: (1) CT Sims summarizes patient appointment information on the CT simulator and patient load; (2) Linac Sims summarizes patient appointment times, setup history, and notes, and patient load; (3) Task Status lists the clinical tasks associated with a treatment plan, their due date, status and ownership, and patient appointment details; (4) Documents provides the status of all documents in the patients\u27 charts; and (5) Diagnoses and Interventions summarizes prescription information, imaging instructions and whether the plan was approved for treatment. Real-time assessment and quantification of progress and delays in a patient\u27s treatment start were achieved. CONCLUSIONS: This study indicates it is feasible to develop and implement a dashboard, tailored to the needs of an interdisciplinary team, which derives and integrates information from the EMR database for real-time analysis and display of resource utilization and clinical workflow in radiation oncology. The framework developed facilitates informed, data-driven decisions on clinical workflow management as we seek to optimize clinical efficiency and patient safety

The effect of breathing irregularities on quantitative accuracy of respiratory gated PET∕CT.

4D positron emission tomography and computed tomography (PET∕CT) can be used to reduce motion artifacts by correlating the raw PET data with the respiratory cycle. The accuracy of each PET phase is dependent on the reproducibility and consistency of the breathing cycle during acquisition. The objective of this study is to evaluate the impact of breathing amplitude and phase irregularities on the quantitative accuracy of 4D PET standardized uptake value (SUV) measurements. In addition, the magnitude of quantitative errors due to respiratory motion and partial volume error are compared

Real‐time analysis and display of quantitative measures to track and improve clinical workflow in Journal of Applied Clinical Medical Physics

PURPOSE: Radiotherapy treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary patient care team. Effective communication and real-time tracking of resources and care path activities are key for clinical efficiency and patient safety. MATERIALS AND METHODS: We designed and implemented a secure, interactive web-based dashboard for patient care path, clinical workflow, and resource utilization management. The dashboard enables visualization of resource utilization and tracks progress in a patient\u27s care path from the time of acquisition of the planning CT to the time of treatment in real-time. It integrates with the departmental electronic medical records (EMR) system without the creation and maintenance of a separate database or duplication of work by clinical staff. Performance measures of workflow were calculated. RESULTS: The dashboard implements a standardized clinical workflow and dynamically consolidates real-time information queried from multiple tables in the EMR database over the following views: (1) CT Sims summarizes patient appointment information on the CT simulator and patient load; (2) Linac Sims summarizes patient appointment times, setup history, and notes, and patient load; (3) Task Status lists the clinical tasks associated with a treatment plan, their due date, status and ownership, and patient appointment details; (4) Documents provides the status of all documents in the patients\u27 charts; and (5) Diagnoses and Interventions summarizes prescription information, imaging instructions and whether the plan was approved for treatment. Real-time assessment and quantification of progress and delays in a patient\u27s treatment start were achieved. CONCLUSIONS: This study indicates it is feasible to develop and implement a dashboard, tailored to the needs of an interdisciplinary team, which derives and integrates information from the EMR database for real-time analysis and display of resource utilization and clinical workflow in radiation oncology. The framework developed facilitates informed, data-driven decisions on clinical workflow management as we seek to optimize clinical efficiency and patient safety