Health and Medical Physics Commons^™

Robust Automated Method Of Spatial Resolution Measurement In Radiotherapy Ct Simulation Images, Pavel Govyadinov, Rick R Layman, Tucker Netherton, Raymond Mumme, Aaron K Jones, Laurence E Court, Moiz Ahmad

Faculty, Staff and Student Publications

Background: Variation in imaging protocol, patient positioning, and the presence of artifacts can vary image quality in CT images used for radiotherapy planning. Automated methods for spatial resolution (SR) estimation exist but require further investigation and validation for wider adoption.

Purpose: To validated previously existing algorithm for SR estimation and introduce improvements that make it robust to patient positioning, CT protocol, site, and artifacts.

Method: A reference algorithm based on the previous gold standard was recreated and modified to improve robustness. The algorithms were tested on three different datasets: (1) a cylindrical ACR CT QC phantom scanned using a Siemens …

Evaluating Automatically Generated Normal Tissue Contours For Safe Use In Head And Neck And Cervical Cancer Treatment Planning, Raphael Douglas, Adenike Olanrewaju, Raymond Mumme, Lifei Zhang, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.

Methods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan …

A Radiotherapy Community Data-Driven Approach To Determine Which Complexity Metrics Best Predict The Impact Of Atypical Tps Beam Modeling On Clinical Dose Calculation Accuracy, Fre'etta Mae Dayo Brooks, Mallory Carson Glenn, Victor Hernandez, Jordi Saez, Hunter Mehrens, Julianne Marie Pollard-Larkin, Rebecca Maureen Howell, Christine Burns Peterson, Christopher Lee Nelson, Catharine Helen Clark, Stephen Frasier Kry

Faculty, Staff and Student Publications

PURPOSE: To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy.

METHODS: Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes …

Identifying The Optimal Deep Learning Architecture And Parameters For Automatic Beam Aperture Definition In 3d Radiotherapy, Skylar S Gay, Kelly D Kisling, Brian M Anderson, Lifei Zhang, Dong Joo Rhee, Callistus Nguyen, Tucker Netherton, Jinzhong Yang, Kristy Brock, Anuja Jhingran, Hannah Simonds, Ann Klopp, Beth M Beadle, Laurence E Court, Carlos E Cardenas

Faculty, Staff and Student Publications

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation.

METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer …

Outcomes Of Breakthrough Covid-19 Infections In Patients With Hematologic Malignancies, Kelly S Chien, Christine B Peterson, Elliana Young, Dai Chihara, Elizabet E Manasanch, Jeremy L Ramdial, Philip A Thompson

Faculty, Staff and Student Publications

Patients with hematologic malignancies have both an increased risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and higher morbidity/mortality. They have lower seroconversion rates after vaccination, potentially leading to inferior coronavirus disease 2019 (COVID-19) outcomes, despite vaccination. We consequently evaluated the clinical outcomes of COVID-19 infections in 243 vaccinated and 175 unvaccinated patients with hematologic malignancies. Hospitalization rates were lower in the vaccinated group when compared with the unvaccinated group (31.3% vs 52.6%). However, the rates of COVID-19-associated death were similar at 7.0% and 8.6% in vaccinated and unvaccinated patients, respectively. By univariate logistic regression, females, older patients, …

Analysis Of Performance And Failure Modes Of The Iroc Proton Liver Phantom, Hunter Mehrens, Paige Taylor, Paola Alvarez, Stephen Kry

Faculty, Staff and Student Publications

Purpose: To analyze trends in institutional performance and failure modes for the Imaging and Radiation Oncology Core’s (IROC’s) proton liver phantom.

Materials and Methods: Results of 66 phantom irradiations from 28 institutions between 2015 and 2020 were retrospectively analyzed. Univariate analysis and random forest models were used to associate irradiation conditions with phantom results. Phantom results included pass/fail classification, average thermoluminescent dosimeter (TLD) ratio of both targets, and percentage of pixels passing gamma of both targets. The following categories were evaluated in terms of how they predicted these outcomes: irradiation year, treatment planning system (TPS), TPS algorithm, treatment machine, number …

Resection Cavity Auto-Contouring For Patients With Pediatric Medulloblastoma Using Only Ct Information, Soleil Hernandez, Callistus Nguyen, Skylar Gay, Jack Duryea, Rebecca Howell, David Fuentes, Jeannette Parkes, Hester Burger, Carlos Cardenas, Arnold C Paulino, Julianne Pollard-Larkin, Laurence Court

Faculty, Staff and Student Publications

PURPOSE: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma.

METHODS: One hundred four retrospective pediatric CT scans were used to train a GTV auto-contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number …

Combining Physics-Based Models With Deep Learning Image Synthesis And Uncertainty In Intraoperative Cone-Beam Ct Of The Brain, Xiaoxuan Zhang, Alejandro Sisniega, Wojciech B Zbijewski, Junghoon Lee, Craig K Jones, Pengwei Wu, Runze Han, Ali Uneri, Prasad Vagdargi, Patrick A Helm, Mark Luciano, William S Anderson, Jeffrey H Siewerdsen

Faculty, Staff and Student Publications

BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention.

PURPOSE: To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality.

METHODS: The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network …

A Model For Gastrointestinal Tract Motility In A 4d Imaging Phantom Of Human Anatomy, Ergys Subashi, Paul Segars, Harini Veeraraghavan, Joseph Deasy, Neelam Tyagi

Faculty, Staff and Student Publications

BACKGROUND: Gastrointestinal (GI) tract motility is one of the main sources for intra/inter-fraction variability and uncertainty in radiation therapy for abdominal targets. Models for GI motility can improve the assessment of delivered dose and contribute to the development, testing, and validation of deformable image registration (DIR) and dose-accumulation algorithms.

PURPOSE: To implement GI tract motion in the 4D extended cardiac-torso (XCAT) digital phantom of human anatomy.

MATERIALS AND METHODS: Motility modes that exhibit large amplitude changes in the diameter of the GI tract and may persist over timescales comparable to online adaptive planning and radiotherapy delivery were identified based on …

Quality Assurance Assessment Of Intra-Acquisition Diffusion-Weighted And T2-Weighted Magnetic Resonance Imaging Registration And Contour Propagation For Head And Neck Cancer Radiotherapy, Mohamed A Naser, Kareem A Wahid, Sara Ahmed, Vivian Salama, Cem Dede, Benjamin W Edwards, Ruitao Lin, Brigid Mcdonald, Travis C Salzillo, Renjie He, Yao Ding, Moamen Abobakr Abdelaal, Daniel Thill, Nicolette O'Connell, Virgil Willcut, John P Christodouleas, Stephen Y Lai, Clifton D Fuller, Abdallah S R Mohamed

Faculty, Staff and Student Publications

BACKGROUND/PURPOSE: Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images.

MATERIALS/METHODS: We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same …

Optimization Of Mesh Generation For Geometric Accuracy, Robustness, And Efficiency Of Biomechanical-Model-Based Deformable Image Registration, Yulun He, Brian M Anderson, Guillaume Cazoulat, Bastien Rigaud, Lusmeralis Almodovar-Abreu, Julianne Pollard-Larkin, Peter Balter, Zhongxing Liao, Radhe Mohan, Bruno Odisio, Stina Svensson, Kristy K Brock

Faculty, Staff and Student Publications

BACKGROUND: Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow.

PURPOSE: To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes.

METHODS: Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality …

Evaluation Of Plan Quality And Treatment Efficiency For Single-Isocenter/Two-Lesion Lung Stereotactic Body Radiation Therapy, Lana Sanford, Janelle A. Molloy, Sameera S. Kumar, Marcus Randall, Ronald C. Mcgarry, Damodar Pokhrel

Radiation Medicine Faculty Publications

Purpose/objectives: To evaluate the plan quality and treatment delivery efficiency of single‐isocenter/two‐lesions volumetric modulated arc therapy (VMAT) lung stereotactic body radiation therapy (SBRT).

Materials/methods: Eight consecutive patients with two peripherally located early stage nonsmall‐cell‐lung cancer (NSCLC) lung lesions underwent single‐isocenter highly conformal noncoplanar VMAT SBRT treatment in our institution. A single‐isocenter was placed between the two lesions. Doses were 54 or 50 Gy in 3 and 5 fractions respectively. Patients were treated every other day. Plans were calculated in Eclipse with AcurosXB algorithm and normalized to at least 95% of the planning target volume (PTV) receiving 100% of the prescribed …