Health and Medical Physics Commons^™

Developing Reference Plans For Evaluating Global Clinical Trials Credentialing And Psqa Systems, Fre'etta M D Brooks, Mohammad Hussein, Jessica Lye, Christopher L Nelson, Nakamura Mitsuhiro, Mallory C Glenn, Patricia Diez, Rushil Patel, Maddison Shaw, Ileana Silvestre Patallo, Miriam Barry, Catharine H Clark, Joerg Lehmann, Stephen F Kry

Faculty, Staff and Student Publications

Purpose: To develop a practical framework for creating a diverse set of validated reference plans (varying in complexity) and implement a workflow to introduce beam modeling, calibration, and delivery errors into the reference cohort to test and compare various dosimetry audit methodologies.

Methods: Sixteen IMRT and VMAT reference plans were created, using RayStation software, for four phantom geometries based on established credentialing cases from participating Global Harmonization Group (GHG) members. These reference plans were first validated in a multi-ion-chamber phantom. Nine dosimetric errors (perturbations) were introduced into the plans by modifying beam model and/or delivery parameters (MLC-offset, MLC-transmission, leaf-tip-width, PDD, …

Dose Prediction Via Deep Learning To Enhance Treatment Planning Of Lung Radiotherapy Including Simultaneous Integrated Boost Techniques, Wenhua Cao, Mary Gronberg, Stephen Bilton, Hana Baroudi, Skylar Gay, Christopher Peeler, Zhongxing Liao, Thomas J Whitaker, Karen Hoffman, Laurence E Court

Faculty, Staff and Student Publications

Background: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses including simultaneous integrated boost (SIB), that is, using multiple prescription doses within the same plan, and benefit in improving plan quality should be validated.

Purpose: To investigate the feasibility and potential benefit of using deep learning to predict dose distribution of volumetric modulated arc therapy (VMAT) including SIB techniques and improve treatment planning for patients with lung cancer.

Methods: The dose prediction model was trained with 93 retrospective …

T2-Weighted Imaging Of Rectal Cancer Using A 3d Fast Spin Echo Sequence With And Without Deep Learning Reconstruction: A Reader Study, Dan Nguyen, Sarah Palmquist, Ken-Pin Hwang, Jingfei Ma, Usama Salem, Jia Sun, Xinzeng Wang, Jong Bum Son, Randy Ernst, Peng Wei, Harmeet Kaur, Nir Stanietzky

Faculty, Staff and Student Publications

Purpose: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).

Methods: The study included 50 patients with rectal cancer who underwent rectal MRI consecutively between July 7, 2020 and January 20, 2021 using a T2W 3D FSE sequence with DLR and conventional reconstruction. Three radiologists reviewed the two sets of images, scoring overall SNR, motion artifacts, and overall image quality on a 3-point scale and indicating clinical preference for DLR or conventional reconstruction based on those three …

An Automated Treatment Planning Portfolio For Whole Breast Radiotherapy, Hana Baroudi, Leonard Che Fru, Deborah Schofield, Dominique L Roniger, Callistus Nguyen, Donald Hancock, Christine Chung, Beth M Beadle, Kent A Gifford, Tucker Netherton, Joshua S Niedzielski, Adam Melancon, Manickam Muruganandham, Meena Khan, Simona F Shaitelman, Sanjay Shete, Patricia Murina, Daniel Venencia, Sheeba Thengumpallil, Conny Vrieling, Joy Zhang, Melissa P Mitchell, Laurence E Court

Faculty, Staff and Student Publications

Background: Automation in radiotherapy presents a promising solution to the increasing cancer burden and workforce shortages. However, existing automated methods for breast radiotherapy lack a comprehensive, end-to-end solution that meets varying standards of care.

Purpose: This study aims to develop a complete portfolio of automated radiotherapy treatment planning for intact breasts, tailored to individual patient factors, clinical approaches, and available resources.

Methods: We developed five automated conventional treatment approaches and utilized an established RapidPlan model for volumetric arc therapy. These approaches include conventional tangents for whole breast treatment, two variants for supraclavicular nodes (SCLV) treatment with/without axillary nodes, and two …

Clinical Use Of Gafchromic Ebt4 Film For In Vivo Dosimetry For Total Body Irradiation, Emily Draeger, Fada Guan, Min-Young Lee, Dae Yup Han, William Donahue, Zhe Jay Chen

Faculty, Staff and Student Publications

Purpose: In vivo dosimetry is a common requirement to validate dose accuracy/uniformity in total body irradiation (TBI). Several detectors can be used for in vivo dosimetry, including thermoluminescent dosimeters (TLDs), diodes, ion chambers, optically stimulated luminescent dosimeters (OSLDs), and film. TLDs are well established for use in vivo but required expertise and clinical system availability may make them impractical for multifractionated TBI. OSLDs offer quick readout, but recalls have restricted their use. The purpose of this work was to validate the newly available Gafchromic EBT4 film for TBI in vivo dosimetry.

Methods: Film calibration curves were created under standard conditions …

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 …

A Multiple X-Ray-Source Array (Mxa) System With A Planar Two-Dimensional Source Distribution For Digital Breast Tomosynthesis, Alejandro Sisniega, Andrew M Hernandez, Shadi A Shakeri, Elizabeth A Morris, John M Boone, Jeffrey H Siewerdsen, Paul R Schwoebel

Faculty, Staff and Student Publications

Background: Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one-dimensional (1D) scan geometry, x-ray source motion during acquisition, and patient motion artifacts from long exam times.

Purpose: A thermionic cathode x-ray system employing two-dimensional (2D, planar) multiple x-ray-source arrays (MXA) is proposed to improve DBT image quality.

Methods: A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of Δd …

Automatic Vessel Attenuation Measurement For Quality Control Of Contrast-Enhanced Ct: Validation On The Portal Vein, Kevin Mccoy, Sujay Marisetty, Dominique Tan, Corey T Jensen, Jeffrey H Siewerdsen, Christine B Peterson, Moiz Ahmad

Faculty, Staff and Student Publications

Background: Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast-enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.

Purpose: The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast-enhanced abdomen CT examinations.

Methods: Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers …

Landmark-Based Auto-Contouring Of Clinical Target Volumes For Radiotherapy Of Nasopharyngeal Cancer, Carlos Sjogreen, Tucker J Netherton, Anna Lee, Moaaz Soliman, Skylar S Gay, Callistus Nguyen, Raymond Mumme, Ivan Vazquez, Dong Joo Rhee, Carlos E Cardenas, Mary K Martel, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Background: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease.

Purpose: The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines.

Methods: The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of …

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 …

Evaluating The Relationship Between Magnetic Resonance Image Quality Metrics And Deep Learning-Based Segmentation Accuracy Of Brain Tumors, Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P Yung, James P Long, Satish E Viswanath, Daniel S Marcus, Caroline Chung, David Fuentes

Faculty, Staff and Student Publications

Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy.

Purpose: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models …

Optimized Scoring Of End-To-End Dosimetry Audits For Passive Motion Management – A Simulation Study Using The Iroc Thorax Phantom, Alex Burton, Mathieu Gaudreault, Nicholas Hardcastle, Jessica Lye, Sabeena Beveridge, Stephen F Kry, Rick Franich

Faculty, Staff and Student Publications

Dosimetry audits for passive motion management require dynamically-acquired measurements in a moving phantom to be compared to statically calculated planned doses. This study aimed to characterise the relationship between planning and delivery errors, and the measured dose in the Imaging and Radiation Oncology Core (IROC) thorax phantom, to assess different audit scoring approaches. Treatment plans were created using a 4DCT scan of the IROC phantom, equipped with film and thermoluminescent dosimeters (TLDs). Plans were created on the average intensity projection from all bins. Three levels of aperture complexity were explored: dynamic conformal arcs (DCAT), low-, and high-complexity volumetric modulated arcs …

Incorporating Variable Rbe In Impt Optimization For Ependymoma, Hadis Moazami Goudarzi, Gino Lim, David Grosshans, Radhe Mohan, Wenhua Cao

Faculty, Staff and Student Publications

PURPOSE: To study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity-modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)-based optimization.

METHODS: This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard-of-care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose-averaged LET (LET

RESULTS: We found that the LETopt plans consistently achieved increased LET in …

Investigation Of Autosegmentation Techniques On T2-Weighted Mri For Off-Line Dose Reconstruction In Mr-Linac Workflow For Head And Neck Cancers, Brigid A Mcdonald, Carlos E Cardenas, Nicolette O'Connell, Sara Ahmed, Mohamed A Naser, Kareem A Wahid, Jiaofeng Xu, Dan Thill, Raed J Zuhour, Shane Mesko, Alexander Augustyn, Samantha M Buszek, Stephen Grant, Bhavana V Chapman, Alexander F Bagley, Renjie He, Abdallah S R Mohamed, John Christodouleas, Kristy K Brock, Clifton D Fuller

Faculty, Staff and Student Publications

Background: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction.

Purpose: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose.

Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by …

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 …

Deep Learning-Based Dose Prediction To Improve The Plan Quality Of Volumetric Modulated Arc Therapy For Gynecologic Cancers, Mary P Gronberg, Anuja Jhingran, Tucker J Netherton, Skylar S Gay, Carlos E Cardenas, Christine Chung, David Fuentes, Clifton D Fuller, Rebecca M Howell, Meena Khan, Tze Yee Lim, Barbara Marquez, Adenike M Olanrewaju, Christine B Peterson, Ivan Vazquez, Thomas J Whitaker, Zachary Wooten, Ming Yang, Laurence E Court

Faculty, Staff and Student Publications

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.

Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the …

Hazard Testing To Reduce Risk In The Development Of Automated Planning Tools, Kelly A Nealon, Raphael J Douglas, Eun Young Han, Stephen F Kry, Valerie K Reed, Samantha J Simiele, Laurence E Court

Faculty, Staff and Student Publications

PURPOSE: Hazard scenarios were created to assess and reduce the risk of planning errors in automated planning processes. This was accomplished through iterative testing and improvement of examined user interfaces.

METHODS: Automated planning requires three user inputs: a computed tomography (CT), a prescription document, known as the service request, and contours. We investigated the ability of users to catch errors that were intentionally introduced into each of these three stages, according to an FMEA analysis. Five radiation therapists each reviewed 15 patient CTs, containing three errors: inappropriate field of view, incorrect superior border, and incorrect identification of isocenter. Four radiation …

Compensation Cycle Consistent Generative Adversarial Networks (Comp-Gan) For Synthetic Ct Generation From Mr Scans With Truncated Anatomy, Yao Zhao, He Wang, Cenji Yu, Laurence E Court, Xin Wang, Qianxia Wang, Tinsu Pan, Yao Ding, Jack Phan, Jinzhong Yang

Faculty, Staff and Student Publications

BACKGROUND: MR scans used in radiotherapy can be partially truncated due to the limited field of view (FOV), affecting dose calculation accuracy in MR-based radiation treatment planning.

PURPOSE: We proposed a novel Compensation-cycleGAN (Comp-cycleGAN) by modifying the cycle-consistent generative adversarial network (cycleGAN), to simultaneously create synthetic CT (sCT) images and compensate the missing anatomy from the truncated MR images.

METHODS: Computed tomography (CT) and T1 MR images with complete anatomy of 79 head-and-neck patients were used for this study. The original MR images were manually cropped 10-25 mm off at the posterior head to simulate clinically truncated MR images. Fifteen …

Parametric Delineation Uncertainties Contouring (Pduc) Modeling On Ct Scans Of Prostate Cancer Patients, Vi Ly, Lizhong Liu, Carlos Cardenas, Sean Maroongroge, Brian De, Daniel El Basha, Laurence Court, Xi Luo

Faculty, Staff and Student Publications

PURPOSE: Variability in contouring contributes to large variations in radiation therapy planning and treatment outcomes. The development and testing of tools to automatically detect contouring errors require a source of contours that includes well-understood and realistic errors. The purpose of this work was to develop a simulation algorithm that intentionally injects errors of varying magnitudes into clinically accepted contours and produces realistic contours with different levels of variability.

METHODS: We used a dataset of CT scans from 14 prostate cancer patients with clinician-drawn contours of the regions of interest (ROI) of the prostate, bladder, and rectum. Using our newly developed …

Intensity Modulated Proton Arc Therapy Via Geometry-Based Energy Selection For Ependymoma, Wenhua Cao, Yupeng Li, Xiaodong Zhang, Falk Poenisch, Pablo Yepes, Narayan Sahoo, David Grosshans, Susan Mcgovern, G Brandon Gunn, Steven J Frank, Xiaorong R Zhu

Faculty, Staff and Student Publications

PURPOSE: We developed and tested a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources similar to those for regular intensity-modulated proton therapy (IMPT) plans and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries.

METHODS: Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle …

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 …

A Virtual Audit System For Intensity-Modulated Radiation Therapy Credentialing In Japan Clinical Oncology Group Clinical Trials: A Pilot Study, Mitsuhiro Nakamura, Dejun Zhou, Toshiyuki Minemura, Satoshi Kito, Hiroyuki Okamoto, Naoki Tohyama, Masahiko Kurooka, Yu Kumazaki, Masayori Ishikawa, Catharine H Clark, Elizabeth Miles, Joerg Lehmann, Nicolaus Andratschke, Stephen Kry, Satoshi Ishikura, Takashi Mizowaki, Teiji Nishio

Faculty, Staff and Student Publications

Purpose: The Medical Physics Working Group of the Radiation Therapy Study Group at the Japan Clinical Oncology Group is currently developing a virtual audit system for intensity-modulated radiation therapy dosimetry credentialing. The target dosimeters include films and array detectors, such as ArcCHECK (Sun Nuclear Corporation, Melbourne, Florida, USA) and Delta4 (ScandiDos, Uppsala, Sweden). This pilot study investigated the feasibility of our virtual audit system using previously acquired data.

Methods: We analyzed 46 films (32 and 14 in the axial and coronal planes, respectively) from 29 institutions. Global gamma analysis between measured and planned dose distributions used the following settings: 3%/3 …

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 …

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 …

Customizable Landmark-Based Field Aperture Design For Automated Whole-Brain Radiotherapy Treatment Planning, Yao Xiao, Carlos Cardenas, Dong Joo Rhee, Tucker Netherton, Lifei Zhang, Callistus Nguyen, Raphael Douglas, Raymond Mumme, Stephen Skett, Tina Patel, Chris Trauernicht, Caroline Chung, Hannah Simonds, Ajay Aggarwal, Laurence Court

Faculty, Staff and Student Publications

Purpose: To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design.

Methods: The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original …

Comparison Of Setup Accuracy And Efficiency Between The Klarity System And Bodyfix System For Spine Stereotactic Body Radiation Therapy, Enzhuo Quan, Shane P Krafft, Tina M Briere, Marissa J Vaccarelli, Amol J Ghia, Andrew J Bishop, Debra N Yeboa, Todd A Swanson, Eun Young Han

Faculty, Staff and Student Publications

BACKGROUND: Spine stereotactic body radiation therapy (SBRT) uses highly conformal dose distributions and sharp dose gradients to cover targets in proximity to the spinal cord or cauda equina, which requires precise patient positioning and immobilization to deliver safe treatments.

AIMS: Given some limitations with the BodyFIX system in our practice, we sought to evaluate the accuracy and efficiency of the Klarity SBRT patient immobilization system in comparison to the BodyFIX system.

METHODS: Twenty-three patients with 26 metastatic spinal lesions (78 fractions) were enrolled in this prospective observational study with one of two systems - BodyFIX (n = 11) or Klarity …

Dosimetric Analysis Of Mr-Linac Treatment Plans For Salvage Spine Sbrt Re-Irradiation, Eun Young Han, Debra N Yeboa, Tina M Briere, Jinzhong Yang, He Wang

Faculty, Staff and Student Publications

PURPOSE: We investigated the feasibility of thoracic spine stereotactic body radiotherapy (SBRT) using the Elekta Unity magnetic resonance-guided linear accelerator (MRL) in patients who received prior radiotherapy. We hypothesized that Monaco treatment plans can improve the gross tumor volume minimum dose (GTVmin) with spinal cord preservation and maintain consistent plan quality during daily adaptation.

METHODS: Pinnacle clinical plans for 10 patients who underwent thoracic spine SBRT (after prior radiotherapy) were regenerated in the Monaco treatment planning system for the Elekta Unity MRL using 9 and 13 intensity-modulated radiotherapy (IMRT) beams. Monaco adapt-to-position (ATP) and adapt-to-shape (ATS) workflow plans were generated …

Clinical Acceptability Of Fully Automated External Beam Radiotherapy For Cervical Cancer With Three Different Beam Delivery Techniques, Dong Joo Rhee, Anuja Jhingran, Kai Huang, Tucker J Netherton, Nazia Fakie, Ingrid White, Alicia Sherriff, Carlos E Cardenas, Lifei Zhang, Surendra Prajapati, Stephen F Kry, Beth M Beadle, William Shaw, Frederika O'Reilly, Jeannette Parkes, Hester Burger, Chris Trauernicht, Hannah Simonds, Laurence E Court

Faculty, Staff and Student Publications

Purpose: To fully automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques.

Methods: We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4-field-box (4-field-box), 3D conformal radiotherapy (3D-CRT), and volumetric modulated arc therapy (VMAT). These auto-planning algorithms were combined with a previously developed auto-contouring system. To improve the quality of the 4-field-box and 3D-CRT plans, we used an in-house, field-in-field (FIF) automation program. Thirty-five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was …

Development And Validation Of A Checklist For Use With Automatically Generated Radiotherapy Plans, Kelly A Nealon, Laurence E Court, Raphael J Douglas, Lifei Zhang, Eun Young Han

Faculty, Staff and Student Publications

Purpose: To develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans.

Methods: A custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence-generated resident …

Assessing The Practicality Of Using A Single Knowledge-Based Planning Model For Multiple Linac Vendors, Raphael J Douglas, Adenike Olanrewaju, Lifei Zhang, Beth M Beadle, Laurence E Court

Faculty, Staff and Student Publications

Purpose: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines.

Materials and methods: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, …