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Recent Submissions
Empowering Tomorrow through Student Engagement in Generative AI
(2024-09-13) Schraer, Elliot; Mitra, Rita
Supporting Interdisciplinary Microbiology Education for Diverse Learners: How Place-Based Curriculum Emphasizing Data Literacy in Agricultural Science Can Support Motivation and Learning
(2024-09-13) Thacker, Ian; Shields-Menard, Sara; Schroeder, Rebecca
How to Get Involved in GenAI
(2024-09-13) Schroeder, Rebecca; Malshe, Ashwin
Phase I quality control framework for monitoring organ-at-risk dose
(IOP, 2024-05-01) Sivabhaskar, Sruthi; Buatti, Jacob S.; Yeh, Arthur B.; Papanikolaou, Niko; Roy, Arkajyoti
Objective. The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling's
chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC). Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans. Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40's plan and updating the DVH and control chart, P40 was IC, but P14* (P14's re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans. Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.
A Computationally Time-Efficient Method for Implementing Pressure Load to FE Models with Lagrangian Elements
(2024-09-22) Shahriar, Adnan; Majlesi, Arsalan; Montoya, Arturo
A computationally time-efficient method is introduced to implement pressure load to a Finite element model. Hexahedron elements of the Lagrangian family with Gauss–Lobatto nodes and integration quadrature are utilized, where the integration points follow the same sequence as the nodes. This method calculates the equivalent nodal force due to pressure load using a single Hadamard multiplication. The arithmetic operations of this method are determined, which affirms its computational efficiency. Finally, the method is tested with finite element implementation and observed to increase the runtime ratio compared to the conventional method by over 20 times. This method can benefit the implementation of finite element models in fields where computational time is crucial, such as real-time and cyber–physical testbed implementation.