3 citations · 3 across the 10 of their papers we have counts for
14 papers
Quantifying the Impact of Upright Patient Positioning on Cardiac Substructures Using Deep Learning
Nicholas Summerfield, Yuhao Yan, Chase Ruff +4
Upright patient positioners with diagnostic-quality vertical CT at treatment isocenter may improve image-guided radiation therapy (RT). However, cardiac substructure (CS) geometry…
Cardiac segmentation for ventricular tachycardia radioablation and radiotherapy patients with cardiac implants
Nicholas Summerfield, Dustin Jacqmin, Chase Ruff +8
Deep-learning (DL) segmentation enables cardiac substructure (CS) delineation for advanced cardiac-sparing radiation therapy (RT). Patients presenting for RT may have implantable d…
Bridging cone-beam CT and MRI to stopping power ratio maps: a modality-agnostic Brownian bridge approach to support robust adaptive proton therapy
Yuhao Yan, Qisheng He, Minglei Kang +4
Purpose: Adaptive proton therapy (APT) relies on routine offline CT simulations for updated anatomy. We present a modality-agnostic regularized Brownian bridge (rBBrg) framework to…
Characterizing the Reliability of a Novel Upright CT for Proton Therapy
Yuhao Yan, Jordan Slagowski, Jessica Miller +4
Purpose: To evaluate reliability of upright CT for proton dose calculation and feasibility of a simplified phantom configuration for accelerated routine QA. Methods: A calibration…
Technical assessment of a novel vertical CT system for upright radiotherapy simulation and treatment planning
Jordan M. Slagowski, Yuhao Yan, Jessica R. Miller +4
Purpose: To characterize image quality, imaging dose, and dose calculation accuracy for an upright CT scanner with a six-degree-of-freedom patient positioning system. Methods: Imag…
Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation
Nicholas Summerfield, Qisheng He, Alex Kuo +9
Cardiac substructure delineation is emerging in treatment planning to minimize the risk of radiation-induced heart disease. Deep learning offers efficient methods to reduce contour…
Deducing Cardiorespiratory Motion of Cardiac Substructures Using a Novel 5D-MRI Workflow for Radiotherapy
Chase Ruff, Tarun Naren, Oliver Wieben +5
Objective: Cardiotoxicity is a devastating complication of thoracic radiotherapy. Current radiotherapy imaging protocols are insufficient to decouple and quantify cardiac motion, l…
Deterministic Medical Image Translation via High-fidelity Brownian Bridges
Qisheng He, Nicholas Summerfield, Peiyong Wang +2
Recent studies have shown that diffusion models produce superior synthetic images when compared to Generative Adversarial Networks (GANs). However, their outputs are often non-dete…
Evaluation of a Novel Quantitative Multiparametric MR Sequence for Radiation Therapy Treatment Response Assessment
Yuhao Yan, R. Adam Bayliss, Adam R. Burr +5
Purpose: To evaluate a Deep-Learning-enhanced MUlti-PArametric MR sequence (DL-MUPA) for treatment response assessment for brain metastases patients undergoing stereotactic radiosu…
Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
Behzad Hejrati, Soumyanil Banerjee, Carri Glide-Hurst +1
Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention…
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
Qisheng He, Nicholas Summerfield, Ming Dong +1
Medical image segmentation of tumors and organs at risk is a time-consuming yet critical process in the clinic that utilizes multi-modality imaging (e.g, different acquisitions, da…
Volumetric medical image segmentation through dual self-distillation in U-shaped networks
Soumyanil Banerjee, Nicholas Summerfield, Ming Dong +1
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in…
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
Hajar Emami, Ming Dong, Siamak Nejad-Davarani +1
In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by in…
Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer
Hajar Emami, Ming Dong, Carri K. Glide-Hurst
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that co…