activity
20192026
most citedDeep Learning in Medical Image Registration: A Review

666 citations · 918 across the 43 of their papers we have counts for

collaborators
Showing 2023Show all

11 papers · 1 filter

physics.med-ph2023

High-resolution 3T to 7T MRI Synthesis with a Hybrid CNN-Transformer Model

Zach Eidex, Jing Wang, Mojtaba Safari +6

7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC…

physics.med-ph2023

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss

Junbo Peng, Chih-Wei Chang, Huiqiao Xie +6

Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise…

physics.med-ph2023

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT

Qian Wang, Huiqiao Xie, Tonghe Wang +7

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification…

eess.IV2023★ 7 cited

Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model

Shaoyan Pan, Elham Abouei, Junbo Peng +8

Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is betwee…

physics.med-ph2023

Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning

Yang Lei, Yifu Ding, Richard L. J. Qiu +6

Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniq…

eess.IV2023★ 10 cited

Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model

Shaoyan Pan, Elham Abouei, Jacob Wynne +10

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone…

physics.med-ph2023★ 4 cited

Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model

Shaoyan Pan, Chih-Wei Chang, Marian Axente +5

The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escala…

eess.IV2023★ 18 cited

Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis

Shaoyan Pan, Chih-Wei Chang, Junbo Peng +7

This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each…

physics.med-ph2023

Deep Learning in MRI-guided Radiation Therapy: A Systematic Review

Zach Eidex, Yifu Ding, Jing Wang +5

MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematic…

physics.med-ph2023★ 117 cited

CBCT-Based Synthetic CT Image Generation Using Conditional Denoising Diffusion Probabilistic Model

Junbo Peng, Richard L. J. Qiu, Jacob F Wynne +8

Background: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, makin…

eess.IV2023★ 1 cited

Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation

Shaoyan Pan, Shao-Yuan Lo, Min Huang +5

In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR)…