Patient-Specific Self-Supervised Resolution-Enhancing Network for High-Resolution MR Imaging in MRI-Guided Radiotherapy
Purpose/Objective (s) The application of MRI significantly improves the accuracy and
reliability of target delineation for many disease sites in radiotherapy (RT) due to its superior …
reliability of target delineation for many disease sites in radiotherapy (RT) due to its superior …
MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown …
J Chun, H Zhang, HM Gach, S Olberg, T Mazur… - Medical …, 2019 - Wiley Online Library
Purpose Deep learning (DL)‐based super‐resolution (SR) reconstruction for magnetic
resonance imaging (MRI) has recently been receiving attention due to the significant …
resonance imaging (MRI) has recently been receiving attention due to the significant …
Super-resolution MRI using deep convolutional neural network for adaptive MR-guided radiotherapy: a pilot study
MR-guided radiotherapy (MRgRT) is creating new perspectives towards an individualized
precise radiation therapy solution. However, spatial resolution of fractional MRI can be much …
precise radiation therapy solution. However, spatial resolution of fractional MRI can be much …
High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling
Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …
Evaluation of super-resolution on 50 pancreatic cancer patients with real-time cine MRI from 0.35 T MRgRT
J Chun, B Lewis, Z Ji, JI Shin, JC Park… - Biomedical Physics & …, 2021 - iopscience.iop.org
MR-guided radiotherapy (MRgRT) systems provide excellent soft tissue imaging
immediately prior to and in real time during radiation delivery for cancer treatment. However …
immediately prior to and in real time during radiation delivery for cancer treatment. However …
Patient-specific Self-supervised Resolution-enhancing Networks for Synthesizing High-resolution Magnetic Resonance Images
Synopsis Keywords: Quantitative Imaging, Data ProcessingThis study aims to develop an
efficient and clinically applicable method using patient-specific self-supervised resolution …
efficient and clinically applicable method using patient-specific self-supervised resolution …
Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution
Purpose/Objective (s) High-resolution (HR) images are important in precision radiation
oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; …
oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; …
Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy
Background Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue
imaging of the human body. However, extensive data sampling requirements severely …
imaging of the human body. However, extensive data sampling requirements severely …
Deep residual learning of radial under sampling artefacts for real-time MR image guidance during radiotherapy
B Stemkens, C Sital, M Blokker… - Proceedings of the …, 2019 - archive.ismrm.org
MRI-guided radiotherapy using hybrid MR-Linac systems, requires high spatiotemporal
resolution MR images to guide the radiation beam in real time. Here, we investigate the …
resolution MR images to guide the radiation beam in real time. Here, we investigate the …
3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning
H Li, J Liu - arXiv preprint arXiv:2111.14259, 2021 - arxiv.org
Shortening acquisition time and reducing the motion artifacts are two of the most essential
concerns in magnetic resonance imaging. As a promising solution, deep learning-based …
concerns in magnetic resonance imaging. As a promising solution, deep learning-based …