Noisy MRI

Noisy MRI data poses significant challenges for accurate image analysis and downstream medical applications. Current research focuses on developing robust methods for denoising and artifact correction, employing techniques like attention-based neural networks, influence-based training, and iterative learning frameworks incorporating U-Nets and normalizing flows. These advancements aim to improve the reliability of diagnostic tools and quantitative image analysis by mitigating the effects of noise and motion artifacts inherent in MRI acquisitions, ultimately leading to more accurate and efficient medical diagnoses. The development of robust and generalizable methods is crucial for improving the quality and accessibility of MRI-based healthcare.

Papers

August 2, 2024

Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
Daniel A. Serino, Marc L. Klasky, Balasubramanya T. Nadiga, Xiaojian Xu, Trevor Wilcox
Transformer Encoder Dimensional Feature Attention Based Transformer Attention Based Neural Network Hydrodynamic Simulation Noisy MRI Richtmyer Meshkov Instability

March 15, 2024

Robust Influence-based Training Methods for Noisy Brain MRI
Minh-Hao Van, Alycia N. Carey, Xintao Wu
Brain Tumor Active Sampling Training Data Influence Noisy MRI

March 13, 2024

Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI
Lintao Zhang, Mengqi Wu, Lihong Wang, David C. Steffens, Guy G. Potter, Mingxia Liu
Image Denoising Iterative Training 3D Brain Motion Artifact Joint Denoising Noisy MRI

March 8, 2024

Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI
Shoujin Huang, Guanxiong Luo, Xi Wang, Ziran Chen, Yuwan Wang, Huaishui Yang, Pheng-Ann Heng, Lingyan Zhang, Mengye Lyu
Image Reconstruction MRI Reconstruction Accelerated MRI Robust Reconstruction Noisy MRI

December 1, 2023

Heteroscedastic Uncertainty Estimation for Probabilistic Unsupervised Registration of Noisy Medical Images
Xiaoran Zhang, Daniel H. Pak, Shawn S. Ahn, Xiaoxiao Li, Chenyu You, Lawrence Staib, Albert J. Sinusas, Alex Wong, James S. Duncan
Medical Image Image Registration Heteroscedastic Noise Noisy MRI Heteroscedastic Uncertainty Displacement Estimation

August 20, 2023

False Negative/Positive Control for SAM on Noisy Medical Images
Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C Byram, Ipek Oguz
Medical Image Segmentation Segment Anything Model Prompt Augmentation False Negative Enhanced Control Test Time Prompt Tuning Noisy MRI

October 15, 2022

RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Ajay Jaiswal, Kumar Ashutosh, Justin F Rousseau, Yifan Peng, Zhangyang Wang, Ying Ding
Knowledge Distillation Medical Image Classification Task Direct Roi Prediction Noisy MRI

December 24, 2021

Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors
Vincent van de Schaft, Ruud J. G. van Sloun
Denoising Process Neural Reflectance Speckle Reduction Ultrasound Image Quality Flow Prior Noisy MRI

Noisy MRI

Papers

Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks

Robust Influence-based Training Methods for Noisy Brain MRI

Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI

Heteroscedastic Uncertainty Estimation for Probabilistic Unsupervised Registration of Noisy Medical Images

False Negative/Positive Control for SAM on Noisy Medical Images

RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging

Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors