Self-supervised deep learning MRI reconstruction with Noisier2Noise

05/20/2022
by   Charles Millard, et al.
0

In recent years, there has been attention on leveraging the statistical modeling capabilities of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data. Most proposed methods assume the existence of a representative fully-sampled dataset and use fully-supervised training. However, for many applications, fully sampled training data is not available, and may be highly impractical to acquire. The development of self-supervised methods, which use only sub-sampled data for training, are therefore highly desirable. This work extends the Noisier2Noise framework, which was originally constructed for self-supervised denoising tasks, to variable density sub-sampled MRI data. Further, we use the Noisier2Noise framework to analytically explain the performance of Self-Supervised Learning via Data Undersampling (SSDU), a recently proposed method that performs well in practice but until now lacked theoretical justification. We also use Noisier2Noise to propose a modification of SSDU that we find substantially improves its reconstruction quality and robustness, offering a test set mean-squared-error within 1 dataset.

READ FULL TEXT

page 1

page 3

page 5

page 6

page 7

research
11/24/2022

Iterative Data Refinement for Self-Supervised MR Image Reconstruction

Magnetic Resonance Imaging (MRI) has become an important technique in th...
research
08/05/2023

K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets

Although deep learning (DL) methods are powerful for solving inverse pro...
research
11/02/2020

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning

Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvas...
research
10/21/2019

Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data

Deep learning (DL) has emerged as a tool for improving accelerated MRI r...
research
01/14/2019

Self-Supervised Deep Active Accelerated MRI

We propose to simultaneously learn to sample and reconstruct magnetic re...
research
01/26/2022

DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction

Multi-contrast MRI (MC-MRI) captures multiple complementary imaging moda...
research
06/17/2022

Self-supervised deep visual servoing for high precision peg-in-hole insertion

Many industrial assembly tasks involve peg-in-hole like insertions with ...

Please sign up or login with your details

Forgot password? Click here to reset