HULFSynth : An INR based Super-Resolution and Ultra Low-Field MRI Synthesis via Contrast factor estimation

TL;DR

This paper introduces an unsupervised MRI synthesis method that leverages physics-inspired contrast estimation and implicit neural representations to generate ultra-low field MRI images from high-field images, improving contrast and resolution.

Contribution

It presents a novel physics-inspired, unsupervised approach combining contrast factor estimation with INR networks for MRI synthesis and super-resolution tasks.

Findings

WM-GM contrast improved by 52% in synthetic ULF-like images

Contrast improved by 37% in 64mT images

Method demonstrated robustness to contrast, noise, and seeding variations

Abstract

We present an unsupervised single image bidirectional Magnetic Resonance Image (MRI) synthesizer that synthesizes an Ultra-Low Field (ULF) like image from a High-Field (HF) magnitude image and vice-versa. Unlike existing MRI synthesis models, our approach is inspired by the physics that drives contrast changes between HF and ULF MRIs. Our forward model simulates a HF to ULF transformation by estimating the tissue-type Signal-to-Noise ratio (SNR) values based on target contrast values. For the Super-Resolution task, we used an Implicit Neural Representation (INR) network to synthesize HF image by simultaneously predicting tissue-type segmentations and image intensity without observed HF data. The proposed method is evaluated using synthetic ULF-like data from generated from standard 3T T$_1$-weighted images for qualitative assessments and paired 3T-64mT T$_1$-weighted images for validation experiments. WM-GM contrast improved by 52% in synthetic ULF-like images and 37% in 64mT images. Sensitivity experiments demonstrated the robustness of our forward model to variations in target contrast, noise and initial seeding.