NeRF Solves Undersampled MRI Reconstruction

TL;DR

This paper introduces a neural radiance field-based MRI reconstruction method that effectively produces high-quality images from highly undersampled data, enabling diagnostic imaging with limited data acquisition.

Contribution

It presents a novel neural representation approach for MRI reconstruction from undersampled data, requiring only a single measurement set and adapting to specific scan data.

Findings

High-quality images from minimal undersampled data

Effective for diagnostic MRI with limited data

Validated through extensive experiments

Abstract

This article presents a novel undersampled magnetic resonance imaging (MRI) technique that leverages the concept of Neural Radiance Field (NeRF). With radial undersampling, the corresponding imaging problem can be reformulated into an image modeling task from sparse-view rendered data; therefore, a high dimensional MR image is obtainable from undersampled k-space data by taking advantage of implicit neural representation. A multi-layer perceptron, which is designed to output an image intensity from a spatial coordinate, learns the MR physics-driven rendering relation between given measurement data and desired image. Effective undersampling strategies for high-quality neural representation are investigated. The proposed method serves two benefits: (i) The learning is based fully on single undersampled k-space data, not a bunch of measured data and target image sets. It can be used potentially for diagnostic MR imaging, such as fetal MRI, where data acquisition is relatively rare or limited against diversity of clinical images while undersampled reconstruction is highly demanded. (ii) A reconstructed MR image is a scan-specific representation highly adaptive to the given k-space measurement. Numerous experiments validate the feasibility and capability of the proposed approach.