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Subject-Specific Low-Field MRI Synthesis via a Neural Operator

Ziqi Gao, Nicha Dvornek, Xiaoran Zhang, Gigi Galiana, Hemant Tagare, Todd Constable

Abstract

Low-field (LF) magnetic resonance imaging (MRI) improves accessibility and reduces costs but generally has lower signal-to-noise ratios and degraded contrast compared to high field (HF) MRI, limiting its clinical utility. Simulating LF MRI from HF MRI enables virtual evaluation of novel imaging devices and development of LF algorithms. Existing low field simulators rely on noise injection and smoothing, which fail to capture the contrast degradation seen in LF acquisitions. To this end, we introduce an end-to-end LF-MRI synthesis framework that learns HF to LF image degradation directly from a small number of paired HF-LF MRIs. Specifically, we introduce a novel HF to LF coordinate-image decoupled neural operator (H2LO) to model the underlying degradation process, and tailor it to capture high-frequency noise textures and image structure. Experimental results in T1w and T2w MRI demonstrate that H2LO produces more faithful simulated low-field images than existing parameterized noise synthesis models and popular image-to-image translation models. Furthermore, it improves performance in downstream image enhancement tasks, showcasing its potential to enhance LF MRI diagnostic capabilities.

Subject-Specific Low-Field MRI Synthesis via a Neural Operator

Abstract

Low-field (LF) magnetic resonance imaging (MRI) improves accessibility and reduces costs but generally has lower signal-to-noise ratios and degraded contrast compared to high field (HF) MRI, limiting its clinical utility. Simulating LF MRI from HF MRI enables virtual evaluation of novel imaging devices and development of LF algorithms. Existing low field simulators rely on noise injection and smoothing, which fail to capture the contrast degradation seen in LF acquisitions. To this end, we introduce an end-to-end LF-MRI synthesis framework that learns HF to LF image degradation directly from a small number of paired HF-LF MRIs. Specifically, we introduce a novel HF to LF coordinate-image decoupled neural operator (H2LO) to model the underlying degradation process, and tailor it to capture high-frequency noise textures and image structure. Experimental results in T1w and T2w MRI demonstrate that H2LO produces more faithful simulated low-field images than existing parameterized noise synthesis models and popular image-to-image translation models. Furthermore, it improves performance in downstream image enhancement tasks, showcasing its potential to enhance LF MRI diagnostic capabilities.

Paper Structure

This paper contains 15 sections, 4 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Problem Formulation
  4. H2LO: High-to-Low Field Operator
  5. Branch network.
  6. Trunk network.
  7. Training and Inference
  8. Experiments
  9. Low Field MRI Synthesis
  10. Low Field MRI Enhancement
  11. Results
  12. Low Field MRI Synthesis
  13. Low Field MRI Enhancement
  14. Ablation Study
  15. Conclusion and Future Work

Figures (2)

  • Figure 1: Overview of the High-to-Low field Operator (H2LO) framework. The architecture maps HF MRI volumes to a continuous LF representation using a branch-trunk network optimized via pointwise intensity fidelity and gradient regularization.
  • Figure 2: Visualization of generated LF MRIs and ablation studies. (a) Generated T1w (upper rows) and T2w (lower rows) LF images using multiple methods. Even rows visualize the absolute differences between simulated and real LF images. (b) Ablation of multiple components in our model. (c) Testing results of the 5-fold ablation study. (d) Comparison of histograms for LF images generated by Arnold et al. arnold2022simulated and our method.