Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction

TL;DR

Meta-QSM introduces a versatile neural network that reconstructs quantitative susceptibility maps at any image resolution using a single model, improving efficiency over traditional fixed-resolution methods.

Contribution

The paper presents a novel meta-learning approach that enables a single neural network to handle arbitrary image resolutions for QSM reconstruction, reducing computational costs.

Findings

Effective reconstruction across various resolutions

Single model outperforms multiple fixed-resolution models

Validated on synthetic and clinical datasets

Abstract

Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM inverse problem are restricted on fixed image resolution. They trained a specific model for each image resolution which is inefficient in computing. In this work, we proposed a novel method called Meta-QSM to firstly solve QSM reconstruction of arbitrary image resolution with a single model. In Meta-QSM, weight prediction was used to predict the weights of kernels by taking the image resolution as input. The proposed method was evaluated on synthetic data and clinical data with comparison to existing QSM reconstruction methods. The experimental results showed the Meta-QSM can effectively reconstruct susceptibility maps with different image resolution using one neural network training.