Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution

TL;DR

This paper introduces ASPEN, a deep learning method using parcellated neural networks for rapid, artifact-reduced quantitative susceptibility mapping, demonstrating comparable accuracy to existing methods with improved image quality and efficiency.

Contribution

The paper presents a novel parcellated neural network approach, ASPEN, for QSM reconstruction that reduces artifacts and computation time while maintaining accuracy.

Findings

ASPEN achieves similar quantitative accuracy to established methods.

ASPEN significantly reduces streaking artifacts and map blurring.

ASPEN operates in near real-time on standard hardware.

Abstract

Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-of-art QSM reconstruction methods either suffer from image artifacts or long computation times, which limits QSM clinical translation efforts. To overcome these limitations, a deep-learning-based approach is proposed and demonstrated. Methods: An encoder-decoder neural network was trained to infer susceptibility maps on volume parcellated regions. The training data consisted of fabricated susceptibility distributions modeled to mimic the spatial frequency patterns of in-vivo brain susceptibility distributions. Inferred volume parcels were recombined to form composite QSM. This approach is denoted as ASPEN, standing for Approximated Susceptibility through Parcellated Encoder-decoder Networks. ASPEN performance was evaluated relative to several well-established approaches on a gold-standard challenge dataset and on cohort of 200 study subjects. Results: ASPEN provided similar levels of quantitative accuracy compared to the evaluated established approaches on the gold standard ISMRM Challenge dataset, but qualitatively showed marked reductions in streaking artifacts and map blurring. On the large-cohort dataset, ASPEN achieved the highest score compared with other methods in a multi-rater evaluation of streaking artifacts and map resolution. Conclusion: The proposed ASPEN approach can robustly infer susceptibility maps in near real-time on routine computational hardware. This preliminary study establishes ASPEN's parity with existing approaches for quantitative accuracy on a well-curated gold standard dataset and further demonstrates its robustness to streaking artifacts across a large cohort of subjects.