Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

TL;DR

This paper demonstrates that a deep learning method called xQSM can accurately perform quantitative susceptibility mapping of deep grey matter from small brain coverages, reducing scan time and improving resolution.

Contribution

The study introduces and validates the xQSM deep learning approach for accurate DGM QSM from reduced brain coverages, outperforming conventional methods.

Findings

xQSM reduces susceptibility underestimation by over 20% in simulations.

Less than 5% susceptibility error achieved with 48 mm coverage in vivo.

Conventional methods require at least 112 mm coverage for similar accuracy.

Abstract

Introduction: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation. Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages.Results: The proposed xQSM network led to the lowest DGM contrast lost with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48 mm axial slabs using the xQSM network, while a minimum of 112 mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion. Conclusion: The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.