MORSE-PI: flexible and artefact-free image reconstruction for structural and functional QSM and other phase-critical imaging applications
Year: 2026
Type: article
Abstract: PURPOSE: Phase imaging applications such as QSM are highly sensitive to noise amplifications, phase singularities, and other artefacts, particularly in challenging scenarios such as ultra-high field (7 T), under-sampled or single-echo acquisitions. We present a novel image reconstruction method, MORSE-PI, designed to produce high-SNR, artefact-free, and singularity-free phase images for both structural (SWI, QSM) and functional (fQSM) phase-based brain imaging. METHODS: MORSE-PI extends our previous approach, MORSE, by introducing a Virtual Reference Coil (VRC). The VRC is constructed as a linear combination of coil sensitivity maps, with correlations enhanced between coil elements using the noise covariance matrix. Such a VRC ensures robust signal support across the entire brain and is used to correct phase offsets in the MORSE-derived coil sensitivity estimates, resulting in artefact-free, high-SNR phase. RESULTS: Compared to GRAPPA with ASPIRE phase correction, MORSE-PI demonstrates greater robustness to artefacts such as noise amplification and aliasing, and shows improved reproducibility in structural imaging at both 3 T and 7 T. Unlike ESPIRiT and GRAPPA combined with adaptive coil combination methods, MORSE-PI yields singularity-free phase maps. MORSE-PI enables high-SNR reconstructions even for the most challenging scenarios, such as single-echo EPI at 7 T. Its efficient, containerised implementation using the Gadgetron framework supports deployment on the MRI scanner console during measurements. CONCLUSION: MORSE-PI offers a flexible and computationally efficient solution for generating high-SNR, artefact- and singularity-free phase images in both single- and multi-echo GRE and EPI acquisitions. This makes it particularly well-suited for structural and functional QSM, as well as other phase-based MRI applications. Its robustness and rapid computational time facilitate efficient deployment on scanners across field strengths.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,090
Funder: Wellcome Trust
OpenAlex ID: W7225455904
