Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi‐Site Reproducibility and Single‐Site Robustness

Abstract

Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment. METHODS: A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm 3 reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm 3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION: Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.

Multi-centre, multi-vendor reproducibility of 7T QSM and R2* in the human brain: Results from the UK7T study

“…Previous studies at 1.5T and 3T have shown good reproducibility for χ and R 2 * data acquired on the same scanner or across sites (1.5T and 3T) ( Hinoda et al., 2015 ; Cobzas et al., 2015 ; Deh et al., 2015 ; Lin et al., 2015 ; Santin et al., 2017 ; Feng et al., 2018 ; Spincemaille et al., 2019 ). In terms of QSM and depending on the subcortical region, intra-scanner 3T repeatability studies report an SD w of 0.002–0.005 ppm ( Feng et al., 2018 ) and 0.004–0.006 ppm ( Santin et al., 2017 ), and the cross-site 3T study by Lin et al.…”

“…R 2 * imaging and QSM have been shown to provide reproducible results in single-site and cross-site studies at 1.5T and 3T ( Hinoda et al., 2015 ; Cobzas et al., 2015 ; Deh et al., 2015 ; Lin et al., 2015 ; Santin et al., 2017 ; Feng et al., 2018 ; Spincemaille et al., 2019 ).…”

Initial Experience of Challenge-Free MRI-Based Oxygen Extraction Fraction Mapping of Ischemic Stroke at Various Stages: Comparison With Perfusion and Diffusion Mapping

“…This CAT QSM + qBOLD OEF mapping eliminates assumptions in and, therefore, is theoretically more accurate than other OEF mapping methods ( Kudo et al, 2016 ; Uwano et al, 2017 ; Zhang et al, 2017 , 2018 ). QSM ( de Rochefort et al, 2010 ; Wang and Liu, 2015 ; Liu et al, 2018 ) has been shown to be highly reproducible ( Deh et al, 2015 ; Spincemaille et al, 2019 ), and recent technical developments in preconditioning for optimization execution ( Liu et al, 2016 ) and in phase unwrapping ( Dong et al, 2015 ) promise to make QSM robust even in brain regions near the air–tissue interface. The challenge of qBOLD prone to noise is effectively addressed by the CAT algorithm.…”

Cerebral oxygen extraction fraction: Comparison of dual‐gas challenge calibrated BOLD with CBF and challenge‐free gradient echo QSM+qBOLD

“…31 Finally, morphologyenabled dipole inversion with automatic uniform cerebrospinal fluid (CSF) 0 reference (MEDI+0) algorithm was used to compute susceptibility. [32][33][34][35][36][37] The mGRE magnitude was modeled according to qBOLD and further denoised according to the cluster analysis of time evolution (CAT) by following the approach outlined by Cho et al 27 with incorporating tissue type information by tripling the number of clusters into gray matter (GM), white matter (WM), cerebrospinal fluid clusters, and without the OEF regularization in the QQ-CAT optimization. The cluster-specific v values are initialized to be 4/2/0% for GM/ WM/cerebrospinal fluid clusters.…”