Magnetic resonance biomarker assessment software (MR-BIAS): an automated open-source tool for the ISMRM/NIST system phantom
Publication Name
Physics in Medicine and Biology
Abstract
Objective. To provide an open-source software for repeatable and efficient quantification of T 1 and T 2 relaxation times with the ISMRM/NIST system phantom. Quantitative magnetic resonance imaging (qMRI) biomarkers have the potential to improve disease detection, staging and monitoring of treatment response. Reference objects, such as the system phantom, play a major role in translating qMRI methods into the clinic. The currently available open-source software for ISMRM/NIST system phantom analysis, Phantom Viewer (PV), includes manual steps that are subject to variability. Approach. We developed the Magnetic Resonance BIomarker Assessment Software (MR-BIAS) to automatically extract system phantom relaxation times. The inter-observer variability (IOV) and time efficiency of MR-BIAS and PV was observed in six volunteers analysing three phantom datasets. The IOV was measured with the coefficient of variation (CV) of percent bias (%bias) in T 1 and T 2 with respect to NMR reference values. The accuracy of MR-BIAS was compared to a custom script from a published study of twelve phantom datasets. This included comparison of overall bias and %bias for variable inversion recovery (T 1VIR), variable flip angle (T 1VFA) and multiple spin-echo (T 2MSE) relaxation models. Main results. MR-BIAS had a lower mean CV with T 1VIR (0.03%) and T 2MSE (0.05%) in comparison to PV with T 1VIR (1.28%) and T 2MSE (4.55%). The mean analysis duration was 9.7 times faster for MR-BIAS (0.8 min) than PV (7.6 min). There was no statistically significant difference in the overall bias, or the %bias for the majority of ROIs, as calculated by MR-BIAS or the custom script for all models. Significance. MR-BIAS has demonstrated repeatable and efficient analysis of the ISMRM/NIST system phantom, with comparable accuracy to previous studies. The software is freely available to the MRI community, providing a framework to automate required analysis tasks, with the flexibility to explore open questions and accelerate biomarker research.
Open Access Status
This publication may be available as open access
Volume
68
Issue
6
Article Number
06NT01