A polynomial regression-based approach to estimate relaxation rate maps suitable for multiparametric segmentation of clinical brain MRI studies in multiple sclerosis. (August 2022)

• Record Type: Journal Article
• Authors: Pirozzi, Maria Agnese; Tranfa, Mario; Tortora, Mario; Lanzillo, Roberta; Brescia Morra, Vincenzo; Brunetti, Arturo; Alfano, Bruno; Quarantelli, Mario
• Is Part Of: Computer methods and programs in biomedicine. Volume 223(2022)
• Publication Date: 2022-08
• Subjects: MRI -- Multiple sclerosis -- Polynomial regression -- Relaxation rates -- Brain Segmentation; AWM Abnormal White Matter -- CSE Conventional Spin-Echo -- CSF CerebroSpinal Fluid -- DSC Dice Similarity Coefficient -- EDSS Expanded Disability Status Scale -- fCSF fractional CerebroSpinal Fluid -- FLAIR FLuid Attenuated Inversion Recovery -- fGM fractional Gray Matter -- fWM fractional White Matter -- GLM General Linear Model -- GM Gray Matter -- ICC Intraclass Correlation Coefficient -- LL Lesion Load -- LST Lesion Segmentation Tool -- MNI Montreal Neurological Institute -- MRI Magnetic Resonance Imaging -- MS Multiple Sclerosis -- NR1 NeuroRadiologist 1 -- NR2 NeuroRadiologist 2 -- NR3 NeuroRadiologist 3 -- PD Proton Density -- PL Potential Lesions -- R1 Longitudinal Relaxation Rate (1/T1) -- R2 Transversal Relaxation Rate (1/T2) -- RMSE Root Mean Square Error -- RPM Relaxation Parameter Maps -- SPM Statistical Parametric Mapping -- T1w T1-weighted -- T2w T2-weighted -- TPM Tissue Probability Maps -- WM White Matter; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106957
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Relaxation parameters maps (RPMs) can be calculated from spin-echo data. Faster sequences replaced conventional spin-echo sequences in clinical MRI studies. RPMs can be estimated from clinical sequences through a regression-based approach. Estimated RPMs avoid the need for dedicated sequences for segmentation purposes. Estimated RPMs are suitable for accurate brain segmentation in multiple sclerosis. Abstract: Background and Objective: Relaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been largely replaced by faster ones, which do not allow the calculation a-posteriori of RPMs from clinical studies. Aim of the study was to develop and validate a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-Gradient Echo T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS. Methods: The proposed method processes spatially normalized clinical MRI studies through a multistep pipeline, to collect a set of data points of matched signal intensities (from MRI studies) and relaxation parameters (from a CSE-derived digital template and an MS lesion database), which are then fitted by a multiple and multivariate 4- th degree polynomial regression, providing pseudo-RPMs. The method was applied to a dataset of 59 clinical MRI studies providing pseudo-RPMs that were segmented through a method originally developed for the CSE-derived RPMs. Results of the segmentation in 12 studies were used to iteratively optimize method parameters. Accuracy of segmentation of normal-appearing brain tissues from the pseudo-RPMs was assessed by comparing their age-related changes, as measured in 47 clinical studies, against those measured acquired using CSE sequences in a comparable dataset of 47 patients. Lesion segmentation was validated against manual segmentation carried out by three neuroradiologists. Results: Age-related changes of normal-appearing brain tissue volumes measured using the pseudo-RPMs substantially overlapped those measured using the RPMs obtained from CSE sequences, and segmentation of MS lesions showed a moderate-high spatial overlap with manual segmentation, comparable to that achieved by the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement. Conclusions: The proposed approach allows calculation from clinical studies of pseudo-RPMs, which are equivalent to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.