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

• Published in: Computer methods and programs in biomedicine 2022-08, Vol.223, p.106957-106957, Article 106957
• Main Authors: Pirozzi, Maria Agnese, Tranfa, Mario, Tortora, Mario, Lanzillo, Roberta, Morra, Vincenzo Brescia, Brunetti, Arturo, Alfano, Bruno, Quarantelli, Mario
• Format: Article
• Language: English
• Subjects: Brain Segmentation; MRI; Multiple sclerosis; Polynomial regression; Relaxation rates
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106957

•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 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. 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. 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 volum