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Published on: December 15, 2023
A polynomial regression-based approach to estimate relaxation rate maps suitable for multiparametric segmentation of
Maria Agnese Pirozzi1, Mario Tranfa2, Mario Tortora2
1Institute of Biostructures and Bioimaging, Italian National Research Council, Naples, Italy; Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy.
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.
Insights
This study introduces a new method to estimate relaxation parameter maps (RPMs) from routine MRI scans, enabling accurate segmentation of brain tissues and multiple sclerosis (MS) lesions without special sequences.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Multiple Sclerosis Research
Background:
- Relaxation parameter maps (RPMs) from spin-echo data are crucial for segmenting brain tissues and multiple sclerosis (MS) lesions.
- Conventional Spin-Echo (CSE) sequences are being replaced by faster MRI sequences, limiting post-acquisition RPM calculation.
- This necessitates a method to derive RPMs from widely available clinical MRI protocols.
Purpose of the Study:
- To develop and validate a method for estimating pseudo-RPMs from routine clinical MRI sequences (3D-T1w, FLAIR, fast-T2w).
- To enable fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS patients.
- To overcome the limitations of CSE sequences in modern clinical practice.
Main Methods:
- A multistep pipeline processes spatially normalized clinical MRI studies.
- Signal intensities are matched with relaxation parameters from a CSE-derived template and MS lesion database.
- Multiple and multivariate 4th-degree polynomial regression is used to generate pseudo-RPMs.
- Segmentation accuracy was assessed by comparing age-related changes in normal-appearing brain tissues and lesion overlap with manual segmentation.
Main Results:
- Pseudo-RPMs derived from clinical MRI showed age-related changes in normal-appearing brain tissues comparable to those from CSE sequences.
- Segmentation of MS lesions using pseudo-RPMs demonstrated moderate-to-high spatial overlap with manual segmentation.
- Volumetric agreement for MS lesion segmentation was superior to the Lesion Segmentation Tool on FLAIR images.
Conclusions:
- The developed approach successfully calculates pseudo-RPMs from routine clinical MRI studies.
- These pseudo-RPMs are equivalent to those obtained from CSE sequences.
- This method eliminates the need for acquiring additional, dedicated MRI sequences for segmentation purposes in MS research.
