Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study

Abstract

Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test–retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.

Multiparametric MRI

“…Although multiparametric imaging techniques have the potential to allow a more comprehensive characterization of pathological processes and therapeutic responses, the stability of the quantitative maps is a prerequisite for the acquired maps to be used in multi-institutional studies. The repeatability and reproducibility of the maps acquired with multiparametric imaging techniques are currently under evaluation, usually in multisite and multiplatform settings 53–58 . The standardization of the acquisition protocols is an active field of research.…”

“…The repeatability and reproducibility of the maps acquired with multiparametric imaging techniques are currently under evaluation, usually in multisite and multiplatform settings. [53][54][55][56][57][58] The standardization of the acquisition protocols is an active field of research. The quantitative MRI techniques referenced in this section have the potential to offer more reproducible results compared with conventional contrast-weighted images owing to their quantitative nature.…”

Magnetic Resonance Fingerprinting

“…If the resolution of these maps can be sufficiently high (to match conventional weighted images), the possibility exists to reconstruct weighted images or even to replace weighted images altogether, and there is significant ongoing work in this direction. 28,40,182 As new technical advances arise in acquisition and reconstruction, it is also very important to make these advances directly available at the scanner so that clinical adoption can be facilitated. To do this, some advances (particularly in reconstruction and large dictionary generation) must often be coupled with appropriate implementation via graphic processing units and tensor processing units, which is not a trivial computing challenge.…”

“…Examples include MRF techniques for measurement of combined T1, T2, and T2* mapping 30–33 ; MRF with b-tensor encoding for combined T1, T2, and apparent diffusion coefficient (ADC) mapping 34 ; and CEST-MRF for brain tumors, 35 vascular MR fingerprinting, 36 and MRF–arterial spin labeling (ASL) for cerebral perfusion 37 . The test-retest repeatability as well as interscanner repeatability across multiple sites have been established for both 2D MRF and 3D MRF sequences 7,38–40 …”

Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images

“…In fact, qMRI measures properties of brain tissue which do not change more than few percentages depending on MR scanner brand or the field strength (Warntjes et al 2008, Gracien et al 2020. For MR fingerprinting, which is a related technique for measuring T 1 and T 2 (Ma et al 2013), the reproducibility of the relaxation rates was also shown to be high (Körzdörfer et al 2019, Fujita et al 2023. Thus, collecting qMRI data at many different sites (similar to BraTS) would potentially not require any harmonization of the images (which deep learning models normally are sensitive to).…”