Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI.

Abstract

Background: Physics-informed deep learning (DL) reconstructions show promise in accelerating MRI yet have not been extensively validated, particularly for 3D fluid-attenuated inversion recovery (FLAIR) sequence.

Purpose: To evaluate the diagnostic quality and interchangeability of DL-based 3D FLAIR with a state-of-the-art acceleration technique (wave-controlled aliasing in parallel imaging [Wave-CAIPI] FLAIR) in a clinical setting with 3 T brain MRI.

Materials and methods: Participants undergoing evaluation for demyelinating disease between October and December of 2023 were prospectively enrolled at a single center. For each participant, state-of-the-art Wave-CAIPI FLAIR and a resolution-matched 6-fold-under-sampled Cartesian FLAIR acquisition with DL reconstruction were performed at 3-T system (MAGNETOM Vida, Siemens Healthineers, Erlangen, Germany). Four neuroradiologists evaluated overall image quality, anatomic conspicuity, lesion conspicuity, and imaging artifacts. Lesion count, volume, and regional brain volume were compared between imaging methods. Inter-reader agreement was assessed using quadratic weighted Cohen's kappa and Kendall's correlation coefficient. Agreement of continuous metrics was evaluated using intraclass correlation coefficients (ICCs), linear regression, and Bland-Altman analysis. Interchangeability regarding the quantitative metrics was evaluated with individual equivalence index (IEI).

Results: Totally, 88 participants (61 women [69%], 47 ± 13 years) were evaluated. DL-FLAIR reduced scan time (1:53 vs. 2:50) and showed higher overall image quality, anatomic conspicuity, lesion conspicuity, and imaging artifacts compared with state-of-the-art technique (all Ps < .001). DL-FLAIR also demonstrated higher signal-to-noise ratio and contrast-to-noise ratio compared to Wave-CAIPI-FLAIR, with high agreement in lesion and regional brain volumes between both methods (ICC(2, k) range, 0.91 to 0.99). DL-FLAIR proved interchangeable with Wave-CAIPI-FLAIR for lesion count (IEI: 0.10, acceptable proportion: 0.977, 95% CI: [0.943, 1.000]) and for lesion volume (IEI: 0.32, acceptable proportion: 0.966, 95% CI: [0.930, 1.000]).

Conclusion: Deep learning reconstruction of 3D-FLAIR provides higher image quality compared to a state-of-the-art technique with 30% less scan time while maintaining excellent agreement and interchangeability in quantitative evaluation.