Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging

Abstract

Purpose: To improve the signal-to-noise ratio (SNR) and image sharpness for whole brain isotropic 0.5 mm three-dimensional (3D) T₁ weighted (T₁w) turbo spin echo (TSE) intracranial vessel wall imaging (IVWI) at 3 T.

Methods: The variable flip angle (VFA) method enables useful optimization across scan efficiency, SNR and relaxation induced point spread function (PSF) for TSE imaging. A convolutional neural network (CNN) was developed to retrospectively enhance the acquired TSE image with PSF blurring. The previously developed VFA method to increase SNR at the expense of blur can be combined with the presented PSF correction to yield long echo train length (ETL) scan while the acquired image remains high SNR and sharp. The overall approach can enable an optimized solution for accelerated whole brain high-resolution 3D T₁w TSE IVWI. Its performance was evaluated on healthy volunteers and patients.

Results: The PSF blurred image acquired by a long ETL scan can be enhanced by CNN to restore similar sharpness as a short ETL scan, which outperforms the traditional linear PSF enhancement approach. For accelerated whole brain IVWI on volunteers, the optimized isotropic 0.5 mm 3D T₁w TSE sequence with CNN based PSF enhancement provides sufficient flow suppression and improved image quality. Preliminary results on patients further demonstrated its improved delineation for intracranial vessel wall and plaque morphology.

Conclusion: The CNN enhanced VFA TSE imaging enables an overall image quality improvement for high-resolution 3D T₁w IVWI, and may provide a better tradeoff across scan efficiency, SNR and PSF for 3D TSE acquisitions.

Keywords: Deep learning; Generative adversarial network; Intracranial vessel wall imaging; Point spread function; Turbo spin echo; Variable flip angle.