Fast Submillimeter Whole-Brain T₂*-Weighted Imaging Using 3D-EPI With CAIPIRINHA and Deep-Learning Denoising at 3T

Recent updates to the diagnostic criteria of multiple sclerosis (MS) require whole-brain T₂*-weighted (T₂*w) imaging with submillimeter resolution to detect novel diagnostic biomarkers such as the central vein sign. However, to achieve the needed submillimeter spatial resolution, conventional T₂*w 3D gradient-echo scans sequences are limited by prohibitively long scan times for clinical use. Here, we evaluated a different approach based on a segmented 3D echo planar imaging (3D-EPI) sequence, accelerated with 2D Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA) undersampling and denoised with a deep learning-based network.

Fifty-two research participants were imaged at 3T using the 3D-EPI sequence acquired at different CAIPIRINHA acceleration factors (R = 2, 3, and 4) and denoised using a dedicated denoising convolutional neural network (DnCNN). Quantitative assessment of the accelerated T₂*w 3D-EPI scans, before and after denoising, was performed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and tissue contrasts. A neuroradiologist separately assessed image quality in a blinded manner using predetermined scoring criteria.

T₂*w 3D-EPI with CAIPIRINHA acceleration enabled fast submillimeter isotropic (0.65 mm) imaging of the entire brain with scan times ranging between 3 min 22 s (R = 2) down to 1 min 56 s (R = 4). Even for the fastest scan (R = 4), accelerated T₂*w 3D-EPI images denoised with DnCNN exhibited superior PSNR (3 dB increase), SSIM (13% increase) and lesion-to-vein tissue contrast (9% increase) compared to the non-denoised images.

The 3D-EPI sequence combined with CAIPIRINHA and deep learning denoising enables fast submillimeter whole-brain T₂*w imaging at 3T.