DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning-Based Image Quality Improvement for Vessel Wall MRI.

Abstract

Background and purpose: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.

Materials and methods: Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.

Results: DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1-1.75] versus 3 [3-4], P < .001) and venous flow artifacts (1 [1-2] versus 3 [3-4], P < .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1-2], versus 3 [2-3], P < .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) (P < .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.

Conclusions: DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.