Vessel Wall Imaging in 1.5 T MRI Using Deep Learning Reconstruction: Prospective Evaluation of Interchangeability With Standard 3 T MRI.

Abstract

Objectives: Vessel wall magnetic resonance imaging is important in the diagnosis of intracranial vascular diseases. Until now, adequate clinical implementation has required the use of 3 T scanners, which limits wider availability. This study aims to evaluate the interchangeability of a 3D turbo spin echo sequence with variable flip angle MRI sequence (T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution, SPACE) with deep learning image reconstruction for a 1.5 T scanner, with an established sequence (T1 3D SPACE) for a 3 T scanner. In addition, image quality parameters and diagnostic confidence are to be compared.

Materials and methods: Patients were prospectively examined using a T1-weighted turbo spin echo with variable flip angle at 3 T and a similar newly developed sequence with deep learning-based image reconstruction at 1.5 T. The images were read by 3 experienced radiologists regarding the presence of pathologic enhancement of the vessel wall for the interchangeability analysis. Image quality and diagnostic confidence were evaluated using 5-point Likert scales. Interreader and intrareader reliability were calculated for all parameters.

Results: A total of 50 participants (42.6 ± 21.2 y, 26 males) were included. Regarding the presence of pathologic vessel wall enhancement, there was a high interreader agreement [Fleiss kappa = 0.72 (95% CI: 0.62; 0.81) for 1.5T, 0.78 (0.69; 0.88) for 3 T]. The sequences at 1.5 T and 3 T were interchangeable (IEI = 0.008). Image quality at 1.5 T was diagnostic in all cases, but rated superior at 3 T (P < 0.001). Diagnostic confidence was also slightly but significantly higher at 3 T (P < 0.001).

Conclusions: The utilization of deep learning reconstruction enables vessel wall imaging in 1.5 T MRI. This new MRI sequence was interchangeable with established sequences at 3 T for the detection of pathologic vessel wall enhancement. This development could significantly improve the availability of vessel wall imaging in the clinical routine.