Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction

[摘要] Background: This study aims to evaluate the impact of a novel deep learning-based image reconstruction (DLIR) algorithm on the image quality in computed tomographic angiography (CTA) for pre-interventional planning of transcatheter aortic valve implantation (TAVI). Methods: We analyzed 50 consecutive patients (median age 80 years, 25 men) who underwent TAVI planning CT on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) and DLIR. Intravascular image noise, edge sharpness, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified for ascending aorta, descending aorta, abdominal aorta and iliac arteries. Two readers (one radiologist and one interventional cardiologist) scored task-specific subjective image quality on a five-point scale. Results: DLIR significantly reduced median image noise by 29–57% at all anatomical locations (all P<0.001). Accordingly, median SNR improved by 44–133% (all P<0.001) and median CNR improved by 44–125% (all P0.05). Conclusions: DLIR significantly improves objective and subjective image quality in TAVI planning CT compared to a state-of-the-art iterative reconstruction without affecting measurements of the aortic annulus. This may provide an opportunity for further reductions in contrast medium volume in this population.

[学科分类] 外科医学

[关键词] Transcatheter aortic valve implantation (TAVI);deep learning;image reconstruction;image quality;computed tomography