The Feasibility of Deep Learning–Based Reconstruction for Low-Tube-Voltage CT Angiography for Transcatheter Aortic Valve Implantation
Tsukasa Kojima, Yuzo Yamasaki, Yuko Matsuura, Ryoji Mikayama, Takashi Shirasaka, Masatoshi Kondo, Takeshi Kamitani, Toyoyuki Kato, Kousei Ishigami, Hidetake Yabuuchi- Radiology, Nuclear Medicine and imaging
Objective
The purpose of this study is to evaluate the efficacy of deep learning reconstruction (DLR) on low-tube-voltage computed tomographic angiography (CTA) for transcatheter aortic valve implantation (TAVI).
Methods
We enrolled 30 patients who underwent TAVI-CT on a 320-row CT scanner. Electrocardiogram-gated coronary CTA (CCTA) was performed at 100 kV, followed by nongated aortoiliac CTA at 80 kV using a single bolus of contrast material. We used hybrid-iterative reconstruction (HIR), model-based IR (MBIR), and DLR to reconstruct these images. The contrast-to-noise ratios (CNRs) were calculated. Five-point scales were used for the overall image quality analysis. The diameter of the aortic annulus was measured in each reconstructed image, and we compared the interobserver and intraobserver agreements.
Results
In the CCTA, the CNR and image quality score for DLR were significantly higher than those for HIR and MBIR (
Conclusions
In low tube voltage TAVI-CT, DLR provides higher image quality than HIR, and DLR provides higher image quality than MBIR in CCTA and is visually comparable to MBIR in aortoiliac CTA.