Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study.

OBJECTIVES: Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We used deep learning reconstruction to quantify stent strut thickness and lumen vessel diameter at the stent and compared it with values obtained using conventional reconstruction strategies. METHODS: We examined 166 stents in 85 consecutive patients who underwent CT and invasive coronary angiography (ICA) within 3 months of each other from 2019-2021 after percutaneous coronary intervention with coronary stent placement. The presence of ISR was defined as percent diameter stenosis ≥ 50% on ICA. We compared a super-resolution deep learning reconstruction, Precise IQ Engine (PIQE), and a model-based iterative reconstruction, Forward projected model-based Iterative Reconstruction SoluTion (FIRST). All images were reconstructed using PIQE and FIRST and assessed by two blinded cardiovascular radiographers. RESULTS: PIQE had a larger full width at half maximum of the lumen and smaller strut than FIRST. The image quality score in PIQE was higher than that in FIRST (4.2 ± 1.1 versus 2.7 ± 1.2, p < 0.05). In addition, the specificity and accuracy of ISR detection were better in PIQE than in FIRST (p < 0.05 for both), with particularly pronounced differences for stent diameters < 3.0 mm. CONCLUSION: PIQE provides superior image quality and diagnostic accuracy for ISR, even with stents measuring < 3.0 mm in diameter. CLINICAL RELEVANCE STATEMENT: With improvements in the diagnostic accuracy of in-stent stenosis, CT angiography could become a gatekeeper for ICA in post-stenting cases, obviating ICA in many patients after recent stenting with infrequent ISR and allowing non-invasive ISR detection in the late phase. KEY POINTS: • Despite CT technology advancements, evaluating in-stent stenosis severity, especially in small-diameter stents, remains challenging. • Compared with conventional methods, the Precise IQ Engine uses deep learning to improve spatial resolution. • Improved diagnostic accuracy of CT angiography helps avoid invasive coronary angiography after coronary artery stenting.

Vagus Nerve Visualization Using Fused Images of 3D-CT Angiography and MRI as Preoperative Evaluation for Vagus Nerve Stimulation.

Vagus nerve stimulation (VNS) is an effective surgical option for intractable epilepsy. Although the surgical procedure is not so complicated, vagus nerve detection is sometimes difficult due to its anatomical variations, which may lead to surgical manipulation-associated complications. Thus, this study aimed to visualize the vagus nerve location preoperatively by fused images of three-dimensional computed tomography angiography (3D-CTA) and magnetic resonance imaging (MRI). This technique was applied to two cases. The neck 3D-CTA and MRI were performed, and the fused images were generated using the software. The vagus nerve and its anatomical relationship with the internal jugular vein (IJV) and common carotid artery were clearly visualized. The authors predicted that the vagus nerve was detected by laterally pulling the IJV according to the images. Intraoperatively, the vagus nerve was located as the authors predicted. The time of the surgery until the vagus nerve detection was <60 min in both cases. This novel radiological technique for visualizing the vagus nerve is effective to quickly detect the vagus nerve, which has anatomical variations, during the VNS.