ArticleQuantitative imaging in medicine and surgery2023
Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality.Scientific reports · 2026Article
- The application of deep learning-based artificial intelligence algorithms combined with low-dose scanning protocols in chest CT.Quantitative imaging in medicine and surgery · 2025Article
- Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.Abdominal radiology (New York) · 2025Article
- Objective assessment of diagnostic image quality in CT scans: what radiologists and researchers need to know.Insights into imaging · 2025Review
- Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.BMC medical imaging · 2025Article
- State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.Radiographics : a review publication of the Radiological Society of North America, Inc · 2024Review
- A Review of Factors Affecting Radiation Dose and Image Quality in Coronary CTA Performed with Wide-Detector CT.Tomography (Ann Arbor, Mich.) · 2024Review
- Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint.Quantitative imaging in medicine and surgery · 2024Article
- Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.Scientific reports · 2024Article
- The 10 Commandments for Transapical TAVI.Innovations (Philadelphia, Pa.)Article
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8 authors.
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Abstract
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 P<0.001). DLIR significantly improved subjective image quality for all four pre-specified TAVI-specific tasks (measuring the annulus, assessing valve morphology and calcifications, the coronary ostia, and the suitability of the aorto-iliac access route) for both the radiologist and the interventional cardiologist (P≤0.001). Measurements of the aortic annulus circumference, area and diameter did not differ between ASIR-V and DLIR reconstructions (all P>0.05). 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.
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