ArticleRadiology2024
Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study.
Article in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Evaluation of deep-learning iterative reconstruction combined with high-frequency kernels in CT: a task-based image quality study.European radiology experimental · 2026Article
- Detection of hypodense hepatic and renal lesions on abdominal CT reconstructed with deep learning image reconstruction technique in patients with large body habitus: A multi-reader study.Abdominal radiology (New York) · 2026Article
- Transferability of radiomics models between deep learning and conventional CT reconstruction algorithms: A task-based assessment for stratifying acute pancreatitis severity.Journal of applied clinical medical physics · 2026Article
- Anatomically constrained liver CT anomaly detection using healthy priors with diffusion-based inpainting.Research square · 2026Article
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Deep learning reconstruction for detection of liver lesions at standard-dose and reduced-dose abdominal CT.European radiology · 2025Article
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13 authors.
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Abstract
Background CT deep learning image reconstruction (DLIR) improves image quality by reducing noise compared with adaptive statistical iterative reconstruction-V (ASIR-V). However, objective assessment of low-contrast lesion detectability is lacking. Purpose To investigate low-contrast detectability of hypoattenuating liver lesions on CT scans reconstructed with DLIR compared with CT scans reconstructed with ASIR-V in a patient and a phantom study. Materials and Methods This single-center retrospective study included patients undergoing portal venous phase abdominal CT between February and May 2021 and a low-contrast-resolution phantom scanned with the same protocol. Four reconstructions (ASIR-V at 40% strength [ASIR-V 40] and DLIR at three strengths) were generated. Five radiologists qualitatively assessed the images using the five-point Likert scale for image quality, lesion diagnostic confidence, conspicuity, and small lesion (≤1 cm) visibility. Up to two key lesions per patient, confirmed at histopathologic testing or at prior or follow-up imaging studies, were included. Lesion-to-background contrast-to-noise ratio was calculated. Interreader variability was analyzed. Intergroup qualitative and quantitative metrics were compared between DLIR and ASIR-V 40 using proportional odds logistic regression models. Results Eighty-six liver lesions (mean size, 15 mm ± 9.5 [SD]) in 50 patients (median age, 62 years [IQR, 57-73 years]; 27 [54%] female patients) were included. Differences were not detected for various qualitative low-contrast detectability metrics between ASIR-V 40 and DLIR (
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