ArticleInsights into imaging2024
Deep learning CT reconstruction improves liver metastases detection.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Optimization of hypovascular liver lesion detectability in dual-energy CT using deep learning image reconstruction: a phantom study for potential iodine dose reduction.European radiology experimental · 2026Article
- Artificial intelligence iterative imaging reconstruction with low-dose CT scans for hepatic metastases.Scientific reports · 2026Article
- Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.Scientific reports · 2026Article
- Evaluation of optimal ClearInfinity artificial intelligence deep learning reconstruction algorithm weights with a 70-kVp tube voltage combined with "dual-low" abdominal computed tomography angiography.Quantitative imaging in medicine and surgery · 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
- Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Anatomically constrained liver CT anomaly detection using healthy priors with diffusion-based inpainting.Research square · 2026Article
- Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.European radiology · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- The utility of low-dose pre-operative CT of ovarian tumor with artificial intelligence iterative reconstruction for diagnosing peritoneal invasion, lymph node and hepatic metastasis.Abdominal radiology (New York) · 2025Article
- Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.Frontiers in big data · 2025Article
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8 authors.
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Abstract
objectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.
methodsCT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.
resultsA higher number of metastases was detected by one reader with DLIR-high: 7 (2-10) (median (Q₁-Q₃); total 733) versus 5 (2-10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.
conclusionDLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected. CRITICAL RELEVANCE STATEMENT: Deep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT. KEY POINTS: Detection of liver metastases is crucial but limited with standard CT reconstructions. More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction. Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.
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