⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

April 13, 2026Journal of Computer Assisted TomographyOpen Access

Ability of Deep Learning Image Reconstruction to Preserve Detail and Remove Noise in Coronary Computed Tomography Angiography: A Clinical Analysis

Aria M. Salyapongse Prashant Nagpal Martin G. Wagner Sarv Priya Jakub Siembida Timothy P. Szczykutowicz

View Full Paper

Ask AI

Bookmark

Share

Key Points

This research aims to evaluate the effectiveness of deep learning image reconstruction (DLIR) on coronary computed tomography angiography.
Assessment of DLIR levels in image reconstruction
Comparison of image quality using objective and subjective measures
Higher levels of DLIR significantly improved image quality
Edge detail preservation was maintained despite enhanced noise reduction

Implication

Clinical analysis demonstrates improved image quality without losing detail in coronary imaging, suggesting enhanced diagnostic potential.

Abstract

By both objective and subjective measures, the higher levels of DLIR resulted in improved image quality without loss of edge details.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Salyapongse et al. (2026) studied this question.

synapsesocial.com/papers/69df2c88e4eeef8a2a6b1b9b https://doi.org/10.1097/rct.0000000000001866

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart diseasecohort · 2025 · 2 citations
2Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT AngiographyOriginal Research · 2023 · 41 citations
3Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research2024 · 3 citations
4Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom2024 · 4 citations
5Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography2024 · 4 citations

Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index2024
Clinical Consequences of Deep Learning Image Reconstruction at CT2025
Deep learning-based reconstruction improves image quality in low-dose head CT angiography2025
Cognitive Bias in Emergency Radiology: Implications for Trauma Torso CTJournal of Computer Assisted Tomography · 2026
Split-Bolus CT Urography Combined With Renal CT Angiography: Evaluation of Image Quality, Radiation Dose, and Clinical FeasibilityJournal of Computer Assisted Tomography · 2026

More from Journal of Computer Assisted Tomography