ArticleEuropean radiology2023
Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Deep Learning Algorithm-Reconstructed Triple-Rule-Out CT: Image Quality and Performance in Evaluating Coronary Lumen Stenosis.Journal of imaging informatics in medicine · 2026Article
- Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.European radiology · 2026Article
- Feasibility of deep learning-based image reconstruction using TrueFidelity for coronary calcium scoring.PloS one · 2026Article
- Distribution of coronary artery calcium in a large European all-comer population referred for cardiac imaging.International journal of cardiology. Heart & vasculature · 2025Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- Influence of deep learning-based super-resolution reconstruction on Agatston score.European radiology · 2025Article
- Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease.BMC medical informatics and decision making · 2025Article
- Effect of temporal resolution on calcium scoring: insights from photon-counting detector CT.The international journal of cardiovascular imaging · 2025Article
- Artificial Intelligence in Coronary Artery Calcium Scoring.Journal of clinical medicine · 2024Review
- Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.The international journal of cardiovascular imaging · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP.
methodsOne hundred patients were consecutively enrolled. Image quality-associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1-99 Agatston score (mildly increased risk), Agatston 100-299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk).
resultsIn comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%.
conclusionDLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment. KEY POINTS: • In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise. • Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score. • Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.