ArticleEuropean heart journal. Digital health2023
AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance.
Article in European heart journal. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Society for cardiovascular magnetic resonance expert consensus statement on quantitative myocardial perfusion cardiovascular magnetic resonance imaging.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceGuideline
- Quantitative myocardial perfusion imaging across PET, SPECT, CMR, and CT.Frontiers in radiology · 2026Review
- Artificial Intelligence-Based Arterial Input Function for the Quantitative Assessment of Myocardial Blood Flow and Perfusion Reserve in Cardiac Magnetic Resonance: A Validation Study.Diagnostics (Basel, Switzerland) · 2025Article
- Impaired Coronary Microcirculation and Myocardial Systolic Function: A Narrative Review on Non-Invasive Assessment in Cardiovascular Diseases.Life (Basel, Switzerland) · 2025Review
- Review
- Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.Tomography (Ann Arbor, Mich.) · 2024Article
- High-resolution quantification of stress perfusion defects by cardiac magnetic resonance.European heart journal. Imaging methods and practice · 2024Article
- Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification.Quantitative imaging in medicine and surgery · 2023Article
- Assessment of Microvascular Disease in Heart and Brain by MRI: Application in Heart Failure with Preserved Ejection Fraction and Cerebral Small Vessel Disease.Medicina (Kaunas, Lithuania) · 2023Review
- A simplified method to correct saturation of arterial input function for cardiac magnetic resonance first-pass perfusion imaging: validation with simultaneously acquired PET.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Aims: One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training. Methods and results: A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 ( Conclusion: Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.
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