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AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic

Cian M Scannell1,2, Ebraham Alskaf1, Noor Sharrack3

  • 1School of Biomedical Engineering & Imaging Sciences, King's College London, 4th Floor Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.

European Heart Journal. Digital Health
|February 6, 2023
PubMed
Summary

A deep learning model accurately predicts myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) imaging. This AI-based approach corrects for signal saturation, enabling precise MBF quantification with a single imaging sequence and contrast injection.

Keywords:
Arterial input functionArtificial intelligenceCardiac magnetic resonanceQuantitative myocardial perfusion

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Quantifying myocardial blood flow (MBF) using stress perfusion cardiac magnetic resonance (CMR) is hindered by signal saturation effects in the arterial input function (AIF).
  • The non-linear relationship between gadolinium concentration and MR signal complicates accurate AIF estimation, impacting MBF quantification.
  • Accurate AIF is critical for reliable MBF measurements in CMR, especially in clinical settings.

Purpose of the Study:

  • To develop and validate a deep learning model for predicting an unsaturated arterial input function (AIF) from standard cardiac magnetic resonance (CMR) images.
  • To enable accurate quantification of myocardial blood flow (MBF) using a single-sequence acquisition and a single contrast injection.
  • To overcome signal saturation challenges in AIF estimation for stress perfusion CMR.

Main Methods:

  • A 1D U-Net deep learning model was trained to predict unsaturated AIF from saturated AIF derived from standard CMR images.
  • Training data utilized reference dual-sequence acquisition AIFs (DS-AIFs) from 201 patients.
  • The model was tested on independent cohorts from two centers (n=44), comparing AI-AIF derived MBF with DS-AIF quantified MBF.

Main Results:

  • The AI-AIF method showed no statistically significant difference in MBF quantification compared to the DS-AIF method (2.77 vs. 2.79 mL/min/g, P=0.33).
  • Bland-Altman analysis revealed minimal bias (-0.11 mL/min/g) between AI-AIF and DS-AIF for quantitative MBF.
  • MBF diagnosis classification using AI-AIF agreed with DS-AIF in 95% of myocardial segments.

Conclusions:

  • Stress perfusion CMR for MBF quantification is feasible using a single-sequence acquisition and single contrast injection.
  • An AI-based correction of the arterial input function (AIF) effectively addresses signal saturation issues.
  • This deep learning approach facilitates accurate and efficient MBF assessment in clinical practice.