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An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN.

Shengyu Fan1,2,3, Yueyan Bian2, Erling Wang4

  • 1School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, China.

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Summary

This study introduces an automated method for estimating the arterial input function (AIF) in perfusion imaging using a 3D CNN. The new approach improves accuracy in calculating hemodynamic variables for better tissue vascular status evaluation.

Keywords:
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neuroimaging

Background:

  • Accurate estimation of the arterial input function (AIF) from perfusion images is crucial for calculating hemodynamic variables and assessing tissue vascular status.
  • Current AIF estimation relies on manual annotations, which are time-consuming and require prior knowledge.
  • Developing automated methods for AIF estimation can enhance efficiency and consistency in clinical practice.

Purpose of the Study:

  • To develop and validate an automated method for estimating the AIF in perfusion images using a multi-stream 3D Convolutional Neural Network (CNN).
  • To compare the performance of the proposed automated method against traditional manual and non-CNN-based approaches.
  • To assess the impact of automated AIF estimation on the calculation of key hemodynamic parameters and clinical decision-making tools.

Main Methods:

  • A multi-stream 3D CNN was designed to integrate spatial and temporal features for automated AIF region of interest (ROI) segmentation.
  • The CNN model was trained and tested on 100 cases of perfusion-weighted imaging, using manual annotations as ground truth.
  • AIF was calculated by averaging voxels within the segmented ROI, and subsequent hemodynamic parameters (Tmax, rCBF, mismatch volume) were derived.

Main Results:

  • The automated AIF ROI segmentation achieved a Dice similarity coefficient of 0.79, outperforming traditional methods.
  • The derived mismatch volume using the automated AIF method reached 93.32% of the manual method's results, compared to 85.04% and 83.04% for other methods.
  • The developed method was successfully applied on cloud (Estroke) and local (NeuBrainCare) platforms for evaluating ischemic penumbra and infarct core volumes.

Conclusions:

  • The proposed multi-stream 3D CNN effectively automates AIF estimation in perfusion imaging, offering improved accuracy and efficiency.
  • Automated AIF estimation leads to more reliable calculation of hemodynamic parameters, particularly mismatch volume, aiding in treatment decisions for conditions like stroke.
  • Integration into clinical platforms like Estroke and NeuBrainCare demonstrates the practical utility of this automated approach for assessing cerebrovascular status.