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Deep learning-based key point detection algorithm assisting vessel centerline extraction.

Xiqian Zhang1,2, Wanqing Sun3, Hui Zhang4

  • 1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Quantitative Imaging in Medicine and Surgery
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

A new key point detection algorithm improves vessel centerline extraction accuracy for plaque analysis. This method enhances precision in tortuous vessels and significantly reduces processing time.

Keywords:
Key point detectionmagnetic resonance vessel wall imaging (MR-VWI)vessel centerline

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Vascular Biology

Background:

  • Vessel centerline extraction is crucial for quantitative plaque analysis.
  • Current methods struggle with tortuous vessels, leading to inaccurate results.
  • This study introduces a key point detection algorithm to improve accuracy.

Purpose of the Study:

  • To develop and evaluate a key point detection algorithm for enhancing vessel centerline extraction.
  • To improve the accuracy of quantitative plaque analysis in cerebrovascular diseases.
  • To address limitations of existing algorithms in handling complex vessel geometries.

Main Methods:

  • Retrospective analysis of 539 patients with cerebrovascular disease using 3.0-T MRI.
  • Selection of 32 critical key points (e.g., carotid siphon, bifurcations).
  • Evaluation using undetected points, erroneous points, point accuracy, and average centerline distance (ACD).

Main Results:

  • The algorithm achieved an average accuracy of 88.99% for 32 key points, exceeding 90% for 18 points.
  • High accuracy (97%) was observed in sharp bends of the carotid siphon.
  • Average centerline distance (ACD) improved from 0.529±0.334 mm to 0.484±0.321 mm; detection time reduced from ~320s to ~2s.

Conclusions:

  • The proposed algorithm automatically and accurately detects key points, especially in the internal carotid and middle cerebral arteries.
  • This enhances vessel centerline extraction accuracy, aiding plaque assessment.
  • The algorithm offers a significant improvement for quantitative analysis of cerebrovascular plaque.