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Rapid CNN-based needle localization for automatic slice alignment in MR-guided interventions using 3D undersampled

Jonas Frederik Faust1,2, Axel Joachim Krafft2, Daniel Polak2

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|September 18, 2024
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Summary

This study introduces a rapid 3D needle localization method using a convolutional neural network (CNN) for MR-guided interventions. The technique enables automatic imaging slice alignment, improving workflow efficiency and targeting accuracy.

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device localizationinterventional MRIpercutaneous needle intervention

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Area of Science:

  • Medical Imaging
  • Interventional Radiology
  • Machine Learning

Background:

  • 2D imaging in MR-guided interventions can lead to needle visibility loss and workflow delays.
  • Manual slice adjustments are time-consuming during interventions.
  • Automated needle alignment is crucial for improving MR-guided procedures.

Purpose of the Study:

  • To develop and evaluate a rapid 3D needle localization algorithm using a CNN.
  • To enable automatic imaging slice alignment in MR-guided interventions.
  • To utilize undersampled white-marker contrast acquisition for efficient localization.

Main Methods:

  • Implemented a radial 3D gradient echo MR sequence with white-marker encoding.
  • Employed a CNN-based algorithm for needle position and orientation extraction.
  • Trained the CNN on porcine tissue phantoms and evaluated performance in ex vivo and in vivo studies.

Main Results:

  • Achieved median localization accuracy of 1.9 mm and 2.6° angular deviation ex vivo.
  • Demonstrated feasibility of rapid localization with highly accelerated acquisition (0.4 s, 32 spokes).
  • In vivo results showed 1.4 mm accuracy and 3.4° deviation with 0.48 s acquisition.

Conclusions:

  • The developed approach enables passive 3D biopsy needle localization.
  • Real-time applicability is feasible for needles oriented perpendicular to B0.
  • This method can significantly enhance MR-guided interventions through automatic slice alignment.