Abstract #2040

3D Keypoint-Based Neural Network for Rapid Needle Localization on Multislice 2D MRI

Wenqi Zhou^1,2, Qing Dai^1,2, Omar Curiel³, David S. Lu¹, Jason Chiang¹, Tsu-Chin Tsao³, and Holden H. Wu¹

¹Department of Radiology, University of California, Los Angeles, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: MR-Guided Interventions, MR-Guided Interventions

Motivation: Rapid, accurate needle localization on multislice images is essential for guiding needle-based MRI-guided interventions. Compared to segmentation-based methods, a keypoint-based approach can simplify reference creation and directly predict needle coordinates

Goal(s): Develop a 3D keypoint-based method for needle localization on multislice 2D MRI.

Approach: A 3D stacked hourglass neural network model with part affinity fields was developed for needle keypoint detection and compared to 3D UNet segmentation-based results and human-annotated references.

Results: The keypoint model achieved median needle tip and axis errors of 2.1 mm and 0.9°, with ~30ms computational time. The accuracy outperformed 3D UNet and was comparable to human intra-reader variation.

Impact: This work developed a 3D keypoint-based method for rapid, accurate needle localization on multislice 2D MRI, outperforming 3D UNet and matching human intra-reader variation. The keypoint-based method could be adapted to assist different needle-based MRI-guided interventions.

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