Abstract #4144

Physical Needle Localization using Mask R-CNN for MRI-Guided Percutaneous Interventions

Xinzhou Li^1,2, Yu-Hsiu Lee³, Tsu-Chin Tsao³, and Holden H. Wu^1,2

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

Discrepancy between the physical needle location and the MRI passive needle feature could lead to needle localization errors in MRI-guided percutaneous interventions. By leveraging physics-based simulations with different needle orientations and MR imaging parameters, we designed and trained a Mask Regional Convolutional Neural Network (R-CNN) to automatically localize the physical needle tip and axis orientation based on the MRI passive needle feature. The Mask R-CNN framework was tested on a separate set of actual phantom MR images and achieved physical needle localization with median tip error of 0.74 mm and median axis error of 0.95°.

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