Abstract #0861

Deep Learning-Driven Automatic Scan Plane Alignment for Needle Tracking in MRI-Guided Interventions

Xinzhou Li^1,2, Yu-Hsiu Lee³, David S. Lu¹, 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

Misalignment between the MRI scan plane and needle trajectory degrades visualization and localization of the needle. This may prolong procedure time and increase errors in MRI-guided interventions. By leveraging an accurate deep learning-based needle localization algorithm, this work proposed an automatic workflow to realign the MRI scan plane with the needle. A scan plane control module was implemented for scan parameter updates. In one degree-of-freedom needle insertion experiments, the automatic workflow accurately aligned the scan plane with the needle (orientation difference 1.9°) with processing time <2 sec.

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