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Abstract #0455

Transformer-Based Automatic Pipeline for 3D Needle Localization on Intra-Procedural 3D MRI

Wenqi Zhou1, Xinzhou Li1, Fatemeh Zabihollahy1, David S. Lu1, and Holden H. Wu1
1Department of Radiological Sciences, UCLA, Los Angeles, CA, United States

Synopsis

Keywords: MR-Guided Interventions, Segmentation

Motivation: Needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions, but current manual methods are time-consuming.

Goal(s): To develop a transformer-based pipeline for accurate and rapid automatic 3D needle localization on intra-procedural 3D MRI.

Approach: The proposed pipeline adopted a coarse-to-fine segmentation strategy by combining 3D and 2D shifted window (Swin) Transformer networks. The performance was evaluated in pre-clinical pig datasets and compared with human-annotated references.

Results: Computation time was 6 sec/volume. The median 3D needle tip and axis localization errors were 1.48 mm (1.09 pixels) and 0.98°, which were comparable to human-level accuracy.

Impact: The automatic transformer-based pipeline developed in this work achieved rapid (~6s) and accurate pixel-level 3D needle localization on intra-procedural 3D MRI. This new pipeline has the potential to improve MRI-guided percutaneous interventions.

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