Differentiable Image-Based Needle Localization in MR-Guided Interventions

Motivation: MR-guided needle interventions require accurate and precise knowledge of the needle position and orientation, which is limited by image distortions. Goal(s): Provide differentiable image-based needle localization that inherently leverages image distortions that typically produce positioning errors. Approach: Needle susceptibility maps were modeled with a mixture of Gaussian functions that are related differentiably to a simulated distorted image. The simulated image was iteratively compared to a measured image to recover the true needle position and orientation. Simulations validated the method compared to a PCA approach. Results: The proposed approach achieved up to 50% lower position and angular errors than the PCA approach. Impact: Accurate needle localization is key to the safety and efficacy of interventional MR procedures. The proposed differentiable image-based method recovers needle position and pose accurately, regardless of needle-induced image distortions.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/4844
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Differentiable Image-Based Needle Localization in MR-Guided Interventions

William A. Grissom, Sarah Garrow
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Differentiable Image-Based Needle Localization in MR-Guided Interventions

William A. Grissom, Sarah Garrow
article en

Abstract

Motivation: MR-guided needle interventions require accurate and precise knowledge of the needle position and orientation, which is limited by image distortions. Goal(s): Provide differentiable image-based needle localization that inherently leverages image distortions that typically produce positioning errors. Approach: Needle susceptibility maps were modeled with a mixture of Gaussian functions that are related differentiably to a simulated distorted image. The simulated image was iteratively compared to a measured image to recover the true needle position and orientation. Simulations validated the method compared to a PCA approach. Results: The proposed approach achieved up to 50% lower position and angular errors than the PCA approach. Impact: Accurate needle localization is key to the safety and efficacy of interventional MR procedures. The proposed differentiable image-based method recovers needle position and pose accurately, regardless of needle-induced image distortions.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Radiomics and Machine Learning in Medical Imaging
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