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Abstract #4851

Unsupervised anomaly detection based on diffusion models for needle detection in MR-guided interventions

Lei Guo1, Tao Guo1, Lin Chen2, and Bensheng Qiu1
1Medical Imaging Center,Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, China, 2Anhui Fuging Medical Equipment Co., Ltd., Hefei, Anhui, China

Synopsis

Keywords: MR-Guided Interventions, Segmentation

Motivation: Rapid and robust needle detection are crucial for real-time interventional MRI, but it is easily disturbed by low image signal-to-noise ratio (SNR) and diverse needle void features

Goal(s): To develop a rapid needle detection approach in MR-guided interventions, which is noise-robust and insensitive to the diverse needle void features

Approach: We present an unsupervised anomaly detection scheme based on diffusion model. Moreover, we dynamically select informative features from a pre-trained VAE to enhance representation.

Results: Our method was evaluated on a simulated human brain intervention dataset, and the results showed effective detection and recognition of different noise levels and various needle-like features.

Impact: The proposed method is noise-robust and insensitive to shape characteristics, has the potential to be applied in real-time interventional magnetic resonance imaging (i-MRI)

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