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Abstract #2434

A novel feature based image reconstruction for neuro-interventional MRI

Kang Yan1, Blanca Zufiria1,2, Alexa Singer1,2, Xudong Chen1, Zhiyu Yang1, Shuo Li1, Suhao Qiu1, Huajun She1, Bomin Sun3, Yiping Du1, Zhipei Liang4, and Yuan Feng1

1Biomedeical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2KTH Royal Institute of Technology, Stockholm, Sweden, 3Functional Neurosurgery, Shanghai Jiao Tong University, Shanghai, China, 4Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Interventional MRI (I-MRI) provides exceptional advantages to other imaging modalities in image-guided neurosurgery. However, real-time imaging presents great challenges for temporal/spatial resolution, image contrast, and SNR. We presented a novel feature based image reconstruction algorithm using golden-angle sampling and compressed sensing. Images were decomposed into the reference part and the novel feature reflecting the interventional process. Experiments of using porcine brain for biopsy showed the proposed method had better performance in terms of SNR and computational time. It demonstrated that the proposed method have potentials in applications of MR-guided intervention such as image-guided epilepsy treatment.

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