Abstract #2434

A novel feature based image reconstruction for neuro-interventional MRI

Kang Yan¹, Blanca Zufiria^1,2, Alexa Singer^1,2, 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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