Abstract #4783

Clinical feasibility of artificial intelligence-assisted compressed sensing for accelerated MR imaging in nasopharyngeal carcinoma

Qin Zhao¹, Song Zhang¹, Liyun Zheng², and Yongming Dai³

¹Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China, ²Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China, ³MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: Data Acquisition, Machine Learning/Artificial Intelligence

To improve the scanning efficiency of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma, this study investigated the value of accelerating technique, artificial intelligence-assisted compressed sensing (ACS), in comparison to conventional sequences without accelerating technique and accelerating MRI using parallel imaging (PI). Eleven patients diagnosed with nasopharyngeal carcinoma were prospectively enrolled. As a result, ACS achieved the shortest acquisition time, with similar or even better image quality and SNR than conventional sequences. ACS has the potential to provide sufficient image quality for T1- and T2-weighted imaging in nasopharyngeal carcinoma and could be an alternative to conventional sequences in clinical practice.

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