Abstract #0011

CloudBrain-ReconAI: A Cloud Computing Platform for Online MRI Reconstruction and Radiologists' Image Quality Evaluation

Yirong Zhou¹, Mingkai Huang¹, Jianshu Chen¹, Jingkai Zhou¹, Taishan Kang², Jianzhong Lin², Ling Qian³, Shaoxing Liu³, Yuan Long³, Qing Hong⁴, Liuhong Zhu⁵, Jianjun Zhou⁵, Di Guo⁶, and Xiaobo Qu¹

¹Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Institute of Artificial Intelligence, Xiamen University, Xiamen, China, ²Department of Radiology, Zhongshan Hospital affiliated to Xiamen University, Xiamen, China, ³China Mobile (Suzhou) Software Technology Company Limited, Suzhou, China, ⁴China Mobile Group, Xiamen, China, ⁵Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China, ⁶School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Synopsis

Keywords: AI/ML Software, Software Tools, Cloud Computing Platform

Motivation: MRI suffers from long data acquisition times. MRI reconstruction requires radiologist evaluation. The isolation of image data across different healthcare institutions impedes collaboration. Moreover, there is a lack of integrated platforms for image processing and analysis that can directly interface with MRI acquisition devices.

Goal(s): To address the existing challenges in medical image processing.

Approach: Integrating cloud computing platform for reconstruction and evaluation, which is starting from the k-space raw data for MRI acquisition devices.

Results: CloudBrain-ReconAI offers a seamless service from the k-space raw data in MRI devices to reconstruction and evaluation.

Impact: The integration of direct k-space rawdata acquisition from MRI devices into CloudBrain-ReconAI enhances the efficiency and timeliness of MRI data processing, facilitating faster clinical decisions and research advancements.

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