Abstract #4203

Assessment of Multi-modal MR Imaging for Glioma Based on a Deep Learning Reconstruction Approach with the Denoising Method

Jun Sun¹, Yiding Guo¹, Zhizheng Zhuo¹, Siyao Xu¹, Min Guo¹, Li Chai¹, Junjie Li¹, Liying Qu¹, Minghao Wu¹, Juan Wei², Mingna Li³, Tong Li³, Jinyuan Weng⁴, Xiaodong Gong⁵, Yunyun Duan¹, Dabiao Zhou¹, and Yaou Liu¹

¹Capital Medical Universtiy, Beijing Tiantan Hospital, Beijing, China, ²MR Research, GE Healthcare, Beijing, China, ³BioMind Inc., Beijing, China, ⁴Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, China, ⁵Department of Medical Imaging Product, Neusoft, Group Ltd., Beijing, China

Synopsis

Keywords: Tumors, BrainDeep learning reconstruction (DLR) approach with denoising can improve image quality of magnetic resonance (MR) images. However, its applications on multi-modal glioma imaging have not been assessed.Multi-modal images of 107 glioma patients were evaluated by signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, visual assessment, diagnosis accuracy and efficiency. Contrasted with conventionally reconstructed images, the DLR images showed higher tumor/residual tumor SNR, higher tumor to white/gray matter CNR, better results of the visual assessment, and a trend of improved diagnosis efficiency and comparable accuracy. DLR can improve image quality of multi-modal glioma images which should benefit the glioma diagnosis.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.