Enhancing Consistency in Neuroimaging With AI and Image Harmonization: Lianrui Zuo, MSE
The PhD student in the department of electrical and computer engineering at Johns Hopkins University discussed the use of artificial intelligence and image harmonization techniques to address the challenges caused by multisite effects in neuroimaging. [WATCH TIME: 4 minutes]
WATCH TIME: 4 minutes
“After harmonization, we observed that the images are more consistent as if they were acquired using a standardized, consistent protocol. We know that it's easier for radiologists to compare data across different centers, different time points, and also for machine learning other computer algorithms to more accurately more consistently processed data."
In the advancing field of medical research and diagnostics, neuroimaging plays an important role in understanding complex neurological disorders such as
Recently, Lianrui Zuo, MSE, a PhD student in the Department of Electrical and Computer Engineering at Johns Hopkins University, presented on impacts and solutions of inconsistent imaging data acquisition in a platform session focused on imaging topics at the
Zuo sat down in an interview with NeurologyLive® at the meeting to provide an overview of his research based on the presentation. He discussed how image harmonization using artificial intelligence (AI) can help to alleviate the challenges caused by multisite effects in neuroimaging. He also talked about the importance of prioritizing consistent data acquisition in studies involving gray matter volume and patient age relationships among patients with MS. Furthermore, Zuo spoke about the role of AI tools in standardizing image data acquired from different centers and manufacturers in the field of neuroimaging.
REFERENCES
1. Zuo L. Inconsistent MR Acquisition in Longitudinal Volumetric Analysis: Impacts and Solutions. Presented at: 2023 CMSC Annual Meeting; May 31-June 3; Aurora, CO. IMG05.
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