Abstract #4611

Estimate the Linear Effect of Inter-Scanner Variability: Insight from Paired Cross-Scanner T1-weighted Images

Chang-Le Chen¹, Mahbaneh Eshaghzadeh Torbati², Weiquan Luo³, Davneet Minhas³, Charles Laymon³, Seong Jae Hwang⁴, Ciprian Crainiceanu⁵, Pauline Maillard⁶, Evan Fletcher⁶, Charles DeCarli⁶, Howard Aizenstein^1,7, and Dana Tudorascu^7,8

¹Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States, ²Intelligent System Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States, ³Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States, ⁴Department of Artificial Intelligence, Yonsei University, Seoul, Korea, Republic of, ⁵Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States, ⁶Department of Neurology, University of California Davis, Davis, CA, United States, ⁷Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States, ⁸Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States

Synopsis

Keywords: Data Processing, Data Processing, HarmonizationTo broaden our knowledge of inter-scanner variability, we established a pipeline to linearly estimate the site effect by incorporating ComBat modeling and superpixel parcellation. We found that the variation prominently manifested in tissue contrast, noise level, and field inhomogeneity with respect to cross-site data. We further used the estimated parameters to harmonize images. The image quality and structural similarity of cross-scanner data can be improved after the harmonization procedure, and the variation in volumetric measures can also be reduced. This study provides further insight for the research focusing on the development of image harmonization methods.

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