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Abstract #0357

Creating Standardized MR Images with Deep Learning to Improve Cross-Vendor Comparability

Enhao Gong1, John Pauly1, and Greg Zaharchuk2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

A very common task for radiologists is to compare sequential imaging studies that were acquired on different MR hardware systems, which can be difficult and inaccurate because of different designs across scanners and vendors. Cross-vendor standardization and transformation is valuable for more quantitative analysis in clinical exams and trials. With in-vivo multi-vendor datasets, we show that it is possible to achieve accurate cross-vendor transformation using the state-of-art Deep Learning Style-transfer algorithm. The method preserves anatomical information while transferring the vendor specific contrast "style". The usage of unsupervised training enable the method to further train and apply on all existing large scale MRI datasets. This technique can lead to a universal MRI style which benefits patients by improving inter-subject reproducibility, enabling quantifiable comparison and pushing MRI to be more quantitative and standardized.

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