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Abstract #2195

Ablation Studies in 3D Encoder-Decoder Networks for Brain MRI-to-PET Cerebral Blood Flow Transformation

Ramy Hussein1, Moss Zhao1, Jia Guo2, Kevin Chen1, David Shin3, Michael Moseley1, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2Bioengineering, University of California, Riverside, Riverside, CA, United States, 3Neuro MR, GE Healthcare, Menlo Park, CA, United States

In this study, we demonstrate that an optimized 3D encoder-decoder structured convolutional neural network with attention gates can effectively integrate brain structural MRI and ASL perfusion images to produce high-quality synthetic PET CBF maps without using radiotracers. We performed experiments to evaluate different loss functions and the role of the attention mechanism. Our results showed that attention-based 3D encoder-decoder network with custom loss function produces the superior PET CBF prediction results, achieving SSIM of 0.94, MSE of 0.00025, and PSNR of 38dB.

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