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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