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Abstract #3230

AV1451 Tau PET Synthesis from T1-weighted MR Images with U-NET

Fernando Vega Lara1,2,3,4 and M.E MacDonald2,3,4
1Radiology, University Medical Center Groningen, Groningen, Netherlands, 2Biomedical Engineering, University of Calgary, Calgary, AB, Canada, 3Electrical & Software Engineering, University of Calgary, Calgary, AB, Canada, 4Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

Synopsis

Keywords: AI/ML Software, Alzheimer's Disease, Tau, MRI, PET, Image-Translation

Motivation: Alzheimer’s Disease hallmarks include tau deposits and brain atrophy, detectable via PET and MRI, respectively. PET is expensive, invasive, exposes patients to ionizing radiation, whereas MRI is a cheaper, non-invasive, free from ionizing radiation but limited to measuring structural changes.

Goal(s): Develop a three-dimensional image-translation model synthesizing tau PET images from T1-weighted MRI, capitalizing on the relationship between tau and brain atrophy.

Approach: The model was trained with 297 PET/MRI pairs and validated with 128 pairs

Results: The model synthesized tau-PET images from T1-weighter MRI with high-degree of similarity reflected in high mean SSIM and PSNR (SSIM=0.96&PSNR=29.76), and low MAE in SUVR synthesis.

Impact: Our model proves the feasibility of synthesizing AV-1451 PET images from T1-weighted MRI, an approach that could enhance the accessibility for large cohort-studies and early dementia detection, while also reducing costs, invasiveness, and enhancing patient safety by limiting radiation exposure.

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Keywords