Image Translation for Estimating Two‐Dimensional Axial Amyloid‐Beta PET From Structural MRI

Author:

Vega Fernando1234,Addeh Abdoljalil1234,Ganesh Aravind345,Smith Eric E.345,MacDonald M. Ethan1234ORCID

Affiliation:

1. Department of Biomedical University of Calgary Calgary Alberta Canada

2. Department of Electrical and Software Engineering University of Calgary Calgary Alberta Canada

3. Department of Radiology University of Calgary Calgary Alberta Canada

4. Hotchkiss Brain Institute University of Calgary Calgary Alberta Canada

5. Department of Clinical Neuroscience University of Calgary Calgary Alberta Canada

Abstract

BackgroundAmyloid‐beta and brain atrophy are hallmarks for Alzheimer's Disease that can be targeted with positron emission tomography (PET) and MRI, respectively. MRI is cheaper, less‐invasive, and more available than PET. There is a known relationship between amyloid‐beta and brain atrophy, meaning PET images could be inferred from MRI.PurposeTo build an image translation model using a Conditional Generative Adversarial Network able to synthesize Amyloid‐beta PET images from structural MRI.Study TypeRetrospective.PopulationEight hundred eighty‐two adults (348 males/534 females) with different stages of cognitive decline (control, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment). Five hundred fifty‐two subjects for model training and 331 for testing (80%:20%).Field Strength/Sequence3 T, T1‐weighted structural (T1w).AssessmentThe testing cohort was used to evaluate the performance of the model using the Structural Similarity Index Measure (SSIM) and Peak Signal‐to‐Noise Ratio (PSNR), comparing the likeness of the overall synthetic PET images created from structural MRI with the overall true PET images. SSIM was computed in the overall image to include the luminance, contrast, and structural similarity components. Experienced observers reviewed the images for quality, performance and tried to determine if they could tell the difference between real and synthetic images.Statistical TestsPixel wise Pearson correlation was significant, and had an R2 greater than 0.96 in example images. From blinded readings, a Pearson Chi‐squared test showed that there was no significant difference between the real and synthetic images by the observers (P = 0.68).ResultsA high degree of likeness across the evaluation set, which had a mean SSIM = 0.905 and PSNR = 2.685. The two observers were not able to determine the difference between the real and synthetic images, with accuracies of 54% and 46%, respectively.ConclusionAmyloid‐beta PET images can be synthesized from structural MRI with a high degree of similarity to the real PET images.Evidence Level3Technical EfficacyStage 1

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fashioning the Future: Could AI Enhanced MRI Put PET Out of Style?;Journal of Magnetic Resonance Imaging;2023-11-03

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