{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T19:04:18Z","timestamp":1776366258784,"version":"3.51.2"},"reference-count":79,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging (NIA)","doi-asserted-by":"publisher","award":["RF1AG073424"],"award-info":[{"award-number":["RF1AG073424"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging (NIA)","doi-asserted-by":"publisher","award":["P30AG072980"],"award-info":[{"award-number":["P30AG072980"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging (NIA)","doi-asserted-by":"publisher","award":["CTR057001"],"award-info":[{"award-number":["CTR057001"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Arizona Department of Health Services (ADHS) and the state of Arizona","award":["RF1AG073424"],"award-info":[{"award-number":["RF1AG073424"]}]},{"name":"Arizona Department of Health Services (ADHS) and the state of Arizona","award":["P30AG072980"],"award-info":[{"award-number":["P30AG072980"]}]},{"name":"Arizona Department of Health Services (ADHS) and the state of Arizona","award":["CTR057001"],"award-info":[{"award-number":["CTR057001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer\u2019s disease (AD), allowing non-invasive detection of amyloid-\u03b2 plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorporates a weighted combination of L1, L2, and MS-SSIM losses at both noise and image scales to enhance MRI-guided reconstruction. We evaluated the model\u2019s performance in improving statistical power for detecting longitudinal changes and enhancing agreement between amyloid PET measurements from different tracers. The results demonstrate that the LDM-RR approach significantly improves PET quantification accuracy, reduces inter-tracer variability, and enhances the detection of subtle changes in amyloid deposition over time. We show that deep learning has the potential to improve PET quantification in AD, effectively contributing to the early detection and monitoring of disease progression.<\/jats:p>","DOI":"10.3390\/life14121580","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T06:58:58Z","timestamp":1733122738000},"page":"1580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3617-2395","authenticated-orcid":false,"given":"Jay","family":"Shah","sequence":"first","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA"},{"name":"ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Che","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA"},{"name":"ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javad","family":"Sohankar","sequence":"additional","affiliation":[{"name":"Banner Alzheimer\u2019s Institute, Banner Health, Phoenix, AZ 85006, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Luo","sequence":"additional","affiliation":[{"name":"Banner Alzheimer\u2019s Institute, Banner Health, Phoenix, AZ 85006, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA"},{"name":"ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-8063","authenticated-orcid":false,"given":"Yi","family":"Su","sequence":"additional","affiliation":[{"name":"Banner Alzheimer\u2019s Institute, Banner Health, Phoenix, AZ 85006, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teresa","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA"},{"name":"ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13S","DOI":"10.2967\/jnumed.121.263195","article-title":"The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review","volume":"63","author":"Chapleau","year":"2022","journal-title":"J. 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