<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Tsai CH</submitter><funding>National Health Research Institutes</funding><funding>National Science and Technology Council</funding><funding>Ministry of Education, Taiwan R.O.C.</funding><pagination>e70168</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12660554</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>52(12)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Amyloid PET imaging has been extensively employed in the noninvasive assessment of amyloid-beta accumulation in Alzheimer's disease. Various amyloid radiotracers are commonly used in clinical settings; however, the limited interchangeability among these radiotracers hinders the feasibility of long-term clinical trials and multicenter comparisons. The Centiloid method was proposed for standardization, though providing a single score per image; voxel-wise translation remains a formidable task.&lt;h4>Purpose&lt;/h4>This paper proposes a U-Net model based on a deformable convolution network (DCNv3-based U-Net) for [ 11C$^{11}\textrm {C}$ ]-Pittsburgh compound B-to-[ 18F$^{18}\textrm {F}$ ]-florbetaben image translation to augment existing datasets for large-scale model training an</pubmed_abstract><journal>Medical physics</journal><pubmed_title>Paired PET-MRI Deep Learning Model for Translating [&amp;lt;sup&amp;gt;11&amp;lt;/sup&amp;gt;C]PiB to [&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt;F]Florbetaben Amyloid Images.</pubmed_title><pmcid>PMC12660554</pmcid><funding_grant_id>114-2628-E-002-019-MY3</funding_grant_id><funding_grant_id>NHRI-EX114- 11205EC</funding_grant_id><funding_grant_id>NTU-114L7872</funding_grant_id><funding_grant_id>NTU-114L900703</funding_grant_id><funding_grant_id>NTU-114V1015-5</funding_grant_id><funding_grant_id>NTU-113V1015-4</funding_grant_id><pubmed_authors>Chen KT</pubmed_authors><pubmed_authors>Tsai CH</pubmed_authors><pubmed_authors>Huang SY</pubmed_authors><pubmed_authors>Hsiao IT</pubmed_authors><pubmed_authors>Wang HW</pubmed_authors><pubmed_authors>Lin YN</pubmed_authors></additional><is_claimable>false</is_claimable><name>Paired PET-MRI Deep Learning Model for Translating [&amp;lt;sup&amp;gt;11&amp;lt;/sup&amp;gt;C]PiB to [&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt;F]Florbetaben Amyloid Images.</name><description>&lt;h4>Background&lt;/h4>Amyloid PET imaging has been extensively employed in the noninvasive assessment of amyloid-beta accumulation in Alzheimer's disease. Various amyloid radiotracers are commonly used in clinical settings; however, the limited interchangeability among these radiotracers hinders the feasibility of long-term clinical trials and multicenter comparisons. The Centiloid method was proposed for standardization, though providing a single score per image; voxel-wise translation remains a formidable task.&lt;h4>Purpose&lt;/h4>This paper proposes a U-Net model based on a deformable convolution network (DCNv3-based U-Net) for [ 11C$^{11}\textrm {C}$ ]-Pittsburgh compound B-to-[ 18F$^{18}\textrm {F}$ ]-florbetaben image translation to augment existing datasets for large-scale model training an</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Dec</publication><modification>2026-06-06T07:17:17.598Z</modification><creation>2026-06-06T03:07:09.006Z</creation></dates><accession>S-EPMC12660554</accession><cross_references><pubmed>41310910</pubmed><doi>10.1002/mp.70168</doi></cross_references></HashMap>