Demystifying T1-MRI to FDG 18 -PET Image Translation via Representational Similarity

Chia Hsiang Kao*, Yong Sheng Chen, Li Fen Chen, Wei Chen Chiu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Recent development of image-to-image translation techniques has enabled the generation of rare medical images (e.g., PET) from common ones (e.g., MRI). Beyond the potential benefits of the reduction in scanning time, acquisition cost, and radiation exposure risks, the translation models in themselves are inscrutable black boxes. In this work, we propose two approaches to demystify the image translation process, where we particularly focus on the T1-MRI to PET translation. First, we adopt the representational similarity analysis and discover that the process of T1-MR to PET image translation includes the stages of brain tissue segmentation and brain region recognition, which unravels the relationship between the structural and functional neuroimaging data. Second, based on our findings, an Explainable and Simplified Image Translation (ESIT) model is proposed to demonstrate the capability of deep learning models for extracting gray matter volume information and identifying brain regions related to normal aging and Alzheimer’s disease, which untangles the biological plausibility hidden in deep learning models.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Pages402-412
Number of pages11
ISBN (Print)9783030871987
DOIs
StatePublished - Sep 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12903 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • Explainability
  • Medical image translation

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