eprintid: 10198880 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/19/88/80 datestamp: 2024-10-25 14:02:41 lastmod: 2024-10-25 14:02:41 status_changed: 2024-10-25 14:02:41 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Puglisi, L creators_name: Alexander, DC creators_name: Ravì, D title: Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge ispublished: pub divisions: UCL divisions: B04 divisions: F48 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. abstract: In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer’s Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://github.com/LemuelPuglisi/BrLP. date: 2024 date_type: published publisher: Springer Nature official_url: http://dx.doi.org/10.1007/978-3-031-72069-7_17 full_text_type: other language: eng verified: verified_manual elements_id: 2329569 doi: 10.1007/978-3-031-72069-7_17 isbn_13: 978-3-031-72068-0 lyricists_name: Alexander, Daniel lyricists_id: DALEX06 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: restricted pres_type: paper series: Lecture Notes in Computer Science publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) volume: 15002 pagerange: 173-183 event_title: 27th International Conference - MICCAI 2024 book_title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 citation: Puglisi, L; Alexander, DC; Ravì, D; (2024) Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. (pp. pp. 173-183). Springer Nature document_url: https://discovery.ucl.ac.uk/id/eprint/10198880/1/0511_paper.pdf