Causal PETS: Causality-Informed PET Synthesis from Multi-modal Data

Yujia Li, Han Li, S Kevin Zhou
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:978-993, 2026.

Abstract

The synthesis of medical images is particularly important when certain modality data are difffcult to obtain, for example, Positron emission tomography (PET). PET is crucial for diagnosing and monitoring neurological disorders. However, the availability is limited due to factors such as high costs, radiation exposure risks, and other constraints. In this study, we propose Causal PETS, a novel causality-informed synthesis model for synthesizing PET images from multi-modal data including MRI, demographic information, and cerebrospinal fluid (CSF) biomarkers. Unlike conventional approaches that involve a straightforward conversion from T1 to PET, our model analyzes the causality between different modality data and seamlessly integrates such causality into PET image generation. Through comprehensive evaluations, we demonstrate that our Causal PETS model outperforms existing non-causal methods in terms of image clarity and accuracy, particularly in identifying regions of interest critical for neurological disorders such as Alzheimer’s Disease (AD). This work underscores the importance of causal reasoning in medical image synthesis and highlights the potential of multimodal integration to advance clinical decision making.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-li26a, title = {Causal PETS: Causality-Informed PET Synthesis from Multi-modal Data}, author = {Li, Yujia and Li, Han and Zhou, S Kevin}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {978--993}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/li26a/li26a.pdf}, url = {https://proceedings.mlr.press/v301/li26a.html}, abstract = {The synthesis of medical images is particularly important when certain modality data are difffcult to obtain, for example, Positron emission tomography (PET). PET is crucial for diagnosing and monitoring neurological disorders. However, the availability is limited due to factors such as high costs, radiation exposure risks, and other constraints. In this study, we propose Causal PETS, a novel causality-informed synthesis model for synthesizing PET images from multi-modal data including MRI, demographic information, and cerebrospinal fluid (CSF) biomarkers. Unlike conventional approaches that involve a straightforward conversion from T1 to PET, our model analyzes the causality between different modality data and seamlessly integrates such causality into PET image generation. Through comprehensive evaluations, we demonstrate that our Causal PETS model outperforms existing non-causal methods in terms of image clarity and accuracy, particularly in identifying regions of interest critical for neurological disorders such as Alzheimer’s Disease (AD). This work underscores the importance of causal reasoning in medical image synthesis and highlights the potential of multimodal integration to advance clinical decision making.} }
Endnote
%0 Conference Paper %T Causal PETS: Causality-Informed PET Synthesis from Multi-modal Data %A Yujia Li %A Han Li %A S Kevin Zhou %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-li26a %I PMLR %P 978--993 %U https://proceedings.mlr.press/v301/li26a.html %V 301 %X The synthesis of medical images is particularly important when certain modality data are difffcult to obtain, for example, Positron emission tomography (PET). PET is crucial for diagnosing and monitoring neurological disorders. However, the availability is limited due to factors such as high costs, radiation exposure risks, and other constraints. In this study, we propose Causal PETS, a novel causality-informed synthesis model for synthesizing PET images from multi-modal data including MRI, demographic information, and cerebrospinal fluid (CSF) biomarkers. Unlike conventional approaches that involve a straightforward conversion from T1 to PET, our model analyzes the causality between different modality data and seamlessly integrates such causality into PET image generation. Through comprehensive evaluations, we demonstrate that our Causal PETS model outperforms existing non-causal methods in terms of image clarity and accuracy, particularly in identifying regions of interest critical for neurological disorders such as Alzheimer’s Disease (AD). This work underscores the importance of causal reasoning in medical image synthesis and highlights the potential of multimodal integration to advance clinical decision making.
APA
Li, Y., Li, H. & Zhou, S.K.. (2026). Causal PETS: Causality-Informed PET Synthesis from Multi-modal Data. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:978-993 Available from https://proceedings.mlr.press/v301/li26a.html.

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