Conditional Learned Reconstruction for Medical Imaging

Nikita Moriakov, George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:754-780, 2026.

Abstract

Medical imaging utilizes a handful of different imaging modalities such as tomography and magnetic resonance (MRI) imaging that require solving an inverse problem to reconstruct an image from the acquired measurements. Reconstruction methods based on learned iterative schemes have been widely explored recently, however, these modalities involve variability in hardware- and protocol-dependent acquisition parameters such as tube current and projection count in case of tomography and acceleration factor or field strength in case of MRI, which are typically not accounted for in the architecture. In this work we propose the framework of conditional learned iterative schemes, where the network weights are explicitly adapted as learned functions of the acquisition parameters. We compare conditional learned iterative schemes to their counterparts without conditioning for both tomography and MRI and demonstrate their effectiveness.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-moriakov26a, title = {Conditional Learned Reconstruction for Medical Imaging}, author = {Moriakov, Nikita and Yiasemis, George and Sonke, Jan-Jakob and Teuwen, Jonas}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {754--780}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/moriakov26a/moriakov26a.pdf}, url = {https://proceedings.mlr.press/v315/moriakov26a.html}, abstract = {Medical imaging utilizes a handful of different imaging modalities such as tomography and magnetic resonance (MRI) imaging that require solving an inverse problem to reconstruct an image from the acquired measurements. Reconstruction methods based on learned iterative schemes have been widely explored recently, however, these modalities involve variability in hardware- and protocol-dependent acquisition parameters such as tube current and projection count in case of tomography and acceleration factor or field strength in case of MRI, which are typically not accounted for in the architecture. In this work we propose the framework of conditional learned iterative schemes, where the network weights are explicitly adapted as learned functions of the acquisition parameters. We compare conditional learned iterative schemes to their counterparts without conditioning for both tomography and MRI and demonstrate their effectiveness.} }
Endnote
%0 Conference Paper %T Conditional Learned Reconstruction for Medical Imaging %A Nikita Moriakov %A George Yiasemis %A Jan-Jakob Sonke %A Jonas Teuwen %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-moriakov26a %I PMLR %P 754--780 %U https://proceedings.mlr.press/v315/moriakov26a.html %V 315 %X Medical imaging utilizes a handful of different imaging modalities such as tomography and magnetic resonance (MRI) imaging that require solving an inverse problem to reconstruct an image from the acquired measurements. Reconstruction methods based on learned iterative schemes have been widely explored recently, however, these modalities involve variability in hardware- and protocol-dependent acquisition parameters such as tube current and projection count in case of tomography and acceleration factor or field strength in case of MRI, which are typically not accounted for in the architecture. In this work we propose the framework of conditional learned iterative schemes, where the network weights are explicitly adapted as learned functions of the acquisition parameters. We compare conditional learned iterative schemes to their counterparts without conditioning for both tomography and MRI and demonstrate their effectiveness.
APA
Moriakov, N., Yiasemis, G., Sonke, J. & Teuwen, J.. (2026). Conditional Learned Reconstruction for Medical Imaging. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:754-780 Available from https://proceedings.mlr.press/v315/moriakov26a.html.

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