Neural fields for tissue attenuation curve reconstruction in sparsely sampled time-resolved CT

Lucas de Vries, Rudolf Leonardus Mirjam van Herten, P. Matthijs van der Sluijs, Ivana Isgum, Bart J. Emmer, Charles B.L.M. Majoie, Henk Marquering, Efstratios Gavves
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:1669-1687, 2026.

Abstract

Time-resolved CT imaging can aid acute ischemic stroke diagnosis by visualizing contrast agent transport through the brain (micro)vasculature. CT perfusion imaging, while widely used for stroke diagnosis, requires approximately 30 sequential scans, leading to extensive radiation exposure and motion sensitivity. As an alternative to CTP perfusion imaging, some hospitals opt for multiphase CT angiography for time-resolved analysis with reduced radiation dose. However, multiphase CT angiography lacks standardized perfusion analysis capabilities, making it more challenging to interpret than CT perfusion imaging. We present Sparse Temporal Attenuation Reconstruction (STAR), a novel approach using conditional neural fields that reconstructs tissue attenuation curves from sparse observations, allowing for reduced radiation exposure and motion sensitivity with CT perfusion, while enabling perfusion analysis from multiphase CT angiography. Our method generates full tissue attenuation curves using only 4 out of 30 observations. The results show that perfusion maps from reconstructed data match the reference perfusion maps, potentially reducing radiation and allowing recovery of motion-corrupted images. Moreover, STAR enables perfusion analysis in centers using multiphase CT angiography. Consequently, STAR has the potential to improve the stroke imaging work-up while making perfusion analysis more widely accessible.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-vries26a, title = {Neural fields for tissue attenuation curve reconstruction in sparsely sampled time-resolved CT}, author = {de Vries, Lucas and van Herten, Rudolf Leonardus Mirjam and van der Sluijs, P. Matthijs and Isgum, Ivana and Emmer, Bart J. and Majoie, Charles B.L.M. and Marquering, Henk and Gavves, Efstratios}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {1669--1687}, 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/vries26a/vries26a.pdf}, url = {https://proceedings.mlr.press/v301/vries26a.html}, abstract = {Time-resolved CT imaging can aid acute ischemic stroke diagnosis by visualizing contrast agent transport through the brain (micro)vasculature. CT perfusion imaging, while widely used for stroke diagnosis, requires approximately 30 sequential scans, leading to extensive radiation exposure and motion sensitivity. As an alternative to CTP perfusion imaging, some hospitals opt for multiphase CT angiography for time-resolved analysis with reduced radiation dose. However, multiphase CT angiography lacks standardized perfusion analysis capabilities, making it more challenging to interpret than CT perfusion imaging. We present Sparse Temporal Attenuation Reconstruction (STAR), a novel approach using conditional neural fields that reconstructs tissue attenuation curves from sparse observations, allowing for reduced radiation exposure and motion sensitivity with CT perfusion, while enabling perfusion analysis from multiphase CT angiography. Our method generates full tissue attenuation curves using only 4 out of 30 observations. The results show that perfusion maps from reconstructed data match the reference perfusion maps, potentially reducing radiation and allowing recovery of motion-corrupted images. Moreover, STAR enables perfusion analysis in centers using multiphase CT angiography. Consequently, STAR has the potential to improve the stroke imaging work-up while making perfusion analysis more widely accessible.} }
Endnote
%0 Conference Paper %T Neural fields for tissue attenuation curve reconstruction in sparsely sampled time-resolved CT %A Lucas de Vries %A Rudolf Leonardus Mirjam van Herten %A P. Matthijs van der Sluijs %A Ivana Isgum %A Bart J. Emmer %A Charles B.L.M. Majoie %A Henk Marquering %A Efstratios Gavves %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-vries26a %I PMLR %P 1669--1687 %U https://proceedings.mlr.press/v301/vries26a.html %V 301 %X Time-resolved CT imaging can aid acute ischemic stroke diagnosis by visualizing contrast agent transport through the brain (micro)vasculature. CT perfusion imaging, while widely used for stroke diagnosis, requires approximately 30 sequential scans, leading to extensive radiation exposure and motion sensitivity. As an alternative to CTP perfusion imaging, some hospitals opt for multiphase CT angiography for time-resolved analysis with reduced radiation dose. However, multiphase CT angiography lacks standardized perfusion analysis capabilities, making it more challenging to interpret than CT perfusion imaging. We present Sparse Temporal Attenuation Reconstruction (STAR), a novel approach using conditional neural fields that reconstructs tissue attenuation curves from sparse observations, allowing for reduced radiation exposure and motion sensitivity with CT perfusion, while enabling perfusion analysis from multiphase CT angiography. Our method generates full tissue attenuation curves using only 4 out of 30 observations. The results show that perfusion maps from reconstructed data match the reference perfusion maps, potentially reducing radiation and allowing recovery of motion-corrupted images. Moreover, STAR enables perfusion analysis in centers using multiphase CT angiography. Consequently, STAR has the potential to improve the stroke imaging work-up while making perfusion analysis more widely accessible.
APA
de Vries, L., van Herten, R.L.M., van der Sluijs, P.M., Isgum, I., Emmer, B.J., Majoie, C.B., Marquering, H. & Gavves, E.. (2026). Neural fields for tissue attenuation curve reconstruction in sparsely sampled time-resolved CT. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:1669-1687 Available from https://proceedings.mlr.press/v301/vries26a.html.

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