AI-Assisted Edema Map Optimization Improves Infarction Detection in Twin-Spiral Dual-Energy CT

Singer L, Heinze D, Möhle T, Sekita A, Mennecke A, Lang S, Gerner S, Schwab S, Dörfler A, Schmidt M (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Brain Sciences MDPI

Book Volume: 15

Article Number: 821

Journal Issue: 8

DOI: 10.3390/brainsci15080821

Abstract

Objective: This study aimed to evaluate whether modifying the post-processing algorithm of Twin-Spiral Dual-Energy computed tomography (DECT) improves infarct detection compared to conventional Dual-Energy CT (DECT) and Single-Energy CT (SECT) following endovascular therapy (EVT) for large vessel occlusion (LVO). Methods: We retrospectively analyzed 52 patients who underwent Twin-Spiral DECT after endovascular stroke therapy. Ten patients were used to generate a device-specific parameter (“y”) using an AI-based neural network (SynthSR). This parameter was integrated into the post-processing algorithm for edema map generation. Quantitative Hounsfield unit (HU) measurements were used to assess density differences in ischemic brain tissue across conventional virtual non-contrast (VNC) images and edema maps. Results: The median HU of infarcted tissue in conventional mixed DECT was 33.73 ± 4.58, compared to 22.96 ± 3.81 in default VNC images. Edema maps with different smoothing filter settings showed values of 14.39 ± 4.96, 14.50 ± 3.75, and 15.05 ± 2.65, respectively. All edema maps demonstrated statistically significant HU differences of infarcted tissue compared to conventional VNC images ((Formula presented.)) while maintaining the density values of non-infarcted brain tissue. Conclusions: Enhancing the post-processing algorithm of conventional virtual non-contrast imaging improves infarct detection compared to standard mixed or virtual non-contrast reconstructions in Dual-Energy CT.

Authors with CRIS profile

Ludwig Singer Neuroradiologisches Institut Daniel Heinze Department of Neuroradiology Tim Möhle Medizinische Fakultät Alexander Sekita Department of Neurology Angelika Mennecke Department of Neuroradiology Stefan Lang Neuroradiologisches Institut Stefan Gerner Department of Neurology Stefan Schwab Lehrstuhl für Neurologie Arnd Dörfler Department of Neuroradiology Manuel Schmidt Department of Neuroradiology

How to cite

APA:

Singer, L., Heinze, D., Möhle, T., Sekita, A., Mennecke, A., Lang, S.,... Schmidt, M. (2025). AI-Assisted Edema Map Optimization Improves Infarction Detection in Twin-Spiral Dual-Energy CT. Brain Sciences, 15(8). https://doi.org/10.3390/brainsci15080821

MLA:

Singer, Ludwig, et al. "AI-Assisted Edema Map Optimization Improves Infarction Detection in Twin-Spiral Dual-Energy CT." Brain Sciences 15.8 (2025).

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