Medical physics · 2025
Spectral virtual non-contrast imaging assisted by artificial intelligence segmentation.
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Soltani Mohsen Beikali, Bouchard Hugo
Аннотация
PURPOSE: The purpose of this study is to adapt a Bayesian dual-virtual non-contrast (VNC) method by integrating prior anatomical knowledge from AI-based multi-organ segmentation and to generalize it for spectral photon-counting CT (PCCT) with an arbitrary number of energy channels.
METHODS: A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open-access whole-body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast-enhanced dual-energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy-relevant parameters such as electron density ( ρ e $\rho _e$ ) and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non-contrast scans by setting contrast agent uptake to zero.
RESULTS: AI-based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and ρ e $\rho _e$ . Mean absolute water-equivalent path length (WEPL) errors confirmed the superiority of segmentation-assisted PCCT over other methods.
CONCLUSION: This proof of concept demonstrates a flexible, AI-assisted Bayesian framework for extracting quantitative information from contrast-enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets is needed.
BACKGROUND: Quantitative VNC methods offer the potential to extract radiotherapy-related parameters from contrast-enhanced spectral CT without the need for additional non-contrast imaging. However, the inherently ill-posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advanced techniques.
METHODS: A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open-access whole-body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast-enhanced dual-energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy-relevant parameters such as electron density ( ρ e $\rho _e$ ) and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non-contrast scans by setting contrast agent uptake to zero.
RESULTS: AI-based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and ρ e $\rho _e$ . Mean absolute water-equivalent path length (WEPL) errors confirmed the superiority of segmentation-assisted PCCT over other methods.
CONCLUSION: This proof of concept demonstrates a flexible, AI-assisted Bayesian framework for extracting quantitative information from contrast-enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets is needed.
BACKGROUND: Quantitative VNC methods offer the potential to extract radiotherapy-related parameters from contrast-enhanced spectral CT without the need for additional non-contrast imaging. However, the inherently ill-posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advanced techniques.