Room: Exhibit Hall | Forum 9
Purpose: Virtual monochromatic analysis (VMA) has been a clinical approach in energy-integration spectral CT (or dual energy CTâ€“DECT) for differentiation of soft tissues, while photon-counting spectral CT is being anticipated to offer even better VMA for clinical applications. We here investigate spectral CTâ€™s potential of VMA for soft tissue differentiation and analyze the technological challenges to be overcome in fulfilling the potentials.
Methods: Mainly because of the suffering from artifacts and noise, the lower energy end for VMA in state-of-the-art DECT is usually set at 40keV, which limits its capability of differentiating soft tissue substantially. By consulting the mass composition and attenuation coefficient of soft tissues offered in official publications, we investigate the ideal case scenario (potential) of VMA for differentiating soft tissue in spectral CT with the lower energy end being pushed to approach 15keV or even lower, in which the soft tissueâ€™s contrast and variation of CT number over energy (spectral profile) are taken as the major features. In addition, we analyze the root causes underlying the strong artifacts and noise that prevent the VMA in spectral CT from going lower than 40keV and investigate the approaches to address the challenges in suppressing noise and artifacts.
Results: The preliminary data show that, by pushing the lower spectral end of VMA in spectral CT, both the contrast and spectral profile can provide more information for soft tissue differentiation, but the key to success lies at suppression of noise and artifacts attributed to the random and systematic inaccuracy respectively in material (or spectral) decomposition in spectral CT.
Conclusion: The reported work is informative to the R&D community of spectral CT with the hope of inspiring more technological investment to address the fundamental challenges towards fulfilling spectral CTâ€™s potential of differentiating soft tissues for clinical applications in oncology, cardiology and neurology.
Funding Support, Disclosures, and Conflict of Interest: Xiangyang Tang is a recipient of research grant from Sinovision Technologies.