Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID)

Abstract

Multi-energy CT acquires simultaneous multiple x-ray attenuation measurements from different energy spectra which facilitates the computation of virtual monoenergetic images (VMI) at a specific photon energy (keV). Since the contrast between iodine attenuation and the attenuation of surrounding soft tissues increases at lower x-ray energies, VMIs in the range of 40–70 keV can be used to improve iodine visualization. However, at lower energy levels, image noise in VMIs is substantially increased, which counteracts the benefits from the increased iodine contrast, resulting in a decreased iodine contrast-to-noise ratio (CNR). There exists considerable data redundancy between multi-energy CT images created from the same acquisition. Similarly, a substantial spatio-spectral data redundancy exists between multi-energy CT images and the corresponding VMIs. In this work, we develop a denoising framework that exploits this data redundancy to improve iodine CNR in the VMIs. We accomplish this by applying prior-knowledge-aware iterative denoising to low-energy VMIs; we refer to the denoised images as mono-PKAID images. The proposed framework was evaluated using phantom and in vivo data acquired on a research whole-body photon-counting-detector CT, as well as using data from a commercial dual-source dual-energy CT system. The results of phantom experiments show that the proposed framework can preserve image resolution and noise texture compared to the original VMIs, while reducing noise to improve iodine CNR. Quantitative measurements show that the iodine CNR of 50 keV VMI is improved by 1.8-fold using the proposed method, relative to the VMI produced using commercial software (Mono+). With mono-PKAID, VMIs at lower keV take full advantage of higher iodine contrast without substantially increasing image noise. These observations were confirmed using patient data sets, which demonstrated that mono-PKAID reduced image noise, improved CNR in anatomical regions with iodine perfusion by 1.8-fold, and potentially enhanced the visibility of focal liver lesions.

Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance

“…The increased noise magnitude of low-keV VMI-energies could be reduced in the future by applying advanced denoising techniques, e.g., prior-knowledge-aware iterative denoising (mono-PKAID) (23). PKAID demonstrated decreased image noise compared to the second-generation VMI algorithm used in this study which consecutively improved iodine CNR (23).…”

“…To compensate for these low signal conditions, the scanner and reconstruction algorithm applied low-signal corrections, which led to a stronger smoothing during the reconstruction process (32). The increased noise magnitude of low-keV VMI-energies could be reduced in the future by applying advanced denoising techniques, e.g., prior-knowledge-aware iterative denoising (mono-PKAID) (23). PKAID demonstrated decreased image noise compared to the second-generation VMI algorithm used in this study which consecutively improved iodine CNR (23).…”

“…Because VMI at certain keVs have been described as being subjectively blotchy or patchy (23), potential differences in noise texture may influence detectability and hamper their widespread implementation into routine clinical practice. Recently, task-based image quality parameters have been investigated for VMI at 40-140 keV among four different DECT approaches (24) as well as for different reconstruction algorithms on a dual-source dual-energy CT (25).…”

Photon-counting x-ray detectors for CT

“…By varying the display energy, the CNR can be optimized for a given imaging task. More advanced ways of forming virtual monoenergetic images include prior-knowledge-aware iterative denoising (Tao et al 2019b) and neural-network-based methods (Feng et al 2019). Approximate material maps and virtual monoenergetic images can also be generated through image-space material decomposition from reconstructed bin images (Zhou et al 2019), although this approach gives less accurate results since it does not eliminate beam hardening.…”

Photon Counting CT: Clinical Applications and Future Developments

“…Optimization-based approaches have been available for the past decade [96] and, while sometimes computationally intensive, can still be applied to PCD CT. Specific variations of this optimization can be posed with the particular goal of improving spectral contrast or reducing noise in spectral images [64], [97]. The correlations that result from basis material decomposition can be exploited [98].…”