Dual-Energy CT in Cardiothoracic Imaging: Current Developments

Abstract

This article describes the technical principles and clinical applications of dual-energy computed tomography (DECT) in the context of cardiothoracic imaging with a focus on current developments and techniques. Since the introduction of DECT, different vendors developed distinct hard and software approaches for generating multi-energy datasets and multiple DECT applications that were developed and clinically investigated for different fields of interest. Benefits for various clinical settings, such as oncology, trauma and emergency radiology, as well as musculoskeletal and cardiovascular imaging, were recently reported in the literature. State-of-the-art applications, such as virtual monoenergetic imaging (VMI), material decomposition, perfused blood volume imaging, virtual non-contrast imaging (VNC), plaque removal, and virtual non-calcium (VNCa) imaging, can significantly improve cardiothoracic CT image workflows and have a high potential for improvement of diagnostic accuracy and patient safety.

Updates on the Applications of Spectral Computed Tomography for Musculoskeletal Imaging

“…Dual-layer CT differs from other DECT techniques, as it uses one X-ray source but has two layered detectors that collect high and low energy spectra, respectively, thus helping reduce artifact and spectral separation [3]. Each of these DECT subsets has advantages and limitations with respect to radiation dose, spectral separation, and imaging speed; thus, these modalities have no one-size-fits-all clinical application [5]. Photon-counting CT is another spectral CT modality that does not fall under the umbrella of DECT.…”

Advantages of Photon-Counting Detector CT in Aortic Imaging

“…The scarcity of suitable cases for rare diseases, the lack of specialized software, and absence of standardized processing protocols also hinder its wider clinical use. In addition, the high cost of PCCT systems, necessitating multiple generators and X-ray tubes, limits their affordability and adoption [ 1 , 23 , 45 ]. Moreover, the lack of molecular imaging agents for clinical applications does not permit an advance tissue characterization.…”

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

“…Recent developments in hardware and software technology, particularly with the introduction of artificial intelligence (AI) tools, are improving image quality of CCTA, increasing the detectable features of CAD (i.e., evaluation and quantification of coronary stenosis, plaque characterization, assessment of myocardial ischemia) and expanding the prognostic role of CCTA with machine-learning (ML) algorithms [ 9 , 10 ]. Moreover, the use of dual energy CT (DECT) and the recent introduction of photon-counting detector scanners (PCD-CT) enabled the acquisition of ultra-high resolution images, with spectral information obtained along with each CT scan (material decomposition) and the reduction of blooming or movement artifacts together with the elimination of electronic noise [ 11 ]. The introduction of sophisticated postprocessing tools gave rise to innovative imaging biomarkers, which are, by definition, “parameters that can be measured and that may influence or predict the incidence of outcome of diseases” [ 12 ].…”