Kauczor, Hans‐Ulrich

Abstract Purpose The aim of this study was to characterize a second‐generation wide‐detector dual‐layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities. Methods A phantom with multiple tissue‐mimicking and material‐specific inserts was scanned with a dual‐layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver‐mimicking inserts with adipose and iron were analyzed with a two‐base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported. Results Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from ‐0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively. Conclusion The second‐generation dual‐layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

There are physiological consequences of overeating that can lead to increased morbidity and mortality. The purpose of this review article is to acquaint the reader with the current state of the art in the non–cardiac-gated, noncontrast chest computed tomographic (NCCT) imaging biomarkers of the metabolic syndrome and their prognostic significance found in the lower neck and chest. NCCT imaging biomarkers associated with metabolic syndrome in the chest include premature coronary artery calcification, acceleration of large vessel arterial and valvular calcifications associated with atherosclerosis, and pulmonary arterial enlargement from pulmonary hypertension associated with sleep apnea. These easily identified imaging biomarkers have prognostic implications for major adverse cardiac events (MACE). These NCCT chest-imaging biomarkers are likely targets for artificial intelligence algorithms to harvest for longitudinal assessment of their individual and multifactorial contributions to chronic disease, MACE, and mortality. Early recognition and treatment of these common disorders may help improve patient outcomes and quality of life while decreasing medical costs.