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In x-ray computed tomography (CT), materials with different elemental compositions can have identical CT number values, depending on the mass density of each material and the energy of the detected x-ray beam. Differentiating and classifying different tissue types and contrast agents can thus be extremely challenging. In multienergy CT, one or more additional attenuation measurements are obtained at a second, third or more energy. This allows the differentiation of at least two materials. Commercial dual-energy CT systems (only two energy measurements) are now available either using sequential acquisitions of low- and high-tube potential scans, fast tube-potential switching, beam filtration combined with spiral scanning, dual-source, or dual-layer detector approaches. The use of energy-resolving, photon-counting detectors is now being evaluated on research systems. Irrespective of the technological approach to data acquisition, all commercial multienergy CT systems circa 2020 provide dual-energy data. Material decomposition algorithms are then used to identify specific materials according to their effective atomic number and/or to quantitate mass density. These algorithms are applied to either projection or image data. Since 2006, a number of clinical applications have been developed for commercial release, including those that automatically (a) remove the calcium signal from bony anatomy and/or calcified plaque; (b) create iodine concentration maps from contrast-enhanced CT data and/or quantify absolute iodine concentration; (c) create virtual non-contrast-enhanced images from contrast-enhanced scans; (d) identify perfused blood volume in lung parenchyma or the myocardium; and (e) characterize materials according to their elemental compositions, which can allow in vivo differentiation between uric acid and non-uric acid urinary stones or uric acid (gout) or non-uric acid (calcium pyrophosphate) deposits in articulating joints and surrounding tissues. In this report, the underlying physical principles of multienergy CT are reviewed and each of the current technical approaches are described. In addition, current and evolving clinical applications are introduced. Finally, the impact of multienergy CT technology on patient radiation dose is summarized.

Purpose: To evaluate the attenuation response of iodine and the accuracy of iodine quantification on a detector-based spectral CT scanner. Methods: A Gammex 461A phantom was scanned using a dual-layer detector (IQon, Philips) at 120 kVp using helical acquisition with a CDTIvol of 15 mGy to approximate the hospital's clinical body protocol. No modifications to the standard protocol were necessary to enable spectral imaging. Iodine inserts at 6 concentrations (2, 5, 7.5, 10, 15, 20 mg/ml) were scanned individually at the center of the phantom and the 20 mg/ml insert was additionally scanned at the 3, 6, and 12 o'clock positions. Scans were repeated 10 times. Conventional, virtual monoenergetic (40–200 keV) and iodine-no-water images (with pixel values equal to iodine concentration of corresponding tissue) were reconstructed from acquired data. A circular ROI (diameter=30 pixels) was used in each conventional and monoenergetic image to measure the mean and standard deviation of the CT number in HU and in each iodine-no-water image to measure iodine concentration in mg/ml. Results: Mean CT number and contrast-to-noise ratio (CNR) measured from monoenergetic images increased with decreasing keV for all iodine concentrations and matched measurements from conventional images at 75 keV. Measurements from the 20 ml insert showed the CT number is independent of location and CNR is a function only of noise, which was higher in the center. Measured concentration from iodine-no-water images matched phantom manufacturer suggested concentration to within 6% on average for inserts at the center of the phantom. Measured concentrations were systematically higher due to optimization of iodine quantification parameters for clinical mixtures of iodine and blood/tissue. Conclusion: Spectral acquisition and reconstruction with a dual-layer detector represents the physical behavior of iodine as expected and accurately quantifies the material concentration. This should permit a variety of clinical applications including lesion characterization, vessel patency, and myocardial perfusion. This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.

Purpose: To obtain objective reference data for the spectral performance on a dual-layer detector CT platform (IQon, Philips) and compare virtual monoenergetic to conventional CT images. Methods: Scanning was performed using the hospital's clinical adult body protocol: helical acquisition at 120kVp, with CTDIvol=15mGy. Multiple modules (591, 515, 528) of a CATPHAN 600 phantom and a 20 cm diameter cylindrical water phantom were scanned. No modifications to the standard protocol were necessary to enable spectral imaging. Both conventional and virtual monoenergetic images were generated from acquired data. Noise characteristics were assessed through Noise Power Spectra (NPS) and pixel standard deviation from water phantom images. Spatial resolution was evaluated using Modulation Transfer Functions (MTF) of a tungsten wire as well as resolution bars. Low-contrast detectability was studied using contrast-to-noise ratio (CNR) of a low contrast object. Results: MTF curves of monoenergetic and conventional images were almost identical. MTF 50%, 10%, and 5% levels for monoenergetic images agreed with conventional images within 0.05lp/cm. These observations were verified by the resolution bars, which were clearly resolved at 7lp/cm but started blurring at 8lp/cm for this protocol in both conventional and 70 keV images. NPS curves indicated that, compared to conventional images, the noise power distribution of 70 keV monoenergetic images is similar (i.e. noise texture is similar) but exhibit a low frequency peak at keVs higher and lower than 70 keV. Standard deviation measurements show monoenergetic images have lower noise except at 40 keV where it is slightly higher. CNR of monoenergetic images is mostly flat across keV values and is superior to that of conventional images. Conclusion: Values for standard image quality metrics are the same or better for monoenergetic images compared to conventional images. Results indicate virtual monoenergetic images can be used without any loss in image quality or noise penalties relative to conventional images. This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.