Dual-Energy CT–Based Differentiation of Benign Posttreatment Changes From Primary or Recurrent Malignancy of the Head and Neck: Comparison of Spectral Hounsfield Units at 40 and 70 keV and Iodine Concentration

Yamauchi, Hideomi; Buehler, Mark; Goodsitt, Mitchell M.; Keshavarzi, Nahid; Srinivasan, Ashok

doi:10.2214/ajr.15.14896

Cited by 55 publications

(22 citation statements)

References 19 publications

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“…7,8,[10][11][12][13][14][15][16][17][18] In the head and neck region, several studies showed iodine concentration (IC) could be useful in CT imaging of cervical lymph nodes or patients with underlying neck malignancy. [19][20][21][22] Until now, a few studies have evaluated IC of thyroid nodules using the DECT data sets to find the malignancy or intranodular hemorrhage; however, they decided the individual CT images for quantification without the detailed topographic knowledge based on ultrasound, which is widely considered as reference imaging modality to assess thyroid nodule. [23][24][25][26] In order to reduce the potential mismatch between the DECT images and other diagnostic methods, representative images of thyroid nodule have to be selected meticulously.…”

Section: Introductionmentioning

confidence: 99%

Dual‐energy CT iodine quantification for characterizing focal thyroid lesions

Lee

Seo

et al. 2018

Head & Neck

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Background To determine the usefulness of dual‐energy CT (DECT) iodine quantification to classify the focal thyroid lesions. Methods We retrospectively enrolled a total of 76 cytopathologically confirmed focal thyroid lesions (mean size: 1.9 cm). After drawing a region of interest on the DECT‐derived iodine maps, the obtained iodine concentration values of thyroid nodules (IC_N) and normalized IC_N were compared between 3 groups: papillary thyroid carcinoma (PTC), benign nodule, and cyst. Results From all lesions, 46, 17, and 13 were assigned to the PTC, benign nodule, and cyst groups. IC_N was the highest in the benign nodule, lower in the PTC, and the lowest in the cyst (median [interquartile range]: 4.3 [3.13‐5.48], 3.15 [2.29‐4.01], 0.60 [0.33‐0.88], all P < .001). Similarly, the normalized IC_N values were all statistically different from each other (P < .05).The multi‐class area under the curves using the optimal cutoff values were 0.931 for IC_N and 0.918, 0.920 for normalized IC, respectively. Conclusion DECT iodine quantification could be helpful to classify the focal thyroid lesions.

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Section: Introductionmentioning

confidence: 99%

Dual‐energy CT iodine quantification for characterizing focal thyroid lesions

Lee

Seo

et al. 2018

Head & Neck

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“…110 Another study employing fast kV-switching dual-energy technology on 40 patients established that 40 and 70 keV were useful for identifying malignant from benign tissues, also in the head and neck region. 111 Another study using the fast kV-switching dual-energy CT platform on 25 unenhanced head scans revealed that maximal grey matter signal-to-noise ratio, white matter signal-to-noise ratio, and grey matter to white matter contrast-to-noise ratio were obtained using 65 keV virtual monoenergetic images. 112 Optimal posterior fossa image quality was obtained at 75 keV.…”

Section: B3 Creation Of Quantitative Virtual Monoenergetic Imagesmentioning

confidence: 99%

Principles and applications of multienergy CT: Report of AAPM Task Group 291

McCollough

Boedeker

Cody³

et al. 2020

Medical Physics

161

195

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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 noncontrast-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.

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“…VMI and iodine characterization of DECT may play a major role in patients with head and neck cancer in the detection and delineation of the tumor, resulting in more accurate staging [12], equivalent to the perfusion map of perfusion CT of head and neck cancer [13]. It can differentiate normal, inflammatory, and metastatic squamous cell carcinoma cervical lymph nodes based on iodine concentration [14], as well as between benign post-treatment changes from the primary or recurrent head and neck malignancies [15]. Three material differentiation algorithms for identification of iodine and calcium can be used to assess cartilage and bone marrow infiltration, the latter being a new application in head and neck DECT [16].…”

Section: Discussionmentioning

confidence: 99%

Clinical application of multi-material artifact reduction (MMAR) technique in Revolution CT to reduce metallic dental artifacts

et al. 2020

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Background: This study aimed to explore the performance of Revolution CT virtual monoenergetic images (VMI) combined with the multi-material artifact reduction (MMAR) technique in reducing metal artifacts in oral and maxillofacial imaging. Results: There were significant differences in image quality scores between VMI + MMAR images and VMI+MARS (multiple artifact reduction system) images at each monochromatic energy level (p = 0.000). Compared with the MARS technology, the MMAR technology further reduced metal artifacts and improved the image quality. At VMI 90 keV and VMI 110 keV , the SD, CNR, and AI in the Revolution CT group were significantly lower than in the Discovery CT, but no significant differences in these parameters were found between two groups at VMI 50 keV , VMI 70 keV , and VMI 130 keV (p > 0.05). The attenuation was comparable between two groups at any energy level (p > 0.05). Conclusions:Compared with the MARS reconstruction technique of Discovery CT, the MMAR technique of Revolution CT is better to reduce the artifacts of dental implants in oral and maxillofacial imaging, which improves the image quality and the diagnostic value of surrounding soft tissues.Compared to the 64-slice Discovery CT VMI + MARS technique for image reconstruction, 256-slice Revolution CT VMI + MMAR technique for image reconstruction is better to reduce metal artifacts and background SD. The combined use of VMI 110 keV + MMAR technique is helpful for the observation and evaluation of small structures around the metal implants. The combined use of VMI 110 keV + MMAR technique also provides a better diagnostic tool in clinical practice.

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Dual-Energy CT–Based Differentiation of Benign Posttreatment Changes From Primary or Recurrent Malignancy of the Head and Neck: Comparison of Spectral Hounsfield Units at 40 and 70 keV and Iodine Concentration

Abstract: DECT-derived spectral Hounsfield units at 40 keV and iodine concentration may be superior to spectral Hounsfield units at 70 keV, which is similar to MDCT, in differentiating benign posttreatment changes from primary or recurrent head and neck malignancies.

Cited by 55 publications

References 19 publications

Dual‐energy CT iodine quantification for characterizing focal thyroid lesions

Dual‐energy CT iodine quantification for characterizing focal thyroid lesions

Principles and applications of multienergy CT: Report of AAPM Task Group 291

Clinical application of multi-material artifact reduction (MMAR) technique in Revolution CT to reduce metallic dental artifacts

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