dual energy ct
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2022 ◽  
Nicola Dalbeth ◽  
Mariam Alhilali ◽  
Peter Riordan ◽  
Ravi Narang ◽  
Ashika Chhana ◽  

2022 ◽  
pp. 084653712110651
Yet Yen Yan ◽  
Hugue A. Ouellette ◽  
Mayuran Saththianathan ◽  
Peter L. Munk ◽  
Paul I. Mallinson ◽  

Purpose: To determine the sensitivity and specificity of dual-energy CT (DECT) virtual noncalcium images (VNCa) with bone and soft tissue reconstructions in the diagnosis of osteomyelitis. Materials & Methods: Between December 1, 2014 to December 1, 2020, 91 patients who had 99 DECT performed for a clinical indication of osteomyelitis with corresponding MRI, triphasic bone scan and/or white blood cell scintigraphy with CT/SPECT performed either 2 weeks before or 1 month after the DECT were retrospectively identified. The presence or absence of osteomyelitis was established using a second imaging test, bone biopsy or surgery. Two radiologists interpreted VNCa images alone and with bone and soft tissue reconstructions for osteomyelitis. Fleiss k statistics was used to assess inter-level agreement. Results: Osteomyelitis was present in 26 cases (26.2%), of which 4 cases (4%) had co-existing septic arthritis. DECT was performed at the following sites: ankle/foot (n = 59), calf (n = 12), knee (n = 3), thigh (n = 7), hip (n = 9), pelvis (n = 6), wrist/hand (n = 1), and shoulder (n = 2). Sensitivity with VNCa images alone was 53.8% and 73.1% and specificity was 84.9% and 71.2%. Sensitivity with VNCa images and bone and soft tissue reconstructions was 80.8% and 80.8% and specificity was 80.8% and 72.6%. Interobserver agreement was 76.7% (76 of 99 cases), for VNCa images alone (k = .487), and 66.7% (66 of 99 patients) for bone and soft tissue reconstructions with VNCa images together (k = .390). Conclusion: When VNCa images were combined with bone and soft tissue reconstructions, there is improved sensitivity in the diagnosis of osteomyelitis.

Fuminari Tatsugami ◽  
Toru Higaki ◽  
Yuko Nakamura ◽  
Yukiko Honda ◽  
Kazuo Awai

AbstractDual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.

2022 ◽  
Vol 17 (01) ◽  
pp. C01028
J. Dudak ◽  
J. Zemlicka

Abstract X-ray micro-CT has become a popular and widely used tool for the purposes of scientific research. Although the current state-of-the-art micro-CT is on a high technology level, it still has some known limitations. One of the relevant issues is an inability to clearly identify and quantify specific materials. The mentioned drawback can be solved by the energy-sensitive CT approach. Dual-energy CT, which is already frequently used in human medicine, offers the identification of two different materials; for example, it differentiates an intravenous contrast agent from bone or it can indicate the composition of urinary stones. Resolving a larger number of material components within a single object is beyond the capabilities of dual-energy CT. Such an approach requires a higher number of measurements using different photon energies. A possible solution for multi bin, or so-called spectral CT, is the application of photon-counting detectors. Photon counting technology offers an integrated circuitry capable of resolving the energy of incoming photons in each pixel. Therefore, it is possible to collect data in user-defined energy windows. This contribution evaluates the applicability of the large-area photon-counting detector Timepix for multi bin energy-sensitive micro-CT. It presents an experimental phantom study focused on the simultaneous K-edge-based identification and quantification of multiple contrast agents within a single object. The paper describes the collection of multiple energy bins using the Timepix detector operated in the photon counting mode, explains the data processing, and demonstrates the results obtained from an in-house implemented basis material decomposition algorithm.

2022 ◽  
pp. 110151
Seokjin Hong ◽  
Ji Eun Kim ◽  
Jae Min Cho ◽  
Ho Cheol Choi ◽  
Jung Ho Won ◽  

Tomography ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 22-32
Andreas S. Brendlin ◽  
Markus Mader ◽  
Sebastian Faby ◽  
Bernhard Schmidt ◽  
Ahmed E. Othman ◽  

(1) To explore the potential impact of an AI dual-energy CT (DECT) prototype on decision making and workflows by investigating its capabilities to differentiate COVID-19 from immunotherapy-related pneumonitis. (2) Methods: From 3 April 2020 to 12 February 2021, DECT from biometrically matching patients with COVID-19, pneumonitis, and inconspicuous findings were selected from our clinical routine. Three blinded readers independently scored each pulmonary lobe analogous to CO-RADS. Inter-rater agreement was determined with an intraclass correlation coefficient (ICC). Averaged perfusion metrics per lobe (iodine uptake in mg, volume without vessels in ml, iodine concentration in mg/mL) were extracted using manual segmentation and an AI DECT prototype. A generalized linear mixed model was used to investigate metric validity and potential distinctions at equal CO-RADS scores. Multinomial regression measured the contribution “Reader”, “CO-RADS score”, and “perfusion metrics” to diagnosis. The time to diagnosis was measured for manual vs. AI segmentation. (3) Results: We included 105 patients (62 ± 13 years, mean BMI 27 ± 2). There were no significant differences between manually and AI-extracted perfusion metrics (p = 0.999). Regardless of the CO-RADS score, iodine uptake and concentration per lobe were significantly higher in COVID-19 than in pneumonitis (p < 0.001). In regression, iodine uptake had a greater contribution to diagnosis than CO-RADS scoring (Odds Ratio (OR) = 1.82 [95%CI 1.10–2.99] vs. OR = 0.20 [95%CI 0.14–0.29]). The AI prototype extracted the relevant perfusion metrics significantly faster than radiologists (10 ± 1 vs. 15 ± 2 min, p < 0.001). (4) Conclusions: The investigated AI prototype positively impacts decision making and workflows by extracting perfusion metrics that differentiate COVID-19 from visually similar pneumonitis significantly faster than radiologists.

2021 ◽  
Xuelong Chen ◽  
Zhizhuo Li ◽  
Hui Fang ◽  
Xiangyang Yin ◽  
Chengxin Li ◽  

Abstract Background: The prevalence of knee injury is high and early diagnosis is significant to guide clinical treatment. MRI is recognized as the gold standard for detecting bone marrow edema (BME) in patients with acute knee injury, but limitations still exist. Dual-energy CT (DECT) is investigated as a promising alternative.Methods: We systematically retrieved studies from EMBASE, Scopus, PUBMED, and the Cochrane Library and collected gray literatures. According to PRISMA-DTA guidelines, a systematic review was performed from inception to July 31, 2021, assessing the diagnostic accuracy of DECT for detecting BME in at least 10 adult patients with acute knee injuries and with an MRI reference standard. Study details were independently extracted by two reviewers. Meta-analysis was performed using a bivariate mixed-effects regression model with subgroup analysis performed to evaluate for sources of variability. Results: Nine studies evaluating 290 patients between the ages of 23–53 with acute knee injuries undergoing DECT and MRI were included in analysis. Summary sensitivity, specificity, and AUC values for BME were 85% (95% confidence interval (CI) 77–90%), 96% (95% CI 93–97%), and 0.97 (95% CI 0.95–0.98), respectively. There were no statistically significant differences in specificity and sensitivity amongst comparative subgroups to account for presumed variability amongst studies.Conclusion: DECT is accurate for detecting BME in patients with acute knee injuries and can be used as an alternative to MRI, particularly when MRI is contraindicated or unavailable.

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