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PURPOSE: To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. MATERIAL AND METHODS: Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120 keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. RESULTS: Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120 keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. CONCLUSION: The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80 keV.

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The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVis) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function. Furthermore, a deep-learning algorithm for automatic contouring was applied (IV). An intermodality comparison of the above-mentioned strategies for contouring GTVis was performed. For the mean GTVref (standard deviation (SD)), the interquartile range (IQR)) was 0.68 mL (0.33; 0.34-1.1). GTV segmentation was distributed as follows: (I) 0.61 mL (0.27; 0.36-0.92); (II) 0.41 mL (0.28; 0.23-0.63); (III) 0.65 mL (0.35; 0.32-0.90); and (IV) 0.61 mL (0.29; 0.33-0.95). GTVref was found to be significantly correlated with GTVis (I) p < 0.001, r = 0.989 (III) p = 0.001, r = 0.916, and (IV) p < 0.001, r = 0.986, but not with (II) p = 0.091, r = 0.595. The Sørensen-Dice indices for the semi-automatic tools were 0.74 (I), 0.57 (II) and 0.71 (III). For the semi-automatic, conventional segmentation tools evaluated, the adaptive-brush function (I) performed closest to the reference standard (0). The automatic deep learning tool (IV) showed high performance for auto-segmentation and was close to the reference standard. For high precision radiation therapy, visual control, and, where necessary, manual correction, are mandatory for all evaluated tools.

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CT perfusion (CTP) is used for the evaluation of brain tissue viability in patients with acute ischemic stroke (AIS). We studied the accuracy of three different syngo.via software (SW) settings for acute ischemic core estimation in predicting the final infarct volume (FIV). The ischemic core was defined as follows: Setting A: an area with cerebral blood flow (CBF) < 30% compared to the contralateral healthy hemisphere. Setting B: CBF < 20% compared to contralateral hemisphere. Setting C: area of cerebral blood volume (CBV) < 1.2 mL/100 mL. We studied 47 AIS patients (aged 68 ± 11.2 years) with large vessel occlusion in the anterior circulation, treated in the early time window (up to 6 h), who underwent technically successful endovascular thrombectomy (EVT). FIV was measured on MRI performed 24 ± 2 h after EVT. In general, all three settings correlated with each other; however, the absolute agreement between acute ischemic core volume on CTP and FIV on MRI was poor; intraclass correlation for all three settings was between 0.64 and 0.69, root mean square error of the individual observations was between 58.9 and 66.0. Our results suggest that using CTP syngo.via SW for prediction of FIV in AIS patients in the early time window is not appropriate.

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BACKGROUND: To estimate the optimal density coefficient for conversion of liver volume into liver weight in patients with chronic liver disease based on semiautomated CT-liver volumetry data and the histologic Ishak score of explanted liver. METHODS: A total of 114 patients (39 female; age, 46±20 years) with chronic liver diseases who underwent liver transplantation between January 2010 and September 2020 were identified over a patient chart search at our institution and subsequently analyzed in retrospect. All patients had contrast-enhanced CT-examinations (mean, 24 days) to liver transplantation. Liver volume was calculated by a semiautomated software and results compared with the liver weight registered by the pathologist. Each explanted liver was histologically scored into six classes according to the Ishak classification where the categories were subgrouped based on recommendation of the pathologists into the following categories 0-3, 4-5 and 6. RESULTS: Mean liver volume was 1,870±1,195, 1,162±679 and 1,278±510 mL for the categories 0-3, 4-5 and 6, respectively. Mean liver weight was 1,624±999, 1,082±669 and 1,346±559 g for the categories 0-3, 4-5 and 6, respectively. A coefficient of 0.92±0.22, 0.98±0.28 and 1.06±0.20 g/mL was found at best for conversion of liver volume into liver weight in these subgroups. Differences between Ishak-subgroups proved significant (0.002). In 4 patients with cystic liver disease, density coefficients varied significantly and were found generally lower compared to the other liver disorders. CONCLUSIONS: Our results yielded significant differences between the density coefficients calculated along with the Ishak score and also for the subgroup with cystic liver disease.