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Purpose: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction., Methods: This retrospective study included 16 patients (mean age: 54.2 ± 22.1 years; 8 men and 8 women) with acute hemorrhage who underwent contrast-enhanced CT. Unenhanced axial, arterial phase axial, arterial phase coronal, and delayed phase axial images were reconstructed with deep learning reconstruction, hybrid iterative reconstruction, and filtered back projection, which was used as a control in qualitative analyses. Circular and line regions of interest were placed on the aorta and superior mesenteric artery (SMA), respectively, in quantitative analyses. Using a blind process, 2 radiologists independently evaluated image noise, depiction of arteries, and suitability for the evaluation of hemorrhage in qualitative image analyses., Results: Image noise in deep learning reconstruction was significantly reduced compared with hybrid iterative reconstruction in the quantitative ( P < .001) and qualitative analyses (Reader 1, P ≤ .001 for all series; Reader 2, P = .002, .001, and < .001). The slope at the half maximum in deep learning reconstruction (123.8 ± 63.2 HU/mm) significantly improved compared with hybrid iterative reconstruction (105.3 ± 51.0 HU/mm) in the CT attenuation profile of the SMA ( P < .001). Qualitative analyses revealed a significantly improved depiction of arteries (Reader 1, P < .001 for all series; Reader 2, P = .037, .008, and < .001) and suitability for evaluating acute hemorrhage in the arterial phase image (Reader 1, P < .001 for both series; Reader 2, P = .041 and .004) with deep learning reconstruction compared with hybrid iterative reconstruction., Discussion: Deep learning reconstruction provided images with a significantly better depiction of arteries and more suitable quality arterial phase images for the evaluation of abdominopelvic hemorrhage compared with hybrid iterative reconstruction., Conclusion: Deep learning reconstruction is better for reconstructing abdominopelvic contrast-enhanced CT images when evaluating hemorrhages; however, a prospective study including a large number of patients is needed to strengthen the findings of this study., (© 2024 American Society of Radiologic Technologists.)

Germinomas frequently cause hydrocephalus, and ventriculoperitoneal shunts (VPS) have been commonly used for their management. Although VPS can potentially serve as a route for peritoneal dissemination of germinomas, the abdominal imaging characteristics of this rare yet important complication remain unknown. In this article, we report the computed tomography imaging findings of diffuse peritoneal dissemination of intracranial germinoma., (© 2024. The Author(s).)

Purpose: We aimed to predict the neurological prognosis of cardiac arrest (CA) patients using quantitative imaging biomarkers extracted from brain computed tomography images., Methods: We retrospectively enrolled 86 CA patients (good prognosis, 32; poor prognosis, 54) who were treated at three hospitals between 2017 and 2019. We then extracted 1131 quantitative imaging biomarkers from whole-brain and local volumes of interest in the computed tomography images of the patients. The data were split into training and test sets containing 60 and 26 samples, respectively, and the training set was used to select representative quantitative imaging biomarkers for classification. In univariate analysis, the classification was evaluated using the p-value of the Brunner-Munzel test and area under the receiver operating characteristic curve (AUC) for the test set. In multivariate analysis, machine learning models reflecting nonlinear and complex relations were trained, and they were evaluated using the AUC on the test set., Results: The best performance provided p = 0.009 (<0.01) and an AUC of 0.775 (95% confidence interval, 0.590-0.960) for the univariate analysis and an AUCof0.813 (95% confidence interval, 0.640-0.985) for the multivariate analysis. Overall, the gray level with the maximum gradient in the histogram of the three-dimensionally low-pass-filtered image was an important feature for prediction across the analyses., Conclusions: Quantitative imaging biomarkers can be used in neurological prognosis prediction for CA patients. Relevant biomarkers may contribute to protocolized computed tomography image acquisition to ensure proper decision support in acute care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC., (© 2024. The Author(s).)

Purpose: The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience., Methods: We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists., Results: The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist., Conclusions: Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used., (© 2024. The Author(s).)

Objective: This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) compared with hybrid iterative reconstruction (HIR). Methods: In this retrospective study, 35 consecutive patients suspected of ILD who underwent CT including the chest region were included. High-resolution CT images of the unilateral lung with DLR and HIR were reconstructed for the right and left lungs. A radiologist placed regions of interest on the lung and measured standard deviation of CT attenuation (i.e., quantitative image noise). In the qualitative image analyses, 5 blinded readers assessed the presence of honeycombing and reticulation, qualitative image noise, artifacts, and overall image quality using a 5-point scale (except for artifacts which was evaluated using a 3-point scale). Results: The quantitative and qualitative image noise in DLR was remarkably reduced compared to that in HIR ( P < .001). Artifacts and overall DLR quality were significantly improved compared to those of HIR ( P < .001 for 4 out of 5 readers). Interobserver agreement in the evaluations of honeycombing and reticulation for DLR (0.557 [0.450-0.693] and 0.525 [0.470-0.541], respectively) were higher than those for HIR (0.321 [0.211-0.520] and 0.470 [0.354-0.533], respectively). A statistically significant difference was found for honeycombing ( P = .014). Conclusions: DLR improved interobserver agreement in the evaluation of honeycombing in patients with ILD on CT compared to HIR., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the tracheal diameter and lung volume. In this retrospective study, a total of 221 patients (71.1 ± 12.4 years, 174 males) who underwent vascular dynamic CT examination including chest were included. Unenhanced, arterial phase, and delayed-phase images were scanned. The tracheal luminal diameters at the level of the thoracic inlet and both lung volumes were evaluated by a radiologist using a commercial software, which allows automatic airway and lung segmentation. The tracheal diameter and both lung volumes were compared between the unenhanced vs. arterial and delayed phase using a paired t-test. The Bonferroni correction was performed for multiple group comparisons. The tracheal diameter in the arterial phase (18.6 ± 2.4 mm) was statistically significantly smaller than those in the unenhanced CT (19.1 ± 2.5 mm) (p < 0.001). No statistically significant difference was found in the tracheal diameter between the delayed phase (19.0 ± 2.4 mm) and unenhanced CT (p = 0.077). Both lung volumes in the arterial phase were 4131 ± 1051 mL which was significantly smaller than those in the unenhanced CT (4332 ± 1076 mL) (p < 0.001). No statistically significant difference was found in both lung volumes between the delayed phase (4284 ± 1054 mL) and unenhanced CT (p = 0.068). In conclusion, intravenous infusion of iodine contrast agent transiently decreased the tracheal diameter and both lung volumes., (© 2024. The Author(s).)

Purpose: We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR)., Methods: This retrospective study enrolled 36 patients (15 men, 21 women; age, 53.9 ± 19.5 years) who had undergone high-resolution CT of the temporal bone. Axial and coronal images were reconstructed using DLR, HIR, and filtered back projection (FBP). In qualitative image analyses, two radiologists independently compared the DLR and HIR images with FBP in terms of depiction of structures, image noise, and overall quality, using a 5-point scale (5 = better than FBP, 1 = poorer than FBP) to evaluate image quality. The other two radiologists placed regions of interest on the tympanic cavity and measured the standard deviation of CT attenuation (i.e., quantitative image noise). Scores from the qualitative and quantitative analyses of the DLR and HIR images were compared using, respectively, the Wilcoxon signed-rank test and the paired t-test., Results: Qualitative and quantitative image noise was significantly reduced in DLR images compared with HIR images (all comparisons, p ≤ 0.016). Depiction of the otic capsule, auditory ossicles, and tympanic membrane was significantly improved in DLR images compared with HIR images (both readers, p ≤ 0.003). Overall image quality was significantly superior in DLR images compared with HIR images (both readers, p < 0.001)., Conclusion: Compared with HIR, DLR provided significantly better-quality high-resolution CT images of the temporal bone., (© 2024. The Author(s).)

This study aimed to establish the diagnostic criteria for upper gastrointestinal bleeding (UGIB) using postmortem computed tomography (PMCT). This case-control study enrolled 27 consecutive patients with autopsy-proven UGIB and 170 of the 566 patients without UGIB who died in a university hospital in Japan after treatment and underwent both noncontrast PMCT and conventional autopsy between 2009 and 2020. Patients were randomly allocated to two groups: derivation and validation sets. Imaging findings of the upper gastrointestinal contents, including CT values, were recorded and evaluated for their power to diagnose UGIB in the derivation set and validated in the validation set. In the derivation set, the mean CT value of the upper gastrointestinal contents was 48.2 Hounsfield units (HU) and 22.8 HU in cases with and without UGIB. The optimal cutoff CT value for diagnosing UGIB was ≥27.7 HU derived from the receiver operating characteristic curve analysis (sensitivity, 91.7%; specificity, 81.2%; area under the curve, 0.898). In the validation set, the sensitivity and specificity in diagnosing UGIB for the CT cutoff value of ≥27.7 HU were 84.6% and 77.6%, respectively. In addition to the CT value of ≥27.7 HU, PMCT findings of solid-natured gastrointestinal content and intra/peri-content bubbles ≥4 mm, extracted from the derivation set, increased the specificity for UGIB (96.5% and 98.8%, respectively) but decreased the sensitivity (61.5% and 38.5%, respectively) in the validation set. In diagnosing UGIB on noncontrast PMCT, the cutoff CT value of ≥27.7 HU and solid gastrointestinal content were valid and reproducible diagnostic criteria., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Okimoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT)., Methods: This retrospective study included 29 and 20 patients with and without vertebral masses. CT images were reconstructed using DLR and hybrid-IR. Three readers performed vertebral mass detection tests and evaluated the presence of spinal cord compression, the depiction of vertebral masses, and image noise. Quantitative image noise was measured by placing regions of interest on the aorta and spinal cord., Results: Deep learning reconstruction tended to improve the performance of readers with less diagnostic imaging experience in detecting vertebral masses (area under the receiver operating characteristic curve [AUC] = 0.892-0.966) compared with hybrid-IR (AUC = 0.839-0.917). Diagnostic performance in evaluating spinal cord compression in DLR (AUC = 0.887-0.995) also improved compared with that in hybrid-IR (AUC = 0.866-0.942) for some readers. The depiction of vertebral masses and subjective image noise in DLR were significantly improved compared with those in hybrid-IR ( P < .041). Quantitative image noise in DLR was also significantly reduced compared with that in hybrid-IR ( P < .001)., Conclusion: Deep learning reconstruction improved the depiction of vertebral masses, which resulted in a tendency to improve the performance of CT compared to hybrid-IR in detecting vertebral masses and diagnosing spinal cord compression for some readers., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Postmortem computed tomography (CT) is an essential tool for investigating the causes of death. Postmortem CT has characteristic imaging findings and should not be interpreted in the same manner as clinical antemortem images. In analyzing the cause of death in in-hospital death cases using postmortem images, it is crucial to understand early postmortem and post-resuscitation changes. In addition, it is essential to understand the limitations of diagnosing the cause of death or significant pathology relating to death on non-contrast-enhanced postmortem CT. In Japan, there has also been social demand to establish a system for postmortem imaging at the time of death. To facilitate such a system, clinical radiologists should be prepared to interpret postmortem images and assess the cause of death. This review article provides comprehensive information regarding unenhanced postmortem CT for in-hospital death cases in daily clinical practice in Japan., (© 2023. The Author(s).)

Background: Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality., Purpose: To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT)., Material and Methods: Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers' sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test., Results: With CAD, less experienced radiologists' sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% ( P  = 0.007), while the experienced radiologists' sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P  = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P  = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P  = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P  = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P  = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P  = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist ( P  < 0.001)., Conclusion: CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.

This study was designed to examine the antemortem factors affecting cerebrospinal fluid (CSF) Hounsfield Units (HU) on postmortem computed tomography (PMCT) compared to the antemortem CT (AMCT). Fifty-five participants without brain lesions who died at a university hospital and underwent AMCT, PMCT, and an autopsy were enrolled. We recorded age, sex, time after death, the CSF HU on AMCT and PMCT at multiple measuring points, 4-point-scale brain atrophy grade on AMCT, and the cella media index. We tested the effects of CSF HU factors observed on PMCT. No significant differences were observed between CSF HUs at any of the PMCT measurement points. The average CSF HU on PMCT was positively correlated with the natural logarithm of the time after death (Pearson's correlation coefficient, 0.81; p < 0.001). No other factors showed correlative relationships. Up until approximately 12 h after death, the CSF HU on PMCT depended only on the time since death., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2022 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.)

Objective: This study aimed to test the usefulness of computer-aided detection (CAD) for the detection of brain metastasis (BM) on contrast-enhanced computed tomography., Methods: The test data set included whole-brain axial contrast-enhanced computed tomography images of 25 cases with 62 BMs and 5 cases without BM. Six radiologists from 3 institutions with 2 to 4 years of experience independently reviewed the cases, both in conditions with and without CAD assistance. Sensitivity, positive predictive value, number of false positives, and reading time were compared between the conditions using paired t tests. Subanalysis was also performed for groups of lesions divided according to size. A P value <0.05 was considered statistically significant., Results: With CAD, sensitivity significantly increased from 80.4% to 83.9% ( P = 0.04), whereas positive predictive value significantly decreased from 88.7% to 84.8% ( P = 0.03). Reading time with and without CAD was 112 and 107 seconds, respectively ( P = 0.38), and the number of false positives was 10.5 with CAD and 7.0 without CAD ( P = 0.053). Sensitivity significantly improved for 6- to 12-mm lesions, from 71.2% without CAD to 80.3% with CAD ( P = 0.02). The sensitivity of the CAD (95.2%) was significantly higher than that of any reader (with CAD: P = 0.01; without CAD: P = 0.005)., Conclusions: Computer-aided detection significantly improved BM detection sensitivity without prolonging reading time while marginally increased the false positives., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra-low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. We applied X2CT-FLOW for the reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra-low-dose protocol). We simulated an ultra-low-dose protocol. With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra-low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).

Purpose: We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers., Materials and Methods: We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups., Results: The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively)., Conclusion: AIS and MIA in smoker's lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker's lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma., (© 2021. The Author(s).)

Abstract: Although differentiating benign and malignant thymic epithelial lesions is important to avoid unnecessary treatment and predict prognosis, it is challenging because of overlaps in the chest computed tomography (CT) findings. In this study, we investigated whether the diameter of the thymic vein and other CT findings could differentiate between benign (thymoma and thymic cysts) and malignant (thymic carcinoma, [TCa]) lesions.We conducted a retrospective study across two tertiary referral hospitals in Japan between November 2009 and June 2018. We included 12 patients with TCa, 34 patients with thymomas, and 17 patients with thymic cysts. We analyzed the receiver operating characteristic (ROC) curve to determine the best cut-off values and performed univariate and multivariate analyses of CT findings to distinguish TCa from other benign lesions. Post-hoc analysis was performed for the maximum short axis of the thymic vein using the Mann-Whitney U test, and the number of the maximum short axis of the thymic vein ≥ the cutoff was determined using the Fisher exact test with a family-wise error-correction using Bonferroni's method.ROC analysis showed that a maximum short axis of the thymic vein ≥2 mm was considerably more frequent in TCa than in the other lesions (P < .001 for both), with 83% sensitivity and 86% specificity. Univariate and multivariate analyses revealed the association with TCa of the number of the maximum short axis of the thymic vein ≥2 mm (P = .005, multivariate generalized linear model analysis), ill-defined margin (P = .001), and mediastinal lymphadenopathy (P < .001). Thymic vein diameter was in descendimg order of TCa > thymoma > thymic cysts with statistically significant differences between the groups (Ps < .05).Thymic vein diameter was significantly longer in TCa than in thymoma and thymic cysts. Measurement of the maximum short axis of the thymic vein could be a powerful diagnostic tool to differentiate TCa from thymoma and thymic cysts., Competing Interests: The authors have no funding and conflicts of interests to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Distinguishing coronavirus disease 2019 (COVID-19) pneumonia from other lung diseases is often difficult, especially in a highly comorbid patient population in a low prevalence region. We aimed to distinguish clinical data and computed tomography (CT) images between COVID-19 and other lung diseases in an advanced care hospital., Methods: We assessed clinical characteristics, laboratory data, and chest CT images of patients with COVID-19 and non-COVID-19 patients who were suspected of having COVID-19 between February 20 and May 21, 2020, at the University of Tokyo Hospital., Results: Typical appearance for COVID-19 on CT images were found in 24 of 29 COVID-19 cases and 21 of 168 non-COVID-19 cases, according to the Radiological Society of North America Expert Consensus Statement (for predicting COVID-19, sensitivity 0.828, specificity 0.875, positive predictive value 0.533, negative predictive value 0.967). When we focused on cases with typical CT images, loss of taste or smell, and close contact with COVID-19 patients were exclusive characteristics for the COVID-19 cases. Among laboratory data, high fibrinogen (P < 0.01) and low white blood cell count (P < 0.01) were good predictors for COVID-19 with typical CT images in multivariate analysis., Conclusions: In a relatively low prevalence region, CT screening has high sensitivity to COVID-19 in patients with suspected symptoms. When chest CT findings are typical for COVID-19, close contact, loss of taste or smell, lower white blood cell count, and higher fibrinogen are good predictors for COVID-19., Competing Interests: Conflict of Interest The authors declare that they have no conflicts of interest for this study., (Copyright © 2021 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.)

Abstract: To evaluate the rib fracture detection performance in computed tomography (CT) images using a software based on a deep convolutional neural network (DCNN) and compare it with the rib fracture diagnostic performance of doctors.We included CT images from 39 patients with thoracic injuries who underwent CT scans. In these images, 256 rib fractures were detected by two radiologists. This result was defined as the gold standard. The performances of rib fracture detection by the software and two interns were compared via the McNemar test and the jackknife alternative free-response receiver operating characteristic (JAFROC) analysis.The sensitivity of the DCNN software was significantly higher than those of both Intern A (0.645 vs 0.313; P < .001) and Intern B (0.645 vs 0.258; P < .001). Based on the JAFROC analysis, the differences in the figure-of-merits between the results obtained via the DCNN software and those by Interns A and B were 0.057 (95% confidence interval: -0.081, 0.195) and 0.071 (-0.082, 0.224), respectively. As the non-inferiority margin was set to -0.10, the DCNN software is non-inferior to the rib fracture detection performed by both interns.In the detection of rib fractures, detection by the DCNN software could be an alternative to the interpretation performed by doctors who do not have intensive training experience in image interpretation., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: Identify appropriate reconstruction modes of Forward-projected model-based Iterative Reconstruction SoluTion (FIRST) in temporal bone computed tomography (CT) and investigate the contribution of spatial resolution and noise to the visual assessment., Methods: Sixteen temporal bone CT images were reconstructed. Two blinded radiologists assessed 8 anatomical structures and classified the visual assessment. These visual scores were compared across reconstruction modes among each anatomical structure. Visual scores and contrast-to-noise ratio, noise power spectrum (NPS) at low (NPSLow) and high (NPSHigh) spatial frequencies, and 50% modulation transfer function relationships were evaluated., Results: Visual scores differed significantly for the stapedius muscle and osseous spiral lamina, with CARDIAC SHARP, BONE, and LUNG modes for the osseous spiral lamina scoring highest. Visual scores significantly positively correlated with NPSLow, NPSHigh, and 50% modulation transfer function but negatively with contrast-to-noise ratio., Conclusions: Modes providing higher spatial resolution and lower noise reduction showed an improved visual assessment of CT images reconstructed with FIRST., Competing Interests: This is a collaborative research with Canon Medical Systems Corporation. S.T. and R.T. are employees of Canon Medical Systems Corporation. E.M. and O.A. received honoraria (eg, lecture fee) and research funding from Canon Medical Systems Corporation. J.S. receives research funding from Canon Medical Systems Corporation. All other authors declare no conflict of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

This study aimed to evaluate the visualization of peripheral bronchioles in normal lungs via quarter-detector computed tomography (QDCT). Visualization of bronchioles within 10 mm from the pleura is considered a sign of bronchiectasis. However, it is not known peripheral bronchioles how close to the pleura in normal lungs can be tracked using QDCT. This study included 228 parts in 76 lungs from 38 consecutive patients who underwent QDCT. Reconstruction was performed with different thicknesses, increments, and matrix sizes: 0.5-mm thickness and increment with 512 and 1024 matrixes (Group5 and Group10, respectively) and 0.25-mm thickness and increment with 1024 matrix (Group10Thin). The distance between the most peripheral bronchiole visible and the pleura was determined in the three groups. The distance between the peripheral bronchial duct ends and the nearest pleural surface were significantly shorter in the order of Group10Thin, Group10, and Group5, and the mean distances from the pleura in Group10Thin and Group10 were shorter than 10 mm. These findings suggest the visualization of peripheral bronchioles in QDCT was better with a 1024 axial matrix than with a 512 matrix, and with a 0.25-mm slice thickness/increment than with a 0.5-mm slice thickness/increment. Our study also indicates bronchioles within 10 mm of the pleura do not necessarily indicate pathology., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Eriko Maeda and Osamu Abe receive Honoraria (e.g. lecture fee) and research funding from Canon Medical Systems Corporation. Jiro Sato receives research funding from Canon Medical Systems Corporation. These do not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors declare no conflicts of interest relevant to this study.

Objectives: To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images., Methods: In this Institutional Review Board-approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson's correlation coefficient (r) and area under the receiver operating characteristic curve (AUC), respectively., Results: The estimated BMD values, according to the CNN model (BMD CNN ), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p < 0.001) and 0.840 (p < 0.001) for the internal and external validation datasets, respectively). Using BMD CNN , osteoporosis was diagnosed with AUCs of 0.965 and 0.970 for the internal and external validation datasets, respectively., Conclusions: Using deep learning, the BMD of lumbar vertebrae could be predicted from unenhanced abdominal CT images., Key Points: • By applying a deep learning technique, the bone mineral density (BMD) of lumbar vertebrae can be estimated from unenhanced abdominal CT images. • A strong correlation was observed between the estimated BMD and the BMD obtained with DXA. • By using the estimated BMD, osteoporosis could be diagnosed with high performance.

To evaluate the utility of unenhanced postmortem computed tomography (PMCT) for the investigation of in-hospital nontraumatic death in children up to 3 years of age.This study included the cadavers of children who died from intrinsic diseases before 3 years of age. The major underlying disease and the main organ-disease systems associated with the immediate causes of death were determined by clinical evaluation, PMCT, and autopsy, which were used as a reference standard. The rates of concordance between the former two methods and autopsy were calculated for all cases.In total, 22 cadavers (12 male and 10 female; mean age, 6.1 ± 8.2 months) were included. The rates of concordance between clinical evaluation/PMCT and autopsy for diagnosis of the major underlying disease and main organ-disease systems associated with the immediate causes of death were 100%/36% (P = .0015) and 59%/41% (P = .37), respectively. In cases where the respiratory system was associated with the immediate cause of death, PMCT showed greater diagnostic sensitivity (90%) than did clinical evaluation (20%). In contrast, the diagnostic sensitivity of PMCT was lower than that of clinical evaluation in cases involving disorders of the cardiac system and multiple organ systems (0% vs 100% for both).The findings of this study suggest that the use of unenhanced PMCT with clinical evaluation can result in improved detection of the immediate cause of death in select cases of in-hospital nontraumatic death before 3 years of age.

Purpose: The purpose of this retrospective study was to evaluate the usefulness of serum tumour markers and morphological characteristics in CT/MRI to differentiate between ovarian metastases from colorectal carcinomas (OMCRC) and primary ovarian carcinomas (POC)., Method: Preoperative radiological images of 41 OMCRCs from 27 patients (mean age ± SD: 52.2 ± 10.7 years) and 46 POCs from 36 patients (52.1 ± 12.7 years) were included. Three blinded gynecological radiologists classified tumour morphology into 'mille-feuille sign', 'solid and cystic', 'multicystic without nodules', and 'multicystic with nodules' groups and analysed using Fisher's exact test. Serum carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), and carbohydrate antigen 19-9 levels were compared by Wilcoxon rank-sum test., Results: 'Mille-feuille sign' indicated OMCRC (OMCRC: 8/41, POC: 1/46, specificity = 0.98, p = 0.011) and had excellent interobserver agreement (Fleiss's kappa value = 0.96). 'Solid and cystic' indicated POC (18/41 vs 41/45, p < 0.001) and 'multicystic without nodules' indicated OMCRC (8/41 vs 2/46, p = 0.041). There was no significant difference in 'multicystic with nodules'. CA125 levels were higher in POCs (292.5 U/mL vs. 41.0 U/mL, p = 0.003). CEA levels were higher in OMCRCs (24.5 ng/mL vs 2 ng/mL, p < 0.001). CEA (< 6.3 ng/mL) AND (CA125 (≥87.0 U/mL) OR 'solid and cystic') indicated POC with high accuracy (3/41 vs 44/46, accuracy = 0.94, p < 0.001)., Conclusions: Our new method with morphological classification and tumour markers were useful for differentiating the two tumours. In particular, the 'mille-feuille sign' frequently indicated OMCRC with high specificity and excellent interobserver agreement., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest directly relevant to the content of this article., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Pediatric cardiac computed tomography (CT) can be acquired without electrode placement by using synthetic electrocardiogram (ECG)., Objective: To determine whether the depiction of gross cardiac structures and coronary arteries in 320-row pediatric CT is not inferior when CT is gated with synthetic ECG at 150 beats per minute (bpm), compared to the patients' own ECG., Materials and Methods: Sixty 320-row CT examinations performed in children younger than 3 years old with congenital cardiac anomaly were enrolled in this retrospective study. Thirty examinations were scanned using the children's own ECG for gating and 30 examinations were scanned using synthetic ECG at 150 bpm. The image quality was compared between the two gating modes using a 3-point scale to delineate the following anatomical structures: atrial septum, ventricular septum, right atrium, right ventricle, left atrium, left ventricle, main pulmonary artery, ascending aorta, aortic arch including the patent ductus arteriosus, descending aorta, right coronary artery and left main trunk. Beam-hardening artifacts from contrast enhancement material were evaluated using a 3-point scale, and the overall image quality was evaluated using a 5-point scale., Results: Synthetic ECG was not inferior to the patients' ECG in depicting each structure, beam-hardening artifact and overall image quality. Average indices were clinically acceptable imaging quality, except for subjective image quality of mid and distal coronary arteries., Conclusion: Pediatric cardiac CT in patients younger than 3 years old can be acquired using synthetic ECG gating, with image quality not inferior to the patients' ECG.

We performed present study to investigate whether the localization of primary aldosteronism (PA) can be predicted using quantitative texture analysis on unenhanced computed tomography (CT). Plain CT data of 82 PA patients (54 unilateral (right-sided:left-sided = 24:30), 28 bilateral) were analyzed retrospectively. After semi-automatically setting the region of interest to include the whole adrenal gland, texture analyses were performed with or without a Laplacian of Gaussian filter with various spatial scaling factors (SSFs). Logistic regression analysis was performed using the extracted histogram-based texture features to identify parameters capable of predicting excessive aldosterone production. The result of adrenal venous sampling served as gold standard in present study. As a result, logistic regression analysis indicated that the mean gray level intensity (p = 0.026), the mean value of the positive pixels (p = 0.003) in the unfiltered image, and entropy (p = 0.027) in the filtered image (SSF: 2 mm) were significant parameters. Using the model constructed by logistic regression analysis and the optimum cutoff value, the localization of PA (three multiple choices of left, right or bilateral) was determined with an accuracy of 67.1% (55/82). CT texture analysis may provide a potential avenue for less invasive prediction of the localization of PA.

Purpose: A novel image feature set named histogram of triangular paths in graph (HoTPiG) is presented. The purpose of this study is to evaluate the feasibility of the proposed HoTPiG feature set through two clinical computer-aided detection tasks: nodule detection in lung CT images and aneurysm detection in head MR angiography images., Methods: The HoTPiG feature set is calculated from an undirected graph structure derived from a binarized volume. The features are derived from a 3-D histogram in which each bin represents a triplet of shortest path distances between the target node and all possible node pairs near the target node. First, the vessel structure is extracted from CT/MR volumes. Then, a graph structure is extracted using an 18-neighbor rule. Using this graph, a HoTPiG feature vector is calculated at every foreground voxel. After explicit feature mapping with an exponential-χ 2 kernel, each voxel is judged by a linear support vector machine classifier. The proposed method was evaluated using 300 CT and 300 MR datasets., Results: The proposed method successfully detected lung nodules and cerebral aneurysms. The sensitivity was about 80% when the number of false positives was three per case for both applications., Conclusions: The HoTPiG image feature set was presented, and its high general versatility was shown through two medical lesion detection applications.

OBJECTIVE. The purpose of this study was to investigate whether, in the evaluation of unconscious patients in the emergency department, a new-generation CT scanner that acquires images in ultrafast scan mode (large coverage, fast rotation, high helical pitch) would reduce motion artifacts on whole-body CT images in comparison with those on images obtained with a conventional CT scanner. MATERIALS AND METHODS. Images of a total of 60 unconscious patients presenting to the emergency department were evaluated retrospectively. Of the 60 patients, 30 underwent CT with a new-generation scanner that acquires images in the ultrafast mode, and 30 underwent CT with a conventional scanner. Two radiologists independently evaluated motion artifacts in the aorta, lung, diaphragm, liver, and kidneys. The motion artifacts were graded in severity on a 4-point scale. A value of p < 0.05 was considered to indicate a statistically significant difference. RESULTS. Interobserver agreement on motion artifact ratings was good (κ = 0.80-0.93). Images obtained with the new-generation CT scanner showed a significant reduction in motion artifacts in the aortic root ( p = 0.0003), lower lungs ( p = 0.011), diaphragm ( p = 0.0047), liver ( p = 0.0026), and kidneys ( p = 0.019). However, there were no significant differences between the two groups with respect to motion artifacts of the aortic arch, thoracic descending aorta, abdominal aorta, and upper lungs. CONCLUSION. CT images obtained in the ultrafast scan mode in the evaluation of unconscious patients had a significant reduction in motion artifacts. The ultrafast technique is expected to be useful for diagnostic CT in the emergency department.

Little is known regarding differences between model-based iterative reconstruction (MBIR) and hybrid iterative reconstruction (HIR) in temporal bone computed tomography (CT). This study compared the ability to depict microstructures in temporal bone in quarter-detector CT (QDCT) between MBIR and HIR.Sixty-two temporal bones in 31 consecutive adult patients who underwent QDCT were included. Reconstruction was performed with Forward projected model-based Iterative Reconstruction SoluTion (FIRST) BONE mild mode and Adaptive Iterative Dose Reduction 3D (AIDR3D) enhanced mild mode. Imaging quality was graded for 3 microstructures (spiral osseous lamina, tympanic membrane, and singular canal).Spiral osseous lamina was significantly well-delineated in the AIDR3D enhanced group, compared with the FIRST group. In nearly all cases with FIRST, spiral osseous lamina was poorly defined. Although there was no significant difference, depiction of the tympanic membrane and singular canal tended to be better with AIDR3D enhanced mode.Routine reconstruction for preoperative temporal bone CT should be performed with HIR, rather than MBIR.

Objectives: To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images., Methods: This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance., Results: The F DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using F DLCT scores were 0.74 (0.64-0.85), 0.76 (0.66-0.85) and 0.73 (0.62-0.84), respectively., Conclusions: Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance., Key Points: • Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.

Purpose: To evaluate the effects of the single-energy metal artifact reduction (SEMAR) algorithm on image quality of cerebral CT and CT angiography (CTA) for patients who underwent intracranial aneurysm coiling., Methods: Twenty patients underwent cerebral CT and CTA using a 320-detector row CT after intracranial aneurysm coiling. Images with and without application of the SEMAR algorithm (SEMAR CT and standard CT images, respectively) were reconstructed for each patient. The images were qualitatively assessed by two independent radiologists in a blinded manner for the depiction of anatomical structures around the coil, delineation of the arteries around the coil, and the depiction of the status of coiled aneurysms. Artifact strength was quantitatively assessed by measuring the standard deviation of attenuation values around the coil., Results: The strength of artifacts measured in SEMAR CT images was significantly lower than that in standard CT images (25.7 ± 10.2 H.U. vs. 80.4 ± 67.2 H.U., p < 0.01, Student's paired t test). SEMAR CT images were significantly improved compared with standard CT images in the depiction of anatomical structures around the coil (p < 0.01, the sign test), delineation of the arteries around the coil (p < 0.01), and the depiction of the status of coiled aneurysms (p < 0.01)., Conclusion: The SEMAR algorithm significantly reduces metal artifacts from intracranial aneurysm coiling and improves visualization of anatomical structures and arteries around the coil, and depiction of the status of coiled aneurysms on post-interventional cerebral CT.

Rationale and Objectives: To investigate the impact of random survival forest (RSF) classifier trained by radiomics features over the prediction of the overall survival of patients with resectable hepatocellular carcinoma (HCC)., Materials and Methods: The dynamic computed tomography data of 127 patients (97 men, 30 women; mean age, 68 years) newly diagnosed with resectable HCC were retrospectively analyzed. After manually setting the region of interest to include the tumor within the slice at its maximum diameter, texture analyses were performed with or without a Laplacian of Gaussian filter. Using the extracted 96 histogram based texture features, RSFs were trained using 5-fold cross-validation to predict the individual risk for each patient on disease free survival (DFS) and overall survival (OS). The associations between individual risk and DFS or OS were evaluated using Kaplan-Meier analysis. The effects of the predicted individual risk and clinical variables upon OS were analyzed using a multivariate Cox proportional hazards model., Results: Among the 96 histogram based texture features, RSF extracted 8 of high importance for DFS and 15 for OS. The RSF trained by these features distinguished two patient groups with high and low predicted individual risk (P=1.1×10 -4 for DFS, 4.8×10 -7 for OS). Based on the multivariate Cox proportional hazards model, high predicted individual risk (hazard ratio=1.06 per 1% increase, P=8.4×10 -8 ) and vascular invasion (hazard ratio=1.74, P=0.039) were the only unfavorable prognostic factors., Conclusions: The combination of radiomics analysis and RSF might be useful in predicting the prognosis of patients with resectable HCC., (Copyright © 2018 Soci showét showé françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Objectives: To determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH)., Methods: NECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels. The reference standard was the histological result of a liver biopsy. A predictive model for NASH was developed using parameters derived from the learning dataset that demonstrated areas under the receiver operating characteristic curve (AUC) of >0.65. The resulting model was then applied to the validation dataset., Results: In patients without suspected fibrosis, the texture parameter mean without filter and skewness with a 2-mm filter were selected for the NASH prediction model. The AUC of the predictive model for the validation dataset was 0.94 and the accuracy was 94%. In patients with suspicion of fibrosis, the mean without filtration and kurtosis with a 4-mm filter were selected for the NASH prediction model. The AUC for the validation dataset was 0.60 and the accuracy was 42%., Conclusions: In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH., Key Points: • In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. • The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis. • Hepatic fibrosis masks the characteristic texture features of NASH.

Radiomics, which involves the extraction of large numbers of quantitative features from medical images, has attracted attention in cancer research. In radiomics analysis, tumor segmentation is a crucial step. In this study, we evaluated the potential application of radiomics for predicting the histology of early stage non-small cell lung cancer (NSCLC) by analyzing interobserver variability in tumor delineation. Forty patient datasets were included in this study, 21 involving adenocarcinomas and 19 involving squamous cell carcinomas. All patients underwent stereotactic body radiotherapy treatment. In total, 476 features were extracted from each dataset, representing treatment planning, computed tomography images, and gross tumor volume (GTV). The definition of GTV can significantly affect the histology prediction. Therefore, in the present study, the effect of interobserver tumor delineation variability on radiomic features was evaluated by preparing 4 volumes of interest (VOIs) for each patient, as follows: the original GTV (which was delineated at treatment planning); two GTVs delineated retrospectively by radiation oncologists; and a semi-automatic GTV contoured by a medical physicist. Radiomic features extracted from each VOI were then analyzed using a naïve Bayesian model. Area-under-the-curve (AUC) analysis showed that interobserver variability in delineation is a significant factor in radiomics performance. Nevertheless, with 8 selected features, AUC values averaged over the VOIs were high (0.725 ± 0.070). The present study indicated that radiomics has potential for predicting early stage NSCLC histology despite variability in delineation. The high prediction accuracy implies that noninvasive histology evaluation by radiomics is a promising clinical application.

Objectives: To investigate whether solid anterior mediastinal masses could be differentiated from cysts using quantitative computed tomography (CT) texture analyses in unenhanced CT (UECT) or contrast enhanced CT (CECT)., Materials and Methods: This clinical retrospective study included 76 UECT images (40 men and 36 women, 28 cystic (mean diameter, 29.5 mm) and 48 solid (mean diameter, 48.2 mm)) and 84 CECT images (45 men and 39 women, 26 cystic (mean diameter, 31.4 mm) and 58 solid (mean diameter, 51.4 mm)) of anterior mediastinal masses, which were diagnosed histopathologically or using imaging criteria. Polygonal regions of interest were placed on these masses. CT histogram analyses for images of masses with or without filtration (Laplacian of Gaussian filters with various spatial scaling factors) were performed. DeLong's test was performed to compare areas under the curve (AUC) with receiver operating characteristic analyses., Results: From logistic regression analyses with a stepwise procedure, a combination of the mean in unfiltered images (mean0; i.e., CT attenuation) and mean in filtered images featuring coarse texture for UECT (AUC = 0.869) and the combination of mean0 and entropy in filtered images featuring fine texture for CECT (AUC = 0.997) were found to predict better the internal characteristics of anterior mediastinal masses. In UECT and CECT, diagnostic performance using these combinations tended to be high compared to mean0 alone (AUC = 0.780 [p = 0.033] and AUC = 0.983 [p = 0.130], respectively)., Conclusion: Solid anterior mediastinal masses can be differentiated from cysts using quantitative CT texture analyses with moderate and high diagnostic performance in UECT and CECT, respectively., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Artifacts caused by metallic implants appear as dark and bright streaks at computed tomography (CT), which severely degrade the image quality and decrease the diagnostic value of the examination. When x-rays pass through a metal object, depending on its size and composition, different physical effects negatively affect the measurements in the detector, most notably the effects of photon starvation and beam hardening. To improve image quality and recover information about underlying structures, several artifact reduction methods have been introduced in modern CT systems. Projection-based metal artifact reduction (MAR) algorithms act in projection space and replace corrupted projections caused by metal with interpolation from neighboring uncorrupted projections. MAR algorithms primarily suppress artifacts that are due to photon starvation. The dual-energy CT technique is characterized by data acquisition at two different energy spectra. Dual-energy CT provides synthesized virtual monochromatic images at different photon energy (kiloelectron volt) levels, and virtual monochromatic images obtained at high kiloelectron volt levels are known to reduce the effects of beam hardening. In clinical practice, although MAR algorithms can be applied after image acquisition, the decision whether to apply dual-energy CT for the patient usually needs to be made before image acquisition. Radiologists should be more familiar with the clinical and technical features of each method and should be able to choose the optimal method according to the clinical situation. © RSNA, 2018.

Postmortem computed tomography (PMCT) of the brain has an important role in detection of subarachnoid hemorrhage (SAH), which has a high mortality rate. However, a phenomenon known as "pseudo-SAH," or high-attenuation areas along the cisterns mimicking SAH, may be seen on CT. The aim of this study was to evaluate the diagnostic accuracy of brain PMCT for SAH and to identify the characteristics of pseudo-SAH. Findings on PMCT (sulcal effacement, asymmetry, maximum thickness of SAH signs, presence of acute/subacute intraventricular/intraparenchymal hemorrhage) and clinical history (left ventricular assist device [LVAD] implantation, anticoagulation therapy/coagulation disorder, global ischemia) were compared between subjects with true SAH and those with pseudo-SAH. Twenty eight of 128 enrolled subjects had positive signs of SAH on PMCT, 20 (71.4%) had SAH on autopsy, and 8 (28.6%) did not. The sensitivity, specificity, positive predictive value, and negative predictive value of SAH signs seen on PMCT were 95.2, 94.6, 71.4, and 99.3%, respectively. Asymmetry of SAH signs and acute/subacute intraventricular and intraparenchymal hemorrhage were significantly more common in true SAH cases than in pseudo-SAH cases. The maximum thickness of SAH signs was significantly greater in true SAH cases. A history of LVAD implantation, anticoagulation therapy, and/or a coagulation disorder were more common in true SAH cases but not significantly so. A history of global ischemia was significantly more common in pseudo-SAH cases. If signs of SAH are observed on PMCT, it is important to look for other signs on PMCT and carefully review the clinical history to avoid a diagnostic error.

Background: Full iterative reconstruction algorithm is available, but its diagnostic quality in pediatric cardiac CT is unknown., Objective: To compare the imaging quality of two algorithms, full and hybrid iterative reconstruction, in pediatric cardiac CT., Materials and Methods: We included 49 children with congenital cardiac anomalies who underwent cardiac CT. We compared quality of images reconstructed using the two algorithms (full and hybrid iterative reconstruction) based on a 3-point scale for the delineation of the following anatomical structures: atrial septum, ventricular septum, right atrium, right ventricle, left atrium, left ventricle, main pulmonary artery, ascending aorta, aortic arch including the patent ductus arteriosus, descending aorta, right coronary artery and left main trunk. We evaluated beam-hardening artifacts from contrast-enhancement material using a 3-point scale, and we evaluated the overall image quality using a 5-point scale. We also compared image noise, signal-to-noise ratio and contrast-to-noise ratio between the algorithms., Results: The overall image quality was significantly higher with full iterative reconstruction than with hybrid iterative reconstruction (3.67±0.79 vs. 3.31±0.89, P=0.0072). The evaluation scores for most of the gross structures were higher with full iterative reconstruction than with hybrid iterative reconstruction. There was no significant difference between full and hybrid iterative reconstruction for the presence of beam-hardening artifacts. Image noise was significantly lower in full iterative reconstruction, while signal-to-noise ratio and contrast-to-noise ratio were significantly higher in full iterative reconstruction., Conclusion: The diagnostic quality was superior in images with cardiac CT reconstructed with electrocardiogram-gated full iterative reconstruction.

A 43-year-old Japanese man presented with a history of bacterial meningitis (BM). He was admitted to our department with a one-day history of headache and was diagnosed with relapse of BM based on the cerebrospinal fluid findings. The conventional imaging studies showed serial findings suggesting left otitis media, a temporal cephalocele, and meningitis. Three-dimensional multi-detector computed tomography (3D-MDCT) showed left petrous bone defects caused by the otitis media, and curative surgical treatment was performed. Skull bone structural abnormalities should be considered a cause of relapsed BM. 3D-MDCT was useful for revealing the causal minimal bone abnormality and performing pre-surgical mapping.

We report a case of kaposiform hemangioendothelioma (KHE) occurring at the sigmoid colon in an 8-year-old male. He had experienced lower abdominal pain and fever for several days. Contrast-enhanced CT images revealed a well-enhanced mass involving several large and small cystic parts with thin, enhanced walls at the sigmoid colon. The diagnosis was made histopathologically after surgical resection; the cystic parts corresponded to dilated lymphatic vessels. KHE is a rare, locally aggressive, vascular tumor that usually occurs in the soft tissues of the extremities. However, a case occurring in the bowel is extremely rare, and usually appears as bowel wall thickening. This is a unique case of KHE appearing as a mass-shaped hypervascular lesion in the bowel. Dilated lymphatic vessels in KHE may appear as a cystic part of the lesion.

Brachial plexus injury (BPI) is a severe neurologic injury that causes functional impairment of the affected upper limb. Imaging studies play an essential role in differentiating between preganglionic and postganglionic injuries, a distinction that is crucial for optimal treatment planning. Findings at standard myelography, computed tomographic (CT) myelography, and conventional magnetic resonance (MR) imaging help determine the location and severity of injuries. MR imaging sometimes demonstrates signal intensity changes in the spinal cord, and enhancement of nerve roots and paraspinal muscles at MR imaging indicates the presence of root avulsion injuries. New techniques including MR myelography, diffusion-weighted neurography, and Bezier surface reformation can also be useful in the evaluation and management of BPI. MR myelography with state-of-the-art technology yields remarkably high-quality images, although it cannot replace CT myelography entirely. Diffusion-weighted neurography is a cutting-edge technique for visualizing postganglionic nerve roots. Bezier surface reformation allows the depiction of entire intradural nerve roots on a single image. CT myelography appears to be the preferred initial imaging modality, with standard myelography and contrast material-enhanced MR imaging being recommended as additional studies. Work-up will vary depending on the equipment used, the management policy of peripheral nerve surgeons, and, most important, the individual patient., ((c) RSNA, 2006.)

The computed tomographic (CT) and magnetic resonance appearance of pseudotumorous enlargement of the paracaval portion (PCP) of the caudate lobe of the liver are presented in two cirrhotic patients. The enlarged PCP was hyperattenuated on precontrast CT and hyperintense on T1-weighted images. The middle and right hepatic veins were displaced around the PCP. The presence of a portal vein branch penetrating the center of the enlarged PCP was important to distinguish it from other hepatic masses in the two patients.

Purpose: To evaluate a coaxial catheter method to sequentially acquire spiral-computed tomography (CT) during arterial portography (CTAP) and hepatic arteriography (CTA) at a single transfer., Patients and Methods: Sixteen patients with malignant hepatic tumours (12 patients with hepatocellular carcinoma, four with metastases) were studied using spiral CT. Depending upon the vascular anatomy revealed by conventional coeliac and superior mesenteric arteriography, an outer and inner catheter were selectively placed to perform CTA and CTAP, respectively. CTAP images were obtained first, while injecting contrast material through an inner catheter followed by the acquisition of the CTA images during injection through an outer catheter. In three patients, the resected specimens were available for comparison with the imaging findings., Results: In 12 patients with standard hepatic arterial anatomy, high quality images of CTAP and CTA were obtained. More lesions were detected by the combination of CTAP and CTA than by CTAP or CTA alone in five patients. In one patient with breast carcinoma and a left hepatic artery arising from the left gastric artery, numerous hepatic metastases were delineated on both sets of images. In three patients with replaced or accessory right hepatic arteries, evaluation of the whole liver was difficult on CTA. These procedures were well tolerated by all 16 patients and no complication or technical failure was experienced., Conclusion: These preliminary data support this new technique as a promising method of performing CTA and CTAP in patients with standard hepatic arterial anatomy with a single catheter insertion.