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Background:In stable patients with blunt abdominal trauma, accurate diagnosis of visceral injuries is crucial.Objectives:To determine whether repeating ultrasound exam will increase the sensitivity of focused abdominal sonography for trauma (FAST) through revealing additional free intraperitoneal fluid in patients with blunt abdominal trauma.Patients and Methods:We performed a prospective observational study by performing primary and secondary ultrasound exams in blunt abdominal trauma patients. All ultrasound exams were performed by four radiology residents who had the experience of more than 400 FAST exams. Five routine intraperitoneal spaces as well as the interloop space were examined by ultrasound in order to find free fluid. All patients who expired or were transferred to the operating room before the second exam were excluded from the study. All positive ultrasound results were compared with intra-operative and computed tomography (CT) findings and/or the clinical status of the patients.Results:Primary ultrasound was performed in 372 patients; 61 of them did not undergo secondary ultrasound exam; thus, were excluded from the study.Three hundred eleven patients underwent both primary and secondary ultrasound exams. One hundred and two of all patients were evaluated by contrast enhanced CT scan and 31 underwent laparotomy. The sensitivity of ultrasound exam in detecting intraperitoneal fluid significantly increased from 70.7% for the primary exam to 92.7% for the secondary exam. Examining the interloop space significantly improved the sensitivity of ultrasonography in both primary (from 36.6% to 70.7%) and secondary (from 65.9% to 92.7%) exams.Conclusions:Performing a secondary ultrasound exam in stable blunt abdominal trauma patients and adding interloop space scan to the routine FAST exam significantly increases the sensitivity of ultrasound in detecting intraperitoneal free fluid.

A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull segmentation. Due to the non-homogeneity of skull intensities in MR images, local statistical parameters are used for adaptive training of Hopfield neural network based on Bayesian classifier error. The experimental results, which are obtained on high resolution T1-weighted MR images of nine neonates with gestational ages between 39 and 42 weeks, show 65% accuracy which consistently exhibits our scheme's superiority in comparison with previous neonatal skull segmentation methods.

Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. The aim of this paper is to develop an accurate and reliable method for segmentation of lung HRCT images using a pixelbased approach.The proposed method combines traditional concepts, such as global-threshold segmentation, mathematical morphology, edge detection and noise reduction, with new ideas, such as performing geometrical computations to achieve the defined ROIs. Two different approaches are proposed and tested on 100 computed-tomography images. Noise tolerance of the algorithm is calculated considering several parameters and objective criteria. In addition, the image segmentation results were visually validated by radiologists.

The convergence rate, computational speed, and accuracy of the UKF algorithm can be improved using the two-stage method.

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Background. Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation to high-risk patients. Purpose. To assess the predictive value of on-admission chest CT characteristics to estimate COVID-19 patients’ outcome and survival time. Materials and Methods. Using a case-control design, we included all laboratory-confirmed COVID-19 patients who were deceased, from June to September 2020, in a tertiary-referral-collegiate hospital and had on-admission chest CT as the case group. The patients who did not die and were equivalent in terms of demographics and other clinical features to cases were considered as the control (survivors) group. The equivalency evaluation was performed by a fellowship-trained radiologist and an expert radiologist. Pulmonary involvement (PI) was scored (0–25) using a semiquantitative scoring tool. The PI density index was calculated by dividing the total PI score by the number of involved lung lobes. All imaging parameters were compared between case and control group members. Survival time was recorded for the case group. All demographic, clinical, and imaging variables were included in the survival analyses. Results. After evaluating 384 cases, a total of 186 patients (93 in each group) were admitted to the studied setting, consisting of 126 (67.7%) male patients with a mean age of 60.4 ± 13.6 years. The PI score and PI density index in the case vs. the control group were on average 8.9 ± 4.5 vs. 10.7 ± 4.4 ( p value: 0.001) and 2.0 ± 0.7 vs. 2.6 ± 0.8 ( p value: 0.01), respectively. Axial distribution (p value: 0.01), cardiomegaly ( p value: 0.005), pleural effusion (p value: 0.001), and pericardial effusion ( p value: 0.04) were mostly observed in deceased patients. Our survival analyses demonstrated that PI score ≥ 10 ( p value: 0.02) and PI density index ≥ 2.2 ( p value: 0.03) were significantly associated with a lower survival rate. Conclusion. On-admission chest CT features, particularly PI score and PI density index, are potential great tools to predict the patient’s clinical outcome.

Background and Objectives:Optic neuritis (ON) and nonarteritic anterior ischemic optic neuropathy (NAION) have some overlapping clinical profiles. We evaluated the usefulness of B-scan ultrasonography in distinguishing ON from NAION by measuring diameter of the optic nerve.Materials and Methods:Consecutive patients with an acute noncompressive unilateral optic neuropathy with relative afferent pupillary defect and onset of visual loss during the last 2 weeks were included. Diagnosis of ON was based on age ≤ 35 years, orbital pain associated with eye movement, and no disk edema, and diagnosis of NAION was based on age ≥ 60 years, no orbital pain associated with eye movement, and presence of disk edema. Age- and gender-matched subjects without ocular disease were selected for comparison. The diameter of the optic nerve was measured by a single radiologist with B-scan ultrasonography.Results:In ON patients, the mean diameter of the affected nerve was significantly larger than that of the unaffected nerve and also larger than that of the right nerve of young controls; P < 0.05. In NAION patients, however, there was no significant difference between the mean diameter of the affected nerve and of the unaffected nerve or the right nerve of elderly controls; P > 0.05. Also, the diameter of the affected nerve was significantly larger in ON than in AION patients; P < 0.05.Conclusion:B-scan ultrasonography is helpful in the early stages of optic neuropathy to distinguish ON from NAION in those cases for which the diagnosis is still uncertain after clinical evaluation.