METHODS: We reviewed children aged
METHODS: The pre- and post-operative CT images of 55 patients undergoing DC surgery were analyzed. The ICV was measured by segmenting every slice of the CT images, and compared with estimated ICV calculated using the 1-in-10 sampling strategy and processed using the SBI method. An independent t test was conducted to compare the ICV measurements between the two different methods. The calculation using this method was repeated three times for reliability analysis using the intraclass correlations coefficient (ICC). The Bland-Altman plot was used to measure agreement between the methods for both pre- and post-operative ICV measurements.
RESULTS: The mean ICV (±SD) were 1341.1±122.1ml (manual) and 1344.11±122.6ml (SBI) for the preoperative CT data. The mean ICV (±SD) were 1396.4±132.4ml (manual) and 1400.53±132.1ml (SBI) for the post-operative CT data. No significant difference was found in ICV measurements using the manual and the SBI methods (p=.983 for pre-op, and p=.960 for post-op). The intrarater ICC showed a significant correlation; ICC=1.00. The Bland-Altman plot showed good agreement between the manual and the SBI method.
CONCLUSION: The shape-based interpolation method with 1-in-10 sampling strategy gave comparable results in estimating ICV compared to manual segmentation. Thus, this method could be used in clinical settings for rapid, reliable and repeatable ICV estimations.
METHODS: A 3D-printed cardiac insert and Catphan 500 phantoms were scanned using CCTA protocols at 120 and 100 kVp tube voltages. All CT acquisitions were reconstructed using filtered back projection (FBP) and Adaptive Statistical Iterative Reconstruction (ASIR) algorithm at 40% and 60% strengths. Image quality characteristics such as image noise, signal-noise ratio (SNR), contrast-noise ratio (CNR), high spatial resolution, and low contrast resolution were analyzed.
RESULTS: There was no significant difference (P > 0.05) between 120 and 100 kVp measures for image noise for FBP vs ASIR 60% (16.6 ± 3.8 vs 16.7 ± 4.8), SNR of ASIR 40% vs ASIR 60% (27.3 ± 5.4 vs 26.4 ± 4.8), and CNR of FBP vs ASIR 40% (31.3 ± 3.9 vs 30.1 ± 4.3), respectively. Based on the Modulation Transfer Function (MTF) analysis, there was a minimal change of image quality for each tube voltage but increases when higher strengths of ASIR were used. The best measure of low contrast detectability was observed at ASIR 60% at 120 kVp.
CONCLUSIONS: Changing the IR strength has yielded different image quality noise characteristics. In this study, the use of 100 kVp and ASIR 60% yielded comparable image quality noise characteristics to the standard CCTA protocols using 120 kVp of ASIR 40%. A combination of 3D-printed and Catphan® 500 phantoms could be used to perform CT dose optimization protocols.
STUDY DESIGN: Diagnostic cross-sectional study.
METHODS: This study included consecutive CRS patients without prior sinus surgery. Computed tomography (CT) scans of the paranasal sinuses were blindly assessed and allergy status was confirmed by serum or skin testing. Individual sinus cavities were defined as either centrally limited or diffuse disease. The radiological pattern that may predict allergy was determined, and its diagnostic accuracy was calculated.
RESULTS: One hundred twelve patients diagnosed to have CRS, representing 224 sides, were assessed (age 46.31 ± 13.57 years, 38.39% female, 41.07% asthma, Lund-Mackay CT score 15.88 ± 4.35, 56.25% atopic). The radiological pattern defined by centrally limited changes in all of the paranasal sinuses was associated with allergy status (73.53% vs. 53.16%, P = .03). This predicted atopy with 90.82% specificity, 73.53% positive predictive value, likelihood positive ratios of 2.16, and diagnostic odds ratio of 4.59.
CONCLUSIONS: A central radiological pattern of mucosal disease is associated with inhalant allergen sensitization. This group may represent a CCAD subgroup of patients with mainly allergic etiology.
LEVEL OF EVIDENCE: 3b Laryngoscope, 128:2015-2021, 2018.
METHODS: Computed tomography scans of 102 wrists from 51 healthy individuals were analyzed using a virtualization software. Four anatomical parameters at the distal radius sigmoid notch, namely, the radius of curvature, depth, version angle, and sagittal slope were measured. Morphological patterns of the sigmoid notch surface were identified. The results were statistically analyzed to assess the reliability of the technique and were compared with previously published literature.
RESULTS: Comparing our findings with previously published values, our study revealed a slightly larger radius of curvature and sagittal slope, while revealing a smaller depth and version. We identified the S-type, C-type, and ski-slope morphological variants. The flat-face morphological variant, however, was not identified. The sigmoid notch at the left and right wrists were similar, except for the radius of curvature.
CONCLUSION: This study demonstrates a noninvasive, fast, reliable, and reproducible technique for analyzing the sigmoid notch of the distal radius. In wrist injuries with intact distal radius sigmoid notch but involving comminuted fractures of the ulnar head, ulnar head replacement may be indicated. In such cases, analysis of the ipsilateral intact sigmoid notch would allow us to prepare an ulnar head prosthesis of appropriate size.
DISCUSSION: It is a set of various methodologies which are used to capture internal or external images of the human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise.
CONCLUSION: This review presents an up-to-date coverage about research topics which include recent literature in the areas of MRI imaging, comparison with other modalities, noise in MRI and machine learning techniques to remove the noise.
OBJECTIVE: The aim of the study was to characterize the perfusion patterns on perfusion computed tomography (PCT) in patients with seizures masquerading as acute stroke.
METHODS: We conducted a study on patients with acute seizures as stroke mimics. The inclusion criteria for this study were patients (1) initially presenting with stroke-like symptoms but finally diagnosed to have seizures and (2) with PCT performed within 72 h of seizures. The PCT of seizure patients (n = 27) was compared with that of revascularized stroke patients (n = 20) as the control group.
RESULTS: Among the 27 patients with seizures as stroke mimics, 70.4% (n = 19) showed characteristic PCT findings compared with the revascularized stroke patients, which were as follows: (1) multi-territorial cortical hyperperfusion {(73.7% [14/19] vs. 0% [0/20], p = 0.002), sensitivity of 73.7%, negative predictive value (NPV) of 80%}, (2) involvement of the ipsilateral thalamus {(57.9% [11/19] vs. 0% [0/20], p = 0.007), sensitivity of 57.9%, NPV of 71.4%}, and (3) reduced perfusion time {(84.2% [16/19] vs. 0% [0/20], p = 0.001), sensitivity of 84.2%, NPV of 87%}. These 3 findings had 100% specificity and positive predictive value in predicting patients with acute seizures in comparison with reperfused stroke patients. Older age was strongly associated with abnormal perfusion changes (p = 0.038), with a mean age of 66.8 ± 14.5 years versus 49.2 ± 27.4 years (in seizure patients with normal perfusion scan).
CONCLUSIONS: PCT is a reliable tool to differentiate acute seizures from acute stroke in the emergency setting.
CASE REPORT: We describe two unusual and diverse incidental adrenal gland lesions, an adenomatoid nodule and a mature ganglioneuroma. Both are deemed 'indeterminate' on radiological assessment. On histology, an adenomatoid nodule is composed of variably-dilated thin-walled cysts lined by bland flattened cells and solid areas of tubules lined by eosinophilic cells with plump nuclei and prominent nucleoli. The lining cells are immunoreactive for calretinin and WT1 while negative for CK5/6, ERG and CD31. Mature ganglioneuroma features fascicles of bland spindle cells with intermixed mature ganglion cells disposed within a background myxoid stroma with no immature neuroblastic component. These spindled Schwann cells are S100 positive.
DISCUSSION: Both adenomatoid nodule and mature ganglioneuroma are rare benign adrenal tumours that need to be differentiated from other, more common adrenal lesions. The management of adrenal incidentalomas is challenging. Surgical excision is indicated if an adrenal incidentaloma is more than 4 cm in size, shows malignant features on imaging or evidence of hormone excess.
METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.