Normal Computed Tomography Images Research Articles

Surgery based on computed tomography images might be feasible for primary aldosteronism patients with visible unilateral adenoma.

Primary aldosteronism (PA) with unilateral adrenal disease can be cured or improved by adrenalectomy. Adrenal venous sampling (AVS) is recommended to identify patients for surgical management. However, surgeries based on computed tomography (CT) images are only advocated for PA patients aged <35 with visible unilateral adenoma. Herein, we aimed to compare CT-based and AVS-based surgery outcomes for PA patients with visible unilateral adenomas for different age groups. A total of 178 PA patients who underwent unilateral adrenalectomy between June 2018 and January 2021 were included in the study based on CT (n=54) or AVS (n=124). Demographics, diagnostics, and follow-up data were retrospectively collected. Clinical and biochemical outcomes were analyzed according to Primary Aldosteronism Surgical Outcome (PASO) criteria at 1-year follow-up. Our results showed that complete clinical success (46.3%vs. 47.6%, p=0.875) and complete biochemical success (88.8%vs. 91.9%, p=0.515) were similar between the two groups. Age stratification revealed that patients >55 years old were likely to have worse biochemical outcomes; however, these were still not significantly different (21.4%vs. 8.6%, p=0.220). Of the 114 AVS-based patients who achieved complete biochemical success, 37 (32.4%) with bilateral normal or bilateral abnormal CT images changed treatment options according to AVS results, 1 (0.9%) avoided adrenalectomy on the wrong side. Our results indicated that surgery based on CT images might be feasible for highly selected PA patients with visible unilateral adenomas and less limited by age, while for those with normal adrenal or bilateral adrenal lesions, treatment strategy must be decided by AVS.

Toraks bilgisayarlı tomografisinin yorumlanmasında acil tıp asistanlarının başarısı ve tanı koymadaki özgüvenleri

Purpose:&#x0D; Interpration of thoracic computed tomography (CT) scans by emergency department (ED) physicians is important especially in crowded EDs. The aim of this study is to assess the proficiency and confidence of ED physicians with varying levels of experience in interpreting thoracic CT.&#x0D; Materials and methods:&#x0D; A total of 25 pathological and 5 normal thoracic CT images were interpreted by 32 ED residents, initially without clinical information, then with. After each session the participants were asked to score their confidence on a scale of 1 to 10. At the end of the study, the results were compared between seniors and junior residents. &#x0D; Results:&#x0D; The median age of the participants was 29 years (24–34). Twenty (62.5%) of the residents were junior residents. There were no significant differences between the two resident groups in terms of accurate diagnosis rates, regardless of the clinical information (p=0.307 and p=0.061). The physicians’ certainty of their own diagnosis mostly does not seem to be statistically different in these diagnoses. The seniors are more confident in the diagnosis of the CT scans they evaluated without clinical information (p=0.004), while when the clinical information is added, the confidence of the junior physicians also increase (p=0.087).&#x0D; Conclusion:&#x0D; Both senior and junior emergency medical residents are able to interpret thoracic CT images with a high degree of accuracy, both for COVID-19 and for other emergency diagnoses. Senior physicians could confidently diagnose CT images solely by visual inspection, whereas junior residents display similar confidence when clinical information is available.

Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis.

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019, and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by ground glass opacity, a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo-lesion generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo-COVID-19 images. The pairs of normal and pseudo-COVID-19 images were then used to train an encoder-decoder architecture-based U-Net for image restoration, which does not require any labeled data. The pretrained encoder was then fine-tuned using labeled data for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis, and the accuracy of the proposed method outperformed the supervised model pretrained on large-scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.

A radiographic study of dorsovolar wrist axes and a database of angular measurements on axial computed tomography images.

This study aims to identify the most accurate dorsovolar principal axis of the distal radius and carpus identified on axial computed tomography (CT) sections and to establish normative data for angular measurements among these axes. Between December 2019 and December 2021, normal axial CT images of wrists of a total of 42 individuals (25 males, 17 females; mean age: 31±8.4 years; range, 18 to 45 years) were retrospectively analyzed. Eight axes were identified on axial CT images: four distal radial axes (the volar cortical, medial cortical, central, and sigmoid notch axes) and four carpal axes (the scapholunate, lunotriquetral, capitohamate, and pisotrapezial axes). Twenty-two angular parameters were measured with reference to four principal axes (the volar cortical, medial cortical, central, and pisotrapezial axes). The mean sigmoid notch rotation (version) angles relative to the four principal axes were 8±5° (range, -2° to 18°), 6±5° (range, -2° to 13°), 1±5° (range, -8° to 14°), and 4±4° (range, -3° to 15°), respectively. The mean scapholunate rotation angles were -13±5° (range, -27° to -6°), -15±6° (range, -29° to -8°), -21±5° (range, -30° to -11°), and -8±5° (range, -28° to -6°), respectively. Among four principal axes, the volar cortical and medial cortical axes were nearly collinear with both of relatively fixed carpal axes. The four principal axes showed angular differences between 2° and 8° with each other. There was no significant difference between men and women for all measurements. The axial CT sections can be used to describe the various angulations between the normal wrist axes such as the sigmoid notch and scapholunate joint rotation angles. Despite slight differences among the four principal axes, the volar cortical and medial cortical axes are more consistent with the relatively fixed carpal axes.

The Use of Foramen Magnum in The Determination of Sex Using Computed Tomography (CT) Scan Images of Sampled Population Attending National Ear Care Centre Kaduna State, Nigeria

Determination of sex is an important element of human identification which becomes challenging when only only fragments of body parts are available for investigation by forensic experts. This study was aimed at assessing the utility of foramen magnum parameters in the determination of sex among the Nigerian population. A retrospective study was conducted on patients attending National Ear Care Center Kaduna between the years 2017-2019. A total of 399 normal Multislice CT images of skulls (236 males and 163 females) of the age of 18 – 95 years were randomly picked from the archives of the Hospital in the computer database of the Radiology Department. The foramen magnum and occipital condyles parameters were measured by an experienced radiologist. Males tend to have a significantly higher mean value of foramen magnum width, Foramen Magnum Index, the length of the right occipital condyle, and the length of left occipital condyles as well as the width of the right occipital condyle at a p-value ≤ 0.05. The width of right occipital condyles was the only variable that got a significant (p ≤ 0.05) impact on our sex estimation following multiple logistic regression. However, by applying discriminate function analysis the length of the left occipital condyle and width of the right occipital condyle were the only discriminatory variables chosen for this study population. In conclusion, this study indicated that five variables of foramen magnum out of ten were statistically significantly different with males having higher mean values than females. Moreover, the only significant discriminatory variables to determine sex in this study population were the length of the left occipital condyle and the width of the right occipital condyle respectively.

Yeni bir Evrişimsel Sinir Ağı Modeli Kullanarak Bilgisayarlı Tomografi Görüntülerinden Akciğer Kanseri Tespiti

Akciğer kanseri, ülkemizde ve dünyada yaygın bir şekilde görülen kanser tipidir ve kansere bağlı ölümlerde ilk sırada yer almaktadır. Akciğer kanserinin erken teşhisi, hastalık seyri hakkında daha bilinçli ilerlemeyi sağlar ve hastanın sağ kalım durumu açısından hayati bir önem taşımaktadır. Son zamanlarda teknolojinin gelişmesiyle birlikte yapay zekâ ve derin öğrenme tabanlı sistemler; Bilgisayarlı Tomografi (BT), Manyetik Rezonans (MR) vb. tıbbi görüntüleme sistemlerinden elde edilmiş verileri kullanarak hastalık teşhisinde hekimlere önemli destek sağlamaktadır. Bu çalışmada akciğer kanserinin BT görüntüleri kullanarak yeni bir Evrişimli Sinir Ağı (ESA) modeli önerilmiştir. Önerilen ESA modelinin sınıflandırma sonuçları, literatürde bulunan diğer ön eğitimli derin öğrenme modellerine göre daha başarılı olduğu için tercih ettiğimiz ResNeXt derin öğrenme modelinin sonuçları ile karşılaştırılmıştır. Modellerin eğitimi ve test aşamaları için açık erişimli akciğer BT görüntülerinin bulunduğu bir veri seti kullanılmıştır. Çalışma sonucunda, önerilen ESA modelinin %99 doğruluk oranı ile ResNeXt mimarisine göre daha yüksek performans sergilediği gözlemlenmiştir. Ayrıca mevcut çalışmadaki görüntülerde herhangi bir özellik çıkarımı yöntemi kullanılmadan görüntüler ham hali ile sınıflandırılmıştır. Ve önerilen ESA modelinin, literatürde yapılan benzer çalışmalarda kullanılan yöntemlere göre daha az katman sayısının olmasının yanında sınıflandırma başarısının da daha yüksek olduğu gözlemlenmiştir.

Measurement of Tracheobronchial Angles of COVID-19 Patients on Computed Tomography and Correlation with Pneumonia Severity in Turkish Population

Aim: This study aimed to evaluate the values of tracheobronchial angles on computed tomography (CT) and to investigate the relationship between angles and CT severity scores (CT-SS) of coronavirus disease 2019 (COVID-19) patients. There is no available literature measuring tracheobronchial angles of adult COVID-19 patients and investigating the relationship of angles with pneumonia severity. Material and Methods: This study was a single-center retrospective analysis of 92 RT-PCR positive patients, aged between 18-40 years, who underwent CT between May and October 2020. The right bronchial angles (RBA), left bronchial angles (LBA), subcarinal angles (SCA), and interbronchial angles (IBA) were measured by a radiologist on coronal CT images with the measurement model used in past. CT-SS was calculated by using a visual scoring system with a global score of 0-25. Results: Thirty-seven (40.2%) patients had normal CT imaging and 55 (59.8%) patients had pulmonary involvement. The CT-SS were ranged from 0 to 24, with a median value of 2.5. The mean IBA was calculated as 81.67±15.20°, mean SCA 77.65±15.78°, mean RBA 39.26±7.51°, and mean LBA 43.35±8.43°. No statistically significant difference was found in SCA, IBA, RBA, and LBA between the groups with and without COVID-19 pneumonia (p=0.277, p=0.389, p=0.218, and p=0.227, respectively). Also, no significant correlation was found between tracheobronchial angles and pneumonia CT-SS of the patients. Conclusion: We calculated the distribution range of tracheobronchial angle values in the adult Turkish COVID-19 patients. According to our study, tracheobronchial angles don’t affect the disease severity and clinical outcome of COVID-19 patients.

Should Primary Stroke Centers Perform Advanced Imaging?

Should Primary Stroke Centers Perform Advanced Imaging?

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

COVID-19 remains to proliferate precipitously in the world. It has significantly influenced public health, the world economy, and the persons’ lives. Hence, there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients. With this explosion of this pandemic, there is a need for automated diagnosis tools to help specialists based on medical images. This paper presents a hybrid Convolutional Neural Network (CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography (CT) images. The proposed approach is employed to classify and segment the COVID-19, pneumonia, and normal CT images. The classification stage is firstly applied to detect and classify the input medical CT images. Then, the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images. The classification stage is implemented based on a simple and efficient CNN deep learning model. This model comprises four Rectified Linear Units (ReLUs), four batch normalization layers, and four convolutional (Conv) layers. The Conv layer depends on filters with sizes of 64, 32, 16, and 8. A 2 × 2 window and a stride of 2 are employed in the utilized four max-pooling layers. A soft-max activation function and a Fully-Connected (FC) layer are utilized in the classification stage to perform the detection process. For the segmentation process, the Simplified Pulse Coupled Neural Network (SPCNN) is utilized in the proposed hybrid approach. The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region, accurately. To summarize the contributions of the paper, we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system. Once the images are accepted by the system, it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images. The region of interest can be assesses both automatically and through experts. This strategy helps so much in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world. The proposed classification approach is applied for different scenarios of 80%, 70%, or 60% of the data for training and 20%, 30, or 40% of the data for testing, respectively. In these scenarios, the proposed approach achieves classification accuracies of 100%, 99.45%, and 98.55%, respectively. Thus, the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.

Computed tomography scan in COVID-19: a systematic review and meta-analysis.

PurposeComputed tomography (CT) scan is a commonly used tool for the diagnosis of the novel coronavirus disease 2019 (COVID-19), similarly to reverse transcription-polymerase chain reaction (RT-PCR). Because of the limitations of RT-PCR, there is growing interest in the usability of the CT scan. The present systematic review and meta-analysis aims to summarize the available data on the CT scan features of COVID-19.Material and methodsWe conducted a systematic search in electronic databases to find eligible studies published between 1 December 2019 and 4 April 2020, which investigated the computed tomographic features of patients with COVID-19. All preprint and peer-reviewed articles were included. No language limitation was applied. For proportional data, pooled prevalence was calculated using a Freeman-Tukey double arcsine transformation, with a 95% confidence interval (CI).ResultsEighty-six studies were eligible to be included in the meta-analysis. For 7956 patients, the most common CT findings were bilateral pattern of involvement (78%; 95% CI: 0.73-0.82; p < 0.001), involvement of more than 1 lobe (75%; 95% CI: 0.68-0.82; p < 0.001), ground-glass opacities (GGO) (73%; 95% CI: 0.67-0.78; p < 0.001), and peripheral distribution of signs (69%; 95% CI: 0.61-0.76; p < 0.001). Only 5% of patients had a normal CT scan (95% CI:0.03-0.07; p < 0.001). The proportion of paediatric patients (age < 18 years) with unremarkable CT findings was higher (40%; 95% CI: 0.27-0.55; p < 0.001). Subgroup analysis showed that patients with the severe or critical type of COVID-19 were more likely to have pleural effusion (RR 7.77; 95% CI: 3.97-15.18; p < 0.001) and consolidation (RR 3.13; 95% CI: 1.57-6.23; p < 0.001). CT results in patients with COVID-19 were comparable with those of people having pneumonia from other causes, except for the lower incidence of consolidation (RR 0.81; 95% CI: 0.71-0.91; p < 0.001) and higher risk of showing GGO (RR 1.45; 95% CI: 1.13-1.86; p < 0.001). The mortality rate was slightly higher in patients with bilateral involvement (RR 3.19; 95% CI: 1.07-9.49; p = 0.04).ConclusionsOur study results show that COVID-19 shares some features with other viral types of pneumonia, despite some differences. They commonly present as GGO along with vascular thickening, air bronchogram and consolidations. Normal CT images, lymphadenopathies, and pleural effusions are not common. Consolidations and pleural effusions correlate with more severe disease. CT features are different between COVID-19 and non-COVID-19 pneumonia. Also, they differ by age, disease severity, and outcomes within COVID-19 patients.

Sectional Anatomy Quiz - VII.

This series involves a quiz pertaining to the identification of key anatomical landmarks and normal structures present at a given level on the computed tomography (CT) image. The current quiz demonstrates examples of normal and abnormal axial CT images at the level of origin of the coeliac artery. The representative image is subsequently followed by further images demonstrating various commonly encountered pathologies found at this level in clinical practice. In each image, readers are expected to identify highlighted anatomical structures and appreciate how given pathologies can alter the appearance of normal structures. This series aims to advance understanding of sectional anatomy and aid nuclear physicians in the interpretation of the CT component of single photon emission computed tomography (SPECT) and positron emission tomography (PET) studies.

COVIDnet: An Efficient Deep Learning Model for COVID-19 Diagnosis on Chest CT Images

A novel coronavirus disease (COVID-19) has been a severe world threat to humans since December 2020. The virus mainly affects the human respiratory system, making breathing difficult. Early detection and Diagnosis are essential to controlling the disease. Radiological imaging, like Computed Tomography (CT) scans, produces clear, high-quality chest images and helps quickly diagnoses lung abnormalities. The recent advancements in Artificial intelligence enable accurate and fast detection of COVID-19 symptoms on chest CT images. This paper presents COVIDnet, an improved and efficient deep learning Model for COVID-19 diagnosis on chest CT images. We developed a chest CT dataset from 220 CT studies from Tamil Nadu, India, to evaluate the proposed model. The final dataset contains 5191 CT images (3820 COVID-infected and 1371 normal CT images). The proposed COVIDnet model aims to produce accurate diagnostics for classifying these two classes. Our experimental result shows that COVIDnet achieved a superior accuracy of 98.98% when compared with three contemporary deep learning models.

A Simplified Framework for the Detection of Intracranial Hemorrhage in CT Brain Images Using Deep Learning.

The need for accurate and timely detection of Intracranial hemorrhage (ICH) is of utmost importance to avoid untoward incidents that may even lead to death. Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on the CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method. Secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored, and finally, a feature selection method, Principal Component Analysis (PCA), has been introduced in the AlexNet-SVM classifier model, and its efficacy is also explored. These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86% and sensitivity and specificity of 0.9986 for the detection of ICH in the brain CT images. This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. The proposed simplified deep learning framework also manifests its ability as a screening tool to assist the radiological trainees in the accurate detection of ICH.

Factors affecting incorrect interpretation of abdominal computed tomography in non-traumatic patients by novice emergency physicians.

ObjectiveAccurate interpretation of computed tomography (CT) scans is critical for patient care in the emergency department. We aimed to identify factors associated with an incorrect interpretation of abdominal CT by novice emergency residents and to analyze the characteristics of incorrectly interpreted scans.MethodsThis retrospective analysis of a prospective observational cohort was conducted at three urban emergency departments. Discrepancies between the interpretations by postgraduate year-1 (PGY-1) emergency residents and the final radiologists’ reports were assessed by independent adjudicators. Potential factors associated with incorrect interpretation included patient age, sex, time of interpretation, and organ category. Adjusted odds ratios (aORs) for incorrect interpretation were calculated using multivariable logistic regression analysis.ResultsAmong 1,628 eligible cases, 270 (16.6%) were incorrect. The urinary system was the most correctly interpreted organ system (95.8%, 365/381), while the biliary tract was the most incorrectly interpreted (28.4%, 48/169). Normal CT images showed high false-positive rates of incorrect interpretation (28.2%, 96/340). Organ category was found to be a major determinant of incorrect interpretation. Using the urinary system as a reference, the aOR for incorrect interpretation of biliary tract disease was 9.20 (95% confidence interval, 5.0–16.90) and the aOR for incorrectly interpreting normal CT images was 8.47 (95% confidence interval, 4.85–14.78).ConclusionBiliary tract disease is a major factor associated with incorrect preliminary interpretations of abdominal CT scans by PGY-1 emergency residents. PGY-1 residents also showed high false-positive interpretation rates for normal CT images. Emergency residents’ training should focus on these two areas to improve abdominal CT interpretation accuracy.

Is small airway dysfunction an abnormal phenomenon for patients with normal forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity?

BackgroundThe clinical significance of small airway dysfunction (SAD) determined with spirometry in patients with normal forced expiratory volume in 1 second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) is controversial. ObjectiveTo determine whether SAD presents histologic abnormalities in the setting of normal computed tomography (CT) imaging and FEV1 and FEV1/FVC. MethodsA cross-sectional study was performed in 64 patients undergoing thoracotomy for pulmonary nodules. Thoracic high-resolution CT (HRCT), bronchodilation test, and fractional exhaled nitric oxide (FENO) and its alveolar component (nitric oxide alveolar concentration [CANO]) were obtained before surgery. Lung pathology and levels of cytokines in lung tissue were measured. The patients were divided into SAD and small airway normal function groups according to forced expiratory flow at 75% and 50% of the FVC (maximal expiratory flow [MEF] 25, MEF50) and maximum midexpiratory flow. ResultsThe MEF50, MEF25, and maximum midexpiratory flow were strongly negatively correlated with CANO (r, –0.42, –0.42, –0.40, respectively; P ≤ .001 for all). The MEFs were mildly negatively correlated with interleukin (IL)-6 and macrophages in lung tissue (r < –0.25, P < .001 for all). The CANO (P < .001), airspace size (mean linear intercept) (P = .02), macrophages (P = .003), IL-6 (P = .003), and IL-8 (P = .008) in lung tissue were higher in patients with SAD (n = 35) than those with small airway normal function (n = 29). A total of 8 patients (22.86%) with SAD and 2 (6.90%) without SAD had pneumatoceles (P = .10). ConclusionPatients with pulmonary nodules and SAD were more likely to have abnormal inflammation and emphysematous destruction than patients without SAD. Thus, SAD indicates histologic abnormalities in patients with normal CT imaging and FEV1 and FEV1/FVC.

3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework

Purpose. Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient. Methods. Ortho2D uses two independent faster R-convolutional neural network networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. The 2D detections are clustered in 3D to localize vertebrae centroids in the volumetric CT and classify the region (cervical, thoracic, lumbar, or sacral) and vertebral level. A post-process sorting method incorporates the confidence in network output to refine classifications and reduce outliers. Ortho2D was evaluated on a publicly available dataset containing 302 normal and pathological spine CT images with and without surgical instrumentation. Labeling accuracy and memory requirements were assessed in comparison to other recently reported methods. The memory efficiency of Ortho2D permitted extension to high-resolution CT to investigate the potential for further boosts to labeling performance. Results. Ortho2D achieved overall vertebrae detection accuracy of 97.1%, region identification accuracy of 94.3%, and individual vertebral level identification accuracy of 91.0%. The framework achieved 95.8% and 83.6% level identification accuracy in images without and with surgical instrumentation, respectively. Ortho2D met or exceeded the performance of previously reported 2D and 3D labeling methods and reduced memory consumption by a factor of ∼50 (at 1 mm voxel size) compared to a 3D U-Net, allowing extension to higher resolution datasets than normally afforded. The accuracy of level identification increased from 80.1% (for standard/low resolution CT) to 95.1% (for high-resolution CT). Conclusions. The Ortho2D method achieved vertebrae labeling performance that is comparable to other recently reported methods with significant reduction in memory consumption, permitting further performance boosts via application to high-resolution CT.

Computed tomography imaging findings in hospitalized patients with coronavirus disease 2019 (COVID-19): a descriptive study of 81 cases.

PurposeSevere acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has proven to be a diagnostic challenge. Early studies have shown that computed tomography (CT) imaging may be useful in diagnosis of these patients. We aim to report CT findings in a series of hospitalized patients.Material and methodsA total of 81 patients were included in this study. All of the patients were hospitalized and had SARS-CoV-2 infection proven by molecular assay. All patients had a CT scan on the first day of admission. Imaging results were reviewed by two separate radiologists, and imaging findings were documented.ResultsSeventy-eight patients had abnormal CT imaging, while 3 had normal CT imaging. The sensitivity of CT in diagnosing coronavirus disease 2019 (COVID-19) was estimated to be 96%. The most common imaging finding was ground glass opacities, followed by septal thickening. Most lesions were located at the periphery and posterior of the lungs. Most lesions were multifocal, and involved the right lower lobe more frequently. Chest X-rays were normal in 38 patients, and the sensitivity of chest X-ray in diagnosing SARS-Cov-2 was 54%.ConclusionsCT scans could be used in diagnosis of patients with a high sensitivity (93%). No common imaging findings may also be seen alongside ground glass opacities, based on the degree of disease progression.

Sectional Anatomy Quiz - VI.

This quiz is part of a series which aims to aid nuclear physicians in interpreting the computed tomography (CT) component of the single photon emission computed tomography (SPECT) and positron emission tomography (PET) studies. The current quiz includes normal and pathological axial CT images at the level of the superior mesenteric artery (SMA) and superior mesenteric vein (SMV). The SMV is normally located to the right of the SMA and is of a larger caliber. Various pathologies such as malignancy, infection or vascular disease can alter the position and/or appearance of the SMA, SMV and the surrounding anatomical structures present at this level. Understanding how normal anatomy is altered by pathology at this level will facilitate improved interpretation of abdominal CT images.

Reversible primary adrenal insufficiency related to anti-programmed cell-death 1 protein active immunotherapy: Insight into an unforeseen outcome of a rare immune-related adverse event

The immune checkpoint inhibitors (ICPi) revolutionize the cancer therapeutics, though not being devoid of toxicity. The immune-related primary adrenal insufficiency (PAI) is a rare, yet potentially life-threatening, adverse event, posing diagnostic and therapeutic challenges. We report the first case of reversible PAI related to nivolumab (programmed cell-death 1 protein inhibitor) in a 42-year-old male with metastatic rectal adenocarcinoma. PAI manifested as profound fatigue, disorientation, hypotension, hyperpigmentation of palmar creases, and hyponatremia without hyperkalemia 16 weeks after initiation of nivolumab. Due to impending adrenal crisis, intravenous stress doses of hydrocortisone and hydration with normal saline were initiated. When the state of patient was stabilized, PAI was confirmed through 250 μg Synacthen test 24 h after temporary cessation of hydrocortisone. Hydrocortisone was fixed at maintenance dose, while mineralocorticoid substitution was not required. PAI was ascribed to nivolumab based on history, physical examination, and laboratory work-up with emphasis on positivity of anti-21-hydroxylase antibodies and exclusion of other causes of PAI by normal imaging of adrenal glands on computed tomography (CT). Reevaluation of adrenal function during follow up demonstrated complete recovery. A review of literature concerning the immune-related PAI indicated that the complete recovery of adrenal function, the normal CT imaging, and the positivity of anti-21-hydroxylase antibodies observed in our patient are exceptional findings of immune-related PAI. Finally, heightened suspicion of immune-related PAI in case of hyponatremia without hyperkalemia and constant vigilance for diagnosis of rare, but real, reversibility of immune-related PAI are of paramount importance.

Identification of Ischemic Stroke by Marker Controlled Watershed Segmentation and Fearture Extraction

In this paper, we will describe a method that distinguishes the ischemic stroke from Computed Tomography (CT) brain images by extracting the statistical and textural features. First, preprocessing of the CT images is done followed by image enhancement. Segmentation of the CT images is performed by Marker Controlled Watershed. After the segmentation, we get the Grey Level Co-occurrence matrix (GLCM) and extract the textural and statistical features. The disadvantage of watershed is the over-segmentation caused by noise and solved by Marker Controlled Watershed as shown experimentally. The features extracted are contrast, correlation, standard deviation, variance, homogeneity, energy and mean. We noticed in our results that the values of homogeneity, energy and mean are bigger in normal CT images than in abnormal CT images where the contrast, correlation, standard deviation and variance of normal CT images are less than those of abnormal CT images (Ischemic Stroke)