Current Medical Imaging - Volume 17, Issue 9, 2021

Ultrasound elastography has become available in everyday practice, allowing direct measurement of tissue elasticity with important and expanding clinical applications. Several studies that have evaluated pathological and non-pathological tissues have demonstrated that ultrasound elastography can actually improve the diagnostic accuracy of the underlying disease process by detecting differences in their elasticity. Ocular and periocular tissues can also be characterized by their elastic properties. In this context, a comprehensive review of literature on ultrasound elastography as well as its current applications in Ophthalmology is presented.

Cases of diverticulosis of the colon continue to increase, especially in the Western countries. In these countries, two-thirds of the population older than 70 years of age are considered to experience this disease. Medical and surgical treatment for diverticulosis actually begun for the complications of diverticulitis and lower gastrointestinal hemorrhage. The first evaluation of complicated diverticular disease is based on patient’s history, physical examination, and laboratory data. But all these exams and data can be inaccurate and are often questionable in the diagnoses of many features of the disease. To describe the position, severity, and presence of complications of a detected diverticulum is crucial to its appropriate treatment. The greater part of the patients have mild disease and can be successfully cured medically. Only a small number of patients admit with acute diverticulitis and need urgent surgical intervention. Determining these patients early is crucial to morbidity and mortality reduction. Radiologic examination is important for exact evaluation of the extent of the course of the disease over the last three decades. This article aims to chart the place of the Computed Tomography (CT) imaging procedure in the assessment of acute complicated diverticular disease.

Background: Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available. Objective: The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and their variants, Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Deep Boltzmann Machine (DBM), and Generative Adversarial Network (GAN). Future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed. Conclusion: Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or are inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.

Background: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection. Methods: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values. Results: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based techniques. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy, and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively. Conclusion: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

Background & Aims: The utility of ultrasound and color Doppler in the diagnosis and evaluation of inflammatory bowel diseases (IBD) has not been studied enough. Therefore, the aim of the current study was to evaluate the importance of conventional abdominal ultrasound and color Doppler in diagnosing IBD and assessing disease activity. Methods: The study was conducted at the National Hepatology and Tropical Medicine Research Institute (NHTMRI) between July 2018 and January 2019, in which 150 patients were suffering from diarrhea, dysentery, tenesmus, or rectal bleeding were evaluated by colonoscopy, high-resolution ultrasound, and color Doppler scans. Results: The present study was conducted on 150 patients; 84 (56%) had ulcerative colitis (UC), 16 (10.7%) had Crohn's disease (CD), and 50 (33.3%) had normal colonoscopy results with a mean age 37.2 ± 9.059. The superior mesenteric Artery Peak Systolic Velocity (SMA-PSV) and End Diastolic Velocity (EDV) were significantly higher in both UC and CD than in the control group; however, pulsatility index (PI) was significantly higher in the control group than both UC and CD. However, there was no significant difference between UC and CD. The inferior mesenteric artery PSV and EDV were significantly higher in both UC and CD than in the control group. Conclusion: Doppler ultrasound findings of SMA and IMA correlate with the incidence of inflammatory bowel disease, the site of disease, and its activity.

Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. Methods: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. Results: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.

Aims and Scope: Computed tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behaviour) with the help of a limited set of projections at different angles. Background: To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used. Objective: The conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from optimal smoothing as the number of iterations increases. Methods: For solving this problem, this paper presents a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means of fuzzy complex diffusion as a regularization term for noise reduction and edge preservation. Results: The proposed model was evaluated on four test cases phantoms. Conclusion: Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compared with the state-of-the-art techniques.

Background: Cardiovascular Disease (CVD) is one of the primary diseases that causes death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, every 34 seconds, a person dies due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. Aims: The main aim of this work is to improve the performance of the heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. Objective: The objective of this investigation is to diagnose heart diseases using feature extraction, reduction based classification and image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system includes three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In the second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. Methods: The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image are calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image are given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image by determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with a regression nature. Due to the regression property, the network is well trained with the feature. The Generalized regression neural network is used for classifying heart diseases. Results: The proposed method achieves the accuracy of 96.23%, sensitivity of 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. Conclusion: In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods, either the feature extraction based classification or the feature reduction based classification.

Background: Excess prostate tissue is trimmed near the prostate capsula boundary during transurethral plasma kinetic enucleation of prostate (PKEP) and transurethral bipolar plasmakinetic resection of prostate (PKRP) surgeries. If a large portion of the tissue is removed, a prostate capsula perforation can potentially occur. As such, real-time accurate prostate capsula (PC) detection is critical for the prevention of these perforations. Objective: This study investigated the potential for using image denoising, image dimension reduction and feature fusion to improve real-time prostate capsula detection with two objectives. First, this paper mainly studied feature selection and input dimension reduction. Secondly, image denoising was evaluated, as it is of paramount importance to transient stability assessment based on neural networks. Methods: Two new feature fusion techniques, maxpooling bilinear interpolation single-shot multibox detector (PBSSD) and bilinear interpolation single shot multibox detector (BSSD) were proposed. Before original images were sent to the neural network, they were processed by principal component analysis (PCA) and adaptive median filter (AMF) for dimension reduction and image denoising. Results: The results showed that the application of PCA and AMF with PBSSD increased the mean average precision (mAP) for prostate capsula images by 8.55% and reached 80.15%, compared with single shot multibox detector (SSD) alone. Application of PCA with BSSD increased the mAP for prostate capsula images by 4.6% compared with SSD alone. Conclusion: Compared with other methods, ours were proven to be more accurate for real-time prostate capsula detection. The improved mAP results suggest that the proposed approaches are powerful tools for improving SSD networks.

Background: Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effective treatment, and non-invasive rapid methods are usually used for diagnosis. Introduction: In this study, we aimed to detect lung cancer using deep learning methods and determine the contribution of deep learning to the classification of lung carcinoma using a convolutional neural network (CNN). Methods: A total of 301 patients diagnosed with lung carcinoma pathologies in our hospital were included in the study. In the thorax, Computed Tomography (CT) was performed for diagnostic purposes prior to the treatment. After tagging the section images, tumor detection, small and non-small cell lung carcinoma differentiation, adenocarcinoma-squamous cell lung carcinoma differentiation, and adenocarcinoma-squamous cell-small cell lung carcinoma differentiation were sequentially performed using deep CNN methods. Results: In total, 301 lung carcinoma images were used to detect tumors, and the model obtained with the deep CNN system exhibited 0.93 sensitivity, 0.82 precision, and 0.87 F1 score in detecting lung carcinoma. In the differentiation of small cell-non-small cell lung carcinoma, the sensitivity, precision and F1 score of the CNN model at the test stage were 0.92, 0.65, and 0.76, respectively. In the adenocarcinoma-squamous cancer differentiation, the sensitivity, precision, and F1 score were 0.95, 0.80, and 0.86, respectively. The patients were finally grouped as small cell lung carcinoma, adenocarcinoma, and squamous cell lung carcinoma, and the CNN model was used to determine whether it could differentiate these groups. The sensitivity, specificity, and F1 score of this model were 0.90, 0.44, and 0.59, respectively, in this differentiation. Conclusion: In this study, we successfully detected tumors and differentiated between adenocarcinoma- squamous cell carcinoma groups with the deep learning method using the CNN model. Due to their non-invasive nature and the success of the deep learning methods, they should be integrated into radiology to diagnose lung carcinoma.

Background: Lungs are the primary organ involved in COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. Aim: We aimed to evaluate the pneumonia severity through the visual and quantitative assessment on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. Methods: We retrospectively evaluated adult COVID-19 patients who underwent chest CT along with theirclinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and the length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between the variables. The Pearson correlation test analyzed the correlation between the variables. The diagnostic performance of the variables was assessed using the receiver operating characteristic (ROC) analysis. Results: The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with the disease severity (p < 0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r = 730, p < 0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. Conclusion: The VSQS and QCT scores can help manage the COVID-19 and predict the duration of the hospitalization.

Background: Cone-Beam Computed Tomography (CBCT) provides a better diagnosis of endodontic lesions. Introduction: The present study would assess the pattern of periapical lesion extension in premolar teeth using CBCT. Methods: In this descriptive study 330 roots in the regions of maxillary and mandibular premolars have been evaluated. Maximum periapical lesion extensions in the three orthogonal planes (axial, coronal, and sagittal) were measured and recorded in millimeters. Measurements were compared based on gender dental arch, tooth type, and root. Statistical analysis was performed using repeated measure ANOVA, Bonferroni, Chi-square tests, and clustering data analysis (K-means method). The significant level was set at 0.05. Results: There were significant differences between the lesion expansions in the three-dimensional planes (p-value<0.001). The highest average of lesion extension in the premolar regions of the examined population was reported in the vertical dimension (4.1± 1.3), followed by horizontal buccolingual dimension (3.4±1.1) and horizontal mesiodistal dimension (3.1±1.0), respectively. According to independent variables, in the premolar region, only tooth roots showed significant differences in the lesion extension (p-value=0.002). Clustering data analysis showed that the majority of the participants were categorized in a cluster with lower lesion extension. Based on clustering data analysis, the small lesions were significantly observed in the first premolar and buccal roots. Conclusion: Since the periapical lesion extension in the buccolingual dimension, which could not be detected in the 2-D imaging techniques, was rather high in the region of premolar teeth, and CBCT, as a 3-D imaging technique, is a suitable option for the precise evaluation of periapical lesion extension. Also, the majority of the lesions in this tooth area are small and located in the buccal roots.

Background: Lymphoma of the female gynecologic tract is extremely rare. Typically, lymphoma is managed nonsurgically unlike other non-lymphomatous malignant tumors raising the importance of differentiation between both entities. Case Presentation: We describe the Magnetic Resonance Imaging (MRI) features of a case of uterovaginal diffuse large B-cell lymphoma in a 50-year-old postmenopausal woman emphasizing Diffusion-Weighted Imaging (DWI) as a diagnostic and follow up tool. We reviewed the literature regarding the diagnostic methods for female genital lymphoma. Forty-five cases, including our patient, were reviewed with an age range from 22 to 85 years. Vaginal bleeding was the most common presentation. The diagnosis was established by Papanicolaou smear, cervical biopsy (25/45), endometrial biopsy (6/45), vaginal biopsy (2/45), pelvic mass biopsy (2/45), iliac LN biopsy (1/45) and surgical diagnosis (8/45). Diffuse Large B-Cell Lymphomas (DLBCL) constitute the vast majority of the cases (82%). The uterine cervix was involved at diagnosis in the majority of these cases (68%), while the uterine body (42%) and vagina (28%) were less involved. Pelvic lymphadenopathy was found in 15 cases, while extra genital lymphomatous infiltration in 13 cases. Sonographic findings were nonspecific, while CT provided excellent data about extra-genital involvement. Thirteen cases underwent pelvic MRI that displayed superior detection of disease extension and parametric involvement. Diffusion restriction was reported only in one case without quantitative analysis of ADC map. Conclusion: MRI shows unique features that differentiate uterovaginal lymphoma from the much more common carcinomas and discriminate post-operative changes from tumor recurrence. It exhibits a marked restricted diffusion pattern with lower ADC values than carcinomas and post-operative changes.

Introduction: Pheochromocytoma are tumors arising from the chromaffin tissue located in the adrenal medulla, associated with typical symptoms and signs. Case Presentation: Occasionally, metastasis, defined as the presence of tumor cells at sites other than the original site, secondary to pheochromocytoma have been reported. Pelvic metastatic malignant pheochromocytoma has rarely been reported in English literature. Conclusion: Here, we have reported a very rare case of pelvic metastatic malignant pheochromocytoma, with a particular focus on sonoelastographic features.

Introduction: Avascular necrosis of humeral trochlea is a very rare condition and was described by Hegemann in 1957. We reported two cases of avascular necrosis of humeral trochlea and also performed a literature review of the reported cases. We expect that this case report will assist clinicians in making a timely diagnosis when encountering similar clinical scenarios. Materials and Methods: We presented cases of an 11-year-old and a 14-year-old with avascular necrosis of the humeral trochlea. The common etiology was idiopathic because there were no recent trauma history and sports activity. Also, there was no history of drug use. We discussed the clinical and radiological findings of these cases. Results: These cases, two teenage boys, were diagnosed withHegemann’s disease with clinical and radiological outcomes. We found that the etiology of both thecases is idiopathic;. The number of previously reported cases in the literature is limited to 64. In our study, there was a lateral crest in one of our two cases and a posteromedial involvement in another. The radiograph of trochleae of these two cases showed irregularity and granular appearance. In our case, heterogeneous signal changed and irregularities were accompanied by hypointensive changes on T1-weighted images. Also, hyperintensive changes on proton density sequences were detected. Conclusion: Radiological evaluation plays an important role in the diagnosis and evaluation of response to treatment in avascular necrosis of the humeral trochlea. Avascular necrosis should be one of the differential lesions involving the trochlea. Recognition of avascular necrosis in the trochlea may prevent the unnecessary biopsy.