Current Medical Imaging - Volume 18, Issue 12, 2022

Acute cholecystitis is a common clinical inflammatory lesion of the gallbladder. With the aggravation of inflammation, ischemic, necrosis, and even acute gangrenous cholecystitis occur in the gallbladder. At the same time, a variety of complications appear, seriously affecting the prognosis of patients. It is recommended that ultrasound can be utilized as the first choice for the diagnosis of acute cholecystitis, due to its fastness, convenience, non-radiation, and low cost. Here, we summarize the latest progress that can predict acute gangrenous cholecystitis in ultrasound, thus assisting us in identifying patients with high risk of gangrene in early stage, and treating these patients in time.

Background: Among brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is challenging due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis, and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques to deep learning through machine learning on MRI of human head scans. Methods: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The primary aim of the paper is to motivate young researchers towards the development of efficient brain tumor segmentation techniques using conventional as well as recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mainly applied for brain tumor detection, whereas deep learning methods were good at segmenting tumor substructures.

Background: In recent years, T1 mapping imaging based on Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) has resulted in new research and clinical applications in hepatic diseases. Objective: The objective of the study is to analyze, prospect, and summarize the Gd-EOB-DTPA- enhanced MRI T1 mapping technology in hepatic diseases in recent years. Main Findings: Gd-EOB-DTPA-enhanced T1 mapping has been used more frequently in liver diseases regardless of 1.5T or 3.0T MRI equipment. Volume interpolated body examination (VIBE) mapping sequence seems to be the recommended MRI scan sequence. In the evaluation of T1 value on liver function, the hepatobiliary phase 10 minutes after enhancement is the recommended time point. The fat fraction and hepatic steatosis grade based on MRI-derived biomarkers are easier to implement and popularize than a liver biopsy. Gd-EOB-DTPA-enhanced MRI T1 mapping can not only be used to evaluate the degree of liver injury, the stage of liver fibrosis, and the liver reserve function of patients with liver cirrhosis but also to distinguish focal liver lesions and predict the differentiation degree of hepatocellular carcinoma. At the same time, it has some value in predicting tumor immunohistochemical indexes, such as Ki67, CD34. Conclusion: Gd-EOB-DTPA-enhanced MRI T1 mapping has great potential in the application of diffuse and focal liver lesions. It is a quantitative study, trying to select homogeneous research objects and try to use the same standards in scanning sequence and scanning time, especially for the study of liver function, which is a focus of future research. The research on the relationship between T1 value and tumor immunohistochemical indexes is worth consideration.

Background: As the mortality rate of lung cancer is enormously high, its impact is also extremely higher than the other types of cancer. Lung malignancy is thus considered one of the deadliest diseases with a high death rate in the world. It is reported that nearly 1.2 million people are diagnosed with this disease and about 1.1 million individuals are died due to this type of cancer every year. The early detection of this disease is the only solution for minimizing the death rate or maximizing the survival rate. However, the timely identification of lung malignant growth is a complex process and hence various imaging algorithms are employed in the process of detecting lung cancer on time. Aim: The Computer-Aided Diagnosis (CAD) is highly beneficial for the radiologist to rapidly detect and diagnose the irregularities in advance. The CAD systems usually focus on identifying and detecting the lung nodules. As the treatment of this disease is provided on the basis of its stages, the early detection of cancer has to be given much importance. The major drawbacks of existing CAD systems are less accuracy in segmenting the nodule and staging the lung cancer. Objective: The major aim of this work is to categorize the lung nodules from the CT image and classify the tumorous cells for identifying the exact position of cancer with higher sensitivity, precision, and accuracy than other strategies. Methods: The methods employed in this study are listed as follows: (i) For the process of de-noising and edge sharpening of lung image, the curvelet transform was used. (ii) The Fuzzy thresholding technique was used to perform lung image binarization and lung boundary corrections. (iii) Segmentation was performed by implementing the K-means algorithm. (iv) By using Convolutional Neural Network (CNN), different stages of lung nodules, like benign and malignant, were identified. Results: The proposed classifier achieves optimal accuracy of 97.3%, a sensitivity of 98.6% and a specificity of 96.1% which are significantly better than the other approaches. Thus, the proposed approach is highly helpful in detecting lung cancer in its early stages. Conclusion: The results validate that the proposed algorithms are highly capable of classifying the lung images into various stages, which effectively helps the radiologist in the decision-making process.

Purpose: Breast cancer ranks first among cancers affecting women's health. Our goal is to develop a fast, high-precision, and fully automated breast cancer detection algorithm to improve the early detection rate of breast cancer. Methods: We compare different object detection algorithms, including anchor-based and anchor-free object detection algorithms for detecting breast lesions. Finally, we find that the fully convolutional onestage object detection (FCOS) showed the best performance in the detection of breast lesions, which is an anchor-free algorithm. 1) Considering that the detection of breast lesions requires the context information of the ultrasound images, we introduce the non-local technique, which models long-range dependency between pixels to the FCOS algorithm, providing the global context information for the detection of the breast lesions. 2) The variety of shapes and sizes of breast lesions makes detection difficult. We propose a new deformable spatial attention (DSA) module and add it to the FCOS algorithm. Results: The detection performance of the original FCOS is that the average precision (AP) for benign lesions is 0.818, and for malignant lesions is 0.888. The FCOS with a non-local module improves the performance of the breast detection; the AP of benign lesions was 0.819, and that of malignant lesions was 0.894. Combining the DSA module with the FCOS improves the performance of breast detection; the AP for benign lesions and malignant lesions is 0.840 and 0.899, respectively. Conclusion: We propose two methods to improve the FCOS algorithm from different perspectives to improve its performance in detecting breast lesions. We find that FCOS combined with DSA is beneficial in improving the localization and classification of breast tumors and can provide auxiliary diagnostic advice for ultrasound physicians, which has a certain clinical application value.

Background: The study aims to assess the changes to left ventricular (LV) function of patients with the coronary slow flow (CSF) in response to stress induced by low dose dobutamine. Methods: Based on coronary angiography (CAG) results, a total of 186 patients undergoing CAG for chest pain and suspected coronary heart disease were assigned to the CSF group (n = 142) and control group (n = 44). Patients in the CSF group underwent two-dimensional speckle-tracking echocardiography (STE) during the dobutamine stress test to evaluate LV systolic and diastolic functions. Results: At rest, there were no statistically significant differences in LV peak systolic longitudinal strain (LS), LV peak systolic longitudinal strain rate (LSRs), LV peak early diastolic longitudinal strain rate (LSRed), LV circumferential strain (CS), or LV circumferential strain rate (CSRed) between the CSF and control groups. In the CSF group, LS and LSRs first increased as the infusion rate was increased to 10 μg/kg/min (all, p < 0.05), before decreasing at infusion rates of 15 and 20 μg/kg/min (all, p < 0.05). CS and CSRed increased in the CSF group at infusion rates of 5, 10, and 15 μg/kg/min, (all, p < 0.05), but decreased significantly at 20 μg/kg/min (all, p < 0.05). Conclusion: At rest, LV systolic and diastolic functions were comparable between the CSF and control groups. However, when blood flow to the heart muscles was insufficient, LSRed decreased first, followed by LS. In terms of sensitivity to myocardial ischemia, LS is a better strain parameter than CS.

Introduction: The disease caused by the novel coronavirus (COVID -19) is a vital public health problem that has now affected approximately 68,037,473 people and caused 1,552,802 deaths around the world. We aimed to correlate the frequency of the lung involvement patterns, the segmental distribution of lung infiltration, and TLSS in COVID-19 pneumonia patients with and without splenomegaly. Material and Methods: This retrospective study included patients admitted to Yunus Emre State Hospital Emergency, Internal Medicine and Infectious Disease Departments between March 11, 2020, and June 10, 2020, and diagnosed with COVID-19 by PCR test with a throat and nasal swab. The thoracic and upper abdomen CTs and the clinical and demographic features of the patients were analyzed at the time of initial diagnosis. Results: Consolidation (group 1 - 18 (47%), group 2 - 69 (28.2%); P = 0017), crazy pavement pattern (15 (39.5%), 42 (17.1%); p = 0.001), pleural band formations (24 (63.2%), 87 (35.5%); p = 0.001), interlobular septal thickening (23 (60.5%), 79 (32.2%); p = 0.001), and sequelae of secondary tuberculosis (4 (10.5%), 8 (3.3%); p = 0.039) were more frequent in the patient with splemomegaly. The total lung severity score was high in the group with splenomegaly (7.32 ± 6.15, 3.69 ± 5.16; p = 0.001). Conclusion:Consolidation, interlobular septal thickening, tuberculosis sequela, pleural band, and crazy pavement patterns were frequent in the COVID-19 pneumonia patients with splenomegaly. The most frequently affected segment was the superior segment of the right lower lobe. TLSS was higher in the COVID-19 pneumonia patients with splenomegaly.

Objective: Non-alcoholic fatty liver disease (NAFLD) may affect many organs and systems, especially the kidney as well as the liver. While NAFLD’s renal impacts can be evaluated via the estimated glomerular filtration rate (eGFR), its effects on renal hemodynamic changes can be monitored with the renal resistive index (RRI). Our aim in this study is to evaluate RRI and eGFR in NAFLD patients. Materials and Methods: In this study, a total of 69 patients with NAFLD and 50 age- and gendermatched healthy controls were included. The basic clinical and laboratory parameters of patient and control groups were assessed and recorded. Results: In the patient group, the RRI was observed as 0.64±0.06 and eGFR value as 86±52 (mL/min/1.73m2), while in the control group, the RRI was 0.61±0.05 and eGFR was 95.40±20.21 (mL/min/1.73m2). Compared to the control group, the RRI and eGFR values were significantly different in the patient group (PRRI=0.003 and PeGFR=0.025). There was a negative correlation observed between the RRI and eGFR (r=-0.347, P=0.003). Receiver-operating characteristic curve analysis suggested that the optimum RRI cut-off value for patients with NAFLD is 0.62, with 65% sensitivity and 60% specificity (p=0.002). There was an independent relationship found between the RRI and eGFR according to the linear regression analysis (β=-0.301, P=0.015). Conclusion: This study shows that RRI may be an indicator of renal functions, such as eGFR in patients with NAFLD.

Background: Distinguishing exophytic renal urothelial carcinoma (ERUC) from exophytic renal clear-cell carcinoma (ERCCC) with collecting system invasion may be difficult as they involve similar locations and collecting system invasion. Objective: The study aimed to characterize the clinical data and computed tomography (CT) features that can aid in differentiating ERUC from ERCCC. Methods: Data from 17 patients with ERUC and 222 patients with ERCCC were retrospectively assessed. CT and clinical features exhibiting significant differences in t-tests/Mann-Whitney U-test and chi-square tests/Fisher’s exact tests were analyzed using receiver operating characteristic (ROC) curves. Variables with an area under the curve (AUC) <0.7 were excluded. Univariate logistic regression analysis was used to analyze the associations of CT and clinical features with ERUC or ERCCC. Variables with odds ratio (OR) values being close to 1 in univariate logistic regression were excluded from multivariate logistic regression. A predictive model was then constructed based on the predictors (p<0 in multivariate logistic regression). Differential diagnostic performance was assessed with AUC values. Results: Multivariate logistic regression analysis identified preserving reniform contour (OR: 45.27, 95% confidence interval [CI]: 4.982–411.39) and infiltrative growth pattern (OR: 21.741, 95% CI: 1.898–249.049) as independent predictors that can be used to distinguish ERUC from ERCCC. AUC values for preserving reniform contour, infiltrative growth pattern, and Model-1 were 0.907 (95% CI: 0.817-0.998), 0.837 (95% CI: 0.729-0.946), and 0.947 (95% CI: 0.874–1), respectively. Conclusion: The independent predictors and predictive model may play an important role in preoperative differentiation between ERUC and ERCCC.

Background: Diagnostic radiology has been linked to several health consequences. Thus, the present study aimed to assess the practice, knowledge, and awareness of the diverse effects of diagnostic radiology among radiology staff and students in Saudi Arabia. Methodology: In this study, 107 participants were recruited from August to December 2020. The study included 56 radiology personnel (staff from Hail City Hospitals) and 51 radiology medical students (students from the University of Ha’il). Results: To the question, “Have you ever been informed of radiation and its effects?” about 6/107 (5.6 %) answered “No,” among which 5/6 (83.3 %) were the staff. In response to the question, “In your opinion, how hazardous is radiation to your body?” about 8/104 (7.7 %) indicated it as nonhazardous (3 were staff and 5 were students). To the question, “Do you believe that radiation in medical sites is harmful?” about 20/104 (19.2 %) answered, “No” (8 were staff and 12 were students). Conclusion: There is a lack of awareness, knowledge, and practice towards ionizing radiation protection measures among radiology medical students and radiology department staff in Northern Saudi Arabia. The paper notifies the health system advisors to include stressing guidelines regarding radiation hazards. More actions are deemed necessary for the safety of patients and radiation workers.

Introduction and Background: In this article, we aimed to present a congenital sialocutaneous fistula originating from the parotid gland in a 4-year-old child with MR findings. Case Presentation: A 4-year-old male patient was admitted to our clinic with a small hole in the inferior right half of the neck and a sticky fluid coming out of this area. Neck ultrasonography (US) and contrast neck Magnetic Resonance Imaging (MRI) were performed on the patient. In the US, a millimeter linear, vaguely hypoechoic appearance was observed in the subcutaneous tissue at this level on the neck. In contrast-enhanced neck MRI imaging, at the level of the right parotid tail, a millimetric linear fistula tract was observed, which was thought to originate from the superficial parotid lobe and extended to the skin at the proximal level of the right sternocleidomastoid (SCM) muscle. After suppressing surrounding fatty tissues in fat-suppressed contrast-enhanced series, this tract area became slightly pronounced. Conclusion: In conclusion, sialocutaneous fistula should be considered among the differential diagnoses in cases of skin fistulization in children. In terms of differential diagnosis, MRI is a very effective imaging method due to its high soft-tissue resolution.