Current Medical Imaging - Volume 21, Issue 1, 2025

Perforation is one of the rarest effects of Meckel's diverticulum and may clinically resemble acute appendicitis.

A 34-year-old woman with pain in the right iliac fossa, nausea, and vomiting for three days was brought to the emergency department. An abdominal examination indicated rebound tenderness in the area of the right iliac fossa. Abdominal ultrasound showed a heterogeneous lesion in the left iliac fossa measuring 5 cm × 3.5 cm × 4 cm with no internal vascularity. Abdominal Computed Tomography (CT) demonstrated a hypodense lesion located left of the midline of the abdomen, which was inseparable from the small bowel at the antimesenteric border. Laparoscopic exploration was performed, and an intraoperative diagnosis of perforated Meckel’s diverticulum with phlegmon formation was made. The patient had an uneventful recovery.

Radiologists should be aware of the possibility of complicated Merkel's diverticulum when encountering cases of acute abdominal pain. If there is a lower abdominal inflammatory process and a normal appendix is identified, there should be a high degree of suspicion when examining the CT scan.

Retroperitoneal nerve sheath tumors are uncommon, representing a small fraction of all primary retroperitoneal neoplasms. Accurate differentiation between benign and malignant forms is essential for optimal clinical management. This study assessed the clinical profiles and sonographic traits of retroperitoneal nerve sheath tumors with the goal of enhancing diagnostic precision and developing therapeutic strategies.

A retrospective analysis of patients diagnosed with retroperitoneal nerve sheath tumors who completed surgical treatment and underwent ultrasound imaging was carried out. Tumors were classified based on sonographic features and blood flow characteristics as per Adler's grading system. Statistical analysis was performed using SPSS 25.0. ROC curve analysis was carried out to determine the optimal diagnostic cutoff values.

A total of 57 patients were included in the study. There were no significant variances in age, gender, or tumor localization among the groups. However, pronounced disparities were observed in tumor number, size, shape, definition of borders, internal echo pattern, structural composition, presence of calcification, and blood flow signals between the classic and malignant groups. Notably, malignant tumors tended to manifest as larger masses with indistinct margins and irregular shapes. The maximum tumor diameter emerged as a discriminating factor for malignancy, with a diagnostic cutoff of 9.9 cm, yielding an AUC of 0.754 from the ROC curve analysis.

This study outlines the distinctive clinical and sonographic features of retroperitoneal nerve sheath tumors, with a particular focus on malignant subtypes. Ultrasonography emerges as a valuable diagnostic tool, contributing to the differentiation of tumor categories and potentially to the development of targeted treatment strategies. The identification of specific sonographic markers may facilitate the early detection and detailed characterization of these tumors, which could contribute to improved patient outcomes.

Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).

Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity. VGGNet-16 and SVM employed for feature extraction and classification, respectively. Performance matrices were evaluated using accuracy, AUC, recall, precision, and F1-score. Both VGGNet-16 and SVM underwent comparative analysis during the training, validation, and testing phases.

The SVMVGGNet-16 model outperformed both, with a training accuracy (97.22%), AUC (99.42%), recall (94.22%), precision (95.28%), and F1-score (94.68%). In testing, our SVMVGGNet-16 model maintained high accuracy (96.72%), with an AUC (96.87%), recall (84.67%), precision (87.40%), and F1-score (85.73%).

Our experimental results demonstrate the potential of SVMVGGNet-16 in improving diagnostic performance, leading to earlier detection and better treatment outcomes. Future work includes refining the model, expanding datasets, conducting clinical trials, and integrating the system into clinical practice to ensure practical usability.

Axitinib is a selective inhibitor of vascular endothelial growth factor receptors and is used in the treatment of many malignancies. Herein, we reported a rare case with axitinib-induced retinal changesand associated toxicity.

A forty-five-year-old female presented with blurred vision who had been taking 7 mg of Axitinib bid for 5 months. Initial Best Corrected Visual Acuity (BCVA) was 20/32 at the right and counting fingers at the left eye. Funduscopic examination revealed bilaterally widespread intraretinal hemorrhages, cotton-wool spots, and hard exudates with a star-like appearance at the macula. The optical coherence tomography revealed central macular edema. There was hyperreflective edema in the inner layers, exudates in the middle retinal layers, and subfoveal subretinal fluid. Fundus fluorescein angiography revealed localized ischaemic findings in the early phase and multifocal perivascular ink-blot fluorescein leakage in the middle and late phases. Axitinib treatment was discontinued immediately, and at the third month of follow-up, the macular edema and fundus findings improved with a final BCVA of 20/20 at the right and 20/32 at the left eye.

Considering the ocular side effects of the patients receiving axitinib is crucial to prevent any potentially persistent visual loss.

Spinal image denoising plays a vital role in the accurate diagnosis of disc herniation (DH).

Traditional denoising algorithms perform less due Limited Directional Selectivity problem and do not adequately capture directional information in pixels. Traditional algorithms' edge representation and texture details are insufficient for the earlier detection of DH. Limited Directional Selectivity leads to inaccurate diagnosis and classification of Disc Herniation (DH) stages. The DH stages are (i) Degeneration (ii) Prolapse (iii) Extrusion and (iv) Sequestration. Moreover, detection of DH size below 2mm using MR image is the major problem.

To solve the above problem, spinal cord MR images fed to the proposed Parrot optimization tuned Denoising Convolutional Neural Network (Po-DnCNN) algorithm for perspective enhancement of nucleus pulposus region in the spinal cord, vertebrae. The perspective enhancement of Spinal cord image led to the accurate classification of stages and earlier detection of DH by using the proposed Hippopotamus optimization- Fast Hybrid Vision Transformer (Ho–FastViT) algorithm. For this study, spinal cord MR images are obtained from the Grand Challenge website – SPIDER dataset.

The proposed Po-DnCNN method and Ho-FastViT results are analysed quantitatively and qualitatively based on the edge, contrast, classification of the stage, and enhancement of the projected nucleus pulposus region in the spinal cord and vertebrae. The predicted DH results using the proposed method are compared with the manual Pfirrman Grade value of the spinal card method.

Proposed method is better than traditional methods for earlier detection of DH. Po-DnCNN and Ho-FastViat methods give high accuracy of about 98% and 97% compared to traditional methods.

Patients with Turner syndrome need hormone replacement therapy for puberty induction. However, it is not known whether inadequate hormone replacement therapy affects the pituitary.

Patients with Turner syndrome (n=35) and healthy control (n=20) (age/gender matched) subjects were included. MRI imaging of the pituitary was used to calculate pituitary volumes. According to the estradiol regimen, patients were divided into two groups; (i) those treated with low-dose conjugated oestrogen (CE, 0.625 mg) and (ii) those treated with combination therapy (ethinyl estradiol+sipropterone acetate; 35 mcg/2 mg). Pituitary measurements were calculated according to pituitary borders and their distances to each other via pituitary MRI.

Pituitary hyperplasia (0.58±0.15 cm³vs. 0.40±0.17 cm³) was determined in patients with low dose conjugated estrogen compared to the other patients or healthy control subjects (0.42±0.16 cm³) (p=0.005). Serum FSH levels of the patients treated with low dose CE were also higher compared to the patients who received combination therapy (p=0.001).

Inadequate hormone replacement therapy can cause devastating effects on the bones and uterine health and disrupts the pituitary structure.

Hypoxic-ischemic encephalopathy (HIE) is a brain injury that is caused by improper oxygen/blood flow. None of the existing works have concentrated on detecting HIE based on the sub-acute injury in the brain.

To enhance the accuracy and specificity of HIE detection, a comprehensive approach that includes SAI identification, BGT segmentation, and volume calculation will be utilized.

This study addresses the critical challenge of detecting hypoxic-schemic encephalopathy (HIE) through advanced image processing techniques applied to brain MRI data. The primary research questions focus on the effectiveness of the proposed CO-GW-RNN method in accurately identifying HIE and the impact of incorporating segmentation and clustering processes on detection performance.

The proposed method achieved remarkable results, demonstrating an accuracy of 98.98% and a specificity of 98.17%, significantly outperforming existing techniques such as the RUB classifier (84.6% accuracy) and the DTL method (94.00% accuracy).

These findings validate the effectiveness of the proposed methodology in improving HIE detection in brain MRI images.

This study aimed to compare automated three-dimensional Intrapulmonary Vessel Volume (IPVV) differences between lung and mediastinal windows in healthy individuals using quantitative measurements obtained from chest Computed Tomography (CT) plain scans.

A total of 258 participants (aged 21–83 years) with negative chest CT scans from routine physical examinations conducted between January to November 2023 were retrospectively enrolled. For each healthy participant, an algorithm was used to automatically extract total lung IPVVs as well as IPVVs for vessels of specific diameter. Differences in IPVVs were then compared between those extracted using the lung window and those extracted using the mediastinal window.

The IPVVs for the entire lung, intrapulmonary arteries, intrapulmonary veins, and small pulmonary vessels (categorized by different diameters) extracted from the lung window were significantly higher than those extracted from the mediastinal window (p<0.01). No significant sex-based differences in IPVV were observed for pulmonary arteries and veins with diameters between 0.8 and 1.6 mm, as well as pulmonary veins with diameters between 2.4 and 3.2 mm. However, in pulmonary arteries and veins with diameters between 1.6 and 2.4 mm, females had significantly higher IPVVs than males. In all other cases, IPVVs were larger in males than in females.

This method of automatic IPVV extraction and quantitative assessment has been proven to be feasible. Automated IPVV expression effectively identified morphological characteristics of intrapulmonary vessels. The study has concluded IPVVs extracted from the lung window to be generally larger than those extracted from the mediastinal window.

Early diagnosis of prostate cancer can improve the survival rate of patients on the premise of high-quality images. The prerequisite for early diagnosis is high-quality images. ZOOMit is a method for high-resolution, zoomed FOV imaging, allowing diffusion-weighted images with high contrast and resolution in short acquisition times. RESOLVE DWI is an advanced MRI technique developed to obtain high-resolution diffusion-weighted images with reduced susceptibility-related artifacts.

This study aimed to compare the image quality of conventional single-shot Echo-planar Imaging (ss-EPI) Diffusion-weighted Imaging (DWI), zoomed FOV imaging (ZOOMit) DWI, and readout segmentation of long variable echo-trains (RESOLVE) DWI sequences for prostate imaging, and optimize the strategy to obtain high-quality Magnetic Resonance Imaging (MRI) in order to discriminate malignant and benign prostate diseases.

Fifty-one patients were enrolled, including 31 with prostate cancer, 11 with prostate benign disease, and 9 with bladder cancer. Patients underwent MRI scans using T2-weighted (T2W), ss-EPI DWI, ZOOMit DWI, and RESOLVE DWI (b = 0, 50, 1400 s/mm²) sequences using a 3.0T MRI scanner. Subjective scores of image quality were evaluated by two independent radiologists. Differences in the subjective scores and objective parameters among the three sequences were compared. The agreement and consistency between the findings of the two raters were evaluated with Kappa or Intra-class Correlation Coefficient (ICC). Receiver Operating Characteristic (ROC) curves were used to distinguish malignant and benign prostate disease.

The agreement of subjective scores of 51 patients was high or moderate between the two radiologists (kappa: 0.529–0.880). ZOOMit displayed the highest clarity and the lowest distortion and artifacts compared to ss-EPI and RESOLVE. The two radiologic technicians obtained moderate or high consistency of objective measurement (ICC: 0.527–0.924). In the ROC analysis, ADCmean and Prostate Imaging Reporting and Data System (PI-RADS) scores for three sequences were comparable in differentiating prostate cancer from benign prostate disease (all p>0.05), in which ZOOMit indicated the highest Area Under the Curve (AUC) (0.930 and 0.790, respectively).

Compared to the other two sequences, ZOOMit can be deemed preferable to improve prostate MRI diffusion imaging as it has exhibited the highest AUC in identifying prostate cancer.

Screening ultrasound proves to be remarkably beneficial in pre-hospital settings, particularly in geographically remote areas with technological constraints and no medical specialties. Urological pathology has a high frequency of occurrence in the emergency department and is part of the wide range of occurrences that can benefit from this ultrasound screening as a clinical guide for patients.

In this case, a patient experiencing lower abdominal pain and symptoms of renal colic sought assistance at a basic emergency service facility. Utilizing a renal screening ultrasound executed by a sonographer, the clinical team identified images indicative of a significant bladder calculus. Subsequently, the patient was referred to a referral hospital for a comprehensive evaluation by medical specialties.

The images obtained in both health units exhibited congruence, indicating that the screening ultrasound, while not intended to replace the specialized orthodox ultrasound executed by a radiologist, served as a crucial tool for diagnostic presumption, providing consistency in clinical decision-making for referring patients. This capability allowed emergency physicians to promptly transfer a patient requiring urgent further investigation to a referral hospital with compelling and substantiated data. This shift in the approach to patient triage in a remote setting could enhance patient safety.

This prospective study aimed to establish the typical viscosity range of the thyroid gland in healthy individuals using a new method called the Sound Touch Viscosity (STVi) technique with a linear array transducer.

Seventy-eight healthy volunteers were enrolled between March, 2023 and April, 2023. Thyroid viscosity was measured using the Resona R9 ultrasound system equipped with a linear array transducer (L15-3WU). Each patient had three valid viscosity measurements taken for each thyroid lobe, and the average values were analyzed. Thyroid gland stiffness was measured and analyzed simultaneously.

The study included 51 women and 27 men with an average age of 48 years. The mean viscosity measurement for a normal thyroid gland was 1.10 ± 0.41 Pa.s (ranging from 0.38 to 2.25 Pa.s). There were no significant differences in viscosity between the left and right lobes of the thyroid gland. We found no significant variations in viscosity based on gender, age, or body mass index (BMI). There was a notable positive correlation between thyroid viscosity and stiffness measurements (r = 0.717, p < 0.001).

Our findings suggest that STVi is a highly reliable method for assessing the thyroid. This technique holds promise as a new, non-invasive approach to evaluating thyroid parenchyma viscosity.

Patients with abdominal Pheochromocytoma and Paraganglioma (PPGL) are prone to a hypertensive crisis during surgery, which may endanger their lives. This study aimed to develop and validate a Computed Tomography (CT) radiomics nomogram for the prediction of intraoperative hypertensive crisis in patients with PPGL.

In this retrospective study, 212 patients with abdominal PPGL underwent abdominal-enhanced CT and surgical resection. Radiomic features were extracted from arterial and venous phases. Multivariable logistic regression models were developed using an internal validation and an external test set. The performance of the nomograms was determined by their discrimination, calibration, and clinical usefulness.

A total of 212 patients with PPGL were included, involving 44 with hypertensive crises. The patients were divided into training (n = 117), validation (n = 51), and test (n = 44) sets. Eighteen radiomics-relevant radiomic features were selected. A history of coronary heart disease and the CT radiomics score were included in the prediction model, which achieved an area under the curve of 0.91 [95% Confidence Interval (CI) 0.85-0.97] in the training set, 0.93 (95% CI 0.84-0.99) in the validation set, and 0.85 (95% CI 0.72-0.97) in the test set. The decision curve analysis demonstrated the radiomics nomogram to be clinically useful.

Our study has developed and validated a CT radiomics nomogram that has demonstrated remarkable potential in predicting intraoperative hypertensive crisis in patients with abdominal pheochromocytoma and paraganglioma. This non-invasive, straightforward approach has exhibited high accuracy, ease of use, and predictive power.

This paper examines the impact of transfer learning and CLAHE (Contrast Limited Adaptive Histogram Equalization) optimization on the classification of medical images, specifically brain images.

Four different setups were tested: normal images without data augmentation, normal images with data augmentation, CLAHE-processed images without data augmentation, and CLAHE-processed images with data augmentation.

The results show that using CLAHE combined with data augmentation significantly improves classification accuracy. Specifically, the combination of CLAHE and data augmentation achieved a precision of 0.90, a recall of 0.87, an F1-score of 0.89, and an accuracy of 0.86, outperforming the other setups.

These findings highlight the effectiveness of CLAHE optimization in the context of transfer learning, particularly when data augmentation techniques are also applied, leading to an overall improvement in the performance of brain image classification models.

Extrahepatic Common Bile Duct Obstruction (EHBDO) is a serious condition that requires accurate diagnosis for effective treatment. Magnetic Resonance Cholangiopancreatography (MRCP) is a widely used noninvasive imaging technique for visualizing bile ducts, but its interpretation can be complex.

This study aimed to develop a deep learning-based classification model that integrates MRCP images and clinical parameters to assist radiologists in diagnosing EHBDO more accurately.

A total of 465 patients with clinical data were included, of whom 143 also had MRCP images. Missing clinical values were addressed through data imputation. Object detection techniques were used to isolate the common bile duct region in the MRCP images. A multimodal deep learning fusion model was developed by combining the extracted imaging features with selected clinical parameters. To account for the varying significance of different features, a weighted loss function was applied. The performance of the fusion model was compared to that of single-modality approaches (using only MRCP images or clinical data), specifically the accuracy, sensitivity, specificity, and Area Under The Curve (AUC).

The performance of the proposed deep learning fusion model was superior to that of models using only MRCP images or clinical parameters. The fusion model achieved an accuracy of 89.8%, AUC of 90.4%, sensitivity of 81.8%, and specificity of 95.7% in diagnosing EHBDO. By integrating MRCP imaging data and clinical parameters, the proposed deep learning model significantly enhanced the accuracy of EHBDO diagnosis.

This proposed multimodal approach outperformed traditional single-modality methods, presenting a valuable tool for improving the diagnostic accuracy of bile duct obstruction.

This study aims to evaluate the clinical efficacy of spectral dual-energy detector computed tomography (SDCT) and its associated parameters in diagnosing acute pulmonary embolism (APE).

Retrospective analysis of imaging data from 86 APE-diagnosed patients using SDCT was conducted. Virtual monoenergetic images (VMIs) at 40, 70, and 100 KeV, Iodine concentration (IC) maps, Electron Cloud Density Map (ECDM), Effective atomic number (Z-eff) maps, and Hounsfield unit attenuation plots (VMI slope) were reconstructed from pulmonary artery phase CT images. The subtraction (SUB) and ratios of VMIs were calculated, and two experienced radiologists evaluated the patients. The Mann-Whitney U test assessed the parameter ability to differentiate between normal and obstructed lung fields and detect emboli in the pulmonary artery. Receiver Operating Characteristic Curves (ROC) were generated for performance evaluation.

Significant differences (p<0.001) in 40KeV, Ratio, SUB, and Z-eff were found between normal and embolized lung fields. Logistic regression demonstrated good detection performance for Z-eff (AUC=0.986), SUB (AUC=0.975), and IC (AUC=0.974). Parameters such as 40KeV (AUC=0.990), 70KeV (AUC=0.980), 100KeV (AUC=0.962), SUB (AUC=0.990), Z-eff (AUC=0.999), and IC (AUC=1.000) exhibited good detection capabilities for identifying emboli in the pulmonary artery.

SDCT facilitates the identification of diseased lung fields and localization of emboli in the pulmonary artery, thereby improving diagnostic efficiency in APE.

Laparoscopically Harvested Pedicled Omental Flap (LHPOF) has become a viable option for breast reconstruction due to advancements in minimally invasive techniques, offering benefits like reduced postoperative pain and minimal scarring.

This study examines the imaging findings in eight patients who underwent breast reconstruction using a LHPOF. Imaging modalities, including mammography, ultrasonography, MRI, and CT, consistently showed reconstructed breasts with fat replacing glandular tissue and numerous internal vessels. One case of recurrence was detected, demonstrating the efficacy of conventional surveillance imaging studies in facilitating the detection of recurrences.

This is the first report detailing imaging findings of breast reconstruction using an LHPOF, including a recurrence case. Understanding these imaging results is crucial for effective surveillance in breast cancer patients with omental flap reconstruction.