Current Medical Imaging - Current Issue

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.