Current Medical Imaging - Volume 20, Issue 1, 2024

Budd-Chiari syndrome is a rare entity that is caused by an obstruction of the flow in the hepatic veins or inferior vena cava.

Herein, we report a rare case of iatrogenic Budd-Chiari syndrome. A 52-year-old woman with chronic renal failure under hemodialysis, presented to our hospital for dyspnea caused by a large pleural effusion. After the placement of the central right jugular vein catheter, she suffered from right upper quadrant acute abdominal pain along with elevation of liver function enzymes in blood tests. An abdominal computed tomography with contrast revealed obstruction of the right hepatic vein by the catheter tip with concomitant thrombosis, thus the diagnosis of Budd-Chiari syndrome was confirmed. Removal of the catheter and anticoagulant therapy were successfully utilized to treat the patient.

Knowledge of the full spectrum of adverse effects of such a procedure is crucial for their early identification and treatment, often with a multidisciplinary approach.

This study aims to optimize the downgrading of BI-RADS class 4a nodules by combining various sectional elastography techniques with age.

We performed conventional ultrasonography, strain elastography (SE), and shear wave elastography (SWE) on patients. Quantitative parameters recorded included age, cross-sectional and longitudinal area ratios (C-EI/B, L-EI/B), strain rate ratios (C-SR, L-SR), overall average elastic modulus values (C-Emean1, L-Emean1), five-point average elastic modulus values (C-Emean2, L-Emean2), and maximum elastic modulus values (C-Emax, L-Emax).

Histopathological evaluations showed that out of 230 lesions, 45 were malignant, and 185 were benign. The sensitivity and specificity of conventional ultrasonography were 100% and 0%, respectively. In contrast, SE and SWE exhibited higher specificity but lower sensitivity. Cross-sectional parameters (C-EI/B, C-SR, C-Emean1, C-Emean2, and C-Emax) outperformed their longitudinal counterparts, with C-SR and C-Emax showing the highest specificity (72.43% and 73.51%) and satisfactory sensitivity (80.00% and 88.89%). Combining age with C-SR and C-Emax significantly improved diagnostic efficiency, achieving a sensitivity of 97.78% and a specificity of 77.30%.

Integrating age with C-SR and C-Emax effectively reduces unnecessary biopsies for most BI-RADS 4a benign lesions while maintaining a very low misdiagnosis rate.

It remains unknown whether the parameters obtained using the Stretched Exponential Model (SEM) and Fractional Order Calculus (FROC) models can help distinguish Hepatocellular Carcinoma (HCC) from Intrahepatic Cholangiocarcinoma (ICC).

This study aimed to evaluate the application value of the parameters of the 3.0T Magnetic Resonance Imaging (MRI) high-order SEM and FROC diffusion model in differentiating HCC and ICC.

Patients with pathologically confirmed HCC and ICC were prospectively enrolled. Diffusion-weighted imaging scans with multiple b-values were acquired 2 weeks before the surgery. The original MRI images were fitted using the mono-exponential model, SEM, and FROC, and several parameters were obtained for the analysis.

In total, 74 patients with HCC and 21 with ICC were included in the study. Significant differences between the HCC and ICC groups were noted in the Apparent Diffusion Coefficient (ADC: p = 0.007), Distributed Diffusion Coefficient (DDC: p < 0.001), and Diffusion coefficient (D: p < 0.001), as each value was significantly lower in the HCC than in the ICC group. The area under the receiver operating characteristic curve of ADC, DDC, and D was 0.694, 0.812, and 0.825, respectively, and the most effective corresponding cut-off values were 1.135 μm²/ms, 1.477 μm²/ms, and 1.104 μm²/ms, respectively.

The diffusion parameters DDC from the SEM and D from the FROC model have been found to be more effective in discriminating HCC and ICC than the ADC from the mono-exponential model. Combining these quantitative parameters can improve the MRI’s diagnostic accuracy, providing useful information for the preoperative differential diagnosis between HCC and ICC.

Pulmonary sclerosing haemangioma (PSH) is a relatively uncommon benign neoplasm that is often asymptomatic and predominantly affects young and middle-aged females. PSH often appears as a single nodule, whereas multiple lesions with a cavity are relatively rare and easily misdiagnosed.

In our study, we report a patient with separated nodules in the same lobe with a cavity and clinical manifestations of cough and sputum with a radiographic presentation similar to that of tuberculosis. The patient underwent percutaneous lung biopsy and thoracoscopic partial pneumonectomy and was diagnosed with multiple PSHs.

We report a rare case of multiple PSHs that were treated with a thoracoscopic partial resection of the left upper lobe. Postoperative pathology confirmed multiple PSHs. Due to the rarity of PSH, it is easily misdiagnosed in clinical practice as lung cancer, tuberculosis, or other diseases. The final diagnosis depends on the pathology, and surgery is considered to be an appropriate treatment that leads to a good prognosis.

Bruxism may cause increased alveolar bone thickness and density and irregular enlargement of the periodontal space.

This study aimed to evaluate the mandibular bone quality using radio-morphometric indices and Fractal Dimension (FD) analysis in orthopantomography (OPG).

OPGs of 100 patients, 50 bruxers and 50 non-bruxers, were included in this study. Values, such as mental index (MI), panoramic mandibular index (PMI), gonial index (GI), antegonial notch depth (AND), mandibular cortical index (MCI), and antegonial index (AI), were calculated in OPG. Eight bilateral areas of interest (ROI) were selected on ort for FD analysis: ROI 1, mandibular condyle; ROI 2, mandibular ramus; ROI 3, mandibular angulus; and ROI 4, mandibular mental area.

MI, PMI, and AND values were higher in bruxers than in the control group (p<0.05). MCI and AI values calculated on both sides were not statistically significantly related in bruxism and control group individuals (p>0.05). As a result of the calculations, the FD values of the left condyle (p=0.02) and left angulus (p=0.03) areas showed a statistically significant difference between individuals with and without bruxer. No statistically significant difference was found in the FD measurements calculated from the ramus and mental areas on the right and left sides (p>0.05). The relationship between FD values and gender in these areas was examined, and no statistically significant difference was found (p<0.05).

In dentistry, bruxism can be diagnosed and treated by measuring MI, PMI, and AND values. No difference was found in mandibular cortical bone thickness in bruxers and non-bruxers, according to AI and MCI. The mean GI measured on the right side differed between groups. FD values of the mandibular trabecular bone were affected by bruxism in the right condyle and right angulus areas.

Currently, most multimodal medical image fusion techniques focus solely on integrating the edge details of image features, often overlooking color preservation from the source images. Hence, this paper proposes a multi-channel fusion algorithm based on gradient domain-guided image filtering.

This study aims to enhance the color preservation of source images in multimodal medical image fusion algorithms.

Utilizing gradient field-guided image filters for image smoothing, the process involves constructing different image layers, decomposing using wavelet transforms, and downsampling. Various fusion rules are then applied before inverse wavelet transformation.

Regarding MSE, CCI, PSNR, SSIM, DD, SM, and other metrics, the proposed algorithm consistently ranks highest compared to alternative methods.

Through both subjective and objective analyses, experimental results substantiate the significant edge-preserving effects of the proposed fusion algorithm while effectively maintaining image fidelity and spectral integrity.

Perivascular Epithelioid Cell tumor (PEComa) is a rare mesenchymal neoplasm characterized by the co-expression of melanocytic and myoid markers. While PEComas can arise in diverse anatomical sites, gastric PEComas are exceedingly rare, with merely nine cases documented in the extant literature.

Herein, we have presented a case of gastric PEComa in a 65-year-old male patient who exhibited a 3-year history of epigastric pain, with notable exacerbation in the two months prior to diagnosis. For the initial evaluation of the patient's condition, Endoscopic Ultrasound-guided Fine Needle Aspiration (EUS-FNA) and Computed Tomography (CT) were employed, which enabled a preoperative diagnosis. Radiological assessment demonstrated a neoplasm exhibiting heterogeneous arterial enhancement, persistent delayed enhancement, and distinct margins. Subsequent to diagnosis, the patient underwent surgical resection and has maintained a disease-free status for one year postoperatively. This case report highlights the crucial role of EUS-FNA in facilitating preoperative histological diagnosis and optimizing surgical planning for gastric PEComa.

This case constitutes the tenth documented instance of gastric PEComa in the global literature. In this case, EUS-FNA facilitated a preoperative histopathological diagnosis, thereby enabling precise surgical planning. An accurate preoperative diagnosis is crucial for devising an optimal treatment strategy.

Various studies have demonstrated large variations in the annual occupational exposure of medical personnel working in interventional cardiology departments, ranging from 0.1 mSv to exceeding the annual effective occupational dose limit of 20 mSv.

The purpose of this study was to investigate the 17-year dynamics in the personal dosimetry records of the medical staff in one interventional cardiology department in Bulgaria.

The study was performed between 2007 and 2023 and included 31 interventional cardiologists. For each of them, data from all individual dosimetry control reports were analysed. The number and complexity of interventional procedures were analysed on an annual basis. A total number of 39639 procedures performed over 17 consecutive years were classified and analysed.

The results have suggested that when a newly formed team gains clinical experience, the focus shifts towards optimizing radiation exposure to patients, and it has been observed to change from 40 Gy.cm² in 2009 to 14.8 Gy.cm² in 2023 for diagnostic and from 146 Gy.cm² in 2009 to 51.2 Gy.cm² in 2023 for interventional procedures, and from 19.5 mSv/year under the lead apron in 2012 and 3.7 mSv/year in 2023 for one of the interventional cardiologists among the medical staff. The optimization process in the department has been found to be slow but consistent, starting with the routine application of basic methods to reduce the likelihood of skin injury. Any practical implementation of a methodology or process requires periodic training to raise awareness of the topic and the use of different strategies to put it into practice. Most of the reported values from individual dosimetry monitoring have been found to be in the range below 4 mSv/year, consistent with the summarised results from other studies.

The radiation protection awareness program introduced in 2014 has been found to result in between a 2- and 6-fold reduction in individual effective doses for some staff members and a 2-fold reduction in typical patient doses.

Controversies still exist regarding the mechanism formation of the blend sign, defined as hypodensity and hyperdensity regions, in Intracerebral Hemorrhage (ICH), and which region associated with bleeding remains unknown. Spot sign is an independent predictor of hematoma expansion (HE) and indicates persistent bleeding focus in the hematoma. Here, we sought to establish the relationship between the spot sign and the blend sign to gain insights into the formation of the blend sign.

Patients were categorized based on the spot sign location within the blend sign in patients with ICH from 2018 to 2023. subjects with a spot sign in the hypodensity region of the blend sign (hypo-spot sign group); subjects with a spot sign in the hyperdensity region of the blend sign (hyper-spot sign group). Subsequently, patients were stratified into two groups based on the presence or absence of HE. Also, we analyzed the relationship between the spot sign and the blend sign, as well as the association between the blend sign and HE.

A total of 205 patients were included, including 190 patients (92.7%) who had the spot sign in the hyper-spot sign and 15 patients (7.3%) who had the spot sign in the hypo-spot sign. HE was observed in 60 patients (29.3%), 59 (98.3%) of whom had the spot sign detected in the hyper-spot sign, while only one (1.7%) had the spot sign in the hypo-spot sign. Univariate logistic regression analysis revealed that the hyper-spot sign group (6.305, 1.810–49.072; P < 0.05) was an independent predictor of HE.

The hyperdensity area of the blend sign may represent fresh bleeding in ICH rather than the hypodensity area.

ClinicalTrials.gov, NCT05548530. Registered on September 21, 2022, Prognostic Analysis of Different Treatment Options for Cerebral Hemorrhage-Full Text View - ClinicalTrials.gov “retrospectively registered.”

Ingestion of gastrointestinal foreign bodies (FB) is a common clinical problem worldwide. Approximately 10–20% of FBs require an endoscopic procedure for removal, and < 1% require surgery.

An 89-year-old male with Alzheimer's disease was hospitalized because of abdominal pain, abdominal distention, vomiting for three days, and cessation of bowel movements for six days. Abdominal computed tomography (CT) scan showed a small intestinal obstruction and an atypical FB in the small intestine. A pill and remaining plastic casing were removed from the small intestine during surgery. FB is a square with four sharp acute angles at its edge. The patient was discharged after two weeks of treatment, and no recurrence or complications were observed during the 6-month follow-up.

Atypical intestinal FBs may cause misdiagnosis and easily lead to serious complications. Therefore, an appropriate radiological examination, such as CT, is necessary for unexplained intestinal obstruction. Symptomatic intestinal FBs should be actively removed to avoid serious complications.

Dural sinus arteriovenous malformation is an uncommon intracranial vascular malformation. The affected cases may suffer from severe neurological injury. Prenatal ultrasound has been used to diagnose fetal intracranial vascular abnormality, but prenatal three-dimensional (3D) ultrasound presents a very rare anomaly; an arteriovenous malformation of the dural sinus has not been reported.

This study aimed to emphasize the diagnostic value of 3D ultrasound in the fetus with dural sinus arteriovenous malformation.

A 38-year-old woman was referred for targeted fetal ultrasonography at 37 weeks of gestation due to an ultrasound that showed a cystic lesion in the posterior cranial fossa. The fetus demonstrated obvious dilatation of the torcular herophili, bilateral transverse sinuses, and bilateral sigmoid sinuses, appearing as a novel bull's horn sign on 3D ultrasound. After birth, cerebral angiography confirmed the diagnosis of dural arteriovenous fistula (DAVF) in the occipital sinus region.

3D ultrasound is an appealing method for prenatal diagnosis of dural sinus arteriovenous malformation.

Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data.

This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized.

In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy.

The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private.

The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacy-preserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.

Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.

These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViT-B-16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.

This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

Guizhi Shaoyao Zhimu Decoction can be used in the treatment of rheumatoid arthritis, but there is scarce literature on using ultrasound combined with electrophysiology to evaluate the efficacy of this traditional Chinese medicine.

This study aimed to explore the clinical effect of Guizhi Shaoyao Zhimu decoction on cold-dampness arthralgia rheumatoid arthritis (RA) by ultrasound and electrophysiological examination.

A total of 64 patients with rheumatoid arthritis were randomly divided into two groups, with 32 cases in each group. The control group was treated with conventional western medicine, and the experimental group was treated with Guizhi Shaoyao Zhimu Decoction in addition to conventional western medicine. After 4 weeks of treatment, traditional Chinese medicine (TCM) symptom scores, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), 28 joint disease range of motion score (DAS28), ultrasonic score, and electrophysiological examination results were observed.

There were significant differences in TCM syndrome scores, ESR, CRP, DAS28, and ultrasound scores in the two groups before and after treatment (P<0.05). Compared between the two groups after treatment, there were statistically significant differences in TCM syndrome scores, ESR, CRP, DAS28, and ultrasound scores (P<0.05). The motor nerve conduction velocity (MNCV), sensory nerve conduction velocity (SNCV), and action potential (AP) of the median nerve and ulnar nerve in the experimental group were significantly increased compared with the control group (P<0.05).

Guizhi Shaoyao Zhimu Decoction combined with conventional western medicine has a significant effect on cold-dampness arthralgia rheumatoid arthritis, and ultrasound and electrophysiological examination can be used to evaluate its curative effect.

The maxillary sinuses are air-filled cavities which vary in size and shape. Sinus radiography has been widely used in the determination of the gender of the individual, especially in forensic investigation for human identification and sexing of individuals. The advanced radiographic techniques like cone beam computed tomography (CBCT), especially the axial and coronal sections, have been considered as a subtle concept in forensic odontology. Aim: The current study aimed in evaluating the parameters of the maxillary sinus using CBCT and to identify its implication in gender determination.

Current study consists of 50 patients who were divided into two groups, group I consisted of 25 males and group II consisted of 25 females, where maxillary sinus dimensions like maximum length (anteroposteriorly), maximum width (mediolaterally) and maximum height (superioinferiorly) were evaluated using CBCT scans in axial and coronal sections respectively.

Shapiro-Wilk test was used to determine the normality and Independent t-test was used to compare the two groups, followed by predictive analysis. Maxillary sinus, right length (p<0.001), right width (p<0.001), right height (p<0.001), left length (p<0.001), left width (p<0.001), left height (p<0.001). Right and left maxillary sinus parameters were different between males and females, with statistical significance indicating the presence of sexual dimorphism.

In this study, maxillary sinus parameters like length, width and height in CBCT were significantly different between males and females. Maxillary sinus can be a useful gender predictor in the forensic identification of the individual.

This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS.

The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis.

This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO.

Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.

This study aimed to establish a model based on Multi-detector Computed Tomography (MDCT), ¹⁸F-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (¹⁸F-FDG PET/CT), and clinical features for predicting different growth patterns of preoperative Invasive Adenocarcinoma (IAC).

This retrospective study included 357 patients diagnosed with IAC who underwent surgical treatment. According to pathological subtypes, IAC was classified into low-risk growth patterns (lepidic, acinar) and high-risk growth patterns (papillary, micropapillary, and solid). The clinical features of patients, preoperative MDCT, and ¹⁸F-FDG PET imaging characteristics were collected. Logistic regression analysis was used to determine the independent risk factors for the high-risk growth pattern of IAC and construct models for predicting the high-/low-risk growth patterns of IAC. Receiver operating characteristics and calibration curves were plotted and Decision Curve Analysis (DCA) was performed to evaluate the performance and clinical benefits of the models, respectively.

Gender, tumor location, size, spiculation, and SUVavg were independent risk factors for high-risk growth patterns of IAC. The PET/CT imaging-clinical characteristics combined model could well identify high-/low-risk growth patterns of IAC (AUC=0.789), which outperformed the CT model (AUC=0.689, p=0.0012), PET model (AUC=0.742, p=0.0022), and clinical model (AUC=0.607, p<0.0001). The calibration curve indicated good coherence between all model predictions and actual observations in both training and test sets (p>0.05). DCA revealed the highest clinical benefit of PET/CT imaging-clinical characteristics combined model in identifying the high-risk growth pattern of IAC.

The PET/CT imaging-clinical model based on multiphase MDCT features, ¹⁸F-FDG PET features, and clinical characteristics could predict the high-risk growth pattern of IAC preoperatively, aiding clinicians in deciding personalized treatment strategies.

Kikuchi-Fujimoto Disease (KFD) is a rare condition, distinguished by its hallmark presentation of regional lymphadenopathy in young adult females. While initially observed to exclusively affect cervical lymph nodes in females under 40 years old, KFD is now known to impact individuals of any age or gender and manifest with adenopathy in various anatomical sites. Nonspecific imaging findings for KFD include enlarged lymph nodes, often exhibiting abnormal morphology.

In this study, we present the case of a 49 year old asymptomatic woman, in whom several enlarged left axillary lymph nodes were incidentally noted during routine mammography. The diagnosis of KFD was determined via ultrasound-guided core needle biopsy. Histological examination of the biopsied lymph node revealed necrotizing lymphadenitis, consistent with KFD.

The uncommon and broad presentation of KFD highlights the significance of acquiring tissue samples to distinguish this condition from resembling malignancies or autoimmune disorders.

The use of Magnetic Resonance Imaging (MRI) and radiomics improves the management of Prostate Cancer (PCa) and helps in differentiating between clinically insignificant and significant PCa. This study has explored the diagnostic value of radiomic analysis based on functional parameter maps from monoexponential and diffusion kurtosis models in MRI for differentiating between clinically insignificant and significant PCa.

In total, 105 PCa cases, including 38 clinically insignificant and 67 clinically significant PCa cases, were retrospectively analyzed. The patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed, and 1,352 radiomic features were extracted from ADC, MD, and MK images. Clinical, radiomic, and clinical–radiomic models were developed and compared using receiver operating characteristic curve analysis, decision curve analysis, and calibration curves.

Clinical variables, such as TPSA, PI-RADS, and FPSA, were identified as independent risk factors for differentiating between clinically insignificant and significant PCa. In radiomics, three features were identified as highly weighted indicators. The clinical–radiomic model based on the clinical and radiomic features demonstrated the highest predictive efficacy for clinically insignificant and significant PCa, with area under the curve values of 0.940 and 0.861 in the training and test sets, respectively.

The predictive model constructed from clinical and radiomic features exhibited substantial diagnostic differentiation capabilities for clinically insignificant and significant PCa. The clinical–radiomic model displayed the highest predictive performance, promising significant contributions to future clinical treatment and assessment of PCa.

Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with congenital and acquired heart disease. However, most 3D whole-heart MRI acquisitions take a long time to perform, and the sequence used is susceptible to banding artifacts.

To validate an unsupervised neural network that can reduce acquisition time and improve image quality for 3D whole-heart MRI by super-resolving low-resolution images.

The results of the super-resolution neural network (SRNN) were compared with bilinear interpolation, a state-of-the-art method known as AdapSR, and the ground truth high-resolution images qualitatively and quantitatively. Thirty pediatric patients with varying congenital and acquired heart diseases were included in this study. Results from the SRNN without a ground truth image were compared qualitatively with the contrast-enhanced whole-heart images. Signal-to-noise ratio (SNR) was used to quantitatively compare each of the methods and the high-resolution ground truth.

As confirmed by both the quantitative and qualitative results, the SRNN improves image quality. Furthermore, because it only requires a low-resolution acquisition, the use of the SRNN reduces acquisition time.

The SRNN lessens noise and eliminates artifacts while maintaining correct anatomical structure in the images.

The permanent canine usually has a single root and a single root canal. A one-rooted canine with two canals or a canine with two roots and two separate canals may also occur at a lower incidence in the permanent dentition. However, bilateral symmetrical mandibular canines with two roots and two separate canals are less common.

This study reported a lower incidence case of bilateral symmetrical mandibular canines with two roots and two separate canals, which was found based on a CBCT examinaton. The patient visited our department and was consulted for orthodontic treatment due to the irregularity of her lower anterior teeth. As the abnormal root morphology of bilateral mandibular canines greatly increased the difficulty of orthodontic treatment, the patient finally gave up orthodontic treatment after communication.

This case report provides supplementary data to better understand the complexities of the root canal system of canines.

Multimodal medical image fusion techniques play an important role in clinical diagnosis and treatment planning. The process of combining multimodal images involves several challenges depending on the type of modality, transformation techniques, and mapping of structural and metabolic information.

Artifacts can form during data acquisition, such as minor movement of patients, or data pre-processing, registration, and normalization. Unlike single-modality images, the detection of an artifact is a more challenging task in complementary fused multimodal images. Many medical image fusion techniques have been developed by various researchers, but not many have tested the robustness of their fusion approaches. The main objective of this study is to identify and classify the noise and artifacts present in the fused MRI-SPECT brain images using transfer learning by fine-tuned CNN networks. Deep neural network-based techniques are capable of detecting minor amounts of noise in images. In this study, three pre-trained convolutional neural network-based models (ResNet50, DenseNet 169, and InceptionV3) were used to detect artifacts and various noises including Gaussian, Speckle, Random, and mixed noises present in fused MRI -SPECT brain image datasets using transfer learning.

The five-fold stratified cross-validation (SCV) is used to evaluate the performance of networks. The obtained performance results for the pre-trained DenseNet169 model for various folds were greater as compared with the rest of the models; the former had an average accuracy of five-fold of 93.8±5.8%, 98%±3.9%, 97.8±1.64%, and 93.8±5.8%, whereas InceptionNetV3 had a value of 90.6±9.8%, 98.8±1.6%, 91.4±9.74%, and 90.6±9.8%, and ResNet50 had a value of 75.8±21%.84.8±7.6%, 73.8±22%, and 75.8±21% for Gaussian, speckle, random and mixed noise, respectively.

Based on the performance results obtained, the pre-trained DenseNet 169 model provides the highest accuracy among the other four used models.

Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a fatal adverse event of immunotherapy. However, there is a lack of methods to identify patients who have a high risk of developing ICI-P in immunotherapy.

We aim at predicting the individualized risk of developing ICI-P by computed tomography (CT) images and deep learning to assist in personalized immunotherapy planning.

We first explored the prognostic value of the commonly used clinical factors. Moreover, we proposed a novel whole-lung analysis deep learning (DL) model, which is constructed using a combination of Densely Connected Convolutional Networks (DenseNet) and Feature Pyramid Networks (FPN). This DL model mines global lung information from CT images for predicting the risk of developing ICI-P, and it is fully automated and does not require manually annotating images. Finally, 157 patients were collected and randomly divided into training and testing sets for performance evaluation.

In the testing set, the clinical model achieved an Area Under the Curve (AUC) of 0.710 and accuracy of 0.625. By mining global lung information, the DL model achieved AUC=0.780 and accuracy=0.729 in the testing set, where the DL score revealed a significant difference between ICI-P and non-ICI-P patients. Through deep learning visualization technique, we found that many areas outside of tumor (e.g., pleural retraction, pleural effusion, and the abnormalities in vessels) are important for predicting the risk of developing ICI-P in immunotherapy.

The whole-lung analysis DL model provides an easy-to-use method for identifying patients at high risk of developing ICI-P by CT images, which is important for individualized treatment planning in immunotherapy. The performance improvement over the clinical model indicates that mining whole-lung information in CT images is effective for prognostic prediction in immunotherapy.

Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to identify and visualize global research trends and hotspots of AI in medical ultrasound to provide guidance for further exploitation.

This study aims to highlight the global research trends and hotspots of the top 100 most-cited papers related to AI in medical ultrasound by combining quantitative and visualization methods.

Articles on AI in medical ultrasound were selected from the WoSCC database and ranked by citation count. After identifying the 100 most-cited papers, we conducted a quantitative and visualized analysis of bibliometric characteristics, including leading research countries, prominent institutions, key authors and journals, author clusters and collaborations, and keyword co-occurrence network analysis.

The top 100 highly cited papers from the WoSCC database were published between 1999 and 2021, with total citations ranging from 91 to 1580. The most cited article was published in IEEE Transactions on Medical Imaging. The top three most prolific countries/regions were the United States, mainland China, and the United Kingdom. The most published institutions and journals were Idaho University and IEEE Transactions on Medical Imaging. Twelve authors published more than four papers, with Suri, JS being the most productive author. The most studied topics were “ultrasound”, “computer-aided diagnosis”, and “segmentation”. Ultrasonography of Superficial Organs was the main site that was studied the most.

This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the most highly cited literature. It serves as a valuable reference for the development and applications of AI, fostering potential collaborations within this domain.

This study aimed to verify whether the Liver-to-portal Ratio (LPR) can assess the adequacy of the Hepatobiliary Phase (HBP) for patients with different liver functions.

A total of 125 patients were prospectively enrolled in the study and graded into the non-cirrhosis group (45), Child-Pugh A group (40), and Child-Pugh B/C group (40). The LPR on HBP was calculated after eight HBPs were obtained within 5-40 minutes. The adequate HBP was determined according to the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus statement. The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were analyzed by paired t-test and Wilcoxon signed-rank test, respectively. The chi-square test was used to test the difference in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP.

The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were significant in Child-Pugh A and Child-Pugh B/C groups (P < 0.05), except for the non-cirrhosis group (P > 0.05). The differences in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP were not statistically significant in all groups (all P > 0.05).

The adequate HBP obtained according to the 2016 ESGAR consensus statement could provide larger LPR and better lesions’ conspicuity than 10-min HBP, especially for cirrhotic patients; however, the efficacy of using an LPR cutoff of 1.462 as the standard of the adequate HBP may be compromised in patients with cirrhosis.

Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a Quantum Deep Neural Network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases.

The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10⁻⁶ and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs.

The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the Convolutional Neural Network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method.

This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors.

Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep learning-based motion correction algorithm (MCA) on motion artifacts in patients with challenging HR conditions, focusing on image quality and diagnostic performance of CCTA.

This retrospective study included 240 patients (mean HR: 88.1 ± 14.5 bpm; mean HRV: 32.6 ± 45.5 bpm) who underwent CCTA between June, 2020 and December, 2020. CCTA images were reconstructed with and without the MCA. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured to assess objective image quality. Subjective image quality was evaluated by two radiologists using a 5-point scale regarding vessel visualization, diagnostic confidence, and overall image quality. Moreover, all vessels with scores ≥ 3 were considered clinically interpretable. The diagnostic performance of CCTA with and without MCA for detecting significant stenosis (≥ 50%) was assessed in 34 patients at both per-vessel and per-patient levels, using invasive coronary angiography as the reference standard.

The MCA significantly improved subjective image quality, increasing the vessel interpretability from 89.9% (CI: 0.88-0.92) to 98.8% (CI: 0.98-0.99) (p < 0.001). The use of MCA resulted in significantly higher diagnostic performance in both patient-based (AUC: 0.83 vs. 0.58, p = 0.04) and vessel-based (AUC: 0.92 vs. 0.81, p < 0.001) analyses, with the vessel-based accuracy notably increased from 79.4% (CI: 0.72-0.86) to 91.2% (CI: 0.85-0.95) (p = 0.01). There were no significant differences in objective image quality between the two reconstructions. The mean effective dose in this study was 2.8 ± 1.1 mSv.

The use of MCA allows for obtaining high-quality CCTA images and superior diagnostic performance with low radiation exposure in patients with elevated HR and HRV.

To verify the multiphase ratio of Computer Tomography-value between the renal cortex and renal medulla, which can be used to concisely evaluate renal function in kidney recipients.

Fifty-eight kidney recipients were retrospectively enrolled and divided into the Normal group (eGFR≥90 mL/min/1.73m²) and Abnormal group (eGFR<90 mL/min/1.73m²) according to Chronicle Kidney Disease Epidemiology Collaboration (eGFR(CKD-EPI)) and the Modular of Diet in Renal Disease (eGFR(MDRD)) formulas respectively. The multiphasic ratios between the renal cortex and medulla in the arterial phase and venous phase were noted as A(RatioC/M) and V(RatioC/M), and the difference between those two was recorded as D(RatioC/M). Correlation/regression analysis, student t-test, and ROC curves analysis were used to test the ability of multiphasic ratios to assess renal function.

Both A(RatioC/M) and V(RatioC/M) were moderately correlated with eGFR(CKD-EPI) (Y =20.41*X + 28.20, r=0.40 (95%Cl, 0.13-0.58), P<0.01; Y =-16.57*X + 109.8, r=-0.29 (95%Cl, -0.51--0.04), P=0.03) and eGFR(MDRD) (Y =23.72*X + 23.52, r=0.38 (95%Cl, 0.13-0.58), P<0.01; Y =-19.88*X + 119.5, r=-0.30 (95%Cl, -0.52--0.05), P=0.02). However, D(RatioC/M) was strongly positive correlated with eGFR(CKD-EPI) (Y = 30.95*X + 60.71, r=0.61 (95%Cl,0.42-0.75), P<0.001) and eGFR(MDRD) (Y = 36.47*X + 61.01, r=0.62 (95%Cl, 0.44-0.76), P<0.001), respectively, and both regression lines were not significant different (slope: P=0.496, intercept: P=0.378). The differences in D(RatioC/M) between the two groups were significant (all P<0.05). The ROC curve analysis provided the cutoff values of D(RatioC/M) for assessing eGFR (AUC:0.863 and AUC:0.822, all P<0.001).

The D(RatioC/M) can be used to assess renal function for kidney recipients.

The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout.

A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance.

Five CT radiomics features were significantly different between gout cases and non-gout cases (P < 0.05). In the logic regression, the AUC, sensitivity, specificity, and accuracy were 0.738, 77% (46/60), 70% (28/40), and 74% (74/100), respectively. In the Random forest, Xgboost, and support vector machine analysis, the accuracy was 0.901, 0.833, and 0.875, respectively.

From this study, it can be concluded that muscle CT radiomics is feasible in identifying gout.

Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time.

This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations.

A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection.

Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304 (±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast DWI and T2WI images (p>0.05).

DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the kidneys.

Accurately modeling respiratory motion in medical images is crucial for various applications, including radiation therapy planning. However, existing registration methods often struggle to extract local features effectively, limiting their performance.

In this paper, we aimed to propose a new framework called CvTMorph, which utilizes a Convolutional vision Transformer (CvT) and Convolutional Neural Networks (CNN) to improve local feature extraction.

CvTMorph integrates CvT and CNN to construct a hybrid model that combines the strengths of both approaches. Additionally, scaling and square layers are added to enhance the registration performance. We have evaluated the performance of CvTMorph on the 4D-Lung and DIR-Lab datasets and compared it with state-of-the-art methods to demonstrate its effectiveness.

The experimental results have demonstrated CvTMorph to outperform the existing methods in terms of accuracy and robustness for respiratory motion modeling in 4D images. The incorporation of the convolutional vision transformer has significantly improved the registration performance and enhanced the representation of local structures.

CvTMorph offers a promising solution for accurately modeling respiratory motion in 4D medical images. The hybrid model, leveraging convolutional vision transformer and convolutional neural networks, has proven effective in extracting local features and improving registration performance. The results have highlighted the potential of CvTMorph for various applications, such as radiation therapy planning, and provided a basis for further research in this field.

Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians. Deep learning, a promising technique for automated image analysis, can extract hierarchal patterns from images without human intervention.

To enhance the performance of deep learning-based segmentation models, we integrate textual data from diagnostic reports with SPECT bone scans, aiming to develop an automated analysis method that outperforms purely unimodal data-driven segmentation models.

We propose a dual-path segmentation framework to extract features from bone scan images and diagnostic reports separately. In the first path, an encoder-decoder network is employed to learn hierarchical representations of features from SPECT bone scan images. In the second path, the Chinese version of the MacBERT model is utilized to develop a text encoder for extracting features from diagnostic reports. The extracted textual features are then fused with image features during the decoding stage in the first path, enhancing the overall segmentation performance.

Experimental evaluation conducted on real-world clinical data demonstrated the superior performance of the proposed segmentation model. Our model achieved a 0.0209 increase in the DSC (Dice Similarity Coefficient) score compared to the well-known U-Net model.

The proposed multimodal data-driven method effectively identifies and isolates metastasis lesions in SPECT bone scans, outperforming existing classical deep learning models. This study demonstrates the value of incorporating textual data in the deep learning-based segmentation of low-resolution SPECT bone scans.

Eosinophilic gastroenteritis (EGE) is a rare immune-mediated chronic inflammatory disorder, which is classified into 3 types according to the affected gastric wall layer. The serosal-type EGE is the least common type. Gastric oral contrast-enhanced ultrasonography (OCEUS) may show some specific changes in the serosal-type EGE. Herein, we reported OCEUS findings in a serosal-type EGE case.

A 60-year-old man with unexplained abdominal pain accompanied by diarrhea, which lasted for half a month, consulted the hospital. Laboratory findings revealed peripheral eosinophilia and elevated carbohydrate antigen 125(CA125). OCEUS showed a thickened gastric antrum wall and ascites, with distinct layers (thickening of the muscularis propria layer was most obvious), which is rare and specific. Endoscopy showed normal mucosa of the esophagus and stomach and scattered hyperemia spots in the mucoua of the duodenal bulb and small intestine. Microscopy evaluation revealed few eosinophils infiltration in the lamina propria. A large number of eosinophils were seen in peritoneal lavage fluid and the greater omentum. Eventually, the patient was diagnosed with serosal type EGE.

Significant thickening of the digestive tract walls and ascites at ultrasonography (US)-examination, with distinct layers and predominant thickening of the muscularis propria layer at OCEUS, can indicate EGE.

The automatic computer-assisted mammogram classification system is important for women patients to detect and diagnose the cancer regions. In this work, the mammogram images are classified into three cases: healthy, benign and cancer, using the proposed Resource Efficient Convolutional Neural Network (RECNN architecture).

The proposed mammogram image classification system consists of Data Augmentation (DA) module and Spatial transformation module and CNN architecture with a segmentation module. The DA methods are used to increase the mammogram image count and Spatial Gabor Transform is used as the spatial transformation module for transforming the spatial pixels into spatial-frequency pixels. Then, the proposed RECNN architecture is used to perform the classification of mammogram images into healthy, benign and cancer cases. Further, the cancer mammogram images are diagnosed as either ‘Early’ or ‘Severe’ using the proposed RECNN architecture in this work.

The proposed MCDS obtains 98.65% SeDR, 98.93% SpDR and 98.84% ADR for benign case mammogram images on DDSM dataset and also obtains 98.84% SeDR, 98.7% SpDR and 98.92% ADR for cancer case mammogram images on DDSM dataset. The proposed MCDS obtains 98.94% SeDR, 98.86% SpDR and 98.96% ADR for benign case mammogram images on MIAS dataset and also obtains 98.89% SeDR, 98.88% SpDR and 99.03% ADR for cancer case mammogram images on MIAS dataset.

This proposed method is tested on the mammogram images from DDSM and MIAS datasets and the experimental results are compared with other similar mammogram classification works in this paper. Based on several performance evaluation measures, the experimental results show that MCDS outperforms the state-of-the-art methods currently used for the diagnosis and detection of mammography cancer.

Capitellar injury (CI) includes capitellar cartilage injury (CCI) and capitellar fracture (CF). A comprehensive classification of CI concurrent with radial head fracture (RHF) that can guide surgical strategy is lacking in the literature. Therefore, this study aimed to introduce a comprehensive classification of CI concurrent with RHF and investigate its value.

A total of 35 patients with CI concurrent with RHF confirmed by surgical exploration were retrospectively analyzed, including males in 19 cases and females in 16 cases. RHF was classified according to the Mason classification, and CI was classified into six types, including 3 types of CCI and CF, each based on the site and degrees of injuries (comprehensive classification method proposed in this study). The classification results were analyzed. Two radiologists were selected to independently classify the CI, and the inter- and intra-observer agreements were analyzed with kappa statistics.

Mason Type I, II, III, and IV RHF accounted for 14.3%, 48.6%, 37.1%, and 0% of cases, respectively. Type I, II, III, IV, V, and VI CIs accounted for 22.9%, 34.3%, 25.7%, 11.4%, 2.9%, and 2.9% of cases, respectively. There was no obvious relationship between the CI and RHF types (p > 0.05). All Type I CIs underwent removal, 9 Type II CIs underwent microfracture repair, and 3 Type II CIs underwent removal. All Type III CIs underwent fixation, one Type IV CI underwent removal, and 3 Type IV CIs underwent fixation, one Type V CI underwent fixation, and one Type VI CI underwent arthroplasty. The inter- and intra-observer kappa coefficients were 0.830 ~ 0.905 and 0.805 ~ 0.892, respectively. At 12 months postoperatively, the elbow function evaluated by MEPS was 91, with an excellent and good rate of 97%.

Different types of CI differ not only in pathology but also in treatment methods. The CI comprehensive classification put forth in this paper for the first time reflects different types of pathology well, with high consistency and repeatability, and can guide the selection of surgical methods, leading to satisfactory postoperative results.

Mediastinal venous malformation (MVM) and fusiform superior vena cava aneurysm (F-SVCA) are both rare congenital vascular anomalies.

A 46-year-old male presented with acute ischemic stroke of unknown etiology. Computed tomography (CT) angiography revealed the coexistence of MVM and F-SVCA. Diagnostic venography demonstrated a significant reduction in blood flow velocity within the F-SVCA, but failed to identify a direct connection to the left heart system or pulmonary vein. The patient expired due to extensive brain damage caused by a stroke.

This case may increase the necessity of meticulous radiological evaluation and preventive management for these anomalies, as mediastinal vascular anomalies can result in thromboembolic complications.

Intrahepatic primary osteosarcoma is a rare disease with a very low incidence but a very poor prognosis. A total of 12 cases have been previously reported, and in most of these cases, intra-focal calcification was observed. This paper aims to report a case of non-calcified intrahepatic primary osteosarcoma.

We hereby report a female patient with hepatitis B for 20 years, identified during a routine examination due to a mass in the right lobe of the liver. The patient experienced no significant discomfort, and the serological tumor markers were not elevated. Surgical resection was performed after comprehensive examinations, and postoperative pathology showed primary osteosarcoma of the liver. The patient experienced recurrence and metastasis seven months postoperatively and died eight and a half months postoperatively.

Intrahepatic primary osteosarcoma is an extremely rare disease, and it currently requires a combination of clinical, radiological, and pathological findings to make a diagnosis of exclusion. Further, patients may benefit from early diagnosis, aggressive surgery, and post-operative chemotherapy.

“Pelvic rib”, “pelvic digit (finger)”, or “eleventh digit (finger)” is a rare congenital anomaly, in which a finger-like bony structure is present in the soft tissue in the pelvic or abdomen (less common) area.

This case report presents a symptomatic “pelvic digit” discovered in a patient referred to the radiology department after prolonged unilateral hip pain, especially during long walks. To our knowledge, this is the first case report of unilateral pelvic digit occurrence in our region. It is an extremely rare condition that is often discovered incidentally due to the lack of clinical symptoms.

To the best of the authors’ knowledge, this is the tenth reported case of symptomatic pelvic digit in the literature and four of them have required surgical intervention.

Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms to automatically analyze and identify X-ray images, determining whether specific pathologies or structural abnormalities exist in the images.

We present the MMPDenseNet network designed specifically for chest multi-label disease classification.

Initially, the network employs the adaptive activation function Meta-ACON to enhance feature representation. Subsequently, the network incorporates a multi-head self-attention mechanism, merging the conventional convolutional neural network with the Transformer, thereby bolstering the ability to extract both local and global features. Ultimately, the network integrates a pyramid squeeze attention module to capture spatial information and enrich the feature space.

The concluding experiment yielded an average AUC of 0.898, marking an average accuracy improvement of 0.6% over the baseline model. When compared with the original network, the experimental results highlight that MMPDenseNet considerably elevates the classification accuracy of various chest diseases.

It can be concluded that the network, thus, holds substantial value for clinical applications.

The neural mechanisms underlying Congenital Sensorineural Hearing Loss (CSNHL) remain elusive.

This study evaluated the function of the glymphatic system in children with CSNHL compared to normal-hearing children using the DTI-ALPS approach, which utilizes diffusion tensor imaging along the perivascular space.

Twenty-six children with CSNHL and 30 age- and sex-matched Healthy Controls (HCs) with normal hearing thresholds were recruited. The DTI-ALPS index was calculated for each group. We analyzed the discrepancies in the DTI-ALPS index between patients with CSNHL and healthy controls. Additionally, Spearman's correlation analysis was performed to investigate the relationship between the DTI-ALPS index and age in children with CSNHL.

Significant differences in the DTI-ALPS index were observed between the two groups. Compared with HCs, the DTI-ALPS index in CSNHL patients was significantly lower (1.49388±0.11441 vs. 1.61402±0.15430, p=0.002). In addition, diffusivity along the z-axis in the association fiber (Dzzassoc) index was significantly higher in the CSNHL group than in the HC group (0.00041±0.00006 vs. 0.00036±0.00004, p=0.003). Furthermore, we discovered a noteworthy downward correlation between the DTI-ALPS index and age in children with CSNHL (rho = -0.544, p=0.005).

In this present study, glymphatic system activity in CSNHL children was investigated for the first time using the DTI-ALPS index. A significant decrease in glymphatic system function was detected in CSNHL children, which correlated well with age. The DTI-ALPS index could serve as a valuable biomarker for tracking disease progression and treatment in CSNHL and unraveling the neural mechanisms of early hearing deprivation in children with CSNHL.

Transcatheter radiofrequency ablation is one of the main treatments for atrial fibrillation, but related complications of this surgery are uncommon.

Here, we report a 70-year-old elderly male patient with atrial fibrillation who experienced severe abdominal pain early after undergoing radiofrequency ablation; related imaging examinations suggested that the patient had intestinal edema and thickening, combined with hepatic portal vein gas accumulation. The reason was that the patient experienced intestinal necrosis due to superior mesenteric artery embolism related to radiofrequency surgery. The surgeon suggested laparotomy for exploration. However, after multidisciplinary consideration, we ultimately chose conservative treatment. After fasting, gastrointestinal decompression, spasmolysis, pain relief, somatostatin inhibition of intestinal edema, anti-infection, and anticoagulation, the patient's condition improved, and he was discharged. We followed the patient for 1 month after discharge, and there was no special discomfort.

Hepatoportal vein gas accumulation after radiofrequency ablation of atrial fibrillation is rare, and imaging findings have important guiding significance for the diagnosis and treatment of the disease.

The purpose of this study was to compare the image quality of different MRI sequences regarding the presentation of Dermatofibrosarcoma Protuberans (DFSP).

We retrospectively collected MRI images of 40 patients who had been pathologically diagnosed with DFSP, including 21 primary tumors and 19 recurrent tumors. The image quality of different MRI sequences was assessed subjectively by two radiologists, taking into account the display of the lesions, artifacts, and distortions, as well as the overall impact of the image quality.

Among the 40 cases, 22 cases involved the trunk, 14 cases involved the shoulders and limbs, 2 cases involved the head and neck, 1 case involved the breast, and 1 case involved the groin. In terms of image quality, fat suppression T2-weighted images were superior to T1-weighted images and T2-weighted images (P<0.05). The difference between fat suppression T2-weighted images and contrast-enhanced images was not significant (P>0.05). As far as lesion contrast is concerned, diffusion-weighted images, fat suppression T2-weighted images, and contrast-enhanced images did not differ significantly (P>0.05). On the DWI images, there were severe magnetic artifacts and deformations.

Fat suppression T2-weighted images and enhanced sequences produce the highest quality images, while diffusion-weighted images provide the best lesion contrast.

Osteoid osteoma (OO) is a painful benign bone tumor. Typically, it causes pain that is most noticeable during the night, which is improved by nonsteroidal anti-inflammatory drugs. In the treatment of symptomatic lesions, open surgery for nidus removal is the gold standard. However, surgical technical difficulties and morbidities vary by location. Percutaneous radiofrequency ablation (RFA) therapy guided by computed tomography (CT) is now a popular treatment option for OO. This study aims to assess our single-center experience with the technique, complications, and procedure effectiveness.

The study included fifteen patients who were treated between 2017 and 2021. A retrospective analysis was carried out on archive images and file records. The lesions' location, nidus width, and affected area (cortical, medullary) were all recorded. The procedure and technical success, as well as postoperative complications and the need for repeat ablation, were all documented.

A total of 20 patients, 18 men, and 2 women, were included in the study, and 12 of them were pediatric patients. The patients' mean age was 16.9±7.3 years old, and the mean nidus diameter was 7.1±8.7 mm. There were 13 cortical niduses, 2 intramedullary niduses, and 5 corticomedullary niduses. The lesions were in the femur (n=12), tibia (n=6), scapula (n=1), and vertebrae (n=1). Two recurrences (10%) were observed in our patients during the follow-up. Patient with a femoral OO, the pain started again 12 weeks after the procedure and we performed additional RFA. The patient with vertebral OO had fewer symptoms and full recovery was not achieved. Therefore, the vertebral OO was ablated again 4 months later, and clinical success was achieved. One patient had a minor burn at the entry site that went away on its own after a short period of time. Except for the patient who was scheduled for a repeat RFA, no recurrence has been observed so far. The primary and secondary success rates are, respectively, 90% (18/20) and 100% (20/20).

RFA has a high success rate in treating OO. The procedure failure and recurrence rates are low. There are possibilities for posttreatment pain relief, early discharge, and a quick return to daily life. For inappropriate lesion localization, the RFA process replaces surgical treatment. The procedure-related complication rate is low. On the other hand, the burn during the procedure can be a serious problem.

Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions.

A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data;

The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters;

These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

This study aimed to enhance the existing nursing model in imaging departments by implementing a characteristic seamless nursing care approach and assessing its impact on patient and medical staff satisfaction, nursing quality, examination efficiency, and patient awareness. We hypothesized that the implementation of a seamless nursing care model would be associated with higher patient satisfaction, improved nursing quality, increased examination efficiency, and better patient awareness compared to the traditional nursing model.

This prospective cohort study included 300 patients undergoing imaging examinations from January 2019 to January 2022. Subjects were divided into control and observation groups (n=150 each) based on different nursing methods. The control group received routine care, and the observation group received seamless care. The following outcome measures were assessed using validated questionnaires: patient satisfaction (measured using a 5-point Likert scale), medical staff satisfaction with patient examination cooperation (measured using a 5-point Likert scale), nursing quality compliance rate (percentage of nursing tasks performed according to established guidelines), dissatisfaction rate (percentage expressing dissatisfaction with examination cooperation), and effect evaluation [measured using a knowledge test validated in previous studies (Chung et al., 2020) with a total score range of 0-20].

Average imaging examination and nursing times were significantly lower in the observation group compared to the control group (P<0.05). The examination cooperation dissatisfaction rate was significantly lower in the observation group (P<0.05). There were significant differences in examination precautions, procedures, breathing training methods, and injection comparisons between the groups (all P<0.05).

The application of seamless nursing care may be associated with improved patient satisfaction, nursing service quality, imaging examination efficiency, and patient awareness of imaging examinations. However, further research is needed to establish causal relationships.