Current Medical Imaging - Volume 21, Issue 1, 2025

Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.

To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.

By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.

The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors’ diagnosis results, with an overall error rate of less than 2%.

The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

This study aimed to retrospectively analyze the curative effect and influencing factors of lauromacrogol in the treatment of symptomatic hepatic cysts of <10 cm.

In this study, a total of 51 patients with symptomatic hepatic cysts (maximum diameter ranging from 5 cm to 10 cm) were included. Polycystic Liver Disease Questionnaire (PLD-Q) was used to evaluate the symptoms of patients prior to treatment. The patients were followed up at 1, 3, 6, and 12 months after treatment. At the 12-month follow-up, patients were asked to fill out the PLD-Q to assess their symptoms. The improvement rate of patients' symptoms was evaluated using a 5-point Likert scale (worse, 1; slight difference, 2; roughly the same, 3; good, and 4; better, 5. Volume reduction rate (VRR) was calculated by measuring the volume of the cyst cavity via ultrasound. Treatment success at the 12-month follow-up was determined using two criteria: symptom improvement and changes in cyst volume. Symptom improvement was assessed using a Likert Scale, with a score greater than 3 points indicating significant improvement. Additionally, a volume reduction rate (VRR) of 50% or more in cyst size (VRR ≥ 50%) was considered an effective treatment outcome. The relationship between the clinical factors and the ultrasonographic manifestations of hepatic cysts, including the initial maximum diameter of the cyst (measured using ultrasound before operation), the initial volume of the cyst, and the formation of septa after sclerosis of the cyst, was analyzed.

All patients completed at least 12 months of follow-up. After a 12-month follow-up, the effective and ineffective rates were 96.1% (49/51) and 3.9% (2/51), respectively. The logistic regression univariate analysis showed significant differences in the initial cyst volume (p = 0.001), the initial maximum diameter of the cyst (p = 0.005), and the interval formation after cyst sclerosis (p = <0.001) between VRR ≥ 50% and VRR < 50%. Logistic regression analysis demonstrated that septa formation after cyst sclerosis was an independent factor related to treatment failure, with an odds ratio of 3.246 (95% confidence interval, 0.784–4.148).

Lauromacrogol is an effective method for hepatic cyst treatment. Septa formation after cyst sclerosis is an independent factor related to ineffective treatment.

The standard treatment for hydrocephalus is often the placement of a ventriculoperitoneal shunt (VPS), especially in patients with myelomeningocele (MMC). This case report aimed to enrich the existing knowledge by presenting a rare instance of asymptomatic anal extrusion of a VPS catheter in an infant, along with a review of the relevant literature.

A 2-month-old male infant with myelomeningocele (MMC) and hydrocephalus presented with asymptomatic anal extrusion of his ventriculoperitoneal shunt (VPS) catheter, discovered by his mother. Emergency imaging revealed distal catheter migration through the rectosigmoid junction. Surgical management included (1) laparoscopic-assisted catheter removal with bowel repair using Vicryl sutures, (2) intraoperative external ventricular drain (EVD) placement, and (3) 14-day antibiotic prophylaxis. Cerebrospinal fluid analysis remained normal throughout the treatment. Following three weeks of infection monitoring, contralateral VPS replacement was performed successfully, with postoperative imaging confirming optimal shunt function and resolved hydrocephalus. This case highlighted the importance of caregiver vigilance in identifying this rare but serious complication, even in asymptomatic patients (Fig. 1).

Although anal extrusion of a VPS catheter is an uncommon but serious complication, primarily seen in pediatric patients, it can lead to life-threatening infections if untreated. Prompt surgical intervention along with broad-spectrum antibiotic therapy is critical. This report highlights the need for recognizing classic symptoms of intestinal perforation and catheter migration in pediatric patients.

This study aimed to detect the performance of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for assessing primary biliary cholangitis (PBC).

Seventy-five patients with PBC were included in this prospective study. Shear wave elastography (SWE) and Gd-EOB-DTPA-enhanced MRI were conducted, and then the signal intensity ratio (SIR) and contrast enhancement index (CEI) in different phases, including portal venous phase (PVP), equilibrium phase (EP), and hepatobiliary phase (HBP), were calculated. Afterward, the results were compared with Child-Pugh grading and non-invasive liver fibrosis models using the Kruskal-Wallis H test or Chi-squared test. The area under the curve (AUC) was applied to evaluate the diagnostic performance of SIRHBP, CEIHBP, and SWE across different Child-Pugh grades.

SWE (p0.001), SIR HBP (p0.001), CEIHBP (p0.001), APRI (p=0.002), and FIB-4(p0.001) showed significant differences in different Child-Pugh grades. Statistically significant differences were found in SIRHBP (p=0.005), CEIHBP (p=0.010), and FIB-4 (p=0.001) of different SWE levels. For the diagnosis of Child-Pugh C, the AUC of SWE, SIRHBP, and CEIHBP were 0.889, 0.778, and 0.761, respectively. Correspondingly, the sensitivity was 75.0%, 64.4%, and 54.2%, and the specificity was 94.9%, 100%, and 100%, respectively. For the diagnosis of Child-Pugh B+C, the AUC of SWE, SIRHBP, and CEIHBP were 0.919, 0.809, and 0.814, respectively.

Our study confirmed that Gd-EOB-DTPA-enhanced MRI is an effective and objective method for assessing liver function in patients with PBC.

SIRHBP and CEIHBP could be regarded as a novel imaging biomarker to evaluate liver function. Gd-EOB-DTPA-enhanced MRI and SWE outperformed serum-based models in sensitivity and specificity, strengthening the value of imaging in clinical decision-making.

The aim of this study is to assess the identification of incidental myocardial infarction on non-electrocardiogram-gated computed tomographic scans of the chest and its prognostic significance.

The increased utilization and abundance of thoracic computed tomographic (CT) scans have provided a substrate for potential screening purposes.

The objective of this study was to evaluate the detection of incidental myocardial infarction on routine non-gated thoracic CT performed for non-cardiac reasons and its associated major cardiovascular events and survival.

We retrospectively assessed routine non-gated thoracic CT scans of all consecutive individuals aged 18 or above who underwent thoracic CT scans as outpatients at the University of California Davis from January 2015 to December 2015. We evaluated the presence and location of incidental MI on non-gated thoracic CT and compared major adverse cardiac events (MACE) and overall survival in CT-positive infarct individuals with a CT-negative infarct control group.

We reviewed routine thoracic CT scans of 1157 individuals and identified 12 individuals with incidental MI. The mean age of individuals with infarction was 71.4 ± 14.1 years, and 50% were female. All individuals with incidental MI had coronary calcification. Individuals with incidental MI had a higher rate of MACE endpoint (92% vs. 28%, p=0.0001), number of MACE events (1.1 vs. 0.3, p<0.001), and lower overall survival (median survival of 67 months vs. not reached, p=0.023) compared with age and sex-matched controls without incidental MI.

Although small in number relative to the total number of individuals evaluated, subjects with incidental MI on routine non-gated thoracic CT scans have worse cardiovascular outcomes and survival compared with controls without infarction. This study highlights the potential opportunistic screening utility of routine thoracic CTs, which could lead to improved risk stratification and intervention.

Electrical Impedance Tomography (EIT) is widely used for bedside ventilation monitoring but is limited in reconstructing cardiac-related signals due to the dominance of lung impedance changes. This study aims to reconstruct heart-related impedance imaging from lung EIT using a novel semi-Siamese U-Net architecture.

A deep learning model was developed with a shared encoder and two decoders designed to segment lung and heart regions independently. The model was trained and validated on FEM-based EIT simulations and tested on real human EIT data. A weighted binary cross-entropy loss was applied to emphasize cardiac-related learning.

The model achieved a Dice coefficient >0.99 and MAE <0.1% on simulation data. It successfully separated lung and heart regions on human EIT frames without additional fine-tuning, demonstrating strong generalization capacity.

These findings reveal that the semi-Siamese U-Net can overcome signal dominance and improve cardiac-related EIT reconstruction. However, promising results are currently limited to qualitative evaluation of real data and simulation-based training.

The proposed method offers a potential pathway for simultaneous lung-heart monitoring in ICU settings. Future work will focus on clinical validation and real-time implementation.

To address the low efficiency of diagnosing pulmonary nodules using computed tomography (CT) images and the difficulty in obtaining the key signs of malignant pulmonary nodules, a ghost convolution residual network incorporating hybrid normalization (GCHN-net) is proposed.

Firstly, a three-dimensional ghost convolution with a small kernel is embedded in the GCHN-net. Secondly, we designed a hybrid normalized-activation module (TMNAM) that can handle the rich and complex features of lung nodules in both the deep and shallow layers of the network, and incorporating two different normalization methods. This allows the network to comprehensively learn the intricate relationships underlying the intrinsic features of lung nodules and enhances its capacity to classify the properties of unknown nodules. Additionally, to enhance the accuracy and detail of the category activation map, GradCAM++ is integrated into the third layer of the GCHN-net. This integration enables the visualization of specific regions within three-dimensional lung nodules that the model focuses on during its predictions.

The accuracy of the GCHN-net on the Lung Nodule Analysis 16 (LUNA16) dataset was 90.22%, with an F1-score of 88.31% and a G-mean of 90.48%.

Compared with existing methods, the proposed method can greatly improve the classification of pulmonary nodules and can effectively assist doctors in diagnosing patients with pulmonary nodules.

Recently, deep learning (DL) algorithms use Arithmetic Units (AU) in CPU/GPU hardware for processing images/data. AU operates in fixed precision and limits the representation of weights and activations in DL. The problem leads to quantization errors, which reduce accuracy during cancer cell segmentation.

In this study, arithmetic multiplication in convolution layers is replaced with Vedic multiplication in the proposed DnCNN algorithm. Next, Vedic multiplication-based convolution layers in the DnCNN architecture are optimized using POA (Pelican Optimization Algorithm), and the resulting POA-DnCNN is implemented on an FPGA device for breast cancer detection, segmentation, and classification of benign and malignant breast lesions.

In the convolution layer of DnCNN, floating-point operations are performed through the Hybrid-Vedic (HV) multiplier called ‘CUTIN,’ which is the combination of Urdhva Tryambakam and Nikhilam Sutra with the upasutra ‘Anurupyena.’ Larger image sizes increase processor size and gate count.

The proposed HV-FPGA-based breast cancer detection system, employing Vedic multiplication in the convolution layers of DnCNN and hyperparameters optimized by POA, detects stages of breast cancer with an accuracy of 96.3%, precision of 94.54%, specificity of 92.37%, F-score of 93.56%, IoU of 94.78%, and DSC of 95.45%, outperforming existing methods.

The proposed CUTIN multiplier uses a CSA (carry save adder) with simplified sum-carry generation logic (CSCGL), achieving lower area-delay, high speed, and improved precision.

Despite the increasing prevalence of hyperuricemia and gout, there remains a relative paucity of research focused on the use of straightforward clinical and laboratory markers to predict urate crystal formation. The identification of such predictive markers is crucial, as they would greatly enhance the ability of clinicians to make timely and accurate diagnoses, leading to more effective and targeted therapeutic interventions.

The aim of this study was to evaluate the diagnostic value of various easily obtainable clinical and laboratory indicators and to establish a decision tree (DT) model to analyze their predictive significance for monosodium urate (MSU) deposition in the first metatarsophalangeal (MTP) joint.

A retrospective study was conducted on 317 patients who presented to the outpatient clinic with a gout flare between January 2023 and June 2024 (181 cases with MSU deposition in the first MTP joint and 136 cases without such deposition). Clinical and laboratory indicators included gender, age, disease course, serum uric acid (SUA), glomerular filtration rate (GFR), serum creatinine (SCR), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Statistical analysis methods, including T-test, logistic regression and decision tree, were used to analyze the predictors of MSU deposition in the first MTP joint. The performance of the DT model was evaluated using receiver operating characteristic (ROC) curves and a 5-fold cross-validation method was used to ensure the robustness of the study results.

Disease course, GFR, SUA, age, and SCR emerged as significant predictors of MSU deposition in the first MTP joint in both LR and DT analyses. The DT model exhibited superior diagnostic performance compared to the LR model, with a sensitivity of 83.4% (151/181), specificity of 56.6% (77/136), and overall accuracy of 71.9% (228/317). The importance of predictive variables in the DT model showed disease course, GFR, SUA, age, and SCR as 53.36%, 21.51%, 15.1%, 5.5% and 4.53%, respectively. The area under the ROC curve predicted by the DT model was 0.752 (95% CI: 0.700~0.800).

The DT model demonstrates strong predictive capability. Disease duration, GFR, SUA, age, and SCR are pivotal factors for predicting MSU deposition at the first MTP joint, with disease course being the most critical factor.

Reversible cerebral vasoconstriction syndrome (RCVS) is a condition characterized by thunderclap headaches, which are sudden and severe headaches that peak within a few seconds. These headaches present diagnostic difficulties due to their diversity and low specificity, often leading to misdiagnoses and patient dissatisfaction.

We present the case of a 52-year-old woman with a 10-day history of recurrent thunderclap headaches. Initial imaging revealed no abnormalities, but she experienced further episodes of thunderclap headaches during hospitalization. Subsequent neurovascular imaging revealed multiple intracranial stenoses with a “string of beads” appearance, confirming the diagnosis of reversible cerebral vasoconstriction syndrome. She was treated with nimodipine, and most symptoms had resolved upon discharge, with no recurrence of headache reported during a 3-month follow-up.

Prior reviews on reversible cerebral vasoconstriction syndrome predominantly emphasized isolated symptoms or advanced neuroimaging findings, offering limited applicability in primary care services. More attention should be given to identifying clinical manifestations warranting heightened reversible cerebral vasoconstriction syndrome suspicion.

Early recognition of reversible cerebral vasoconstriction syndrome counts in primary care services. We proposed a revised diagnostic routine that begins with clinical suspicion prompted by typical manifestations, like recurrent thunderclap headaches, female sex, and specific triggers, and recommends advanced neurovascular imaging when accessible. Extreme headache severity or deviation from prior migraine patterns should raise suspicion for reversible cerebral vasoconstriction syndrome, while diagnostic consideration should still remain in patients with transient neurological deficits, seizures, or cerebrovascular events.

Cor Triatriatum Sinister (CTS) is a rare congenital anomaly, accounting for 0.1%- 0.4% of congenital heart diseases. While often diagnosed and treated in infancy, some cases remain asymptomatic until adulthood due to large fenestrations. This report presents a unique case of CTS in an adult coexisting with chronic adhesive pericarditis, which may have contributed to chronic atrial dilatation, a condition not previously documented.

A 60-year-old asymptomatic Taiwanese male underwent a routine medical examination. Coronary computed tomography angiography revealed a fenestrated septum dividing the left atrium, consistent with CTS. Virtual endoscopy confirmed two wide fenestrations. Notably, chronic adhesive pericarditis, evidenced by curvilinear calcifications, was diagnosed. This condition likely exacerbated the hemodynamic impact of CTS, contributing to left atrial dilation and atrial fibrillation. Atrial fibrillation was identified, and the patient was treated with an anticoagulant for stroke prevention.

This is the first reported case of CTS coexisting with chronic adhesive pericarditis. Advanced imaging modalities, including cardiac computed tomography, angiography, and virtual endoscopy, are crucial for diagnosis and anatomical evaluation. Chronic adhesive pericarditis may amplify the effects of CTS, leading to complications, including atrial fibrillation. Anticoagulation is essential for stroke prevention in such cases.

Type 2 diabetes mellitus (T2DM) complicated with interstitial lung abnormalities (ILAs) is often overlooked and can progress to severe diabetes-induced pulmonary fibrosis (DiPF). Therefore, early diagnosis of T2DM complicated with ILAs is crucial. Chest computed tomography (CT) is an important method for diagnosing T2DM complicated with ILAs. Quantitative computed tomography (QCT) is more objective and accurate than visual assessment on CT. However, there are currently limited studies on T2DM complicated with ILAs based on quantitative CT.

This study aimed to explore the utility of quantitative computed tomography for early detection of lung injury in individuals with T2DM by examining CT-derived metrics in T2DM complicated with ILAs.

We collected data from 135 T2DM complicated with ILAs on chest CT scans retrospectively, alongside 135 non-diabetic controls with normal CT findings. Employing digital lung software, chest CT images were processed to extract quantitative parameters: total lung volume (TLV), emphysema index (LAA-950%, the percentage of lung area with attenuation < –950 Hu to total lung volume), pulmonary fibrosis index (LAA-700~-200%, the percentage of lung area with attenuation from –700Hu to –200 Hu to the total lung volume), and pulmonary peripheral vascular index (ratio TAV/TNV, the number of blood vessels TNV, the cross-sectional area of blood vessels TAV). Statistical comparisons between groups utilized Mann-Whitney U or t-tests. Correlations between Hemoglobin A1c (HbA1c) levels and CT parameters were assessed via Pearson or Spearman correlations. Parameters showing statistical significance were further examined through receiver operating characteristic (ROC) analysis.

The T2DM-ILAs cohort displayed a significantly higher LAA-700~-200% compared to controls (Z = -7.639, P< 0.001), indicative of increased fibrotic changes. Conversely, TLV (Z =-3.120, P=0.002), TAV/TNV (Z = -9.564, P< 0.001), and LAA-950% (Z = -4.926, P < 0.001) were reduced in T2DM-ILAs patients. The correlation between HbA1c and various CT quantitative indicators was not significant, HbA1c and TLV (r=-0.043, P=0.618), HbA1c and TAV (r=0.143, P=0.099), HbA1c and TNV (r=0.064, P=0.461), HbA1c and LAA-700~-200% (r=0.102, P=0.239), HbA1c and LAA-950% (r=-0.170, P=0.049), HbA1c and TAV/TNV (r=0.175, P=0.043). The peripheral vascular marker, TAV/TNV, excelled in distinguishing T2DM-related lung changes (AUC=0.84, P<0.001), outperforming LAA-700~-200% (AUC=0.77,P<0.001). A composite index incorporating multiple quantitative parameters achieved the highest diagnostic accuracy (AUC = 0.91, P< 0.001).

Quantitative CT parameters distinguish T2DM complicated with ILAs from non-diabetic individuals, suggesting a distinct pattern of lung injury. Our findings imply a particular susceptibility of small pulmonary blood vessels to injury in T2DM.

Non-gestational Ovarian Choriocarcinoma (NGOC) is an extremely rare and highly malignant ovarian germ cell tumor with nonspecific clinical manifestations, making early diagnosis challenging. At present, detailed reports on the clinical and imaging characteristics of NGOC are scarce. This case report discusses a rare instance of NGOC in a prepubertal adolescent, complemented by a literature review to enhance clinicians’ understanding of its presentation, diagnosis, and treatment.

A 10-year-old female with no history of menstruation or sexual activity presented with persistent lower abdominal pain and vaginal bleeding. Preoperative imaging revealed a large pelvic mass with heterogeneous echogenicity and vascularity. Serum Human Chorionic Gonadotropin (hCG) levels were markedly elevated (>297,000 IU/L).

Ultrasonography and CT demonstrated a large, heterogeneous, hypervascular adnexal mass with features of necrosis and cystic changes, suggesting malignancy.

The mass, originating from the right adnexa, was removed via laparotomy. Histopathology confirmed NGOC, supported by immunohistochemistry, showing strong positivity for markers like CD146, CK18, HCG, and HPL, along with a high Ki-67 index (>90%).

In young females with no sexual life, significantly elevated HCG levels and imaging findings of a large heterogeneous adnexal mass should raise suspicion for NGOC. Early recognition and multimodal diagnostic approaches, including imaging, biochemical, and pathological assessments, are essential for timely intervention, reducing metastatic risk and improving prognosis. This report contributes to the understanding of NGOC and emphasizes the importance of accurate diagnosis for better patient outcomes.

Pain with a persistent and recurrent onset is one of the most important symptoms of temporomandibular disorders (TMD). Recent evidence indicated the dysfunction of the central nervous system was more linked to TMD pain. This study aimed to explore the abnormal structural and perfusion alterations in patients with painful TMD (p-TMD) to understand the comprehension of neuro-pathophysiological mechanisms.

Forty-one p-TMD patients and 33 normal controls (NC) were recruited, and high-resolution structural brain and 3D PCASL data were obtained from a 3.0T MR scanner. The voxel-based analysis of the whole cerebral gray matter (GMV) was performed, and the GMV and cerebral blood flow (CBF) value of the altered positive areas were extracted to investigate the significant correlation with clinical variables.

The brain regions with significantly increased GMV in p-TMD group were listed as follows: right putamen, right superior frontal gyrus, left superior frontal gyrus medial segment, right supplementary motor cortex, left postcentral gyrus, right middle temporal gyrus, right postcentral gyrus medial segment, right temporal pole, right inferior temporal gyrus and right opercular part of the inferior frontal gyrus (Punc<0.001, cluster>39). However, there were no brain regions with significantly decreased GMV in the p-TMD group. Cerebral perfusion analysis identified that only the right postcentral gyrus medial segment presented significantly higher CBF value in the p-TMD group than in the NC group over all the brain regions with increased GMV. Within the p-TMD group, pain intensity, anxiety, depression, and jaw functional limitation scores were differentially associated with GMV and CBF value.

The voxel-based morphometric and perfusion findings collectively implicate maladaptive plasticity in both the sensory-discriminative and affective-motivational dimensions of pain processing in p-TMD pathophysiology.

Modic changes (MCs) are a common manifestation of lumbar degenerative disease, classified into three types. However, the relationship between Bone Mineral Density (BMD) and each type of MC at the vertebral lesion sites remains unclear.

This study included 144 patients who had both lumbar MR and CT images. The classification and grading of MCs were evaluated using MR images. On the CT images, BMD values, T-scores, and Z-scores were obtained from the normal T12 vertebrae, the corresponding lumbar Modic lesion sites, and the adjacent healthy regions at the same vertebra on the axial plane.

A total of 370 vertebrae (226 MCs and 144 normal T12 vertebrae) were assessed. No significant difference was found in the BMD of normal T12 vertebrae between males and females in the study. MCs were more commonly found in the lumbar 4 and 5 vertebrae. Of the MCs, 80 (36%) were classified as type I, 130 (57%) as type II, and 16 (7%) as type III. The BMD value, T-score, and Z-score of each Modic type lesion site were higher than those of adjacent healthy regions and normal T12 vertebrae. A strong correlation was found between the different Modic types, though no significant differences were observed between grades within the same Modic type.

The presence of any MCs was significantly associated with an increase in BMD in the corresponding lesion sites, with more severe MCs showing a stronger association with higher BMD. This is the first study to explore the relationship between all types of MCs and their BMD values.

Intrauterine growth restriction (IUGR) is associated with long-term metabolic disturbances, including obesity. Changes in hepatic lipid metabolism and adipose tissue function, mediated by lipin-1 and lipin-2, may contribute to these outcomes.

This study aimed to investigate the correlation between lipin-1 in visceral adipose tissues (VATs) and lipin-2 in the liver. It also examined hepatic T1 values using T1 mapping in IUGR rats.

The objective of this study was to explore the metabolic mechanisms linking IUGR and adult obesity by analyzing molecular and imaging markers.

Pregnant rats were fed either a low-protein diet (10%) to induce IUGR or a normal-protein diet (21%) as a control. Male offspring underwent conventional magnetic resonance imaging and native T1 mapping using a 3.0 T whole-body MR scanner at days 21, 56, and 84 post-birth. Liver tissues and VATs were collected for analysis. Lipin-1 and lipin-2 expression levels were measured using Western blot and real-time quantitative PCR.

The IUGR group exhibited significantly higher mRNA and protein expression levels of lipin-1 and lipin-2 compared to the control group at days 21, 56, and 84 after birth. Additionally, the IUGR group demonstrated significantly higher hepatic T1 values than the control group at the corresponding time points. Positive correlations were observed between the protein and mRNA expression levels of lipin-1 and hepatic T1 values. Similarly, the protein and mRNA expression levels of lipin-2 were positively correlated with hepatic T1 values. All results were statistically significant (P<0.05).

The upregulation of lipin-1 and lipin-2 expressions was found to be linked to elevated hepatic T1 values, potentially contributing to adult obesity in IUGR rats.

Semantic segmentation algorithms are essential for identifying and segmenting human organs and lesions in medical images. However, as U-Net variants enhance segmentation accuracy, they often increase in parameter count, demanding more sophisticated and costly hardware for training.

This study aims to introduce a lightweight U-Net that optimizes the trade-off between network parameters and segmentation accuracy, while fully leveraging the encoder's feature extraction capabilities.

We propose a lightweight full-encoder U-shaped network, termed LFE-UNet, which employs full-encoder skip connections, encompassing all encoder layers. This model is designed with a reduced number of basic channels—specifically, 8 instead of the typical 64 or 32—to achieve a more efficient architecture.

The LFE-UNet, when integrated with ResNet34, achieved a Dice score of 0.97385 on the ISBI LiTS 2017 liver dataset. For the BraTS 2018 brain tumor dataset, it obtained 0.87510, 0.93759, 0.87301, and 0.81469 on average, WT, TC, and ET, respectively. The paper also discusses the impact of varying basic channel numbers n and encoder layer counts N on the network's parameter efficiency, as well as the model's robustness to different levels of Gaussian noise in images and salt and pepper noise in labels. Additionally, the influence of different loss functions is explored.

The LFE-UNet proves that high segmentation accuracy can be attained with a markedly lower parameters, fully utilizing the full-scale encoder's feature extraction. It also highlights the significance of loss function selection and the effects of noise on segmentation accuracy.

The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space.

The main objective is to enhance the compression efficiency through accurate segmentation followed by non-uniform compression through a cascade of encoders.

Due to a sharp growth in digital imaging data, it is highly desirable to reduce the size of medical images by a significant amount, without losing clinically important diagnostic information. The majority of the compression techniques reported in the literature use either manual or traditional segmentation techniques to extract the informative parts of the images. The methods based upon non-uniform compression require accurate extraction of the informative part of the image to achieve higher compression rate.

This research proposes unsupervised machine learning modified fuzzy c-means (FCM) clustering-based segmentation for accurate extraction of informative parts of MR images. The spatial constraints of the images are extracted using an automated region-growing algorithm and incorporated into the objective function of FCM clustering (RG-FCM) to enhance the performance of the segmentation process even in the presence of noise. Further, informative and background parts are subjected to two separate series of encoders, with higher bit rates for the informative part of the image.

Empirical analysis was done on the Magnetic Resonance Imaging (MRI)dataset, and experimental results indicate that the proposed technique outperforms similar existing techniques in terms of segmentation and compression metrics.

This integration of different segmentation techniques exhibits improvement in Jaccard and dice indexes, and cascade of different encoders endorse the superior performance of the proposed compression technique. The proposed technique can help in achieving higher compression of medical images without compromising clinically significant information.

Gastric schwannoma is a rare gastrointestinal mesenchymal tumor with Schwann cell differentiation. In the past, most of the published cases were single gastric schwannoma. Multiple gastric schwannoma is exceedingly rare. We herein report a case of multiple gastric schwannomas.

A 55-year-old male presented with postprandial vomiting of unclear etiology, accompanied by epigastric pain and bloating. Computed tomography revealed marked thickening of the gastric wall at the fundus-body junction along the greater curvature and gastric angle, with intraluminal nodular projections. Multiphase contrast-enhanced computed tomography demonstrated moderate progressive enhancement. The patient was misdiagnosed as having a gastric stromal tumor before the operation and subsequently underwent laparoscopic partial gastrectomy. However, pathological and immunohistochemical analysis confirmed multiple gastric schwannomas. The patient recovered uneventfully and was discharged without complications.

Gastric schwannoma is rare in clinical practice, especially gastric multiple schwannomas, which are easily confused with gastric stromal tumors, as illustrated in this case, where a preoperative misdiagnosis occurred. Clinicians should enhance their recognition of characteristic imaging features (including Computed tomography, Magnetic resonance imaging, and Positron emission tomography) and employ multimodal diagnostic approaches to optimize preoperative diagnosis.

Small cell neuroendocrine carcinoma (SCNEC) of the ureter is extremely rare, and tends to show a mixed histologic profile. Literature on its imaging features is limited.

We herein report the case of a 68-year-old woman who presented with two days of left flank pain. Ultrasound and CT scan revealed a lesion in the left distal ureter. The lesion exhibited intensive tracer activity on ¹⁸F-FDG PET/CT scan, corresponding to a malignant tumor, most likely a high-grade urothelial carcinoma, and no metastases were observed. Then, the patient underwent a radical left nephroureterectomy. Pathology revealed a carcinoma composed of SCNEC (approximately 83%) and urothelial carcinoma (approximately 17%). During one year of follow-up, the patient underwent six cycles of adjuvant chemotherapy (etoposide 100mg d1-3 + cisplatin 30mg d1-3, q3w), and no recurrence or metastases were found on the CT scan.

This case report has presented a case of ureteral SCNEC and explored the value of ¹⁸F-FDG PET/CT in the diagnosis and staging of the disease.