Current Medical Imaging - Volume 20, Issue 1, 2024

Lung cancer patients with post-chemotherapy may have disconnected or weakened function connections within brain networks.

This study aimed to explore the abnormality of brain functional networks in lung cancer patients with post-chemotherapy by modular edge analysis.

Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 40 patients after chemotherapy, 40 patients before chemotherapy and 40 normal controls. Patients in all three groups were age and sex well-matched. Then, modular edge analysis was applied to assess brain functional network alterations.

Post-chemotherapy patients had the worst MoCA scores among the three groups (p < 0.001). In intra-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strengths in the occipital lobe module (p < 0.05). Compared with the non-chemotherapy group, the patients after chemotherapy had decreased connection strengths in the subcortical module (p < 0.05). In inter-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strength in the frontal-temporal lobe modules (p < 0.05). Compared with the non-chemotherapy group, the patients after chemotherapy had decreased connection strength in the subcortical-temporal lobe modules (p < 0.05).

The results reveal that chemotherapy can disrupt connections in brain functional networks. As far as we know, the use of modular edge analysis to report changes in brain functional brain networks associated with chemotherapy was rarely reported. Modular edge analysis may play a crucial part in predicting the clinical outcome for the patients after chemotherapy.

While pulmonary vein filling defects on CT are typically considered diagnostic for thrombus, under certain circumstances, they can be artifactual as a result of flow phenomena.

We report a case of a 53-year-old female with chest pain who was found to have filling defects in pulmonary vein branches on CCTA that were initially treated as thromboses. However, follow-up cardiac MRI was negative for thrombi, and pseudo-thrombosis was therefore diagnosed.

Pulmonary vein pseudo-thrombosis should be considered in the differential diagnosis of pulmonary vein filling defects.

Diffuse-type tenosynovial giant cell tumor (D-TGCT) originates from synovial cells in tendon sheaths and bursae and rarely presents as a calcaneal mass.

A 44-year-old female presented with left heel pain that had persisted for over a year and had worsened over the past six months. A mass was found on the Lateral radiograph of the calcaneus, which was diagnosed as an aneurysmal bone cyst. Non-contrast computed tomography (CT) and magnetic resonance imaging (MRI)diagnosed a benign tumor. Based on light microscopy, special stains, and immunohistochemistry, a final diagnosis of diffuse tenosynovial giant cell tumor (D-TGCT) was rendered.

D-TGCT is a slow-growing, infiltrative tumor that can form single or multiple masses outside the joint, and can also involve adjacent jointsmainly affects weight-bearing joints such as the knee, hip, and ankle. However, D-TGCT presents as a calcaneal mass, which poses a diagnostic challenge for all radiologists.

A calcaneal mass exhibiting well-defined borders, focal cortical destruction, a sclerotic rim, and T2WI hypointensity, the possibility of D-TGCT should be considered.

It is essential to determine whether bone marrow signal changes on magnetic resonance imaging (MRI) represent a physiological response or pathology; at present, the clinical significance of these signal changes is unclear. It is unknown whether a bone marrow biopsy is required when bone marrow signal changes are detected incidentally in individuals without suspected malignancy.

The primary purpose of this study was to determine whether incidentally detected bone marrow signal changes on MRI performed for various reasons (at the time of admission or during follow-up) are clinically significant.

We retrospectively evaluated the bone marrow biopsy clinical and laboratory findings of 42 patients with incidental bone marrow signal changes on MRI between September 2016 and January 2020. We also determined whether the patients were diagnosed with malignancy during admission or follow-up.

Of the 42 patients, three (7%) were diagnosed with hematological malignancies during admission, while two were diagnosed with multiple myeloma and one with B-cell acute lymphoblastic leukemia. Of the 42 patients, 35 had a mean follow-up of 40.6 ± 5.3 months. One patient was diagnosed with monoclonal gammopathy of undetermined significance four months after their first admission.

In addition to MRI, detailed clinical and laboratory evaluations should be performed to inform the decision for bone marrow biopsy and exclude hematological malignancy. If there is any doubt, a bone marrow biopsy should be performed. Moreover, since bone marrow signal changes may be a preliminary finding, follow-up of these patients is essential.

Pyogenic liver abscess (PLA) is a purulent disease caused by microbial contamination of liver parenchyma and includes amoebic liver abscess, fungal liver abscess, and the most common bacterial liver abscess.

Explore the efficacy of contrast-enhanced ultrasound (CEUS) via vessels and surgical drain guidance percutaneous catheter drainage (PCD) in the treatment of pyogenic liver abscesses (PLA).

A total of 86 PLA patients who underwent PCD treatment in our hospital from May 2018 to February 2023 were retrospectively selected. Of them, 41 patients were treated under intravenous CEUS guidance (Control group), and 45 patients were treated under CEUS via vessels and surgical drain guidance (study group). Perioperative characteristics, treatment effectiveness, and incidence of complications were analyzed and compared between groups.

The duration of surgery, drainage, white blood cell recovery, body temperature recovery, and hospitalization in the study group were longer than those in the control group (P<0.05). The total effective rate of the study group (95.56%) was higher than that of the control group (80.49%) (P<0.05). The incidence of complications in the study group (4.44%) was lower than that in the control group (19.51%) (P<0.05).

Compared with intravenous CEUS alone, treatment under CEUS via vessels and surgical drains-guided PCD was associated with shorter surgical time, faster recovery, better treatment effect, lower risk of complications, and ensured treatment safety in PLA patients.

Mesothelioma is an insidious neoplasm that develops from mesothelial cells. About 80% of mesotheliomas originate in the pleural cavity. Other sites where it has been reported are the peritoneal cavity, tunica vaginalis, and the pericardium.

A 45-year-old female complained of abdominal distention and pain for three months. There was a significant weight loss of approximately 15 kg in the past three months, and there was no family history of any malignancy, tuberculosis, substance abuse, or asbestosis exposure. Physical examination revealed signs of muscle wasting, loss of subcutaneous fat, and hollowing of the eye sockets. There was pitting edema in the bilateral lower limbs; per abdomen examination revealed abdominal distension with umbilicus in the midline. No visible peristalsis or dilated veins were seen all over the abdomen. Hernial sites were normal. Gross ascites were present, and no organomegaly, definitive mass, or lump was palpable. The dull note was heard all over the abdomen, and fluid thrill was noted on percussion. Bowel sounds were normal on auscultation. The ascitic fluid examination revealed the presence of atypical cells. An omentectomy was done and it was sent for histopathological examination.

The specimen of omentectomy was in multiple fragments and measured 17x16x3cm; a few of the fragments were nodular, soft to firm on palpation. The cut section of mass was gray and white with areas of necrosis. Microscopic examination showed sheets of malignant cells. These tumor cells were immunoreactive to EMA, cytokeratin, vimentin, calretinin, WT-1, and D2-40 and immune negative to desmin (highlighting only the entrapped reactive mesothelial cells), inhibin, BerEP4, TTF-1, CD 68, napsin, ER, CEA, CDX2, PR, PAX-8, and SALL4. Ki67 labelling index was 15%. The features were of epithelioid mesothelioma.

Image enhancement is a very significant topic in image processing that improves the quality of the images. The methods of image enhancement are classified into 3 categories. They include histogram method, fuzzy logic method and optimal method. Studies on image enhancement are often based on rules: if it's bright then it's brighter, if it's dark then it's darker and using the global approach. So, it's hard to enhance objects in all dark and light areas, as in the medical images.

Input data is downloaded from the link: http://www.med.harvard.edu/AANLIB.

This paper introduces a new algorithm for enhancing medical images that is called the medical image enhancement based on cluster enhancement (MIECE). Firstly, the input image is clustered by the algorithm of fuzzy clustering. Then, the upper bound, and lower bound are calculated according to cluster. Next, the sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels. Because after clustering, each pixel belongs to each cluster with the corresponding membership value. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value.

This paper experiences the method MIECE with input data downloaded from the link: http://www.med.harvard.edu/AANLIB. The experimental results are compared with some recent methods that include: SGHIE (2017), Ying (2017) and KinD++ (2021).

This paper introduces the new algorithm which is based on cluster enhancement (MIECE) to enhance the medical image contrast. The experimental results show that the output images of the proposed algorithm are better than some other recent methods for enhancing dark objects.

Epicardial Adipose Tissue (EAT) accumulation is closely associated with the presence and severity of coronary artery disease (CAD), myocardial ischemia, plaque vulnerability, and major adverse cardiovascular events.

The aim of this study was to investigate the correlation between myocardial ischemia defined by computed tomography-derived fractional flow reserve (CT-FFR) and region-specific EAT in patients with type 2 diabetes mellitus (T2DM).

Between January 2022 and May 2023, 200 T2DM patients were randomly selected from the Department of Endocrinology in The Central Hospital of Wuhan. These patients were divided into two groups based on myocardial ischemia defined by CT-FFR: myocardial ischemia group (152 cases) and control group (48 cases). Both groups of patients used a post-treatment workstation to measure the thickness of region-specific EAT. Receiver operating characteristic (ROC) curve analysis and binary logistic regression were used to evaluate the correlation between various parameters and myocardial ischemia.

Patients in the myocardial ischemia group had significantly higher values of age, male gender, systolic blood pressure, total cholesterol, triglycerides, LDL, HDL, fasting blood glucose, fasting insulin, HOMA-IR, EAT thickness in right ventricular wall, left atrioventricular groove, and superior and inferior interventricular groove. ROC curve analysis results showed that EAT thickness in the left atrioventricular groove had the largest area under the ROC curve for diagnosing myocardial ischemia (0.837 [95% CI 0.766-0.865]; P < 0.001). Binary logistic regression analysis showed that EAT thickness in the left atrioventricular groove was an independent risk factor for myocardial ischemia in patients with T2DM (P < 0.05).

The EAT thickness in the left atrioventricular groove is an independent risk factor for myocardial ischemia in patients with T2DM. Adipose tissue in the left atrioventricular groove region plays a major role in EAT-mediated CAD.

Some patients with suspected brain metastases (BM) could not tolerate longer scanning examinations according to the standardized MRI protocol.

The purpose of this study was to evaluate the clinical value of contrast-enhanced fast fluid-attenuated inversion recovery (CE FLAIR) imaging in combination with contrast-enhanced T1 weighted imaging (CE T1WI) in detecting BM of lung cancer and explore a quick and effective MRI protocol.

In 201 patients with lung cancers and suspected BM, T1WI and FLAIR were performed before and after administration of gadopentetate dimeglumine. Two radiologists reviewed pre- and post-contrast images to determine the presence of abnormal contrast enhancement or signal intensity and decided whether it was metastatic or not on CE T1WI (Group 1) and CE FLAIR (Group 2). The number, locations and features of abnormal findings in two groups were recorded. Receiver Operating Characteristic (ROC) analyses were conducted in three groups: Group 1, 2 and 3(combination of CE FLAIR and CE T1WI).

A total of 714 abnormal findings were revealed, of which 672 were considered as BM and 42 nonmetastatic. Superficial and small metastases(≤10mm) in parenchyma and ependyma, leptomeningeal and non-expansive skull metastases were typically better seen on CE FLAIR. The areas under ROC in the three groups were 0.720,0.887 and 0.973, respectively. Group 3 was significantly better in diagnostic efficiency of BMs than Group 1 (p<0.0001) or Group 2 (p=0.0006).

The combination of CE T1WI and CE FLAIR promotes diagnostic performance and results in better observation and characterization of BM in patients with lung cancers. It provides a quick and efficient way of detecting BM.

Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification.

This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques.

This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets.

SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets.

These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.

To evaluate the usefulness of unenhanced CT added to the portal venous phase in the diagnostic accuracy of acute colonic diverticulitis/sigmoiditis.

Between January 1^st and December 31^st, 2020, all consecutive adult patients referred to the radiology department for clinical suspicion of acute colonic diverticulitis/sigmoiditis were retrospectively screened. To be included, patients must have undergone a CT with both unenhanced (UCT) and contrast-enhanced portal venous phase CT (CECT). CT examinations were assessed for features of diverticulitis, complications, differential diagnosis and incidental findings using UCT + CECT association, medical management, and follow-up as the reference. Radiation doses were recorded on our image archiving system and assessed.

Of the 114 patients included (mean age was 67±18 years; 60% were female), 46 had acute colonic diverticulitis/sigmoiditis. No diagnosis of sigmoiditis/diverticulitis, complication or differential diagnosis was missed with the CECT alone. Apart from diverticulitis, only one 2 mm meatal urinary microlithiasis was missed with no impact on patient management. The confidence level in diagnosis was not increased by UCT. The average DLP of CECT was 450 mGy.cm, and 382 mGy.cm for UCT. The use of a single-phase CECT acquisition allowed a reduction of 45.9% of the irradiation.

Unenhanced CT is not necessary for patients addressed with clinical suspicion of acute colonic diverticulitis/sigmoiditis, and CECT alone protocol must be used.

During the collection process, the prostate capsula is prone to introduce salt and pepper noise due to gastrointestinal peristalsis, which will affect the precision of subsequent object detection.

A cascade optimization scheme for image denoising based on image fusion was proposed to improve the peak signal-to-noise ratio (PSNR) and contour protection performance of heterogeneous medical images after image denoising.

Anisotropic diffusion fusion (ADF) was used to decompose the images denoised by adaptive median filter, non-local adaptive median filter and artificial neural network to generate the base layer and detail layer, which were fused by weighted average and Karhunen-Loeve Transform respectively. Finally, the image was reconstructed by linear superposition.

Compared with the traditional denoising method, the image denoised by this method has a higher PSNR while maintaining the image edge contour.

Using the denoised dataset for object detection, the detection precision of the obtained model is higher.

This study aims to comprehensively assess the characteristics of patent foramen ovale (PFO) in relation to Cryptogenic Strok (CS) by utilizing transesophageal echocardiography (TEE) and contrast transthoracic echocardiography (c-TTE) and to identify high-risk factors associated with PFO-related CS.

Transcatheter PFO closure has demonstrated its effectiveness in preventing PFO-related CS. Therefore, understanding the specific structural attributes of PFO associated with CS is imperative.

Enrollment comprised 113 test patients who experienced CS in conjunction with PFO and 117 control patients diagnosed with migraine with PFO but without a history of stroke. The characteristics of the PFO were observed by TEE and c-TTE. A comparative analysis was undertaken to assess the variations in PFO characteristics between the test patients and controls, and to uncover the independent factors relevant to CS.

The patients in the test group were older than the controls. Both the height and length of the PFO during Valsalva exhibited greater dimensions in the test group when contrasted with controls. Notably, the test group presented higher incidence rates of low-angle PFO (defined as an angle between the inferior vena cava (IVC) and PFO ≤ 10°) and atrial septal aneurysm (ASA) as contrasted with the control group. Right-to-left shunt (RLS) III during Valsalva demonstrated a significantly elevated occurrence within the test group as opposed to the controls. Conversely, RLS II during Valsalva exhibited a significantly higher frequency in the controls in contrast to the tests. No significant disparities were observed between the two groups with respect to RLS I during Valsalva and all grades of RLS at rest. Multivariate analysis revealed that the length of the PFO during Valsalva, the presence of ASA, RLS III during Valsalva and low-angle PFO were independent relevant factors associated with CS.

The length of the PFO tunnel, low-angle PFO, RLS III during Valsalva and the presence of ASA were independent risk factors for CS. The combined utilization of TEE and c-TTE may prove valuable in identifying PFO patients at a heightened risk of CS and in facilitating the screening process for transcatheter PFO closure.

Accurate preoperative judgment of lymph node (LN) metastasis is a critical step in creating a treatment strategy and evaluating prognosis in rectal cancer (RC) patients.

This study aimed to explore the value of T1 mapping and amide proton transfer weighted (APTw) imaging in predicting LN metastasis in patients with rectal cancer.

In a retrospective study, twenty-three patients with pathologically confirmed rectal adenocarcinoma who underwent MRI and surgery from August 2019 to August 2021 were selected. Then, 3.0T/MR sequences included conventional sequences (T1WI, T2WI, and DWI), APTw imaging, and T1 mapping. Patients were divided into LN metastasis (group A) and non-LN metastasis groups (group B). The intra-group correlation coefficient (ICC) was used to test the inter-observer consistency. Mann-Whitney U test was used to compare the differences between the two groups. Spearman correlation analysis was performed to evaluate the correlation between T1 and APT values. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the differential performance of each parameter and their combination. The difference between AUCs was compared using the DeLong test.

The APT value in patients with LN metastasis was significantly higher than in those without LN metastasis group (P=0.020). Also, similar results were observed for the T1 values (P=0.001). The area under the ROC curve of the APT value in the prediction of LN metastasis was 0.794; when the cutoff value was 1.73%, the sensitivity and specificity were 71.4% and 88.9%, respectively. The area under the ROC curve of the T1 value was 0.913; when the cutoff value was 1367.36 ms, the sensitivity and specificity were 78.6% and 100.0%, respectively. The area under the ROC curve of T1+APT was 0.929, with a sensitivity of 78.6% and specificity of 100.0%.

APT and T1 values show great diagnostic efficiency in predicting LN metastasis in rectal cancer.

Despite the availability of vaccines, there is an increasing number of SARS-CoV-2-breakthrough-infections.

The aim of this study was to determine whether there is a radiological difference in lung parenchymal involvement between infected vaccinated and unvaccinated patients. Additionally, we aimed to investigate whether vaccination has an impact on the course of illness and the need for intensive care.

This study includes all patients undergoing chest computed tomography (CT) or x-ray imaging in case of a proven SARS-CoV-2 infection between September and November 2021. Anonymized CT and x-ray images were reviewed retrospectively and in consensus by two radiologists, applying an internal severity score scheme for CT and x-ray as well as CARE and BRIXIA scores for x-ray. Radiological findings were compared to vaccination status, comorbidities, inpatient course of the patient’s illness and the subjective onset of symptoms.

In total, 38 patients with acute SARS-CoV-2 infection underwent a CT scan, and 168 patients underwent an x-ray examination during the study period. Of these, 32% were vaccinated in the CT group, and 45% in the x-ray group. For the latter, vaccinated patients exhibited significantly more comorbidities (cardiovascular (p=0.002), haemato-oncological diseases (p=0.016), immunosuppression (p=0.004)), and a higher age (p<0.001). Vaccinated groups showed significantly lower extent of lung involvement (severity scores in CT cohort and x-ray cohort both p≤0.020; ARDS 42% in unvaccinated CT cohort vs. 8% in vaccinated CT cohort). Furthermore, vaccinated patients in the CT cohort had significantly less need for intensive care treatment (p=0.040).

Our data suggest that vaccination, in the case of breakthrough infection, favours a milder course of illness concerning lung parenchymal involvement and the need for intensive care, despite negative predictors, such as immunosuppression or other pre-existing conditions.

Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both transforms.

The proposed method begins by decomposing the input images into curvelet and ridgelet coefficients using respective transform algorithms for Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images.

An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that incorporates the salient features from both input images.

The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology.

The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral information makes it a valuable tool for image fusion tasks.

An anomalous left coronary artery from the pulmonary artery (ALCAPA) is a rare heart malformation, with 90% of patients dying during the first year of life. If the right coronary artery compensation and multiple collateral circulation are sufficiently established, the patient's myocardial ischemia symptoms are mild and appear later, which is called the adult type ALCAPA.

A 42-year-old woman presented to our hospital with one-month history of the aggravation of active shortness of breath which gradually progressed to nocturnal paroxysmal shortness of breath and cough. Admission physical examination suggested mild edema of both lower limbs. Transthoracic echocardiography (TTE) showed that a small vessel shadow was abnormally connected to the pulmonary artery (PA), and moderate pulmonary artery hypertension. Coronary computed tomography angiography (CTA) showed an anomalous origin of the left main coronary artery (LMCA) dividing into the left anterior descending (LAD) and left circumflex (LCX) artery from the PA, with no clear connection to the left coronary sinus. The right coronary artery (RCA) was significantly dilated and originated from the normal Valsalva sinus. It was accompanied by multiple collateral circulations, most of which traveled anterior to the right ventricular free wall and anterior interventricular sulcus, and some emanated from the posterior descending branch of the posterior interventricular sulcus and walked toward the posterolateral wall of the left ventricle.

Coronary computed tomography angiography (CTA) can be used to visualize the abnormal origin and distribution of the coronary artery's course and may be the first choice in the diagnosis of ALCAPA.

A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions.

Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image.

In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms.

The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency.

Furthermore, it provides a roadmap for the direction of future research.

Exploring the relationship between the signal-to-noise ratio (SNR) of organs and size-specific dose estimate (SSDE) in tube current modulation (TCM) chest CT examination.

Forty patients who received TCM chest CT scanning were retrospectively collected and divided into four groups according to the tube voltage and sexes. We chose to set up the region of interest (ROI) at the tracheal bifurcation and its upper and lower parts in slice images of the heart, aorta, lungs, paracranial muscles, and female breast, and the SNR of each organ was calculated. We also calculated the corresponding axial volume CT dose index (CTDIvolz) and axial size-specific dose estimate (SSDEz).

The correlation analysis showed that the correlation between the SNR of the slice images of most organs and SSDEz was more significant than 0.8, and that between the SNR and CTDIvol was more significant than 0.7. The simple linear regression analysis results showed that when the sex is the same, the SNR of the same organ at 100kVp was higher than 120kVp, except for the lung. In multiple regression analysis, the result indicated that the determination coefficients of the SNR and SSDEz of the four groups were 0.934, 0.971, 0.905, and 0.709, respectively.

In chest CT examinations with TCM, the correlation between the SNR of each organ in slice images and SSDEz was better than that of CTDIvolz. And when the SSDEz was the same, the SNR at 100 kVp was better than that at 120 kVp.

Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.

This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation.

The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms.

The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset.

The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

Stroke is reportedly the biggest cause of death and disability in the world, according to the World Health Organisation (WHO). The severity of a stroke can be lowered by recognising the many stroke warning indicators early on. Using CNN, the primary goal of this study is to predict the likelihood that a brain stroke would develop at an early stage.

The novelty of the proposed work is to acquire models that can accurately differentiate between stroke and no-stroke (normal) cases using MR Imaging sequences like DWI, SWI, GRE and T2 FLAIR aiding in timely diagnosis and treatment decisions.

A dataset comprising real time MRI scans of patients with stroke and no-stroke conditions was collected and preprocessed for model training. The preprocessing involves standardizing the resolution of the images, normalizing pixel values, and augmenting the dataset to enhance model generalization. The ResNet, DenseNet, EfficientNet, and VGG16 architectures were implemented and trained on the preprocessed dataset. The training process involved optimizing model parameters using stochastic gradient descent and minimizing the loss function.

The results demonstrate promising performance across all models by obtaining an accuracy of 98% for ResNet, DenseNet and EfficientNet, while 97% for VGG16 in differentiating the stroke using real time MRI data.

The proposed work explored the effectiveness of CNN models, including ResNet, DenseNet, EfficientNet, and VGG16, for the differentiation of stroke and no-stroke cases. The models were trained and evaluated using a real-time dataset of brain MR Images. The obtained accuracies highlight the potential of CNN models in accurately differentiating between stroke and non-stroke cases.

The novel coronavirus pandemic has caused a global health crisis, placing immense strain on healthcare systems worldwide. Chest X-ray technology has emerged as a critical tool for the diagnosis and treatment of COVID-19. However, the manual interpretation of chest X-ray films has proven to be inefficient and time-consuming, necessitating the development of an automated classification system.

In response to the challenges posed by the COVID-19 pandemic, we aimed to develop a deep learning model that accurately classifies chest X-ray images, specifically focusing on lung regions, to enhance the efficiency and accuracy of COVID-19 and pneumonia diagnosis.

We have proposed a novel deep network called “FocusNet” for precise segmentation of lung regions in chest radiographs. This segmentation allows for the accurate extraction of lung contours from chest X-ray images, which are then input into the classification network, ResNet18. By training the model on these segmented lung datasets, we sought to improve the accuracy of classification.

The performance of our proposed system was evaluated on three types of lung regions in normal individuals, COVID-19 patients, and those with pneumonia. The average accuracy of the segmentation model (FocusNet) in segmenting lung regions was found to be above 90%. After re-classification of the segmented lung images, the specificities and sensitivities for normal, COVID-19, and pneumonia were excellent, with values of 98.00%, 99.00%, 99.50%, and 98.50%, 100.00%, and 99.00%, respectively. ResNet18 achieved impressive area under the curve (AUC) values of 0.99, 1.00, and 0.99 for classifying normal, COVID-19, and pneumonia, respectively, on the segmented lung datasets. Moreover, the AUC values of the three groups increased by 0.02, 0.02, and 0.06, respectively, when compared to the direct classification of unsegmented original images. Overall, the accuracy of lung region classification after processing the datasets was 99.3%.

Our deep learning-based automated chest X-ray classification system, incorporating lung region segmentation using FocusNet and subsequent classification with ResNet18, has significantly improved the accuracy of diagnosing respiratory lung diseases, including COVID-19. The proposed approach has great potential to revolutionize the diagnosis of COVID-19 and other respiratory lung diseases, offering a valuable tool to support healthcare professionals during health crises.

Immunoglobulin G4 (Ig G4)-related disease is rare; however, it is a fibroinflammatory disease that has been studied a lot so far. Although the expression pattern varies depending on the organ affected, it usually manifests as organ hypertrophy and organ dysfunction.

A 46-year-old man was referred to our otorhinolaryngology department for left submandibular swelling and tenderness that occurred 2 weeks ago. He was treated with antibiotics (augmentin 625mg, per oral) for 2 weeks, but his symptoms did not improve, and his white blood cell (WBC) count was 10,500 /μL (normal 3,800−10,000 /μL).

A mass-like lesion of the submandibular space has been concluded and the laboratory findings have been satisfactory (IgG4 level); IgG4-related disease, which is rare, but recently often reported, can be included in the differential diagnosis.

The purpose of this study was to assess the diagnostic value of magnetic resonance imaging (MRI) in staging and treatment of cervical cancer in pregnancy, and to evaluate the benefit of apparent diffusion coefficient (ADC) during neoadjuvant chemotherapy management.

This was a retrospective cohort study. Patients were divided into two groups according to the stage of cervical cancer. The mean term of pregnancy at the time of the diagnosis was the early second trimester (range 10-27 weeks) and the median age was 33 years (range 26-40 years). The abdominal and pelvic MRI images and clinical data of these patients were reviewed. Tumor size, local tumor spread, and nodal involvement were evaluated using an MRI dataset. The treatment and follow-up imaging were analyzed as well, and the ADC was measured before and after the chemotherapy.

16 patients with histopathologically confirmed cervical cancer during pregnancy were retrospectively enrolled. 7 patients were diagnosed with local cervical cancer (FIGO stage IAI) and designated as early stage group, as the lesion was invisible on MRI. In this group, pregnancies were allowed to continue until cesarean delivery (CD) at 38-41 weeks. The other 9 patients presenting with local or extensive cervical cancer (FIGO stage IB2-IIA2) were designated as the advanced-stage group. The lesion could be measured and analyzed on MRI. They were treated with neo-adjuvant chemotherapy in pregnancy. Among them, 6 patients underwent TP regimen (paclitaxel 135~175 mg/m2 plus cisplatin 70~75 mg/m2), while 3 patients received TC regimen (paclitaxel 135~175 mg/m2 plus carboplatin AUC=5). NACT was performed for 1 to 2 courses before surgery. ADC demonstrated significant differences before and after chemotherapy administered during pregnancy (1.06 ± 0.12 sec/mm2 vs. 1.34 ± 0.21 sec/mm2).

MRI has been found to be helpful in staging cervical cancer in pregnancy. Patients with stage IA confirmed by MRI can choose conservative treatment and continue the pregnancy until term birth. MRI can dynamically monitor the efficacy of chemotherapy for patients with stage IB and above during pregnancy. ADC value can have a potential role in the evaluation of chemotherapy efficacy.

Diabetic Retinopathy (DR) is a growing problem in Asian countries. DR accounts for 5% to 7% of all blindness in the entire area. In India, the record of DR-affected patients will reach around 79.4 million by 2030.

The main objective of the investigation is to utilize 2-D colored fundus retina scans to determine if an individual possesses DR or not. In this regard, Engineering-based techniques such as deep learning and neural networks play a methodical role in fighting against this fatal disease.

In this research work, a Computational Model for detecting DR using Convolutional Neural Network (DRCNN) is proposed. This method contrasts the fundus retina scans of the DR-afflicted eye with the usual human eyes. Using CNN and layers like Conv2D, Pooling, Dense, Flatten, and Dropout, the model aids in comprehending the scan's curve and color-based features. For training and error reduction, the Visual Geometry Group (VGG-16) model and Adaptive Moment Estimation Optimizer are utilized.

The variations in a dataset like 50%, 60%, 70%, 80%, and 90% images are reserved for the training phase, and the rest images are reserved for the testing phase. In the proposed model, the VGG-16 model comprises 138M parameters. The accuracy is achieved maximum rate of 90% when the training dataset is reserved at 80%. The model was validated using other datasets.

The suggested contribution to research determines conclusively whether the provided OCT scan utilizes an effective method for detecting DR-affected individuals within just a few moments.

To evaluate the predictive value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) in the histological grade of rectal adenocarcinoma.

Seventy-one patients with rectal adenocarcinoma confirmed by pathology after surgical resection were collected retrospectively. According to pathology, they were divided into a poorly differentiated group (n=23) and a moderately differentiated group (n=48). The IVIM-DWI parameters and TA characteristics of the two groups were compared, and a prediction model was constructed by multivariate logistic regression analysis. ROC curves were plotted for each individual and combined parameter.

There were statistically significant differences in D and D* values ​​between the two groups (P < 0.05). The three texture parameters SmallAreaEmphasis, Median, and Maximum had statistically significant differences between groups (P = 0.01, 0.004, 0.009, respectively). The logistic regression prediction model showed that D*, the median, and the maximum value were significant independent predictors, and the AUC of the regression prediction model was 0.860, which was significantly higher than other single parameters.

3.0T MRI IVIM-DWI parameters combined with TA can provide valuable information for predicting the histological grades of rectal adenocarcinoma one week before the operation.

With the development of vascular intervention, pseudoaneurysm complications are increasing. Ultrasound-guided thrombin injection (UGTI) is currently the treatment of choice for pseudoaneurysm, but the pharmacological properties of thrombin may trigger acute thrombosis within the vessel lumen. Despite a very low incidence, this type of primary arterial thrombosis is a serious complication of UGTI, and cases involving multiple branches of the lower limb arteries are particularly rare.

Here, we report a case of a 65-year-old male who underwent UGTI for the treatment of an iatrogenic pseudoaneurysm of the femoral artery complicated by acute thrombosis of multiple arteries in the lower limbs, and the patient ultimately underwent a successful thrombectomy.

We reviewed the case and analyzed the possible etiologic causes, providing a reference for future clinical work.

Pulmonary embolism (PE) is a relatively rare vascular complication of acute pancreatitis (AP), and its mortality rate is high. To our knowledge, relevant literature reports still need to be summarized. In this study, we analyzed the clinical characteristics, treatment, and prognosis of five patients with AP complicated by PE and summarized and reviewed the relevant literature.

Clinical data of patients with AP complicated by PE treated in Taizhou Hospital of Zhejiang Province between January 2017 and September 2022 were retrospectively collected. Combined with the relevant literature, the clinical characteristics, treatment, and prognoses of patients with AP combined with PE were analyzed and summarized.

Five patients were eventually enrolled in this study. Among the five patients with AP complicated by PE, all (100%) had symptoms of malaise, primarily chest tightness, shortness of breath, and dyspnea. All patients (100%) had varied degrees of elevated D-dimer levels and a significant decrease in the pressure of partial oxygen (PO2) and pressure of arterial oxygen to fractional inspired oxygen concentration ratio (PaO2/FiO2). Computed tomographic angiography (CTA) or pulmonary ventilation/perfusion imaging revealed a pulmonary artery filling defect in these patients. One patient (20%) had left calf muscular venous thrombosis before the occurrence of PE. Four patients (80%) were treated with low-molecular-weight heparin (LMWH), and one patient (20%) was treated with rivaroxaban during hospitalization; all continued oral anticoagulant therapy after discharge. All patients (100%) were cured and discharged. No patients showed recurrence of AP or PE.

PE is a rare but life-threatening complication of AP. However, once diagnosed, early treatment with anticoagulation or radiological interventional procedures is effective, and the prognosis is good.

Pulmonary embolism (PE) is a rare but life-threatening complication of acute pancreatitis (AP). Its early diagnosis and timely anticoagulation or radiological intervention can reduce mortality. However, only nine cases have been reported in the English literature thus far, and they are all case reports. Our study is the first systematic analysis of patients with AP combined with PE with a review of the relevant literature. Our patients and those reported in the literature were discharged with good prognoses under treatment such as anticoagulation and vascular intervention. These cases remind clinicians that, in patients with AP, especially those with risk factors for venous thrombosis, it is necessary to monitor the D-dimer level dynamically. Clinicians should pay attention to AP patients' symptoms and related examinations to reduce the chance of a missed diagnosis or misdiagnosis of PE.

Non-invasive bio-diagnostics are essential for providing patients with safer treatment. In this subject, significant growth is attained for non-invasive anaemia detection in terms of Hb concentration by means of spectroscopic and image analysis. The lower satisfaction rate is found due to inconsistent results in various patient settings.

This observational study aims to present an adaptable point-of-care Near-Infrared (NIR) spectrophotometric approach with a constructive Machine Learning (ML) algorithm for monitoring Haemoglobin (Hb) concentration by considering dominating influencing factors into account.

To accomplish this objective, 121 subjects (19.2-55.4 years) were enrolled in the study, having a wide range of Hb concentrations (8.2-17.4 g/dL) obtained from two standard Laboratory analyzers. To inspect the performance, the unique dimensionality reduction approaches are applied with numerous regression models using 5-fold cross-validation.

The optimum accuracy is found using support vector regression (SVR) and mutual information having 3 independent features i.e. Pearson correlation (r)= 0.79, standard deviation (SD)= 1.07 g/dL, bias=-0.13 g/dL and limits of agreement (LoA)=-2.22 to 1.97 g/dL. Additionally, comparability between two standard laboratory analyzers is found as; r=0.97, SD=0.50 g/dL, bias=0.21 g/dL, and LoA= -0.77 to 1.19 g/dL.

The precision of ±1 g/dL in 5-fold cross-validation ensures the same performance irrespective of different age groups, gender, BMI, smoking level, drinking level, and skin type. The outcomes with the offered NIR sensing system and an exclusive ML algorithm can accelerate its’ requirement at remote locative rural areas and critical care units where continuous Hb monitoring is compulsory.

In order to perform a full surgical resection on non-palpable breast lesions, a current method necessitates correct intraoperative localization. Additionally, because it is an important prognostic factor for these patients, the examination of the lymph node status is crucial.

The aim of this study was to evaluate the efficiency of the sentinel node and occult lesion localization (SNOLL) technique in localizing non-palpable breast lesions together with sentinel lymph node (SLN) using a single radiotracer, that is, nanocolloid particles of human serum albumin (NC) labeled with technetium-99m (^99mTc).

39 patients were included, each having a single non-palpable breast lesion and clinically no evidence of axillary disease. Patients received ^99mTc-NC intratumorally on the same day as surgery under the guidance of ultrasound. Planar and single-photon emission computed tomography/computed tomography lymphoscintigraphy were performed to localize the breast lesion and the SLN. The occult breast lesion and SLN were both localized using a hand-held gamma-probe, which was also utilized to determine the optimal access pathway for surgery. In order to ensure a radical treatment in a single surgical session and reduce the amount of normal tissue that would need to be removed, the surgical field was checked with the gamma probe after the specimen was removed to confirm the lack of residual sources of considerable radioactivity.

Breast lesions were successfully localized and removed in all patients. Pathological findings revealed breast carcinoma in 11/39 patients (28%) and benign lesions in 28 (72%). Axillary SLNs were detected in 31/39 (79.5%) patients. The metastatic involvement of SLN was only seen in two cases.

While the identification rate of the SNOLL technique performed with an intratumoral injection of ^99mTc-NC as the sole radiotracer in non-palpable breast lesions was great, it was not fully satisfactory in SLNs.

Pulmonary enteric adenocarcinoma (PEAC) is an extremely rare variant of lung adenocarcinoma characterized by pathological features similar to those of colorectal adenocarcinoma. It is mostly observed on computed tomography (CT) and positron emission tomography (PET)/CT as solitary or multiple nodules/masses in the lung. It tends to grow rapidly and is difficult to distinguish from lung metastatic colorectal cancer. Herein, we have presented a case of PEAC with special imaging findings.

A chest CT scan of a 72-year-old man with suspected chronic pneumonia revealed a well-defined consolidation in the upper lobe of the left lung. The lesion was slightly enlarged at the 9-month follow-up, and low FDG accumulation was subsequently observed using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT scans. The patient was later diagnosed with PEAC through percutaneous lung biopsy.

Our case has demonstrated specific imaging findings of PEAC.

Immune thrombocytopenic purpura (ITP) is a rare auto-antibody mediated disease of isolated thrombocytopenia (<100,000/µL) with normal haemoglobin levels and leukocyte counts. Only a small number of ITP cases have been reported with accompanying ophthalmological findings. Herein, we report an ITP case with demonstrative retinal haemorrhages.

A fifty-five-year-old woman with a known history of type 2 diabetes mellitus was referred to our clinic with blurred vision. After detailed anamnesis and clinical assessment, she was diagnosed as primary ITP in haematology department, and systemic steroid (1.5mg/kg) therapy was initiated. During her follow-up, a concomitant peripheral facial paralysis (PFP) emerged. In the course of follow-up, her platelet counts increased gradually, the retinal haemorrhages regressed partially, and the PFP recovered completely.

ITP is a rare haematologic disease that sometimes manifests with additional systemic involvements, and this disease should be remembered in the differential diagnosis of unusual retinal haemorrhages, which might be the only presenting feature.

Persistent trigeminal artery (PTA) is the most common vascular anastomosis between the carotid artery and vertebrobasilar systems. We report a very rare case of dissecting aneurysm in the right internal carotid artery (ICA) with ipsilateral PTA and discuss its clinical importance.

A 38-year-old male presented to the emergency department with paroxysmal dysphasia for 6h. Brain magnetic resonance (MR) imaging showed acute cerebral infarction of the right corona radiata and right parietal lobe. Three-dimensional time-of-flight MR angiography (3D TOF MRA) revealed severe stenosis of the petrous segment (C1 portion) of the right internal carotid artery and a PTA originating from the right ICA cavernous segment (C4 portion), with a length of approximately 1.8cm and a diameter of approximately 0.2cm. The ICA segments are all named according to the Bouthilier classification. The basilar artery (BA) under union was well developed. The bilateral posterior communicating arteries were also present. One day later, the high-resolution vessel-wall MR demonstrated a dissecting aneurysm in the C1 portion of the right ICA. The length of the dissecting aneurysm is approximately 4.4cm, the diameter of the true lumen at the most severe stenosis is approximately 0.2cm, and the diameter of the false lumen is approximately 0.8cm. Subsequent digital subtraction angiography (DSA) confirmed a dissecting aneurysm in the C1 portion of the right ICA. The patient was treated conservatively and did not undergo interventional surgery. Four months later, head and neck MRA showed that the right ICA blood flow was smooth and that the dissecting aneurysm had disappeared.

The Ethics Committee of Liaocheng People’s Hospital approved the research protocol in compliance with the Helsinki Declaration. Written informed consent was obtained from the individual for the publication of any potentially identifiable images or data included in this article.

Flow alteration with PTA may have influenced the formation of ICA dissection in this patient. Awareness of this is crucial in clinical practice because it can influence treatment options and intervention procedures.

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) via spatially resolved error estimation.

Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an in vivo mouse liver measurement.

The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as in vivo occurring reasons for the increased errors.

The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain.

To investigate the prediction performance of MRI radiomics for MVI in HCC.

Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses.

13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 – 0.86), specificity of 0.79 (95%CI: 0.76 – 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 – 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05).

MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application.

The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

This study aimed to describe the characteristics of computed tomography (CT) and magnetic resonance imaging (MRI) of clear cell papillary renal cell carcinoma (CCPRCC).

This retrospective study comprised 27 patients diagnosed with 29 tumors of CCPRCC. The study was approved by the Medical Ethics Committee and the requirement for the informed consent was waived. The inclusion criteria stipulated pathology-confirmed CCPRCCs with at least one preoperative imaging examination, including CT or MRI. Two experienced radiologists independently analyzed the imaging characteristics, including size, location, growth mode, morphology, texture, density, and enhancement pattern. Paired t-test was used to compare differences in CT Hounsfield unit values and apparent diffusion coefficient (ADC) imaging between the tumor and the renal cortex.

The mean age of the 27 patients was 57.0 ± 14.2 years. Nineteen patients underwent CT, while 12 underwent MRI (There are 4 patients underwent not only CT but also MRI). Among the cases, 26 (96%) were single, and 1 (4%) was multiple, consisting of three lesions. Out of the 29 tumors, 15 (52%) were located in the left kidney and 14 (48%) in the right kidney. The mean tumor diameter was 3.3 ± 1.7 cm. Furthermore, 19 (66%), 3 (10%), and 7 (24%) tumors were solid, cystic, mixed solid, and cystic type, respectively. The growth mode was endogenous and exogenous in 8 (28%) and 21 (72%) tumors, respectively. The tumor shape was irregular and round in 5 (17%) and 24 (83%) tumors, respectively. The CT value of the tumor was approximately 33.2 ± 9.8 HU, which was not significantly different from that of the renal cortex(31.1 ± 6.3HU)(p = 0.343). Furthermore, 7 (24%), 12 (41%), and 3 (10%) had calcification, cystic degeneration, and hemorrhage, respectively. In 12 tumors, hypointense and hyperintense were predominant on T1 and T2-weighted images, respectively. The tumor capsule was found at the edge of 12 tumors. The average ADC value of the tumor (1.54 ± 0.74 × 10⁻³ mm²/s) and that of the renal cortex(1.68 ± 0.63×10^–3mm² /s) was not statistically significantly different (p = 0.260). The enhancement scanning revealed “wash-in and wash-out” enhancement in 19 (68%) tumors, continuous or progressive enhancement in 6 (21%) tumors, and enhanced cystic wall and central separation in 3 (11%) tumors.

CCPRCC occurs more likely in middle-aged and elderly individuals, and the tumor is prone to cystic degeneration, with rare bleeding and calcification, and no obvious limitation on MRI diffusion-weighted imaging, which enhancement form performs as mainly “wash-in and wash-out,” but the final diagnosis depends on histopathology.

Follicular Dendritic Cell Sarcomas (FDCS)was first found in 1986; the specificity of the disease is its rarity, with an incidence of only 0.4%, numerous doctors for its lack of understanding, the accuracy of imaging diagnosis is not great, which is easy to delay the treatment. This article summarizes several characteristic imaging manifestations of FDCS to provide imaging physicians with an understanding of the imaging properties of this rare disease. When faced with complex cases, the radiologist can consider this disease and include it in the differential diagnosis. FDCS occurs mainly in lymph nodes, mainly in the head and neck. The main symptoms are fatigue, local pain, or painless mass. The treatment method is not uniform, but scholars agree that we should strive for the opportunity of surgery as much as possible.

This paper reported a case of FDCS with pelvic recurrence 3 years after surgery. The patient was suspected to have lymphoma by postoperative pathology in the local hospital, and it is recommended that the patient be reexamined regularly. A soft tissue mass was recently found again in the left pelvic cavity. After an enhanced CT examination, the radiologist was skeptical of the previous diagnosis of lymphoma. Subsequently, a needle biopsy was performed at Peking University Shougang Hospital. The pathological results rejected the prior diagnosis of lymphoma after consultation with additional hospitals, and the patient was diagnosed with FDCS.

The imaging manifestations of FDCS lack absolute specificity, but it also has imaging characteristics, such as large areas of necrosis in the huge mass, rough mass calcification in the mass, enhanced scan showed “fast in and slow out” mode, and there were blood vessels in the tumor. FDCS mainly occurs in lymph nodes and is easily misdiagnosed as GIST, inflammatory myoblastoma, lymphoma, etc. Radiologists should continue to collect cases of this disease and include suspected cases in the differential diagnosis in clinical work.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD.

This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients.

756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD).

The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation.

The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.

Dural sinus malformation (DSM) is a rather rare congenital condition that can be encountered in the fetus and infants. The cause and etiology of DSM remain unclear. Obstetric ultrasound plays a key role in screening fetal brain malformations, and MRI is frequently used as a complementary method to confirm the diagnosis and provide more details.

Here, we present a fetus with DSM by multiple imaging methods to help better understand the imaging characteristics of this malformation.

A 22-year-old primipara was referred to our hospital at 25 weeks of gestation following the detection of a fetal intracranial mass without any symptoms. A prenatal ultrasound performed in our hospital at 25 ^{+ 2} gestational weeks showed a large anechoic mass with liquid dark space, while no blood flow was detected. After the initial evaluation, this primipara received a prenatal MRI in our hospital. This examination at 25 ^{+ 5} gestational weeks delineated a fan-shaped mass in the torcular herophili, which was iso-to hyperintense on T1WI and hypointense on T2WI. At the lower part of this lesion, a quasi-circular hyperintense on T1WI and a signal slightly hyperintense on T2WI could be seen. Meanwhile, the adjacent brain parenchyma was compressed by the mass.

We reviewed the current literature to obtain a better understanding of the mechanisms, imaging characteristics, and survival status of DSM. Although the primipara of the present study regretfully opted for elective termination of pregnancy, the reevaluation of DSM survival deserves more attention because of the better survival data from recent studies.

Mixed epithelial and stromal tumor of the kidney (MESTK) is a rare benign lesion that appears as a solid cystic renal lesion or complex renal cystic lesion on medical imaging. There are no definite imaging criteria for METSK diagnosis.

We present a case of a solid cystic renal mass that was evaluated by contrast-enhanced ultrasound (CEUS) during an imaging workup. The patient underwent nephrectomy and histopathological confirmation of MESTK. The lesions showed hypoenhancement during the process. Quantitative perfusion analysis showed the septation of the solid cystic lesion to have lower peak enhancement with a longer rise time compared to the normal renal cortex.

CEUS can visualize the microcirculation of the organ and reconstruction of the vessels. By providing a more detailed visualization of the microvessel, CEUS is a useful tool for further characterizing renal lesions that show indeterminate enhancement on CT. This study determined the time to peak to be shorter for the cancerous lesion than the normal renal cortex, while peak intensity did not differ between the cancerous lesion and the normal renal cortex.

Quantitative perfusion analysis of CEUS may be useful for differentiating benign and malignant solid cystic renal masses. Further investigation is needed to determine whether peak intensity is a useful parameter in differentiating benign and malignant solid cystic lesions of the kidney.

Cytoarchitectonic mapping has revealed distinct subregions within Broadmann area 4 (BA 4) – BA 4a and BA 4p – with varying functional roles across tasks. We investigate their functional connectivity using resting-state functional magnetic resonance imaging (rsfMRI) to explore bilateral differences and the impact of handedness on connectivity within major brain networks.

This retrospective study involved 54 left- and right-handed subjects. We employed regions-to-regions-network rsfMRI analysis to examine the Cytoarchitectonic mapping of BA 4a and BA 4p functional connectivity with eight major brain networks.

Our findings reveal differential connectivity patterns in both right-handed and left-handed subjects:

Both right-handed subjects' BA 4a and BA 4p subregions exhibit connections to sensorimotor, dorsal attention, frontoparietal, and anterior cerebellar networks. Notably, BA 4a shows unique connectivity to the posterior cerebellum, lateral visual networks, and select salience regions. Similar connectivity patterns are observed in left-handed subjects, with BA 4a linked to sensorimotor, dorsal attention, frontoparietal, and anterior cerebellar networks. However, BA 4a in left-handed subjects shows distinct connectivity only to the posterior cerebellum. In both groups, the right portion of BA 4 demonstrates heightened connectivity compared to the left portion within each subregion.

Our study uncovers complex patterns of functional connectivity within BA 4a and BA 4p, influenced by handedness. These findings emphasize the importance of considering hemisphere-specific and handedness-related factors in functional connectivity analyses, with potential implications for understanding brain organization in health and neurodegenerative diseases.

Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images.

To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model.

A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis).

Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively.

The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of ^99mTc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.

Deep learning reconstruction for free-breathing pulmonary MRI.

To propose a motion-resolved 3D pulmonary MRI reconstruction scheme using the sinusoidal representation network (SIREN).

The proposed scheme learns the registration maps using SIREN to register an averaging image to get the final reconstructions. The learning of the network relies only on the undersampled data from the specific subject. The usage of the network for outputting the registration maps enables a memory-efficient algorithm, as outputting registration maps instead of images only requires small networks. The training of the network based on only undersampled data enables an unsupervised learning scheme, which makes the proposed scheme useful in cases in which fully sampled data is not available.

We compare the proposed SIREN-based motion-resolved reconstruction with two state-of-the-art methods for ten datasets. Both visual and quantitative comparison indicates the better performance of the proposed method.

In conclusion, the use of SIREN for 3D pulmonary MRI reconstruction allows for the efficient and accurate reconstruction of data that has been undersampled.

The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report.

A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics.

The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization.

The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%.

Renal cell carcinoma, especially in small renal masses (≤ 4 cm) (SRM), has increased. Pathological analysis revealed a high proportion of benign masses, highlighting the urgent need for precise SRM differentiation.

This research aimed to independently validate the performance of machine learning-based ultrasound (US) radiomics analysis in differentiating benign from malignant SRM, and to compare its performance with that of radiologists.

A total of 499 patients from two hospitals were retrospectively included in this study and divided into two cohorts. US images were used to extract radiomics features. To obtain the most robust features, inter-observer correlation coefficient, Spearman correlation coefficient, and least absolute shrinkage and selection operator methods were applied for feature selection. Three models were developed in the training data using the stochastic gradient boosting algorithm, including a clinical model, a radiomics model, and a combined model that integrated clinical factors and radiomics features. The performance of these models was evaluated in the independent external validation data, including discrimination, calibration, and clinical usefulness, and compared with pooled radiologists' assessments.

The AUCs of the clinical, radiomics, and combined models were 0.844, 0.942, and 0.954, respectively. The radiomics and combined models significantly outperformed the clinical model (all p < 0.05), while no significant difference was observed between them (p = 0.32). The radiomics and combined models showed good discrimination and calibration. Decision curve analysis exhibited that the combined model had clinical usefulness. Compared with the pooled radiologists’ assessment (AUC, 0.799), the combined model showed superior classification results (p < 0.01) and higher specificity (p < 0.01) with similar sensitivity (p = 0.62).

The combined model incorporating clinical factors and radiomics features accurately distinguished benign from malignant SRM.

Multisystem information, including musculoskeletal information, can be captured from chest CT scans of patients with COVID-19 without further examination.

This study aims to assess the relationship between chest CT-extracted baseline bone mineral density (BMD) and body composition parameters and the length of hospital stay in these patients.

A retrospective analysis was performed in a cohort of 88 patients with COVID-19. Correlation analysis and a generalized linear model (GLM) were used to assess the associations between the length of hospital stay and covariates, including age, sex, body mass index (BMI), BMD and body composition variables.

The mean length of hospital stay was 27.4±8.7 days. The length of hospital stay was significantly positively associated with age (r=0.202, p=0.046) and the paraspinal muscle fat ratio (r=0.246, p=0.021). The GLM involving age, sex, BMD, paraspinal muscle fat ratio, subcutaneous adipose tissue (SAT) area, visceral adipose tissue (VAT) area, and liver fat fraction (LFF) showed that the length of hospital stay was positively correlated with VAT area (β coefficients, 95% CI: 9.304, 1.141-17.478, p=0.025).

The musculoskeletal features extracted from chest CT correlated with the prognosis of COVID-19 patients. Factors including old age, a higher paraspinal muscle fat ratio and a larger VAT area in patients with COVID-19 were associated with longer hospital stays.

Coronary artery diseases are the leading cause of death worldwide. Stenting or angioplasty of coronary arteries as interventional management requires knowledge about the morphology of the coronary tree, including luminal diameters.

This work aimed to study the diameters of the left main coronary artery and its branches measured by QCA in relation to the diameters derived by Finet’s law.

This was a cross-sectional, retrospective, hospital-based study. The number of angiograms used was 357. The diameters of the left main coronary artery (LM1), left anterior interventricular artery (LAD1), and left circumflex artery (LCx1) were measured by QCA. The diameter of LM1 was measured by 5 mm before its termination, and the diameters of LAD1 and LCx1 were obtained by 5 mm from their origins. Finet’s law was used to calculate the diameters of LM2, LAD2 and LCx2 using the QCA measurements.

The mean age of participants was 53.3±8.8 years. Female patients represented 58.9%. The mean diameter of the left main coronary using QCA was 3.75±0.85 mm, and the diameter calculated using Finet’s law was 3.89±0.80 mm. The diameters of LAD1 and LCx1 were larger than those calculated with Finet’s law. The Z-test showed a significant difference between the diameter of the LM1 calculated by Finet’s law; both diameters were positively associated. The diameters of LAD1 and LAD2 showed a non-significant correlation (r = 0.0653, P = 0.259526) and a negative correlation between LCx1 and LCX2 (r = -0.2659, P = 0.00001). The Z-test showed a significant difference in the diameter of LAD and LCx measured by QCA and Finet’s law.

An association was found between the diameter of LM measured by QCA and calculated with Finet’s law; the diameter calculated by Finet’s law was larger. The diameters of LAD and LCx measured by QCA were larger than those calculated by Finet’s law. A positive correlation existed between the diameters measured by QCA and Finet’s law, and they had significant differences. Finet’s law can assist in the selection of stent size. Despite the literature about Finet’s law, generalising its use requires more studies on different ethnicities.