ObjectiveThe purpose of this study was to better delineate the clinical spectrum of periventricular nodular heterotopia (PNH) in a large patient population to better understand social support in people with PNH and epilepsy in west China. Specifically, this study aimed to relate PNH subtypes to clinical or epileptic outcomes and epileptic discharges by analyzing anatomical features. MethodsThe study included 70 patients with radiologically confirmed nodular heterotopias and epilepsy. We also recruited healthy controls from nearby urban and rural areas. People with PNH and epilepsy and healthy controls were gender-and age-matched. Two-sided Chi-square test and Fisher's exact t-test were used to assess associations between the distribution of PNHs and specific clinical features. ResultsBased on imaging data, patients were subdivided into three groups: (a) classical (bilateral frontal and body, n=25), (b) bilateral asymmetrical or posterior (n=9) and (c) unilateral heterotopia (n=36). Most patients with classical heterotopia were females, but were mostly seizure-free. Patients with unilateral heterotopia were prone to develop refractory epilepsy. ConclusionsEach group's distinctive genetic mutations, epileptic discharge patterns and overall clinical outcomes confirm that the proposed classification system is reliable. These findings could not only be an indicator of a more severe morphological and clinical phenotype, but could also have clinical implications with respect to the epilepsy management and optimization of therapeutic options.
Acute aortic dissection is featured as sudden onset and high mortality. Regardless early optimal surgical intervention and strict medical therapy, incidence of late complications is still high. Thus, specific imaging techniques and precise measurement of biomarkers to predict complications are needed. In the present study, we reviewed related papers to compare traditional imaging techniques (computed tomography, echocardiography) and magnetic resonance imaging (MRI) in the diagnosis of chronic aortic dissection. In addition, we discussed how to further evaluate aortic dissection by MRI.
Urokinase plasminogen activator receptor (uPAR) is a membrane protein which is attached to the cellular external membrane. The uPAR expression can be observed both in tumor cells and in tumor-associated stromal cells. Thus, in the present study, the human amino-terminal fragment (hATF), as a targeting element to uPAR, is used to conjugate to the surface of superparamagnetic iron nanoparticle (SPIO). Flowcytometry was used to examine the uPAR expression in different tumor cell lines. The specificity of hATF-SPIO was verified by Prussian blue stain and cell phantom test. The imaging properties of hATF-SPIO were confirmed in vivo magnetic resonance imaging (MRI) of uPAR-elevated colon tumor. Finally, the distribution of hATF-SPIO in tumor tissue was confirmed by pathological staining. Results showed that the three cells in which we screened, presented different expression characteristics, i.e., Hela cells strongly expressed uPAR, HT29 cells moderately expressed uPAR, but Lovo cells didn't express uPAR. In vitro, after incubating with Hela cells, hATF-SPIO could specifically combined to and be subsequently internalized by uPAR positive cells, which could be observed via Prussian blue staining. Meanwhile T2WI signal intensity of Hela cells, after incubation with targeted probe, significantly decreased, and otherwise no obvious changes in Lovo cells both by Prussian blue staining and MRI scans. In vivo, hATF-SPIO could be systematically delivered to HT29 xenograft and accumulated in the tumor tissue which was confirmed by Prussian Blue stain compared to Lovo xenografts. Twenty-four hours after injection of targeting probe, the signal intensity of HT29 xenografts was lower than Lovo ones which was statistically significant. This targeting nanoparticles enabled not only in vitro specifically combining to uPAR positive cells but also in vivo imaging of uPAR moderately elevated colon cancer lesions.
To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translation method based on diffusion generative adversarial networks. First, an unsupervised translation module is used to convert magnetic resonance imaging (MRI) into pseudo-computed tomography (CT) images. Subsequently, a nonlinear frequency decomposition module is used to extract high-frequency CT images. Finally, the pseudo-CT image is input into the forward process, while the high-frequency CT image as a conditional input is used to guide the reverse process to generate the final CT image. The proposed model is evaluated on the SynthRAD2023 dataset, which is used for CT image generation for radiotherapy planning. The generated brain CT images achieve a Fréchet Inception Distance (FID) score of 33.159 7, a structure similarity index measure (SSIM) of 89.84%, a peak signal-to-noise ratio (PSNR) of 35.596 5 dB, and a mean squared error (MSE) of 17.873 9. The generated pelvic CT images yield an FID score of 33.951 6, a structural similarity index of 91.30%, a PSNR of 34.870 7 dB, and an MSE of 17.465 8. Experimental results show that the proposed model generates highly realistic CT images while preserving anatomical accuracy as much as possible. The transformed CT images can be effectively used in radiotherapy planning, further enhancing diagnostic efficiency.
When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.
ObjectiveTo make the model of Wistar suckling rats Focal cortical dysplasia (FCD) by liquid nitrogen freezing brain cortex and verify it. Analysed the electroencephalogram (EEG) and magnetic resonance imaging (MRI) features of the FCD model, in order to provide theoretical and experimental basis for human FCD diagnosis and treatment. MethodsTake the first day of Wistar suckling rats as experimental object, liquid nitrogen freezing Wistar suckling rats brain cortex.Make examination of EEG and MRI for Wistar suckling rats. The Brain tissue slice of Wistar suckling rats model dyed by HE and check with light microscope examination. ResultsIn experiment group, the sample epileptic discharge rate of EEG was about 41.6% on average, and showed visible spike wave, spine slow wave frequency distribution. Experimental Wistar suckling rats MRI showed positive performance for long T1 and long T2 signal, brain tissue slices HE staining showed brain cortex layer structure and columnar structure disorder, exist abnormal neurons and the balloon sample cells. ConclusionThe method of liquid nitrogen freezing Wistar suckling rats cortex can established FCDⅢd animal models successfully, and showed specific EEG and MRI, which has important value for diagnosis and treatment of human FCD.
This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.
ObjectiveTo investigate clinical value of MRI examination in diagnosis of xanthogranulomatous cholecystitis (XGC), and to analyze pathologic correlation of various imaging findings. MethodsMRI imaging data of 7 patients with XGC proved by surgery and pathology who underwent entire MRI sequences examination in Sichuan Provincial People's Hospital from Jan. 2013 to Dec. 2015, were analyzed retrospectively. The thickness and contrast enhancement of gallbladder wall, gallbladder wall nodules, completeness of gallbladder mucosa lines, gallbladder stones, and the changes around the gallbladder were focused in every patient. ResultsIn 7 patients with XGC: gallbladder wall thickening occurred in all patients, in which 2 patients were local thickening, 5 patients were diffuse thickening; ‘hypodense band sign' was found by enhance scan in 4 patients; the multiple intramural nodules were presented in 5 patients, which were low signal intensity on T1WI image and high signal intensity on T2WI image; the mucosal lines were continuous in 6 patients and discontinuous in 1 patient; 6 patients combined with cholecystolithiasis. The fat layer around the gallbladder was found fuzz in 7 patients, liver and gallbladder boundaries were not clear in 7 patients; temporal enhancement of arterial phase in liver parenchyma was observed in all patients, and 1 patient combined with liver abscess. Hilar bile duct narrowed and intra-hepatic bile duct dilated in 2 patients, intra-hepatic and extra-hepatic bile duct slightly dilated in 2 patients (lower part of the choledochus stone was found in 1 patient), liver cyst was observed in 3 patients, single or double kidney cyst was observed in 4 patients; all patients were not found intraperitoneal or retroperitoneal swelling lymph nodes. ConclusionMRI examination can accurately describe various imaging features of XGC, so MRI has important value in diagnosis of XGC.