scholarly journals Magnetic Resonance Imaging Features under Deep Learning Algorithms in Evaluated Cerebral Protection of Craniotomy Evacuation of Hematoma under Propofol Anesthesia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Deqian Xin ◽  
Zhongzhe An ◽  
Juan Ding ◽  
Zhi Li ◽  
Leyan Qiao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Super Resolution ◽  
Control Group ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Resolution Algorithm ◽  
Experimental Group

This study aimed to explore the value of magnetic resonance imaging (MRI) features based on deep learning super-resolution algorithms in evaluating the value of propofol anesthesia for brain protection of patients undergoing craniotomy evacuation of the hematoma. An optimized super-resolution algorithm was obtained through the multiscale network reconstruction model based on the traditional algorithm. A total of 100 patients undergoing craniotomy evacuation of hematoma were recruited and rolled into sevoflurane control group and propofol experimental group. Both were evaluated using diffusion tensor imaging (DTI) images based on deep learning super-resolution algorithms. The results showed that the fractional anisotropic image (FA) value of the hind limb corticospinal tract of the affected side of the internal capsule of the experimental group after the operation was 0.67 ± 0.28. The National Institute of Health Stroke Scale (NIHSS) score was 6.14 ± 3.29. The oxygen saturation in jugular venous (SjvO2) at T4 and T5 was 61.93 ± 6.58% and 59.38 ± 6.2%, respectively, and cerebral oxygen uptake rate (CO2ER) was 31.12 ± 6.07% and 35.83 ± 7.91%, respectively. The difference in jugular venous oxygen (Da-jvO2) at T3, T4, and T5 was 63.28 ± 10.15 mL/dL, 64.89 ± 13.11 mL/dL, and 66.03 ± 11.78 mL/dL, respectively. The neuron-specific enolase (NSE) and central-nerve-specific protein (S100β) levels at T5 were 53.85 ± 12.31 ng/mL and 7.49 ± 3.16 ng/mL, respectively. In terms of the number of postoperative complications, the patients in the experimental group were better than the control group under sevoflurane anesthesia, and the differences were substantial ( P  < 0.05). In conclusion, MRI images based on deep learning super-resolution algorithm have great clinical value in evaluating the degree of brain injury in patients anesthetized with propofol and the protective effect of propofol on brain nerves.

scholarly journals Deep Learning in Laparoscopic Colorectal Carcinoma Surgery under Magnetic Resonance Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shuguang Pan ◽  
Wei Tang ◽  
Tiejun Zhou ◽  
Wei Luo
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Laparoscopic Surgery ◽  
Magnetic Resonance ◽  
Colorectal Carcinoma ◽  
Control Group ◽  
Resonance Imaging ◽  
Neural Network Algorithm ◽  
Experimental Group

This study aimed to explore the application effect of magnetic resonance imaging (MRI) based on deep learning in laparoscopic surgery for colorectal carcinoma (CRC). 40 patients with CRC who were diagnosed and required laparoscopic surgery were selected in the research. The MRI scan images of all patients were processed based on the convolutional neural network algorithm. The MRI images before and after treatment were set as the control group and the experimental group, respectively. The consistency of MRI results with laparoscopic and postoperative pathological biopsy results was observed. Through the comparative analysis of the research results, in terms of consistency with the surgical plane, the assessment results of the experimental group were more consistent than those of the control group and direct observation under laparoscopy, and the difference was statistically significant ( P < 0.05 ). In terms of tumor T staging, the consistency between the experimental group and pathological biopsy results was superior to that of the control group, with considerable difference ( P < 0.05 ). In conclusion, practically speaking, the application of MR images based on convolutional neural network algorithm in laparoscopic CRC surgery was better than conventional MRI technology. However, the research was a small-scale pathological study, which was not very representative.

scholarly journals Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

2021 ◽  
Vol 11 (2) ◽  
pp. 782 ◽  
Author(s):  
Albert Comelli ◽  
Navdeep Dahiya ◽  
Alessandro Stefano ◽  
Federica Vernuccio ◽  
Marzia Portoghese ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Central Processing ◽  
Prostate Segmentation

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

scholarly journals Support Vector Machine-Based Backprojection Algorithm for Detection of Gastric Cancer Lesions with Abdominal Endoscope Using Magnetic Resonance Imaging Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huajia Dai ◽  
Yuhao Bian ◽  
Libin Wang ◽  
Junfeng Yang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Gastric Cancer ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Hospital Stay ◽  
Radical Resection ◽  
Support Vector ◽  
Control Group ◽  
Resonance Imaging ◽  
Experimental Group

This study was to analyze the diagnostic value of magnetic resonance imaging (MRI) for gastric cancer (GC) lesions and the treatment effect of complete laparoscopic radical resection (CLSRR). A malignant tumor recognition algorithm was constructed in this study based on the backprojection (BP) and support vector machine (SVM), which was named BPS. 78 GC patients were divided into an experimental group (received CLSRR) and a control group (received assisted laparoscopic radical resection (ALSRR)), with 39 cases in each group. It was found that the BPS algorithm showed lower relative mean square error (MSE) in axle x (OMSE, x) and axle y (OMSE, x), but the classification accuracy (CA) was the opposite ( P < 0.05 ). The postoperative hospital stay, analgesia duration, first exhaust time (FET), and first off-bed activity time (FOBA) for patients in the experimental group were less ( P < 0.05 ). The operation time of the experimental group (270.56 ± 90.55 min) was significantly longer than that of the control group (228.07 ± 75.26 min) ( P < 0.05 ). There were 3 cases of anastomotic fistula, 1 case of acute peritonitis, and 2 cases of lung infections in the experimental group, which were greatly less than those in the control group (7 cases, 4 cases, and 3 cases) ( P < 0.05 ). In short, the BPS algorithm was superior in processing MRI images and could improve the diagnostic effect of MRI images. The CLSRR could reduce the length of hospital stay and the probability of complications in GC patients, so it could be used as a surgical plan for the clinical treatment of advanced GC.

scholarly journals Diffusion Tensor Magnetic Resonance Imaging in Patients with Multiple Sclerosis and its Relationship with Disability

2013 ◽  
Vol 26 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Ş. Temel ◽  
H.D. Kekliğkoğlu ◽  
G. Vural ◽  
O. Deniz ◽  
K. Ercan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Water Diffusion ◽  
Control Group ◽  
Healthy Individuals ◽  
Resonance Imaging ◽  
Significant Difference

Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system. Diffusion tensor magnetic resonance imaging (DTI) can yield important information on the in vivo pathological processes affecting water diffusion. The aim of this study was to quantitatively define water diffusion in normal-appearing white matter (NAWM) distant from the plaque, in the plaque, and around the plaque, and to investigate the correlation of these changes with clinical disability. Conventional MRI and DTI scans were conducted in 30 patients with MS and 15 healthy individuals. Fractional anisotropy maps and visible diffusion coefficients were created and integrated with T2-weighted images. Regions of interest (ROIs) were placed on the plaques on the same side, white matter around the plaques and NAWM on the opposite side. Only the white matter of healthy individuals in the control group, and FA and ADC values were obtained for comparison. The highest FA and lowest ADC were detected in the control group at the periventricular region, cerebellar peduncle and at all ROIs irrespective of location. There was a significant difference in comparison to the control group at all ROIs in patients with MS (p < 0.001 for all comparisons). No significant correlation between diffusion parameters and expanded disability state scale (EDSS) scores was found in patients with MS. DTI may provide more accurate information on the damage due to the illness, compared to T2A sequences, but this damage may not be correlated with the clinical disability measured by EDSS score.

Microstructural magnetic resonance imaging of cortical lesions in multiple sclerosis

2012 ◽  
Vol 19 (4) ◽  
pp. 418-426 ◽  
Author(s):  
M Filippi ◽  
P Preziosa ◽  
E Pagani ◽  
M Copetti ◽  
S Mesaros ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Neuronal Damage ◽  
Lesion Volume ◽  
Imaging Features ◽  
Cortical Lesions ◽  
Resonance Imaging

Background: Pathologic and magnetic resonance imaging (MRI) studies have shown that cortical lesions (CLs) are a frequent finding in multiple sclerosis (MS). Objective: To quantify microstructural damage in CLs and normal appearing (NA) cortex in relapse-onset MS patients at different stages of the disease. Methods: Brain double inversion recovery (DIR), diffusion tensor (DT) MRI and 3D T1-weighted scans were acquired from 35 relapsing–remitting (RR) patients, 23 secondary progressive (SP) patients, 12 benign (B) MS patients and 41 healthy controls (HC). Diffusivity values in CLs, cortex, white matter (WM) lesions and normal-appearing white matter (NAWM) were assessed. Results: Compared to HC, MS patients had a significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the cortex and NAWM. CLs had higher FA vs HC cortex and vs patients’ cortex. Compared to RRMS patients, SPMS patients had higher WM lesion volume, higher MD in the cortex, and more severe damage to the NAWM and WM lesions. Compared to SPMS patients, BMS patients had lower MD and FA of CLs. Damage in other compartments was similar between SPMS and BMS patients. Damage in CLs had a high power to discriminate BMS from SPMS (area under the curve: 79–91%), with high specificity (85%), sensitivity (100%) and accuracy (90%). Conclusions: Microstructural imaging features of CLs differ from those of WM lesions and are likely to reflect neuronal damage and microglial activation. The nature and extent of CL damage can be used to help distinguish the different MS clinical phenotypes.

scholarly journals Tic Disorder of Children Analyzed and Diagnosed by Magnetic Resonance Imaging Features under Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chunxia Wu ◽  
Qingerile Si ◽  
Budegerile Su ◽  
Lan Mu ◽  
Gaowa Bao ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Severe Illness ◽  
Control Group ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Normal Children ◽  
Tic Disorder

This work aimed to explore the analysis and diagnosis of children with tic disorder by magnetic resonance imaging (MRI) features under convolutional neural network (CNN), to provide a certain reference basis for clinical identification. A total of 45 children diagnosed with tic disorder in hospital from January 2018 to June 2020 were selected as the research subjects. A total of 30 normal children were selected as the control group. MRI images were collected, and CNN was constructed for image processing. The results showed that the convolutional neural network could significantly improve the speed of MRI reconstruction and can improve the diagnostic accuracy. Compared with normal children, the metabolites in children with tic disorder were slightly increased, but there was no statistical significance P > 0.05 . The results of the Yale score showed that the proportion of children with moderate illness was significantly greater than that of children with mild and severe illness. In short, the pathological changes of tic disorder were effectively discovered by MRI based on CNN algorithms, which can provide a reference for clinical identification.

scholarly journals Magnetic Resonance Imaging Based on Wavelet Algorithm in the Diagnosis and Treatment of Tibial Osteomyelitis Wound Infection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoran Ou ◽  
Qi Wang ◽  
Chunxiao Li ◽  
Hongjin Zhao ◽  
Lei Guo
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Wound Infection ◽  
Control Group ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Dyadic Wavelet ◽  
Experimental Group ◽  
Wavelet Algorithm ◽  
Better Than

This study was to explore the therapeutic effect of magnetic resonance imaging (MRI) images based on the image processing algorithm under the correlation of dyadic wavelet coefficients on the diagnosis of tibial osteomyelitis patients. 32 tibial osteomyelitis patients admitted to hospital were randomly selected as the research objects. According to the patients’ wishes, patients who were willing to use new MRI imaging techniques for disease detection were set as the experimental group and conventional MRI imaging detection methods were set as the control group. The application effect of the new MRI imaging technology was evaluated by comparing the treatment effect of the two groups of patients. It was found that the mean square error (MSE) (38.5642) and signal-to-noise ratio (SNR) (18.5122) processed by the improved wavelet algorithm were much better than those of unimproved dyadic wavelet algorithm (59.1096 and 15.2341) ( P < 0.05 ). The possibilities of soft tissue swelling, bone invasion or destruction, thickening and sclerosis of bone cortex, bone abscess, periosteum response, dense dead bone, and bone sinus of patients in the experimental group were higher than those of the control group, which were 100% vs. 55%, 100% vs. 80%, 92% vs. 65%, 50% vs. 25%, 42% vs. 15%, 67% vs. 45%, and 50% vs. 15%, respectively ( P < 0.05 ). The healing time of osteomyelitis (22.89 ± 2.19 d vs. 32.32 ± 2.81 d) and the recovery of wound infection (14% vs. 45%) in the patients in control and experimental groups showed that the results of the experimental group were obviously better than those of the control group. The kappa value of the diagnosis results and tissue biopsy of the experimental group was higher than that of the control group (0.45 vs. 0.34) ( P < 0.05 ). In conclusion, the results of the enhanced and improved MRI images were relatively more accurate and the treatment methods adopted were more symptomatic, resulting in more effective treatment. In addition, the wavelet algorithm had certain application value in the enhancement processing of medical images and showed a good development prospect.

Comparison of Predictive Ability of Computed Tomography and Magnetic Resonance Imaging in Patients with Carotid Atherosclerosis Complicated with Stroke

2020 ◽  
Author(s):  
Jiangfeng ZHOU ◽  
Tao YANG ◽  
Cuihong XING ◽  
Fengxia JIA ◽  
Hongling CHEN
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Carotid Atherosclerosis ◽  
Control Group ◽  
Resonance Imaging ◽  
Fibrous Cap ◽  
Vascular Stenosis ◽  
Experimental Group ◽  
Ct And Mri

Background: To investigate the characterizations of CT (computed tomography) and MRI (magnetic resonance imaging) in patients with carotid atherosclerosis. Methods: A retrospective analysis was performed on the medical records of 264 patients with carotid atherosclerosis underwent CT and MRI in Linyi Central Hospital, Linyi, China from January 2010 to January 2016. Among them, 142 patients with ischemic stroke were in experimental group (test group), another 122 patients in control group. The lumen stenosis degree, plaque fibrous cap status, calcification information and vascular plaque hemorrhage in the carotid artery fork of patients detected by CT and MRI were collected. Results: The detection rate of the plaque calcification of patients detected by MRI was lower than that detected by CT in the experimental group (P<0.05). Patients in the experimental group had higher average vascular stenosis degree detected by CT and MRI than those in the control group (P<0.01). The average vascular stenosis degree of patients detected by MRI was higher than that detected by CT in the experimental group (P<0.05). Patients in the experimental group had higher unstable fibrous cap number detected by CT and MRI than those in the control group (P<0.01). Patients in the experimental group had significantly higher number of vascular plaque small focus hemorrhage than those in the control group (P<0.05). Conclusion: Patients with carotid atherosclerotic complicated with stroke have higher plaque calcification number, vascular stenosis degree and unstable fibrous cap number. Both CT and MRI can better predict the risk of stroke.

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