scholarly journals Convolutional Neural Network Optimization Algorithm-Based Magnetic Resonance Imaging in Analysis of Chronic Pain Caused by the Myofascial Trigger Point

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Jin ◽  
Lei Fan ◽  
Yongling Yao
Keyword(s):  
Neural Network ◽  
Chronic Pain ◽  
Magnetic Resonance ◽  
Trigger Point ◽  
Data Consistency ◽  
Myofascial Trigger Point ◽  
Resonance Imaging ◽  
Injection Group ◽  
The Right ◽  
Better Than

This study was to explore the value of magnetic resonance imaging (MRI) technology processed by convolutional neural network (CNN) optimization algorithms in the clinical research of patients with chronic pain caused by myofascial trigger points (MTrPs). Firstly, referring to the traditional iterative algorithm, this study iterated the convolution network and data consistency layer as a whole for several times, which increased the fitting ability of the data consistency layer and network. When it was applied to magnetic resonance examination, it could be concluded that the effect of its reconstruction method was better than the traditional convolution neural network without the data consistency layer. The image edge was clear, and the restoration effect of details was better. 100 patients with chronic neck pain caused by MTrP were collected and divided into an ultrasound treatment group and a local anesthetic drug injection group, with 50 cases in each group. In addition, 50 healthy volunteers were selected. After clinical treatment, the results showed that, after 3 weeks of treatment, the visual analog score (VAS) and the pain rating index (PRI) of the injection group were 3.16 ± 1.14 points and 4.92 ± 1.26 points, respectively; the present pain intensity (PPI) score was 2.06 ± 0.85 points, and the number of pain days per month was 7.73 ± 1.15. After 1 month of treatment, the VSA and PRI of the injection group were 1.24 ± 0.89 and 1.31 ± 0.97, respectively; the PPI score was 1.34 ± 0.65, and the number of pain days per month was 5.34 ± 0.98. In addition, there were 38 cases reaching the level of clinical cure, accounting for 76%. Therefore, all indicators in the injection group were better than those in the ultrasound treatment group, and the differences were statistically significant ( P < 0.05 ). The results of MRI examination showed that compared with the healthy control group, patients with chronic pain caused by the myofascial trigger point had reduced axial kurtosis (AK), mean kurtosis (MK), and radial kurtosis (RK) in multiple brain areas such as the right parahippocampal gyrus and the right medial prefrontal cortex. In short, chronic pain caused by the trigger point of the myofascial membrane would affect the microstructure of the gray matter of the patient’s brain. In clinical treatment, the efficacy of local anesthetic injection was better than ultrasound therapy.

scholarly journals Alteration within the Hippocampal Volume in Patients with LHON Disease—7 Tesla MRI Study

2020 ◽  
Vol 10 (1) ◽  
pp. 14
Author(s):  
Cezary Grochowski ◽  
Kamil Jonak ◽  
Marcin Maciejewski ◽  
Andrzej Stępniewski ◽  
Mansur Rahnama-Hezavah
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Molecular Layer ◽  
Three Dimensional ◽  
Hippocampal Volume ◽  
7 Tesla ◽  
Diagnostic Process ◽  
Resonance Imaging ◽  
The Right

Purpose: The aim of this study was to assess the volumetry of the hippocampus in the Leber’s hereditary optic neuropathy (LHON) of blind patients. Methods: A total of 25 patients with LHON were randomly included into the study from the national health database. A total of 15 patients were selected according to the inclusion criteria. The submillimeter segmentation of the hippocampus was based on three-dimensional spoiled gradient recalled acquisition in steady state (3D-SPGR) BRAVO 7T magnetic resonance imaging (MRI) protocol. Results: Statistical analysis revealed that compared to healthy controls (HC), LHON subjects had multiple significant differences only in the right hippocampus, including a significantly higher volume of hippocampal tail (p = 0.009), subiculum body (p = 0.018), CA1 body (p = 0.002), hippocampal fissure (p = 0.046), molecular layer hippocampus (HP) body (p = 0.014), CA3 body (p = 0.006), Granule Cell (GC) and Molecular Layer (ML) of the Dentate Gyrus (DG)–GC ML DG body (p = 0.003), CA4 body (p = 0.001), whole hippocampal body (p = 0.018), and the whole hippocampus volume (p = 0.023). Discussion: The ultra-high-field magnetic resonance imaging allowed hippocampus quality visualization and analysis, serving as a powerful in vivo diagnostic tool in the diagnostic process and LHON disease course assessment. The study confirmed previous reports regarding volumetry of hippocampus in blind individuals.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Intraorbital wooden foreign body detected by computed tomography and magnetic resonance imaging

2016 ◽  
Vol 30 (1) ◽  
pp. 88-91 ◽  
Author(s):  
Alfredo Di Gaeta ◽  
Francesco Giurazza ◽  
Eugenio Capobianco ◽  
Alvaro Diano ◽  
Mario Muto
Keyword(s):  
Computed Tomography ◽  
Foreign Body ◽  
Magnetic Resonance ◽  
Delayed Diagnosis ◽  
Clinical History ◽  
Definitive Diagnosis ◽  
Double Vision ◽  
Resonance Imaging ◽  
Wooden Foreign Body ◽  
The Right

To identify and localize an intraorbital wooden foreign body is often a challenging radiological issue; delayed diagnosis can lead to serious adverse complications. Preliminary radiographic interpretations are often integrated with computed tomography and magnetic resonance, which play a crucial role in reaching the correct definitive diagnosis. We report on a 40 years old male complaining of pain in the right orbit referred to our hospital for evaluation of eyeball pain and double vision with an unclear clinical history. Computed tomography and magnetic resonance scans supposed the presence of an abscess caused by a foreign intraorbital body, confirmed by surgical findings.

Anomalous origin of the right pulmonary artery from the ascending aorta: Diagnosis by magnetic resonance imaging

1995 ◽  
Vol 18 (2) ◽  
pp. 118-121 ◽  
Author(s):  
Tae Kyoung Kim ◽  
Yeon Hyoen Choe ◽  
Hak Soo Kim ◽  
Jae Kon Ko ◽  
Young Tak Lee ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Pulmonary Artery ◽  
Ascending Aorta ◽  
Anomalous Origin ◽  
Resonance Imaging ◽  
Right Pulmonary Artery ◽  
The Right

Magnetic resonance imaging of primary cerebral gliosarcoma: a report of 15 cases

2008 ◽  
Vol 49 (9) ◽  
pp. 1058-1067 ◽  
Author(s):  
L. Han ◽  
X. Zhang ◽  
S. Qiu ◽  
X. Li ◽  
W. Xiong ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Clinical Information ◽  
Cerebellar Hemisphere ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Mr Images ◽  
Peripheral Location ◽  
Pathology Reports ◽  
The Right ◽  
Radiologic Features

Background: Gliosarcomas are rare tumors with mixed glial and mesenchymal components. Many of their radiologic features resemble those of other primary brain malignancies. Purpose: To investigate the magnetic resonance (MR) imaging features of gliosarcomas. Material and Methods: We retrospectively reviewed the MR images, pathology reports, and clinical information of 11 male and four female patients aged 15–71 years to evaluate the location, morphology, enhancement, and other features of their pathologically confirmed gliosarcomas. Results: Apart from one tumor in the right cerebellar hemisphere, all were supratentorial. Two tumors were intraventricular, and four involved the corpus callosum. The tumors were well demarcated, with an inhomogeneous or cystic appearance and moderate-to-extensive surrounding edema. Thick walls with strong rim and ring-like enhancement were observed in 13 (87%). Seven (47%) showed intratumoral paliform enhancement. Conclusion: Gliosarcoma demonstrates certain characteristic MR features, such as supratentorial and peripheral location, well-demarcated, abutting a dural surface, uneven and thick-walled rim-like or ring enhancement, as well as intratumoral strip enhancement. These findings, combined with patient age, can aid the differential diagnosis of gliosarcomas from more common primary brain tumors.

Study of the right ventricle using magnetic resonance imaging

2012 ◽  
Vol 54 (3) ◽  
pp. 231-245 ◽  
Author(s):  
A. Capelastegui Alber ◽  
E. Astigarraga Aguirre ◽  
M.A. de Paz ◽  
J.A. Larena Iturbe ◽  
T. Salinas Yeregui
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Right Ventricle ◽  
Resonance Imaging ◽  
The Right

scholarly journals A New Retrieval Algorithm Based on Pulse Coupled Neural Network for Biomedical Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 9-13
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rapid Expansion ◽  
Image Feature ◽  
Experimental Result ◽  
Feature Descriptor ◽  
Resonance Imaging ◽  
Biomedical Images ◽  
Pulse Coupled Neural Network

The rapid expansion and improvement in medical science and technology lead to the generation of more image data in its regular activity such as computed tomography (CT), X-ray, magnetic resonance imaging (MRI) etc. To manage the medical images properly for clinical decision making, content-based medical image retrieval (CBMIR) system emerged. In this paper, Pulse Coupled Neural Network (PCNN) based feature descriptor is proposed for retrieval of biomedical images. Time series is used as an image feature which contains the entire information of the feature, based on which the similar biomedical images are retrieved in our work. Here, the physician can point out the disorder present in the patient report by retrieving the most similar report from related reference reports. Open Access Series of Imaging Studies (OASIS) magnetic resonance imaging dataset is used for the evaluation of the proposed approach. The experimental result of the proposed system shows that the retrieval efficiency is better than the other existing systems.

scholarly journals Pure epidural spinal cavernous haemangioma

2021 ◽  
Vol 12 ◽  
pp. 523
Author(s):  
Ragavan Manoharan ◽  
Jonathon Parkinson
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Surgical Excision ◽  
Cavernous Haemangioma ◽  
Case Description ◽  
Resonance Imaging ◽  
Cavernous Hemangiomas ◽  
The Right ◽  
Spinal Epidural ◽  
Progressive Paraparesis

Background: Pure epidural spinal cavernous hemangiomas (SCH) account for only 4% of all spinal epidural lesions. Our literature review identified 61 publications reporting on, a total of 175 cases in the magnetic resonance imaging era. Here, we reviewed those cases, and have added our case of what appeared to be a multifocal SCH. Case Description: A 72-year-old male presented with a progressive paraparesis attributed to a T5/T6 dorsolateral extradural mass extending into the right T5/6 foramen. Surgical excision documented the lesion, histologically, was a SCH. A second similar lesion was noted involving the left C7/T1 foramen; as the patient was asymptomatic from this lesion, and no additional biopsy was performed. The patient returned to normal neurological function within 2 months postoperatively. Conclusions: Here, a 72-year-old male presented with a pathologically confirmed T5/T6 epidural SCH and a secondary C7/T1 foraminal lesion suspected to represent a secondary focus of an epidural SCH.

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