Obstacles Facing the Implementation of Functional Magnetic Resonance Imaging （fMRI） of Brain in Jeddah City

fMRI （Functional Magnetic Resonance Imaging） is a relatively new technique that uses MRI （Magnetic Resonance Imaging） to measure the hemodynamic response （change in blood flow） related to neural activity in the brain. This paper aims to explore and identify the obstacles facing the implementation and applications of IMRI in radiology departments within Jeddah city by analyzing related data received by direct questionnaires and interviews with all the people working in MRI units in Jeddah city and finds that the major obstacle is lacking of awareness of fMRI among medical professionals and their training.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Cost Analysis of Diffusion Tensor Imaging and MR Tractography of the Brain

Purpose: To determine the total direct costs (fixed and variable costs) of diffusion tensor imaging (DTI) and MR tractography reconstruction of the brain. Materials and Methods: The direct fixed and variable costs of DTI with MR tractography were determined prospectively with time and motion analysis in a 1.5-Tesla MR scanner using 15 encoding directions. Seventeen patients with seizure disorders, 9 males & 8 females, with mean age of 13 years (age range 2 - 33 years) were studied. Total direct costs were calculated from all direct fixed and variable costs. Sensitivity analyses between 1.5 versus a 3-Tesla MR system, and 15 versus 32 encoding directions were done. Results: The total direct costs of DTI and MR tractography for a 1.5-T system with 15 encoding directions were US $97. Variable cost was $76.80 and fixed cost was $20.20. Total direct costs for a 3-T system with 15 directions decreased to US $94.5 because of the shorter scan time despite the higher cost of the 3-T system. The most costly component of the direct cost was post-processing analysis at US $46.00. Conclusion: DTI with MR tractography has important total direct costs with variable costs higher than the fixed costs. The post processing variable cost is the most expensive component. Developing more accurate automated post-processing software for DTI and MR tractography is important to decrease this variable labor cost. Given the added value of DTI-MR tractography and the costs involved reimbursement codes should be considered.

Preliminary examination of early neuroconnectivity features in the R6/1 mouse model of Huntington's disease by ultra-high field diffusion MRI

During the last decades,advances in the understanding of genetic,cellular,and microstructural alterations associated to Huntington's disease(HD)have improved the understanding of this progressive and fatal illness.However,events related to early neuropathological events,neuroinflammation,deterioration of neuronal connectivity and compensatory mechanisms still remain vastly unknown.Ultra-high field diffusion MRI(UHFD-MRI)techniques can contribute to a more comprehensive analysis of the early microstructural changes observed in HD.In addition,it is possible to evaluate if early imaging microstructural parameters might be linked to histological biomarkers.Moreover,qualitative studies analyzing histological complexity in brain areas susceptible to neurodegeneration could provide information on inflammatory events,compensatory increase of neuroconnectivity and mechanisms of brain repair and regeneration.The application of ultra-high field diffusion-MRI technology in animal models,particularly the R6/1 mice(a common preclinical mammalian model of HD),provide the opportunity to analyze alterations in a physiologically intact model of the disease.Although some disparities in volumetric changes across different brain structures between preclinical and clinical models has been documented,further application of different diffusion MRI techniques used in combination like diffusion tensor imaging,and neurite orientation dispersion and density imaging have proved effective in characterizing early parameters associated to alteration in water diffusion exchange within intracellular and extracellular compartments in brain white and grey matter.Thus,the combination of diffusion MRI imaging techniques and more complex neuropathological analysis could accelerate the discovery of new imaging biomarkers and the early diagnosis and neuromonitoring of patients affected with HD.

Classification of Medical Brain Images

Since brain tumors endanger people’s living quality and even their lives, the accuracy of classification becomes more important. Conventional classifying techniques are used to deal with those datasets with characters and numbers. It is difficult, however, to apply them to datasets that include brain images and medical history (alphanumeric data), especially to guarantee the accuracy. For these datasets, this paper combines the knowledge of medical field and improves the traditional decision tree. The new classification algorithm with the direction of the medical knowledge not only adds the interaction with the doctors, but also enhances the quality of classification. The algorithm has been used on real brain CT images and a precious rule has been gained from the experiments. This paper shows that the algorithm works well for real CT data.

Acupuncture at Waiguan (TE5) influences activation/deactivation of functional brain areas in ischemic stroke patients and healthy people A functional MRI study

In the present study, 10 patients with ischemic stroke in the left hemisphere and six healthy controls were subjected to acupuncture at right Waiguan （TE5）. In ischemic stroke subjects, functional MRI showed enhanced activation in Broadmann areas 5, 6, 7, 18, 19, 24, 32, the hypothalamic inferior lobe, the mamiilary body, and the ventral posterolateral nucleus of the left hemisphere, and Broadmann areas 4, 6, 7, 18, 19 and 32 of the right hemisphere, but attenuated activation of Broadmann area 13, the hypothalamic inferior lobe, the posterior lobe of the tonsil of cerebellum, and the culmen of the anterior lobe of hypophysis, in the left hemisphere and Broadmann area 13 in the right hemisphere. In ischemic stroke subjects, a number of deactivated brain areas were enhanced, including Broadmann areas 6, 11,20, 22, 37, and 47, the culmen of the anterior lobe of hypophysis, alae lingulae cerebella, and the posterior lobe of the tonsil of cerebellum of the left hemisphere, and Broadmann areas 8, 37, 45 and 47, the culmen of the anterior lobe of hypophysis, pars tuberalis adenohypophyseos, inferior border of lentiform nucleus, lateral globus pallidus, inferior temporal gyrus, and the parahippocampal gyrus of the right hemisphere. These subjects also exhibited attenuation of a number of deactivated brain areas, including Broadmann area 7. These data suggest that acupuncture at Waiguan specifically alters brain function in regions associated with sensation, vision, and motion in ischemic stroke patients. By contrast, in normal individuals, acupuncture at Waiguan generally activates brain areas associated with insomnia and other functions.

A brief report on MRI investigation of experimental traumatic brain injury

Traumatic brain injury is a major cause of death and disability. This is a brief report based on a symposium presentation to the 2014 Chinese Neurotrauma Association Meeting in San Francisco, USA. It covers the work from our laboratory in applying multimodal MRI to study experimental traumatic brain injury in rats with comparisons made to behavioral tests and histology. MRI protocols include structural, perfusion, manganese-enhanced, diffusion-tensor MRI, and MRI of blood-brain barrier integrity and cerebrovascular reactivity.

Deep Learning Framework for the Prediction of Childhood Medulloblastoma

This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.

基于MRI影像组学鉴别胶质瘤及单发脑转移瘤的应用研究

目的分析基于多模态磁共振成像(Magnetic Resonance Imaging,MRI)影像组学鉴别胶质瘤及单发脑转移瘤的研究进展,得出提升鉴别准确性的要素。方法通过检索PubMed、Web of Science及FMRS外文医学信息资源检索平台3个数据库,根据纳入排除标准,对纳入的文章提取数据来源、患者数量、MRI设备、MRI序列、肿瘤分割软件、分割方式、分割范围、分割类型、特征提取方法、筛选方法、机器学习分类器、最优的机器学习分类器等数据进行综合分析。结果最终纳入12篇文献进行分析,大多数研究选择MRI传统结构序列,特征筛选方法选择最多的是最小绝对收缩和选择算子,使用最多且表现最佳的机器学习分类器为随机森林。结论MRI影像组学方法在鉴别胶质瘤及单发脑转移瘤方面展现出了较高的准确性,为临床决策提高了较大帮助。

融合CNN与Transformer的MRI脑肿瘤图像分割

为解决卷积神经网络(convolutional neural network,CNN)在学习全局上下文信息和边缘细节方面受到很大限制的问题,提出一种同时学习局语义信息和局部空间细节的级联神经网络用于脑肿瘤医学图像分割。首先将输入体素分别送入CNN和Transformer分支,在编码阶段结束后,采用一种双分支融合模块将2个分支学习到的特征有效地结合起来以实现全局信息与局部信息的融合。双分支融合模块利用哈达玛积对双分支特征之间的细粒度交互进行建模,同时使用多重注意力机制充分提取特征图通道和空间信息并抑制无效的噪声信息。在BraTS竞赛官网评估了本文方法,在BraTS2019验证集上增强型肿瘤区、全肿瘤区和肿瘤核心区的Dice分数分别为77.92%,89.20%和81.20%。相较于其他先进的三维医学图像分割方法,本文方法表现出了更好的分割性能,为临床医生做出准确的脑肿瘤细胞评估和治疗方案提供了可靠依据。

肺癌脑转移瘤的MRI影像特征与病理组织学的相关性分析

目的探讨肺癌脑转移瘤患者的MRI影像学特征以及与病理组织学的相关性。方法选取肺癌脑转移瘤患者34例为研究对象,对患者MRI检查和病理组织学结果进行整理。结果本组34例患者中,23例腺癌,2例鳞癌,9例小细胞肺癌;腺癌多发数目高于鳞癌、小细胞肺癌(P<0.05);腺癌肿瘤大小高于鳞癌、小细胞肺癌(P<0.05);鳞癌脑转移瘤水肿程度最高,其次为小细胞肺癌,三组对比有差异(P<0.05);小细胞肺癌强化不规则状特点最突出,其次为腺癌,三组对比有差异(P<0.05)。结论肺癌脑转移瘤MRI影像学特征与病理组织学之间有密切关联性,故可将MRI可作为识别诊断肺癌脑转移瘤的重要手段。

Physical Activity, Mediterranean Diet and Biomarkers-Assessed Risk of Alzheimer’s: A Multi-Modality Brain Imaging Study

Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer’s disease (AD). Their association has not been investigated with the use of biomarkers. This study examines whether, among cognitively normal (NL) individuals, those who are less physically active and show lower MeDi adherence have brain biomarker abnormalities consistent with AD. Methods: Forty-five NL individuals (age 54 ± 11, 71% women) with complete leisure time physical activity (LTA), dietary information, and cross-sectional 3D T1-weigthed MRI, 11C-Pittsburgh Compound B (PiB) and 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) scans were examined. Voxel-wise multivariate partial least square (PLS) regression was used to examine the effects of LTA, MeDi and their interaction on brain biomarkers. Age, gender, ethnicity, education, caloric intake, BMI, family history of AD, Apolipoprotein E (APOE) genotype, presence of hypertension and insulin resistance were examined as confounds. Subjects were dichotomized into more and less physically active (LTA+ vs. LTA-;n = 21 vs. 24), and into higher vs. lower MeDi adherence groups (n = 18 vs. 27) using published scoring methods. Spatial patterns of brain biomarkers that represented the optimal association between the images and the groups were generated for all modalities using voxel-wise multivariate Partial Least Squares (PLS) regression. Results: Groups were comparable for clinical and neuropsychological measures. Independent effects of LTA and MeDi factors were observed in AD-vulnerable brain regions for all modalities (p β load and lower glucose metabolism) were observed in LTA- compared to LTA+ subjects, and in MeDi- as compared to MeDi+ subjects. A gradient effect was observed for all modalities so that LTA+/MeDi+ subjects had the highest and LTA+/MeDi+ subjects had the lowest AD-burden (p < 0.001), although the LTA × MeDi interaction was significant only for FDG measures (p < 0.03). Adjusting for covariates did not attenuate these relationships. Conclusion: Lower physical activity and MeDi adherence were associated with increased brain AD-burden among NL individuals, in-dicating that lifestyle factors may modulate AD risk. Studies with larger samples and longitudinal evaluations are needed to determine the predictive power of the observed associations.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

A Machine Learning Approach for MRI Brain Tumor Classification

A new method for the denoising,extraction and tumor detection on MRI images is presented in this paper.MRI images help physicians study and diagnose diseases or tumors present in the brain.This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis.The ambiguity of Magnetic Resonance(MR)image features is solved in a simpler manner.The MRI image acquired from the machine is subjected to analysis in the work.The real-time data is used for the analysis.Basic preprocessing is performed using various filters for noise removal.The de-noised image is segmented,and the feature extractions are performed.Features are extracted using the wavelet transform.When compared to other methods,the wavelet transform is more suitable for MRI image feature extraction.The features are given to the classifier which uses binary tree support vectors for classification.The classification process is compared with conventional methods.

面向急性缺血性脑卒中的CT生成MRI算法

急性缺血性脑卒中病灶在计算机断层扫描(CT)上表现不明显,但在核磁共振成像(MRI)上可以清晰显示。然而,当患者体内有金属植入物等特殊情况则无法进行MRI检测,使得患者的治疗受到延误。通过CT生成MRI可在急性缺血性脑卒中的诊断和治疗中起到至关重要的作用,但现有的医学影像跨模态生成方法从CT得到的MRI缺乏病灶信息且边界模糊。为了解决上述问题,提出一种基于影像组学和扩散生成对抗网络的急性缺血性脑卒中CT生成MRI算法,通过影像组学在CT上进行病灶特征增强,突出生成MRI的病灶信息,引入梯度损失为生成图像与真实图像增加边缘感知约束,提升生成MRI的质量。在ISLES2018挑战赛数据集上的实验结果表明,该算法生成的MRI在整体上的峰值信噪比为23.051 dB,结构相似度为0.798,皮尔逊相关系数为0.969,并且病灶区域的互信息为2.075,与现有的生成模型相比,该算法的各项指标均达到最优。此外,经3名经验丰富的放射科医生在生成的MRI上确定病灶并进行阳性/阴性分类,其中生成的MRI中无错误病灶,且分类准确率可达到86.61%,可作为一种辅助工具协助医生进行诊断。

Characteristics of diffusion-tensor imaging for healthy adult rhesus monkey brains

Diffusion-tensor imaging can be used to observe the microstructure of brain tissue. Fractional ani- sotropy reflects the integrity of white matter fibers. Fractional anisotropy of a young adult brain is low in gray matter, high in white matter, and highest in the splenium of the corpus callosum. Thus, we selected the anterior and posterior limbs of the internal capsule, head of the caudate nucleus, semioval center, thalamus, and corpus callosum （splenium and genu） as regions of interest when using diffusion-tensor imaging to observe fractional anisotropy of major white matter fiber tracts and the deep gray matter of healthy rhesus monkeys aged 4-8 years. Results showed no laterality dif- ferences in fractional anisotropy values. Fractional anisotropy values were low in the head of cau- date nucleus and thalamus in gray matter. Fractional anisotropy values were highest in the sple- nium of corpus callosum in the white matter, followed by genu of the corpus callosum and the pos- terior limb of the internal capsule. Fractional anisotropy values were lowest in the semioval center and posterior limb of internal capsule. These results suggest that fractional anisotropy values in major white matter fibers and the deep gray matter of 4-8-year-old rhesus monkeys are similar to those of healthy young people.

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research.Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences.The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal.The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models.One of the best models for image localization and classification is the Visual Geometric Group(VGG)model.In this study,an efficient modified VGG architecture for brain image classification is developed using transfer learning.The pooling layer is modified to enhance the classification capability of VGG architecture.Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5%improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa(REMBRANDT)database.

MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

Digital Image Processing(DIP)is a well-developed field in the biological sciences which involves classification and detection of tumour.In medical science,automatic brain tumor diagnosis is an important phase.Brain tumor detection is performed by Computer-Aided Diagnosis(CAD)systems.The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes.Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research.Brain tumor diagnosis mainly performed for obtaining exact location,orientation and area of abnormal tissues.Cancer and edema regions inference from brain magnetic resonance imaging(MRI)information is considered to be great challenge due to brain tumors complex structure,blurred borders,besides exterior features like noise.The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation.Combined form of Median Modified Wiener filter(CMMWF)is chiefly deployed for denoising,and morphological operations which in turn eliminate nonbrain tissue,efficiently dropping technique’s sensitivity to noise.The proposed system contains the main phases such as preprocessing,brain tumor extraction and post processing.Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering(IPFC)algorithm.The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction.Then,the post processing of images are done through morphological operations along with Hybrid Median filtering(HMF)for attaining exact tumors representations.Additionally,suggested algorithm is substantiated by comparing with other existing segmentation algorithms.The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy,sensitivity,specificity,and recall.

Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.

Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.