Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

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.

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Alzheimer’s disease(AD)is a chronic and common form of dementia that mainly affects elderly individuals.The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear,and there is nomedicinal or surgical treatment available yet forAD.ADcauses loss of memory and functionality control in multiple degrees according to AD’s progression level.However,early diagnosis of AD can hinder its progression.Brain imaging tools such as magnetic resonance imaging(MRI),computed tomography(CT)scans,positron emission tomography(PET),etc.can help in medical diagnosis of AD.Recently,computer-aided diagnosis(CAD)such as deep learning applied to brain images obtained with these tools,has been an established strategic methodology that is widely used for clinical assistance in prognosis of AD.In this study,we proposed an intelligent methodology for building a convolutional neural network(CNN)from scratch to detect AD stages from the brain MRI images dataset and to improve patient care.It is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting problems.Therefore,for better understanding of classifiers and to overcome the model overfitting problem,we applied data augmentation to the minority classes in order to increase the number of MRI images in the dataset.All experiments were conducted using Alzheimer’s MRI dataset consisting of brain MRI scanned images.The performance of the proposed model determines detection of the four stages of AD.Experimental results show high performance of the proposed model in that the model achieved a 99.38%accuracy rate,which is the highest so far.Moreover,the proposed model performance in terms of accuracy,precision,sensitivity,specificity,and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

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.

MRI and PET images fusion based on human retina model

The diagnostic potential of brain positron emission tomography （PET） imaging is limited by low spatial resolution. For solving this problem we propose a technique for the fusion of PET and MRI images. This fusion is a trade-off between the spectral information extracted from PET images and the spatial information extracted from high spatial resolution MRI. The proposed method can control this trade-off. To achieve this goal, it is necessary to build a multiscale fusion model, based on the retinal cell photoreceptors model. This paper introduces general prospects of this model, and its application in multispectral medical image fusion. Results showed that the proposed method preserves more spectral features with less spatial distortion. Comparing with hue-intensity-saturation （HIS）, discrete wavelet transform （DWT）, wavelet-based sharpening and wavelet-a trous transform methods, the best spectral and spatial quality is only achieved simultaneously with the proposed feature-based data fusion method. This method does not require resampling images, which is an advantage over the other methods, and can perform in any aspect ratio between the pixels of MRI and PET images.

CT Reconstruction with Priori MRI Images Through Multi-Group Datasets Expansion

Computed tomography(CT) reconstruction with a well-registered priori magnetic resonance imaging(MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.

Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction

Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.

The parallel-plate resonator:An RF probe for MR and MRI studies over a wide frequency range

We explore the use of the parallel-plate resonator for the study of thin cuboid samples over a wide range of magnetic resonance frequencies.The parallel-plate resonator functions at frequencies from tens to hundreds of MHz.Seven parallel-plate resonators are presented and discussed in a frequency range from 8 to 500 MHz.Magnetic resonance experiments were performed on both horizontal and vertical bore magnet systems with lithium and hydrogen nuclei.Parallel-plate radiofrequency(RF)probes are easy to build and easy to optimize.Experiments and simulations showed good sensitivity of the parallel-plate RF probe geometry with a small decrease in sensitivity at higher frequencies.

Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.

Hypersensitivity Reaction Caused by Intravenous Gadolinium-based MRI Contrast Agents

Objective To present a rare case of skin allergic reaction to gadobutrol,a magnetic resonance imaging(MRI)contrast agent,in a 37-year-old man.Methods The adverse reactions of gadobutrol were analyzed combined with the instructions and related literatures.Results and Conclusion The presence of this patient is consistent with the adverse reactions in the instructions of gadobutrol.The incidence of ADR in gadobutrol is considered to be low,although sometimes patients report a hypersensitivity reaction when undergoing MRI.There are only a few cases of immediate adverse reactions to gadobutrol.However,we should improve the ability of medical staff to use drugs safely and take preventive measures.

Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.

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%.

Clinical study on improving the diagnostic accuracy of adult elbow joint cartilage injury by multisequence magnetic resonance imaging

BACKGROUND Due to frequent and high-risk sports activities,the elbow joint is susceptible to injury,especially to cartilage tissue,which can cause pain,limited movement and even loss of joint function.AIM To evaluate magnetic resonance imaging(MRI)multisequence imaging for improving the diagnostic accuracy of adult elbow cartilage injury.METHODS A total of 60 patients diagnosed with elbow cartilage injury in our hospital from January 2020 to December 2021 were enrolled in this retrospective study.We analyzed the accuracy of conventional MRI sequences(T1-weighted imaging,T2-weighted imaging,proton density weighted imaging,and T2 star weighted image)and Three-Dimensional Coronary Imaging by Spiral Scanning(3D-CISS)in the diagnosis of elbow cartilage injury.Arthroscopy was used as the gold standard to evaluate the diagnostic effect of single and combination sequences in different injury degrees and the consistency with arthroscopy.RESULTS The diagnostic accuracy of 3D-CISS sequence was 89.34%±4.98%,the sensitivity was 90%,and the specificity was 88.33%,which showed the best performance among all sequences(P<0.05).The combined application of the whole sequence had the highest accuracy in all sequence combinations,the accuracy of mild injury was 91.30%,the accuracy of moderate injury was 96.15%,and the accuracy of severe injury was 93.33%(P<0.05).Compared with arthroscopy,the combination of all MRI sequences had the highest consistency of 91.67%,and the kappa value reached 0.890(P<0.001).CONCLUSION Combination of 3D-CISS and each sequence had significant advantages in improving MRI diagnostic accuracy of elbow cartilage injuries in adults.Multisequence MRI is recommended to ensure the best diagnosis and treatment.

Magnetic resonance imaging techniques for lithium-ion batteries:Principles and applications

Operando monitoring of internal and local electrochemical processes within lithium-ion batteries(LIBs)is crucial,necessitating a range of non-invasive,real-time imaging characterization techniques including nuclear magnetic resonance(NMR)techniques.This review provides a comprehensive overview of the recent applications and advancements of non-invasive magnetic resonance imaging(MRI)techniques in LIBs.It initially introduces the principles and hardware of MRI,followed by a detailed summary and comparison of MRI techniques used for characterizing liquid/solid electrolytes,electrodes and commercial batteries.This encompasses the determination of electrolytes'transport properties,acquisition of ion distribution profile,and diagnosis of battery defects.By focusing on experimental parameters and optimization strategies,our goal is to explore MRI methods suitable to a variety of research subjects,aiming to enhance imaging quality across diverse scenarios and offer critical physical/chemical insights into the ongoing operation processes of LIBs.

NON-INVASIVE IN VIVO PHENOTYPING OF SEIPIN MOUSE BY 7T MRI

Lipodystrophies are clinically heterogeneous acquired or inherited disorders characterized by selective loss of the adipose tissue. Non-invasive in vivo phenotyping of adipose tissue deposits in small animal models of the disease is studied using 7T magnetic resonance imaging （MRI）. Pseudo image and multi-weight MRI methods show that the fat of seipin mice is virtually absent compared with WT mice. The three-dimensional （3-D） small animal visualization system for 7T MRI developed in this project facilitates to obtain the interested feature with stroke-based classification method. Student＇s t-test statistic result confirms that total fat and subcutaneous fat are less in seipin mice than those in WT mice. However, the visceral fat difference is not found in the experiment. Based on 7T MIRI, the study gives more reliable information on location and lipid contents of the tissue about seipin mice, thus it is important to explore the pathophysiological characteristics of the disease.

RIGHT INFERIOR FRONTAL GYRUS PROCESSING OF VISUAL EROTIC INFORMATION BASED ON fMRI

Visually-induced erotic arousal evoked by pornographic visual stimuli, such as films or photographs, is a common occurrence in human behavior. The brain activation associated with visual erotic stimuli in heterosexual right handed females is studied. Functional magnetic resonance imaging is used to investigate 15 female partici- panterotic arousal induced by visual stimuli in film and picture forms, respectively, performing three or more times during their menstrual cycle on a 3.0T magnetic resonance imaging scanner. There is activation of a set of bilateral brain areas, including the inferior lateral occipital cortex, the anterior supramarginal gyrus, the parietal operculum cortex, the superior parietal lobules, the right inferior frontal gyrus, the cerebellum, the hypothalamus, the thalamus, the hippocampus, and the mid-brain. From different regions, the brain activation is observed and the inferior frontal gyrus has found to be task-independent. Furthermore, the right inferior frontal gyrus has more activation than the left inferior frontal gyrus. The result shows that the right inferior frontal gyrus plays an important role in pornographic information processing rather than being activated stimuli property specific. It is presented for the first time that the functional laterization of the inferior frontal gyrus is bi-directional rather than single （left） directional.

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.

Value of ^(18)F-FDG PET/CT and MRI in diagnosing primary endometrial small cell carcinoma

Primary small cell carcinoma（SCC） is a group of aggressive neoplasms that mainly arise from the lung and digestive tract. Endometrial small cell carcinoma（ESCC） is extremely rare. To our knowledge, less than 90 cases have been reported, and most of these reports were dedicated to describing the clinicopathologic or immunochemical features of ESCC. Herein, we present a new case of ESCC involving a 51-year-old woman and mainly focus on the magnetic resonance imaging（MRI） and positron emission tomography/computed tomography（PET/CT） findings. MRI showed that the uterus was significantly enlarged（11.6 cm × 11.1 cm × 14.4 cm）, and a giant irregular mass（7.5 cm × 8.4 cm × 8.5 cm） was observed in the uterine cavity. The lesion demonstrated an extremely low apparent diffusion coefficient（ADC） value [（0.553±0.088）×10^–3 mm^2/s] and a high FDG uptake value（22.7）. Multiple metastatic lymph nodes（LNs） were identified at different positions, with diameters ranging from 0.3 to 2.8 cm and a maximum standardized uptake value（SUV max） ranging from 6.9 to 19.3.

3D printing of patient-specific implants for osteochondral defects: workflow for an MRI-guided zonal design

Magnetic resonance imaging(MRI)is a common clinical practice to visualize defects and to distinguish different tissue types and pathologies in the human body.So far,MRI data have not been used to model and generate a patient-specific design of multilayered tissue substitutes in the case of interfacial defects.For orthopedic cases that require highly individual surgical treatment,implant fabrication by additive manufacturing holds great potential.Extrusion-based techniques like 3D plot-ting allow the spatially defined application of several materials,as well as implementation of bioprinting strategies.With the example of a typical multi-zonal osteochondral defect in an osteochondritis dissecans(OCD)patient,this study aimed to close the technological gap between MRI analysis and the additive manufacturing process of an implant based on dif-ferent biomaterial inks.A workflow was developed which covers the processing steps of MRI-based defect identification,segmentation,modeling,implant design adjustment,and implant generation.A model implant was fabricated based on two biomaterial inks with clinically relevant properties that would allow for bioprinting,the direct embedding of a patient’s own cells in the printing process.As demonstrated by the geometric compatibility of the designed and fabricated model implant in a stereolithography(SLA)model of lesioned femoral condyles,a novel versatile CAD/CAM workflow was successfully established that opens up new perspectives for the treatment of multi-zonal(osteochondral)defects.