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.

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.

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.

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.

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.

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.

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%.

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.

Application of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)combined with magnetic resonance spectroscopy(MRS)in prostate cancer diagnosis

Objective The aim of the study was to investigate the application of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)combined with magnetic resonance spectroscopy(MRS)in prostate cancer diagnosis.Methods In the outpatient department of our hospital(Sichuan Cancer Hospital,Chengdu,China),60 patients diagnosed with prostate disease were selected randomly and included in a prostate cancer group,60 patients with benign prostatic hyperplasia were included in a proliferation group,and 60 healthy subjects were included in a control group,from January 2013 to January 2017.Using Siemens Avanto 1.5 T high-field superconducting MRI for DCE-MRI and MRS scans,after the MRS scan was completed,we used the workstation spectroscopy tab spectral analysis,and eventually obtained the crest lines of the prostate metabolites choline(Cho),creatine(Cr),citrate(Cit),and the values of Cho/Cit,and(Cho+Cr)/Cit.Results Participants who had undergone 21-s,1-min,and 2-min dynamic contrast-enhanced MR revealed significant variations among the three groups.The spectral analysis of the three groups revealed a significant variation as well.DCE-MRI and MRS combined had a sensitivity of 89.67%,specificity of 95.78%,and accuracy of 94.34%.Conclusion DCE-MRI combined with MRS is of great value in the diagnosis of prostate cancer.

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.

Glomus Tumor of the Kidney: A Case Report with CT, MRI, and Histopathological Findings

We describe the computed tomographic (CT) and magnetic resonance imaging (MRI) features of a very rare renal neoplasm, a glomus tumor. Our patient was a 68-year-old woman with a history of high grade T1 stage bladder cancer, status post intravesical Bacillus Calmette-Guérin (BCG) therapy and left ureteral stent placement, who presented for routine follow-up imaging evaluation of the urothelial tract. Computed tomographic urography (CTU) incidentally demonstrated a 1.7 cm well-circumscribed, non-calcified, non-fat containing lesion in the left renal cortex with arterial phase continuous peripheral rim enhancement and central hypoattenuation relative to enhanced renal parenchyma. Subsequent MRI showed the lesion to be isointense in signal intensity relative to the renal parenchyma on T1-weighted imaging and hyperintense on T2-weighted imaging. No macroscopic fat or microscopic lipid was seen within the lesion, and there were no foci of susceptibility artifact on T1-weighted images. Diffusion-weighted and apparent diffusion coefficient images demonstrated no restricted diffusion. Contrast-enhanced images demonstrated continuous peripheral rim enhancement in the arterial phase and persistent rim enhancement with partial centripetal fill in of enhancement on venous phase images, similar to the pattern seen on CT. Partial left nephrectomy was performed for the suspected solid renal neoplasm. Histopathological assessment was diagnostic of a renal glomus tumor.

Comparison with Surgical Findings for the Accuracy of Routine MRI in Rotator Cuff Tears

Objective: To evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) for the detection of partial-thickness rotator cuff tears (PTT) and full-thickness rotator cuff tears(FTT) by comparing its findings with surgical findings as the gold standard and to improve the previous MRI accuracy in diagnosing rotator cuff tears (RCT) considering more variables. Methods: In 45 months, 804 patients underwent MRI shoulder joint. Among them, only 95 cases had undergone both MRI imaging and surgery accordingly. The patient records were evaluated retrospectively if MRI and surgery were performed within 40 days of MRI. MRI findings were categorized into PTT, FTT and no tears which were further divided into different types according to four main nominal data as variables viz. site, size, shape and muscle involvement in RCT and were correlated with surgical findings for statistical calculation by using Kappa coefficient and McNemar Bowker test. Results: 81 patients (86 RCTs) underwent surgery within 40 days. On the basis of site as variable, MRI correctly depicted 100% of full thickness tears(FTT), 85% of bursal partial thickness tears(PTT), 80.4% of articular partial thickness tears(PTT). The consistency in diagnosis of RCT between MRI and surgery was moderate (Kappa coefficient 0.645). Overall sensitivity, specificity and accuracy of MRI for diagnosing PTT was 87.3%, 53.3% and 81.3%;and that for FTT was 100%, 98.7% and 98.8% respectively. Likewise on the basis of size, shape and muscles involved, the consistency between MRI and surgery was poor for size and shape and moderate for muscles involved;and the difference in diagnosing RCT by MRI and surgery was significant for shape (P = 0.002) only, but not significant for size (P = 0.16) and for muscles involved (P = 0.206) respectively. The agreement between MRI and surgery in diagnosing calcific tendinitis and shoulder joint hematoma with Kappa coefficient is (0.577) and (0.556) respectively. Conclusion: MRI has better accuracy for detecting FTT and has high sensitivity and positive predictive value in diagnosing both PTT and FTT. Combining more others variables in addition to RCT, MRI offers a great value in diagnosing RCT.

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.

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.

Completed attention convolutional neural network for MRI image segmentation

Attention mechanism combined with convolutional neural network(CNN) achieves promising performance for magnetic resonance imaging(MRI) image segmentation,however these methods only learn attention weights from single scale,resulting in incomplete attention learning.A novel method named completed attention convolutional neural network(CACNN) is proposed for MRI image segmentation.Specifically,the channel-wise attention block(CWAB) and the pixel-wise attention block(PWAB) are designed to learn attention weights from the aspects of channel and pixel levels.As a result,completed attention weights are obtained,which is beneficial to discriminative feature learning.The method is verified on two widely used datasets(HVSMR and MRBrainS),and the experimental results demonstrate that the proposed method achieves better results than the state-of-theart methods.

Prevalence of Bicuspid Aortic Valve in Turner Syndrome Patients Receiving Cardiac MRI and CT: A Meta-Analysis

Turner syndrome(TS)is a rare disorder affecting 25–50 in 100000 female newborns.Bicuspid aortic valve(BAV)is assumed to be the most common congenital heart defect(CHD)in TS.In literature,reported BAV prevalence in TS ranges between 14%and 34%.The specific BAV prevalence in TS is still unknown.The aim of this study was to give a more precise estimation of BAV prevalence in TS by conducting a meta-analysis of TS-studies,which detected BAV by either cardiac magnetic resonance imaging(MRI)or cardiac computed tomography(CT).We searched PubMed,Cochrane Library,and Web of Science databases to collect observational studies including the prevalence of BAV identified by cardiac MRI or cardiac CT in TS patients up to June 4th,2021.After screening for inclusion,data extraction,and quality assessment by two independent reviewers,the meta-analysis was performed with R 4.1.1 software.Results are shown as proportion and weighted mean difference with 95%confidence intervals(95%CI).In total,11 studies involving 1177 patients were included.Pooled data showed that the prevalence of BAV in TS patients was 23.7%(95%CI:21.3%to 26.1%).No high heterogeneity was found between the included studies.The current meta-analysis reveals that BAVcan be detected in 23.7%of TS patients receiving cardiac MRI or cardiac CT.Therefore,BAV can be considered as the most common CHD in TS.Compared to TTE,cardiac MRI and cardiac CT might represent superior imaging modalities in BAV assessment of adult TS patients.