Objective:To investigate the CT and MRI features of salivary ductal carcinoma(SDC).Method:The imaging,clinical and pathological data of 32 patients with SDC confirmed by histomathology and operation were retrospective...Objective:To investigate the CT and MRI features of salivary ductal carcinoma(SDC).Method:The imaging,clinical and pathological data of 32 patients with SDC confirmed by histomathology and operation were retrospectively analyzed.The location,size,shape,boundary,relationship with surrounding tissues,density,signal,enhancement mode,calcification,cystic degeneration and metastasis were observed.Result:Of the 32 patients with SDC,31 cases were isolated,17 were located in the parotid gland,8 in the submandibular gland,2 in the sinuses,2 in the orbit,1 in the part of the eye,and 1 in the sublingual gland.One case had multiple lesions located in the parotid gland.The maximum diameter of tumor was 1.5-7.2cm,and the median diameter was 3.0cm.The tumor showed diffuse growth in 11 cases and focal growth in 21 cases.The boundary was clear in 24 cases and unclear in 8 cases.The lesion may invade parapharyngeal space,soft palate,facial nerve,auditory nerve,skin,surrounding muscle and bone;There were 15 cases(47%)with lymph node metastasis and 1 case with lung metastasis.MRI showed that the solid part of the tumor was dominated by isointensity and low intensity on T1 WI,mixed high intensity on T2 WI,low intensity on T1 WI and high intensity on T2 WI.CT showed uneven tumor density,with equal or low density in 15 cases,high density in 4 cases,and calcification in 7 cases.Contrast-enhanced scan showed moderate to significant enhancement of the solid part.Conclusion:SDC is mostly single,prone to cystic necrosis and calcification,with strong aggressiveness and frequent lymph node metastasis.Understanding the imaging findings of SDC is helpful to improve the accuracy of preoperative diagnosis.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conv...The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.展开更多
Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity ...Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity of pathology,potentially hindering clinical trials in the development of medical treatment.Brain-based subtyping studies utilize magnetic resonance imaging(MRI)and data-driven methods to discover the subtypes of diseases,providing a new perspective on disease heterogeneity.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a co...Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.展开更多
Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration...Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical use.In our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil performances.In this work,we propose to design this transverse gradient coil system with transformed magnetic gradient fields.By shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic performances.With more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency bands.On the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR sequences.It's demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.展开更多
Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while ob...Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results.The authors have(1)carefully compared performances of most-developed segmentation and object detection methods in localising prostate imaging reporting and data system(PIRADS)-labelled prostate lesions on MRI scans;(2)proposed an additional customised set of lesion-level localisation sensitivity and precision;(3)proposed efficient ways to ensemble the segmentation and object detection methods for improved performances.The ground-truth(GT)perspective lesion-level sensitivity and prediction-perspective lesion-level precision are reported,to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions.The two networks are trained independently on 549 clinical patients data with PIRADS-V2 as GT labels,and tested on 161 internal and 100 external MRI scans.At the lesion level,nnDetection outperforms nnUNet for detecting both PIRADS≥3 and PIRADS≥4 lesions in majority cases.For example,at the average false positive prediction per patient being 3,nnDetection achieves a greater Intersection-of-Union(IoU)-based sensitivity than nnUNet for detecting PIRADS≥3 lesions,being 80.78%�1.50%versus 60.40%�1.64%(p<0.01).At the voxel level,nnUnet is in general superior or comparable to nnDetection.The proposed ensemble methods achieve improved or comparable lesion-level accuracy,in all tested clinical scenarios.For example,at 3 false positives,the lesion-wise ensemble method achieves 82.24%�1.43%sensitivity versus 80.78%�1.50%(nnDetection)and 60.40%�1.64%(nnUNet)for detecting PIRADS≥3 lesions.Consistent conclusions are also drawn from results on the external data set.展开更多
In neuropathological diseases such as Alzheimer's Disease(AD),neuroimaging and Magnetic Resonance Imaging(MRI)play crucial roles in the realm of Artificial Intelligence of Medical Things(AIoMT)by leveraging edge i...In neuropathological diseases such as Alzheimer's Disease(AD),neuroimaging and Magnetic Resonance Imaging(MRI)play crucial roles in the realm of Artificial Intelligence of Medical Things(AIoMT)by leveraging edge intelligence resources.However,accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences.To address this challenge,we propose a novel approach aimed at improving the early detection of AD through MRI imaging.This method integrates a Convolutional Neural Network(CNN)with a Cascade Attention Model(CAM-CNN).The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity.In this architecture,the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture.Additionally,two new cost functions,Satisfied Rank Loss(SRL)and Cross-Network Similarity Loss(CNSL),are introduced to enhance collaboration and overall network performance.Finally,a unique entropy addition method is employed in the attention module for network integration,converting intermediate outcomes into the final prediction.These components are designed to work collaboratively and can be sequentially trained for optimal performance,thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR images.Validation using the Kaggle dataset demonstrates the model's accuracy of 99.07%in multiclass classification,ensuring precise classification and early detection of all AD subtypes.Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach,with deviations from the standard criteria of less than 1%.Applied in Alzheimer's patient care,this capability holds promise for enhancing value-based therapy and clinical decision-making.It aids in differentiating Alzheimer's patients from healthy individuals,thereby improving patient care and enabling more targeted therapies.展开更多
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
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 ...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.展开更多
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 illn...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.展开更多
In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic r...In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic resonance imaging(MRI)reconstruction is proposed,which reconstructs the image from highly under-sampled k-space data.In the algorithm,the nonconvex surrogate function replacing the conventional nuclear norm is utilized to enhance the low-rank property inherent in the reconstructed image.An alternative direction multiplier method(ADMM) is applied to solving the resulting non-convex model.Extensive experimental results have demonstrated that the proposed method can consistently recover MRIs efficiently,and outperforms the current state-of-the-art approaches in terms of higher peak signal-to-noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.展开更多
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 ...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.展开更多
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 ...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.展开更多
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 ma...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.展开更多
Energy metabolism is fundamental for life.It encompasses the utilization of carbohydrates,lipids,and proteins for internal processes,while aberrant energy metabolism is implicated in many diseases.In the present study...Energy metabolism is fundamental for life.It encompasses the utilization of carbohydrates,lipids,and proteins for internal processes,while aberrant energy metabolism is implicated in many diseases.In the present study,using three-dimensional(3D)printing from polycarbonate via fused deposition modeling,we propose a multi-nuclear radiofrequency(RF)coil design with integrated 1H birdcage and interchangeable X-nuclei(^(2)H,^(13)C,^(23)Na,and^(31)P)single-loop coils for magnetic resonance imaging(MRI)/magnetic resonance spectroscopy(MRS).The single-loop coil for each nucleus attaches to an arc bracket that slides unrestrictedly along the birdcage coil inner surface,enabling convenient switching among various nuclei and animal handling.Compared to a commercial 1H birdcage coil,the proposed 1H birdcage coil exhibited superior signal-excitation homogeneity and imaging signal-to-noise ratio(SNR).For X-nuclei study,prominent peaks in spectroscopy for phantom solutions showed excellent SNR,and the static and dynamic peaks of in vivo spectroscopy validated the efficacy of the coil design in structural imaging and energy metabolism detection simultaneously.展开更多
Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy fro...Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-展开更多
Cerebral toxoplasmosis is a common opportunistic infectious disease in immunocompromised patients that usually involves the central nervous system.The clinical features and neuroimaging findings of cerebral toxoplasmo...Cerebral toxoplasmosis is a common opportunistic infectious disease in immunocompromised patients that usually involves the central nervous system.The clinical features and neuroimaging findings of cerebral toxoplasmosis are often similar to brain abscess and tuberculoma.We report a case of hepatitis C with cerebral toxoplasmosis,with the aim of enhancing understanding of the imaging manifestations of cerebral toxoplasmosis and thereby improving the differential diagnosis of brain space‐occupying lesions.展开更多
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...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.展开更多
文摘Objective:To investigate the CT and MRI features of salivary ductal carcinoma(SDC).Method:The imaging,clinical and pathological data of 32 patients with SDC confirmed by histomathology and operation were retrospectively analyzed.The location,size,shape,boundary,relationship with surrounding tissues,density,signal,enhancement mode,calcification,cystic degeneration and metastasis were observed.Result:Of the 32 patients with SDC,31 cases were isolated,17 were located in the parotid gland,8 in the submandibular gland,2 in the sinuses,2 in the orbit,1 in the part of the eye,and 1 in the sublingual gland.One case had multiple lesions located in the parotid gland.The maximum diameter of tumor was 1.5-7.2cm,and the median diameter was 3.0cm.The tumor showed diffuse growth in 11 cases and focal growth in 21 cases.The boundary was clear in 24 cases and unclear in 8 cases.The lesion may invade parapharyngeal space,soft palate,facial nerve,auditory nerve,skin,surrounding muscle and bone;There were 15 cases(47%)with lymph node metastasis and 1 case with lung metastasis.MRI showed that the solid part of the tumor was dominated by isointensity and low intensity on T1 WI,mixed high intensity on T2 WI,low intensity on T1 WI and high intensity on T2 WI.CT showed uneven tumor density,with equal or low density in 15 cases,high density in 4 cases,and calcification in 7 cases.Contrast-enhanced scan showed moderate to significant enhancement of the solid part.Conclusion:SDC is mostly single,prone to cystic necrosis and calcification,with strong aggressiveness and frequent lymph node metastasis.Understanding the imaging findings of SDC is helpful to improve the accuracy of preoperative diagnosis.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
文摘The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.
基金supported by the National Natural Science Foundation of China(82102018,62333002,T2425027,and 82327809)Data collection and sharing for this project were supported by the National Natural Science Foundation of China(61633018,81571062,81471120,and 81901101)+30 种基金Data collection and sharing for this project were funded by the ADNI(National Institutes of Health Grant U01 AG024904)the Department of Defense ADNI(award number W81XWH-12-2-0012).The ADNI is funded by the National Institute on Aging,the National Institute of Biomedical Imaging and Bioengineering,and through generous contributions from the following:AbbVie,Alzheimer’s AssociationAlzheimer’s Drug Discovery FoundationAraclon BiotechBioClinica,Inc.BiogenBristol-Myers Squibb Co.CereSpir,Inc.CogstateEisai Inc.Elan Pharmaceuticals,Inc.Eli Lilly and Co.EuroImmunF.Hoffmann-La Roche Ltd and its affiliated company Genentech,Inc.FujirebioG.E.HealthcareIXICO Ltd.Janssen Alzheimer Immunotherapy Research&Development,LLC.Johnson&Johnson Pharmaceutical Research&Development LLC.LumosityLundbeckMerck&Co.,Inc.Meso Scale Diagnostics,LLC.NeuroRx ResearchNeurotrack TechnologiesNovartis Pharmaceuticals Corp.Pfizer Inc.Piramal ImagingServierTakeda Pharmaceutical Co.and Transition Therapeutics.The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada.Private sector contributions are facilitated by the Foundation for the National Institutes of Health(www.fnih.org).The grantee organization was the Northern California Institute for Research and Education,and the study was coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California.ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
文摘Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity of pathology,potentially hindering clinical trials in the development of medical treatment.Brain-based subtyping studies utilize magnetic resonance imaging(MRI)and data-driven methods to discover the subtypes of diseases,providing a new perspective on disease heterogeneity.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
基金supported by Gansu Natural Science Foundation Programme(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。
文摘Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.
基金supported by the Instrument Developing Project of Magnetic Resonance Union of Chinese Academy of Sciences,Grant No.2022GZL002.
文摘Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical use.In our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil performances.In this work,we propose to design this transverse gradient coil system with transformed magnetic gradient fields.By shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic performances.With more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency bands.On the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR sequences.It's demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.
基金National Natural Science Foundation of China,Grant/Award Number:62303275International Alliance for Cancer Early Detection,Grant/Award Numbers:C28070/A30912,C73666/A31378Wellcome/EPSRC Centre for Interventional and Surgical Sciences,Grant/Award Number:203145Z/16/Z。
文摘Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results.The authors have(1)carefully compared performances of most-developed segmentation and object detection methods in localising prostate imaging reporting and data system(PIRADS)-labelled prostate lesions on MRI scans;(2)proposed an additional customised set of lesion-level localisation sensitivity and precision;(3)proposed efficient ways to ensemble the segmentation and object detection methods for improved performances.The ground-truth(GT)perspective lesion-level sensitivity and prediction-perspective lesion-level precision are reported,to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions.The two networks are trained independently on 549 clinical patients data with PIRADS-V2 as GT labels,and tested on 161 internal and 100 external MRI scans.At the lesion level,nnDetection outperforms nnUNet for detecting both PIRADS≥3 and PIRADS≥4 lesions in majority cases.For example,at the average false positive prediction per patient being 3,nnDetection achieves a greater Intersection-of-Union(IoU)-based sensitivity than nnUNet for detecting PIRADS≥3 lesions,being 80.78%�1.50%versus 60.40%�1.64%(p<0.01).At the voxel level,nnUnet is in general superior or comparable to nnDetection.The proposed ensemble methods achieve improved or comparable lesion-level accuracy,in all tested clinical scenarios.For example,at 3 false positives,the lesion-wise ensemble method achieves 82.24%�1.43%sensitivity versus 80.78%�1.50%(nnDetection)and 60.40%�1.64%(nnUNet)for detecting PIRADS≥3 lesions.Consistent conclusions are also drawn from results on the external data set.
基金funded by the National Elites Foundation(No.711.5095).
文摘In neuropathological diseases such as Alzheimer's Disease(AD),neuroimaging and Magnetic Resonance Imaging(MRI)play crucial roles in the realm of Artificial Intelligence of Medical Things(AIoMT)by leveraging edge intelligence resources.However,accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences.To address this challenge,we propose a novel approach aimed at improving the early detection of AD through MRI imaging.This method integrates a Convolutional Neural Network(CNN)with a Cascade Attention Model(CAM-CNN).The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity.In this architecture,the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture.Additionally,two new cost functions,Satisfied Rank Loss(SRL)and Cross-Network Similarity Loss(CNSL),are introduced to enhance collaboration and overall network performance.Finally,a unique entropy addition method is employed in the attention module for network integration,converting intermediate outcomes into the final prediction.These components are designed to work collaboratively and can be sequentially trained for optimal performance,thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR images.Validation using the Kaggle dataset demonstrates the model's accuracy of 99.07%in multiclass classification,ensuring precise classification and early detection of all AD subtypes.Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach,with deviations from the standard criteria of less than 1%.Applied in Alzheimer's patient care,this capability holds promise for enhancing value-based therapy and clinical decision-making.It aids in differentiating Alzheimer's patients from healthy individuals,thereby improving patient care and enabling more targeted therapies.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
文摘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.
基金support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘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.
基金National Natural Science Foundations of China(Nos.61362001,61365013,51165033)the Science and Technology Department of Jiangxi Province of China(Nos.20132BAB211030,20122BAB211015)+1 种基金the Jiangxi Advanced Projects for Postdoctoral Research Funds,China(o.2014KY02)the Innovation Special Fund Project of Nanchang University,China(o.cx2015136)
文摘In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic resonance imaging(MRI)reconstruction is proposed,which reconstructs the image from highly under-sampled k-space data.In the algorithm,the nonconvex surrogate function replacing the conventional nuclear norm is utilized to enhance the low-rank property inherent in the reconstructed image.An alternative direction multiplier method(ADMM) is applied to solving the resulting non-convex model.Extensive experimental results have demonstrated that the proposed method can consistently recover MRIs efficiently,and outperforms the current state-of-the-art approaches in terms of higher peak signal-to-noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.
文摘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.
文摘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.
基金supported by the National Key R&D Program of China,Grant No.2021YFB2401800。
文摘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.
基金This work was supported in part by the STI 2030-Major Projects(No.2021ZD0200401)the National Key Research and Development Program of China(No.2018YFA0701400)+3 种基金the National Natural Science Foundation of China(Nos.52277232,52293424,81701774,and 61771423)the Fundamental Research Funds for the Central Universities(Nos.226-2022-00136 and 226-2023-00125)the Zhejiang Provincial Natural Science Foundation of China(No.LR23E070001),the Key R&D Program of Jiangsu Province(No.BE2022049)the Key-Area R&D Program of Guangdong Province(No.2018B030333001),China.
文摘Energy metabolism is fundamental for life.It encompasses the utilization of carbohydrates,lipids,and proteins for internal processes,while aberrant energy metabolism is implicated in many diseases.In the present study,using three-dimensional(3D)printing from polycarbonate via fused deposition modeling,we propose a multi-nuclear radiofrequency(RF)coil design with integrated 1H birdcage and interchangeable X-nuclei(^(2)H,^(13)C,^(23)Na,and^(31)P)single-loop coils for magnetic resonance imaging(MRI)/magnetic resonance spectroscopy(MRS).The single-loop coil for each nucleus attaches to an arc bracket that slides unrestrictedly along the birdcage coil inner surface,enabling convenient switching among various nuclei and animal handling.Compared to a commercial 1H birdcage coil,the proposed 1H birdcage coil exhibited superior signal-excitation homogeneity and imaging signal-to-noise ratio(SNR).For X-nuclei study,prominent peaks in spectroscopy for phantom solutions showed excellent SNR,and the static and dynamic peaks of in vivo spectroscopy validated the efficacy of the coil design in structural imaging and energy metabolism detection simultaneously.
文摘Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-
基金the Guizhou Epilepsy Basic and Clinical Research Scientific and Technological Innovation Talent Team Project,Grant/Award Number:CXTD[2022]013the Collaborative Innovation Center of Chinese Ministry of Education,Grant/Award Number:2020‐39the Guizhou provincial“hundred”level innovative talents funds,Grant/Award Number:GCC‐2022‐038‐1。
文摘Cerebral toxoplasmosis is a common opportunistic infectious disease in immunocompromised patients that usually involves the central nervous system.The clinical features and neuroimaging findings of cerebral toxoplasmosis are often similar to brain abscess and tuberculoma.We report a case of hepatitis C with cerebral toxoplasmosis,with the aim of enhancing understanding of the imaging manifestations of cerebral toxoplasmosis and thereby improving the differential diagnosis of brain space‐occupying lesions.
基金Supported by the National Natural Science Foundation of China (30830039)the National High Technology Research and Development Program of China ("863"Program) (2007AA02Z211)~~
文摘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.
