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
This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specif...This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.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
目的:研究原发性中枢神经系统淋巴瘤(primary central nervous system lymphoma,PCNSL)MRI图像参数与分子病理的关联性。方法:回顾性分析2020年1月至2023年6月就诊于哈尔滨医科大学附属第一医院26例PCNSL患者资料,根据细胞来源、BCL-2...目的:研究原发性中枢神经系统淋巴瘤(primary central nervous system lymphoma,PCNSL)MRI图像参数与分子病理的关联性。方法:回顾性分析2020年1月至2023年6月就诊于哈尔滨医科大学附属第一医院26例PCNSL患者资料,根据细胞来源、BCL-2表达、Ki-67指数,MAP+布鲁顿酪氨酸激酶抑制剂(Bruton's tyrosine kinase inhibitor,BTKi)治疗反应性将患者归纳至非生发中心B细胞(non-germinal center B-cell,non-GCB)组和生发中心B细胞(germinal center B-cell,GCB)组、Ki-67≥75%组和Ki-67<75%组、BCL-2+组和BCL-2-组、对MAP+BTKi方案治疗有反应组和无反应组。提取患者基线期MRI图像一阶参数如平均值、标准差、方差、变异系数、偏度、峰度、熵,比较其在两组间的差异。结果:方差、峰度、偏度、变异系数等4个参数在组间差异无统计学意义;平均数、标准差、熵这3个参数在Ki-67表达、BCL-2表达组间的差异具有统计学意义;平均数、熵这两个参数在细胞来源、治疗是否有反应性两组间差异具有统计学意义(P<0.05);对于Ki-67指数,3个参数的曲线下面积(AUC)分别为0.731、0.831、0.913;对于BCL-2表达,平均数、标准差的曲线下面积(AUC)分别为0.889和0.938。多参数联合分析时其鉴识效果较利用单个纹理分析定量参数更高。结论:平均值、标准差、熵等3个MRI参数有助于预测PCNSL患者Ki-67、BCL-2的表达,对于治疗具有一定评估作用,有利于术前无创性评估肿瘤的恶性程度并为预后和治疗提供新的依据。展开更多
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 interes...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.展开更多
This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D...This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.展开更多
Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently...Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently proposed constrained estimation framework,called QTI+,improved QTI's resilience to noise and data sparsity,thus increasing the reliability of the method by enforcing relevant positivity constraints.In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model.We show that the additional conditions,which introduce an upper bound on the diffusivity values,further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.展开更多
Amide proton transfer (APT) magnetic resonance imaging (MRI) is an important molecularimaging technique at the protein level in tissue. Neurodegenerative diseases have a highlikelihood of causing abnormal protein accu...Amide proton transfer (APT) magnetic resonance imaging (MRI) is an important molecularimaging technique at the protein level in tissue. Neurodegenerative diseases have a highlikelihood of causing abnormal protein accumulation in the brain, which can be detectedby APT MRI. This article briefly introduces the principles and image processing technologyof APT MRI, and reviews the current state of research on Alzheimer's disease and Parkinson's disease using this technique. Early applications of this approach in these twoneurodegenerative diseases are encouraging, which also suggests continued technicaldevelopment and larger clinical trials to gauge the value of this technique.展开更多
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.
文摘This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.
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.
文摘目的:研究原发性中枢神经系统淋巴瘤(primary central nervous system lymphoma,PCNSL)MRI图像参数与分子病理的关联性。方法:回顾性分析2020年1月至2023年6月就诊于哈尔滨医科大学附属第一医院26例PCNSL患者资料,根据细胞来源、BCL-2表达、Ki-67指数,MAP+布鲁顿酪氨酸激酶抑制剂(Bruton's tyrosine kinase inhibitor,BTKi)治疗反应性将患者归纳至非生发中心B细胞(non-germinal center B-cell,non-GCB)组和生发中心B细胞(germinal center B-cell,GCB)组、Ki-67≥75%组和Ki-67<75%组、BCL-2+组和BCL-2-组、对MAP+BTKi方案治疗有反应组和无反应组。提取患者基线期MRI图像一阶参数如平均值、标准差、方差、变异系数、偏度、峰度、熵,比较其在两组间的差异。结果:方差、峰度、偏度、变异系数等4个参数在组间差异无统计学意义;平均数、标准差、熵这3个参数在Ki-67表达、BCL-2表达组间的差异具有统计学意义;平均数、熵这两个参数在细胞来源、治疗是否有反应性两组间差异具有统计学意义(P<0.05);对于Ki-67指数,3个参数的曲线下面积(AUC)分别为0.731、0.831、0.913;对于BCL-2表达,平均数、标准差的曲线下面积(AUC)分别为0.889和0.938。多参数联合分析时其鉴识效果较利用单个纹理分析定量参数更高。结论:平均值、标准差、熵等3个MRI参数有助于预测PCNSL患者Ki-67、BCL-2的表达,对于治疗具有一定评估作用,有利于术前无创性评估肿瘤的恶性程度并为预后和治疗提供新的依据。
基金supported by the Ministry of Higher Education(MOHE)through the Fundamental Research Grant Scheme(FRGS)(FRGS/1/2020/TK0/UTHM/02/16)the Universiti Tun Hussein Onn Malaysia(UTHM)through an FRGS Research Grant(Vot K304).
文摘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.
文摘This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.
基金funded by Sweden's Innovation Agency(VINNOVA)ASSIST,Analytic Imaging Diagnostic Arena(AIDA),Swedish Foundation for Strategic Research(RMX18-0056)Linkoping University Center for Industrial Information Technology(CENIIT),LiU Cancer Barncancerfonden,and a research grant(00028384)from VILLUM FONDEN。
文摘Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently proposed constrained estimation framework,called QTI+,improved QTI's resilience to noise and data sparsity,thus increasing the reliability of the method by enforcing relevant positivity constraints.In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model.We show that the additional conditions,which introduce an upper bound on the diffusivity values,further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.
文摘Amide proton transfer (APT) magnetic resonance imaging (MRI) is an important molecularimaging technique at the protein level in tissue. Neurodegenerative diseases have a highlikelihood of causing abnormal protein accumulation in the brain, which can be detectedby APT MRI. This article briefly introduces the principles and image processing technologyof APT MRI, and reviews the current state of research on Alzheimer's disease and Parkinson's disease using this technique. Early applications of this approach in these twoneurodegenerative diseases are encouraging, which also suggests continued technicaldevelopment and larger clinical trials to gauge the value of this technique.
