Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(...Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches.展开更多
Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality rates.Detecting and treating ABE promptly is important to prevent further complications and long-ter...Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality rates.Detecting and treating ABE promptly is important to prevent further complications and long-term issues.Recent studies have explored ABE diagnosis.However,they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging(MRI).To tackle this problem,the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI scans.The scans include T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),and apparent diffusion coefficient maps to get indepth information.Initially,the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and Z-score normalisation for data standardisation.An Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection accuracy.Furthermore,a multi-transformer approach was used for feature fusion and identify feature correlations effectively.Finally,accurate ABE diagnosis is achieved through the utilisation of a SoftMax layer.The performance of the proposed Tri-M2MT model is evaluated across various metrics,including accuracy,specificity,sensitivity,F1-score,and ROC curve analysis,and the proposed methodology provides better performance compared to existing methodologies.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring ef...The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.展开更多
Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials prov...Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials provide a promising prospect for imaging-guided precision therapy.Considering that tumor-derived alkaline phosphatase(ALP)is over-expressed in metastatic PCa,it makes a great chance to develop a theranostics system with ALP responsive in the TME.Herein,an ALP-responsive aggregationinduced emission luminogens(AIEgens)nanoprobe AMNF self-assembly was designed for enhancing the diagnosis and treatment of metastatic PCa.The nanoprobe exhibited self-aggregation in the presence of ALP resulted in aggregation-induced fluorescence,and enhanced accumulation and prolonged retention period at the tumor site.In terms of detection,the fluorescence(FL)/computed tomography(CT)/magnetic resonance(MR)multi-mode imaging effect of nanoprobe was significantly improved post-aggregation,enabling precise diagnosis through the amalgamation of multiple imaging modes.Enhanced CT/MR imaging can achieve assist preoperative tumor diagnosis,and enhanced FL imaging technology can achieve“intraoperative visual navigation”,showing its potential application value in clinical tumor detection and surgical guidance.In terms of treatment,AMNF showed strong absorption in the near infrared region after aggregation,which improved the photothermal treatment effect.Overall,our work developed an effective aggregation-enhanced theranostic strategy for ALP-related cancers.展开更多
Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly diffic...Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.展开更多
We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two d...We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two detectors that utilize LYSO-SiPM and were available in our laboratory.One detector consists of a 50×50 array of LYSO crystals,each measuring 0.9mm×0.9mm×10mm with 1 mm pitches,whereas the other detector comprises a 25×25 array of LYSO crystals,each measuring 1.9mm×1.9mm×10mm with 2 mm pitches.These detectors are mounted on a rotational stage,which enables them to function as either a Compton camera or a PET detector pair.The 64-channel signals from the SiPMs of each detector are processed through a capacitive multiplexing circuit to yield four position-weighted outputs.Distinct energy windows were used to discriminate Compton events from PET events.Energy resolution and energy-channel relationships were calibrated via multiple sources.The measured average energy resolutions(full widths at half maximum,FWHMs)for the detectors at 511 keV were 17.5%and 15.2%,respectively.The initial experimental results indicate an angular resolution(FWHM)of 8.6◦for the system in Compton imaging mode.A V-shaped tube injected with 18 F solution was clearly reconstructed,which further verified the imaging capabilities of the system in Compton imaging mode.The results of simulation and experimental imaging studies show that the system can detect tumors as small as 1 mm in diameter when working in PET imaging mode.Mouse bone PET imaging was successfully conducted,with the results matching well with the corresponding CT images.This technology holds great potential for advancing the development of physiological function modalities.展开更多
Aqueous zinc metal batteries(AZMBs)face significant challenges in achieving reversibility and cycling stability,primarily due to hydrogen evolution reactions(HER)and zinc dendrite growth.In this study,by employing car...Aqueous zinc metal batteries(AZMBs)face significant challenges in achieving reversibility and cycling stability,primarily due to hydrogen evolution reactions(HER)and zinc dendrite growth.In this study,by employing carefully designed cells that approximate the structural characteristics of practical batteries,we revisit this widely held view through in-operando X-ray radiography to examine zinc dendrite formation and HER under nearpractical operating conditions.While conventional understanding emphasizes the severity of these processes,our findings suggest that zinc dendrites and HER are noticeably less pronounced in dense,real-operation configurations compared to modified cells,possibly due to a more uniform electric field and the suppression of triple-phase boundaries.This study indicates that other components,such as degradation at the cathode current collector interface and configuration mismatches within the full cell,may also represent important barriers to the practical application of AZMBs,particularly during the early stages of electrodeposition.展开更多
Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience...Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.展开更多
Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment（a MCI） to Alzheimer＇s disease（AD）. As a part of the medial temporal lobe memory sy...Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment（a MCI） to Alzheimer＇s disease（AD）. As a part of the medial temporal lobe memory system,the hippocampus is one of the brain regions affected earliest by AD neuropathology,and shows progressive degeneration as a MCI progresses to AD. Currently,no validated biomarkers can precisely predict the conversion from a MCI to AD. Therefore,there is a great need of sensitive tools for the early detection of AD progression. In this review,we summarize the specifi c structural and functional changes in the hippocampus from recent a MCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile,this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of a MCI to AD.展开更多
Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in hu...Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
Cardiac amyloidosis is a heterogeneous and challenging diagnostic disease with poor prognosis that is now being altered by introduction of new therapies.Echocardiography remains the first-line imaging tool, and when d...Cardiac amyloidosis is a heterogeneous and challenging diagnostic disease with poor prognosis that is now being altered by introduction of new therapies.Echocardiography remains the first-line imaging tool, and when disease is suspected on echocardiography, cardiac magnetic resonance imaging and nuclear imaging play critical roles in the non-invasive diagnosis and evaluation of cardiac amyloidosis. Advances in multi-modality cardiac imaging allowing earlier diagnosis and initiation of novel therapies have significantly improved the outcomes in these patients. Cardiac imaging also plays important roles in the risk stratification of patients presenting with cardiac amyloidosis. In the current review, we provide a clinical and imaging focused update, and importantly outline the imaging protocols, diagnostic and prognostic utility of multimodality cardiac imaging in the assessment of cardiac amyloidosis.展开更多
Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer’s disease (AD). Their association has not been investigated with ...Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer’s disease (AD). Their association has not been investigated with the use of biomarkers. This study examines whether, among cognitively normal (NL) individuals, those who are less physically active and show lower MeDi adherence have brain biomarker abnormalities consistent with AD. Methods: Forty-five NL individuals (age 54 ± 11, 71% women) with complete leisure time physical activity (LTA), dietary information, and cross-sectional 3D T1-weigthed MRI, 11C-Pittsburgh Compound B (PiB) and 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) scans were examined. Voxel-wise multivariate partial least square (PLS) regression was used to examine the effects of LTA, MeDi and their interaction on brain biomarkers. Age, gender, ethnicity, education, caloric intake, BMI, family history of AD, Apolipoprotein E (APOE) genotype, presence of hypertension and insulin resistance were examined as confounds. Subjects were dichotomized into more and less physically active (LTA+ vs. LTA-;n = 21 vs. 24), and into higher vs. lower MeDi adherence groups (n = 18 vs. 27) using published scoring methods. Spatial patterns of brain biomarkers that represented the optimal association between the images and the groups were generated for all modalities using voxel-wise multivariate Partial Least Squares (PLS) regression. Results: Groups were comparable for clinical and neuropsychological measures. Independent effects of LTA and MeDi factors were observed in AD-vulnerable brain regions for all modalities (p β load and lower glucose metabolism) were observed in LTA- compared to LTA+ subjects, and in MeDi- as compared to MeDi+ subjects. A gradient effect was observed for all modalities so that LTA+/MeDi+ subjects had the highest and LTA+/MeDi+ subjects had the lowest AD-burden (p < 0.001), although the LTA × MeDi interaction was significant only for FDG measures (p < 0.03). Adjusting for covariates did not attenuate these relationships. Conclusion: Lower physical activity and MeDi adherence were associated with increased brain AD-burden among NL individuals, in-dicating that lifestyle factors may modulate AD risk. Studies with larger samples and longitudinal evaluations are needed to determine the predictive power of the observed associations.展开更多
The goal of parathyroid imaging in hyperparathyroidism is not diagnosis,rather it is the localization of the cause of hyperparathyroidism for planning the best therapeutic approach.Hence,the role of imaging to accurat...The goal of parathyroid imaging in hyperparathyroidism is not diagnosis,rather it is the localization of the cause of hyperparathyroidism for planning the best therapeutic approach.Hence,the role of imaging to accurately and precisely localize the abnormal parathyroid tissue is more important than ever to facilitate minimally invasive parathyroidectomy over bilateral neck exploration.The common causes include solitary parathyroid adenoma,multiple parathyroid adenomas,parathyroid hyperplasia and parathyroid carcinoma.It is highly imperative for the radiologist to be cautious of the mimics of parathyroid lesions like thyroid nodules and lymph nodes and be able to differentiate them on imaging.The various imaging modalities available include high resolution ultrasound of the neck,nuclear imaging studies,four-dimensional computed tomography(4D CT)and magnetic resonance imaging.Contrast enhanced ultrasound is a novel technique which has been recently added to the armam-entarium to differentiate between parathyroid adenomas and its mimics.Through this review article we wish to review the imaging features of parathyroid lesions on various imaging modalities and present an algorithm to guide their radiological differentiation from mimics.展开更多
In situ monitoring of bone regeneration enables timely diagnosis and intervention by acquiring vital biological parameters.However,an existing gap exists in the availability of effective methodologies for continuous a...In situ monitoring of bone regeneration enables timely diagnosis and intervention by acquiring vital biological parameters.However,an existing gap exists in the availability of effective methodologies for continuous and dynamic monitoring of the bone tissue regeneration process,encompassing the concurrent visualization of bone formation and implant degradation.Here,we present an integrated scaffold designed to facilitate real-time monitoring of both bone formation and implant degradation during the repair of bone defects.Laponite(Lap),CyP-loaded mesoporous silica(CyP@MSNs)and ultrasmall superparamagnetic iron oxide nanoparticles(USPIO@SiO2)were incorporated into a bioink containing bone marrow mesenchymal stem cells(BMSCs)to fabricate functional scaffolds denoted as C@M/GLU using 3D bioprinting technology.In both in vivo and in vitro experiments,the composite scaffold has demonstrated a significant enhancement of bone regeneration through the controlled release of silicon(Si)and magnesium(Mg)ions.Employing near-infrared fluorescence(NIR-FL)imaging,the composite scaffold facilitates the monitoring of alkaline phosphate(ALP)expression,providing an accurate reflection of the scaffold’s initial osteogenic activity.Meanwhile,the degradation of scaffolds was monitored by tracking the changes in the magnetic resonance(MR)signals at various time points.These findings indicate that the designed scaffold holds potential as an in situ bone implant for combined visualization of osteogenesis and implant degradation throughout the bone repair process.展开更多
Transthyretin amyloid(TTR)cardiomyopathy is a disease of insidious onset,which is often accompanied by debilitating neurological and/or cardiac complications.The true prevalence is not fully known due to its elusive p...Transthyretin amyloid(TTR)cardiomyopathy is a disease of insidious onset,which is often accompanied by debilitating neurological and/or cardiac complications.The true prevalence is not fully known due to its elusive presentation,being often under-recognized and usually diagnosed only late in its natural history and in older patients.Because of this,effective treatment options are usually precluded by multiple comorbidities and frailty associated with such patients.Therefore,high clinical suspicion with earlier and better detection of this disease is needed.In this review,the novel applications of multimodality imaging in the diagnostic pathway of TTR cardiomyopathy are explored.These include the complimentary roles of transthoracic echocardiography,cardiac magnetic resonance,nuclear scintigraphy and positron emission tomography in quantifying cardiac dysfunction,diagnosis and risk stratification.Recent advances in novel therapeutic options for TTR have further enhanced the importance of a timely and accurate diagnosis of this disease.展开更多
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.展开更多
A new multi-mode resistivity imaging sonde, with toroidal coils as source, can conduct three resistivity measurements: azimuthal resistivity, lateral resistivity, and bit resistivity measurements. Thus, the logging ti...A new multi-mode resistivity imaging sonde, with toroidal coils as source, can conduct three resistivity measurements: azimuthal resistivity, lateral resistivity, and bit resistivity measurements. Thus, the logging time and cost are greatly saved. The toroidal coils are simplified as an extended voltage dipole and the response equations are derived for a homogenous formation. Based on 3D FEM, the depth of investigation(DOI), vertical resolution, circumferential azimuthal capacity, borehole diameter, mud resistivity, thickness of target formation, and the resistivity of the surrounding formation and mud invasion are simulated. The results suggest that the three measurement modes of the new sonde are different in vertical resolutions and DOIs. The circumferential detection ability of the azimuth button depends on the contrast between the anomaly and formation resistivity and the open angle of the anomaly. Whether the borehole is truncated at the bit or not has a great influence on the simulation results. The borehole and mud invasion affect the apparent resistivity in all modes, but the effects of resistivity of surrounding formation and thickness of the target formation are only corrected for lateral resistivity measurement.展开更多
基金supported by the VTT Technical Research Centre of Finland and the work of Nayef Alqahtani is supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant KFU251882).
文摘Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches.
文摘Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality rates.Detecting and treating ABE promptly is important to prevent further complications and long-term issues.Recent studies have explored ABE diagnosis.However,they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging(MRI).To tackle this problem,the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI scans.The scans include T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),and apparent diffusion coefficient maps to get indepth information.Initially,the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and Z-score normalisation for data standardisation.An Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection accuracy.Furthermore,a multi-transformer approach was used for feature fusion and identify feature correlations effectively.Finally,accurate ABE diagnosis is achieved through the utilisation of a SoftMax layer.The performance of the proposed Tri-M2MT model is evaluated across various metrics,including accuracy,specificity,sensitivity,F1-score,and ROC curve analysis,and the proposed methodology provides better performance compared to existing methodologies.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071315 and 62271336).
文摘The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.
基金supported by Natural Science Foundation of Jilin Province(No.SKL202302002)Key Research and Development project of Jilin Provincial Science and Technology Department(No.20210204142YY)+2 种基金The Science and Technology Development Program of Jilin Province(No.2020122256JC)Beijing Kechuang Medical Development Foundation Fund of China(No.KC2023-JX-0186BQ079)Talent Reserve Program(TRP),the First Hospital of Jilin University(No.JDYY-TRP-2024007)。
文摘Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials provide a promising prospect for imaging-guided precision therapy.Considering that tumor-derived alkaline phosphatase(ALP)is over-expressed in metastatic PCa,it makes a great chance to develop a theranostics system with ALP responsive in the TME.Herein,an ALP-responsive aggregationinduced emission luminogens(AIEgens)nanoprobe AMNF self-assembly was designed for enhancing the diagnosis and treatment of metastatic PCa.The nanoprobe exhibited self-aggregation in the presence of ALP resulted in aggregation-induced fluorescence,and enhanced accumulation and prolonged retention period at the tumor site.In terms of detection,the fluorescence(FL)/computed tomography(CT)/magnetic resonance(MR)multi-mode imaging effect of nanoprobe was significantly improved post-aggregation,enabling precise diagnosis through the amalgamation of multiple imaging modes.Enhanced CT/MR imaging can achieve assist preoperative tumor diagnosis,and enhanced FL imaging technology can achieve“intraoperative visual navigation”,showing its potential application value in clinical tumor detection and surgical guidance.In terms of treatment,AMNF showed strong absorption in the near infrared region after aggregation,which improved the photothermal treatment effect.Overall,our work developed an effective aggregation-enhanced theranostic strategy for ALP-related cancers.
基金funded by the National Natural Science Foundation of China(Nos.52474165 and 52522404)。
文摘Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.
基金supported by the National Natural Science Foundation of China(No.12105018)the Beijing Nova Program(Nos.Z211100002121129 and 20230484413)the Beijing Normal University Start-up Grant(No.312232104).
文摘We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two detectors that utilize LYSO-SiPM and were available in our laboratory.One detector consists of a 50×50 array of LYSO crystals,each measuring 0.9mm×0.9mm×10mm with 1 mm pitches,whereas the other detector comprises a 25×25 array of LYSO crystals,each measuring 1.9mm×1.9mm×10mm with 2 mm pitches.These detectors are mounted on a rotational stage,which enables them to function as either a Compton camera or a PET detector pair.The 64-channel signals from the SiPMs of each detector are processed through a capacitive multiplexing circuit to yield four position-weighted outputs.Distinct energy windows were used to discriminate Compton events from PET events.Energy resolution and energy-channel relationships were calibrated via multiple sources.The measured average energy resolutions(full widths at half maximum,FWHMs)for the detectors at 511 keV were 17.5%and 15.2%,respectively.The initial experimental results indicate an angular resolution(FWHM)of 8.6◦for the system in Compton imaging mode.A V-shaped tube injected with 18 F solution was clearly reconstructed,which further verified the imaging capabilities of the system in Compton imaging mode.The results of simulation and experimental imaging studies show that the system can detect tumors as small as 1 mm in diameter when working in PET imaging mode.Mouse bone PET imaging was successfully conducted,with the results matching well with the corresponding CT images.This technology holds great potential for advancing the development of physiological function modalities.
基金the fundamental Research Funds for the central Universities(x2wjD2240360)for the funding supportMeanwhile,Engineering and Physical Sciences Research Council(EPSRC,EP/V027433/3)+2 种基金UK Research and Innovation(UKRI)under the UK government’s Horizon Europe funding(101077226,EP/Y008707/1)Faraday Institution(EP/S003053/1)Degradation project(FIRG001),Royal Society(IEC\NSFC\233361),QUB Agility Fund and Wright Technology and Research Centre(W-Tech,R5240MEE)Funding from UK aid from the UK Government through the Faraday Institution and the Transforming Energy Access Programme(Grant number FIRG050-Device engineering of Zn-based hybrid micro-flow batteries and by-product H2 collection for Emerging Economies)。
文摘Aqueous zinc metal batteries(AZMBs)face significant challenges in achieving reversibility and cycling stability,primarily due to hydrogen evolution reactions(HER)and zinc dendrite growth.In this study,by employing carefully designed cells that approximate the structural characteristics of practical batteries,we revisit this widely held view through in-operando X-ray radiography to examine zinc dendrite formation and HER under nearpractical operating conditions.While conventional understanding emphasizes the severity of these processes,our findings suggest that zinc dendrites and HER are noticeably less pronounced in dense,real-operation configurations compared to modified cells,possibly due to a more uniform electric field and the suppression of triple-phase boundaries.This study indicates that other components,such as degradation at the cathode current collector interface and configuration mismatches within the full cell,may also represent important barriers to the practical application of AZMBs,particularly during the early stages of electrodeposition.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
基金supported by the National Natural Science Foundation of China,No.31760290,82160688the Key Development Areas Project of Ganzhou Science and Technology,No.2022B-SF9554(all to XL)。
文摘Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.
基金supported by the National Natural Science Foundation of China (91332000,81171021,and 91132727)the Key Program for Clinical Medicine and Science and Technology,Jiangsu Provence,China ( BL2013025 and BL2014077)
文摘Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment（a MCI） to Alzheimer＇s disease（AD）. As a part of the medial temporal lobe memory system,the hippocampus is one of the brain regions affected earliest by AD neuropathology,and shows progressive degeneration as a MCI progresses to AD. Currently,no validated biomarkers can precisely predict the conversion from a MCI to AD. Therefore,there is a great need of sensitive tools for the early detection of AD progression. In this review,we summarize the specifi c structural and functional changes in the hippocampus from recent a MCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile,this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of a MCI to AD.
基金supported by the National Key R&D Program of China,Nos.2017YFA0104302(to NG and XM)and 2017YFA0104304(to BW and ZZ)
文摘Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
文摘Cardiac amyloidosis is a heterogeneous and challenging diagnostic disease with poor prognosis that is now being altered by introduction of new therapies.Echocardiography remains the first-line imaging tool, and when disease is suspected on echocardiography, cardiac magnetic resonance imaging and nuclear imaging play critical roles in the non-invasive diagnosis and evaluation of cardiac amyloidosis. Advances in multi-modality cardiac imaging allowing earlier diagnosis and initiation of novel therapies have significantly improved the outcomes in these patients. Cardiac imaging also plays important roles in the risk stratification of patients presenting with cardiac amyloidosis. In the current review, we provide a clinical and imaging focused update, and importantly outline the imaging protocols, diagnostic and prognostic utility of multimodality cardiac imaging in the assessment of cardiac amyloidosis.
文摘Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer’s disease (AD). Their association has not been investigated with the use of biomarkers. This study examines whether, among cognitively normal (NL) individuals, those who are less physically active and show lower MeDi adherence have brain biomarker abnormalities consistent with AD. Methods: Forty-five NL individuals (age 54 ± 11, 71% women) with complete leisure time physical activity (LTA), dietary information, and cross-sectional 3D T1-weigthed MRI, 11C-Pittsburgh Compound B (PiB) and 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) scans were examined. Voxel-wise multivariate partial least square (PLS) regression was used to examine the effects of LTA, MeDi and their interaction on brain biomarkers. Age, gender, ethnicity, education, caloric intake, BMI, family history of AD, Apolipoprotein E (APOE) genotype, presence of hypertension and insulin resistance were examined as confounds. Subjects were dichotomized into more and less physically active (LTA+ vs. LTA-;n = 21 vs. 24), and into higher vs. lower MeDi adherence groups (n = 18 vs. 27) using published scoring methods. Spatial patterns of brain biomarkers that represented the optimal association between the images and the groups were generated for all modalities using voxel-wise multivariate Partial Least Squares (PLS) regression. Results: Groups were comparable for clinical and neuropsychological measures. Independent effects of LTA and MeDi factors were observed in AD-vulnerable brain regions for all modalities (p β load and lower glucose metabolism) were observed in LTA- compared to LTA+ subjects, and in MeDi- as compared to MeDi+ subjects. A gradient effect was observed for all modalities so that LTA+/MeDi+ subjects had the highest and LTA+/MeDi+ subjects had the lowest AD-burden (p < 0.001), although the LTA × MeDi interaction was significant only for FDG measures (p < 0.03). Adjusting for covariates did not attenuate these relationships. Conclusion: Lower physical activity and MeDi adherence were associated with increased brain AD-burden among NL individuals, in-dicating that lifestyle factors may modulate AD risk. Studies with larger samples and longitudinal evaluations are needed to determine the predictive power of the observed associations.
文摘The goal of parathyroid imaging in hyperparathyroidism is not diagnosis,rather it is the localization of the cause of hyperparathyroidism for planning the best therapeutic approach.Hence,the role of imaging to accurately and precisely localize the abnormal parathyroid tissue is more important than ever to facilitate minimally invasive parathyroidectomy over bilateral neck exploration.The common causes include solitary parathyroid adenoma,multiple parathyroid adenomas,parathyroid hyperplasia and parathyroid carcinoma.It is highly imperative for the radiologist to be cautious of the mimics of parathyroid lesions like thyroid nodules and lymph nodes and be able to differentiate them on imaging.The various imaging modalities available include high resolution ultrasound of the neck,nuclear imaging studies,four-dimensional computed tomography(4D CT)and magnetic resonance imaging.Contrast enhanced ultrasound is a novel technique which has been recently added to the armam-entarium to differentiate between parathyroid adenomas and its mimics.Through this review article we wish to review the imaging features of parathyroid lesions on various imaging modalities and present an algorithm to guide their radiological differentiation from mimics.
基金support from various resources,including the National Natural Science Foundation of China (grant numbers 32071350,32271412,32171404)the Shanghai Rising-Star Program (grant numbers 22QA1400100)+1 种基金the Fundamental Research Funds for the Central Universities (grant numbers 2232019A3-06,2232021D-10)the Science and Technology Commission of Shanghai Municipality (grant numbers 21ZR1403100,19440741600,20DZ2254900).
文摘In situ monitoring of bone regeneration enables timely diagnosis and intervention by acquiring vital biological parameters.However,an existing gap exists in the availability of effective methodologies for continuous and dynamic monitoring of the bone tissue regeneration process,encompassing the concurrent visualization of bone formation and implant degradation.Here,we present an integrated scaffold designed to facilitate real-time monitoring of both bone formation and implant degradation during the repair of bone defects.Laponite(Lap),CyP-loaded mesoporous silica(CyP@MSNs)and ultrasmall superparamagnetic iron oxide nanoparticles(USPIO@SiO2)were incorporated into a bioink containing bone marrow mesenchymal stem cells(BMSCs)to fabricate functional scaffolds denoted as C@M/GLU using 3D bioprinting technology.In both in vivo and in vitro experiments,the composite scaffold has demonstrated a significant enhancement of bone regeneration through the controlled release of silicon(Si)and magnesium(Mg)ions.Employing near-infrared fluorescence(NIR-FL)imaging,the composite scaffold facilitates the monitoring of alkaline phosphate(ALP)expression,providing an accurate reflection of the scaffold’s initial osteogenic activity.Meanwhile,the degradation of scaffolds was monitored by tracking the changes in the magnetic resonance(MR)signals at various time points.These findings indicate that the designed scaffold holds potential as an in situ bone implant for combined visualization of osteogenesis and implant degradation throughout the bone repair process.
文摘Transthyretin amyloid(TTR)cardiomyopathy is a disease of insidious onset,which is often accompanied by debilitating neurological and/or cardiac complications.The true prevalence is not fully known due to its elusive presentation,being often under-recognized and usually diagnosed only late in its natural history and in older patients.Because of this,effective treatment options are usually precluded by multiple comorbidities and frailty associated with such patients.Therefore,high clinical suspicion with earlier and better detection of this disease is needed.In this review,the novel applications of multimodality imaging in the diagnostic pathway of TTR cardiomyopathy are explored.These include the complimentary roles of transthoracic echocardiography,cardiac magnetic resonance,nuclear scintigraphy and positron emission tomography in quantifying cardiac dysfunction,diagnosis and risk stratification.Recent advances in novel therapeutic options for TTR have further enhanced the importance of a timely and accurate diagnosis of this disease.
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
基金sponsored by Study on High-Precision Logging While Drilling Imaging Technology of Low-Permeability Reservoirs(No.2016ZX05021-002)
文摘A new multi-mode resistivity imaging sonde, with toroidal coils as source, can conduct three resistivity measurements: azimuthal resistivity, lateral resistivity, and bit resistivity measurements. Thus, the logging time and cost are greatly saved. The toroidal coils are simplified as an extended voltage dipole and the response equations are derived for a homogenous formation. Based on 3D FEM, the depth of investigation(DOI), vertical resolution, circumferential azimuthal capacity, borehole diameter, mud resistivity, thickness of target formation, and the resistivity of the surrounding formation and mud invasion are simulated. The results suggest that the three measurement modes of the new sonde are different in vertical resolutions and DOIs. The circumferential detection ability of the azimuth button depends on the contrast between the anomaly and formation resistivity and the open angle of the anomaly. Whether the borehole is truncated at the bit or not has a great influence on the simulation results. The borehole and mud invasion affect the apparent resistivity in all modes, but the effects of resistivity of surrounding formation and thickness of the target formation are only corrected for lateral resistivity measurement.
