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
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
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
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
A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence(AI)in medical imaging.The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologi...A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence(AI)in medical imaging.The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologies,especially deep learning,that performs image recognition,feature extraction,and pattern analysis.Furthermore,AI has demonstrated significant promise in assessing the effects of treatments and forecasting the course of diseases.It also provides doctors with more advanced tools for managing the conditions of their patients.AI is poised to play a more significant role in medical imaging,especially in real-time image processing and multimodal fusion.By integrating multiple forms of image data,multimodal fusion technology provides more comprehensive disease information,whereas real-time image analysis can assist surgeons in making more precise de-cisions.By tailoring treatment regimens to each patient's unique needs,AI enhances both the effectiveness of treatment and the patient experience.Overall,AI in medical imaging promises a bright future,significantly enhancing diagnostic precision and therapeutic efficacy,and ultimately delivering higher-quality medical care to patients.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is i...The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.展开更多
BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evalu...BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm^(3))being significantly lower than before correction(825.632 cm^(3),P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.展开更多
0 INTRODUCTION.According to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 a.m.local time.The epicenter is located at 28.5°N,...0 INTRODUCTION.According to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 a.m.local time.The epicenter is located at 28.5°N,87.45°E,with a depth of~10 km.展开更多
Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method...Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.展开更多
Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism....Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.展开更多
Fetal intracranial tumors are rare,accounting for approximately 0.5%–1.9%of all pediatric tumors,though the true incidence may be underestimated.These tumors often present with distinct histopathological features,ima...Fetal intracranial tumors are rare,accounting for approximately 0.5%–1.9%of all pediatric tumors,though the true incidence may be underestimated.These tumors often present with distinct histopathological features,imaging characteristics,and clinical behavior compared to their postnatal counterparts.This review summarizes the current understanding of the prenatal diagnosis and characterization of fetal brain tumors,with a particular focus on the role of fetal magnetic resonance imaging(MRI).We discuss the advantages of advanced MR sequences in enhancing lesion detection and anatomical delineation following suspicious findings on obstetric ultrasound.Common tumor types encountered in utero—including teratomas,as-trocytomas,medulloblastomas,choroid plexus papillomas,and craniopharyngiomas—are reviewed in terms of imaging fea-tures,differential diagnosis,and clinical implications.Furthermore,the review addresses the diagnostic challenges,prognostic considerations,and the potential role of fetal MRI in guiding perinatal management and parental counseling.展开更多
Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ul...Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.展开更多
Background:Congenital hepatic hemangioma(CHH)is a rare benign vascular tumor that occurs prenatally.However,only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH,an...Background:Congenital hepatic hemangioma(CHH)is a rare benign vascular tumor that occurs prenatally.However,only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH,and no studies have conducted long-term follow-up on it.This study aimed to explore the ultrasound and magnetic resonance features,growth patterns,and clinical outcomes of CHH.Methods:Thirty-six pregnancies with a prenatal fetal diagnosis and postnatal diagnosis of CHH were studied.CHHs were grouped into those with a diameter≥4 cm and those with a diameter<4 cm according to the largest diameter.Fisher's exact test was used to compare the imaging characteristics between the groups.The volume of CHHs was measured at each follow-up visit to plot the growth pattern of the tumors,and the volume of CHHs was compared before and after birth using a rank sum test analysis.Results:Thirty-three cases of CHHs were confirmed by postnatal imaging,and three were confirmed by a biopsy.Mixed echoes were more common in the diameter≥4 cm group than in the diameter<4 cm group(p=0.026).Complications were more likely to occur in the large-diameter group.Eighteen(54.5%)cases were classified as rapidly involuting congenital hemangioma,nine(27.3%)as partially involuting congenital hemangioma,and two(6.1%)as noninvoluting congenital hemangioma.A new type of CHH was identified in which four(12.1%)cases continued to proliferate after birth and spontaneously subsided in subsequent months.The CHH volume decreased with age and was significantly decreased at 9 months postnatal compared to birth(p=0.001).Conclusion:This study showed the imaging features of CHH were associated with the lesion size.Based on postnatal follow-up,a new type of CHH was identified.If there are no complications at birth in CHH cases,a good prognosis is indicated.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer...Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.展开更多
The fluorescence imaging (FLI) in the second near-infrared window (NIR-II, 1000–1700nm) has attracted considerable attention in the past decade. In contrast to conventional NIR-I window excitation (808nm/980nm), FLI ...The fluorescence imaging (FLI) in the second near-infrared window (NIR-II, 1000–1700nm) has attracted considerable attention in the past decade. In contrast to conventional NIR-I window excitation (808nm/980nm), FLI with NIR-II window excitation (1064nm/other wavelength beyond 1000nm) can afford deeper tissue penetration depth with high clarity due to the merits of suppressed photon scattering and diminished autofluorescence. In this review, we have summarized NIR-II window excitable/emissive organic/polymeric fluorophores recently developed. The characteristics of these fluorophores such as chemical structures and photophysical properties have also been critically discussed. Furthermore, the latest development of noninvasive in vivo FLI with NIR-II excitation was highlighted. The ideal imaging results emphasized the importance of NIR-II excitation of these fluorophores in enabling deep tissue penetration and high-resolution imaging. Finally, a perspective on the challenges and prospects of NIR-II excitable/emissive organic/polymeric fluorophores was also discussed. We expected this review will be served as a source of inspiration for researchers, stimulating the creation of novel NIR-II excitable fluorophores and fostering the development of bioimaging applications.展开更多
BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the cor...BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
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.
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.
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 the National Natural Science Foundation of China(Grant Nos.82360266,81960224,and 81860248)Guizhou Provincial Basic Research Program(Grant Nos.Qiankehe basic-ZK[2023]general 395+2 种基金Qiankehe basic-ZK[2023]general 324Qiankehe basic-MS[2025]548)Key Lab of Acute Brain Injury and Function Repair at Guizhou Medical University(Grant No.[2024]fy0071).
文摘A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence(AI)in medical imaging.The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologies,especially deep learning,that performs image recognition,feature extraction,and pattern analysis.Furthermore,AI has demonstrated significant promise in assessing the effects of treatments and forecasting the course of diseases.It also provides doctors with more advanced tools for managing the conditions of their patients.AI is poised to play a more significant role in medical imaging,especially in real-time image processing and multimodal fusion.By integrating multiple forms of image data,multimodal fusion technology provides more comprehensive disease information,whereas real-time image analysis can assist surgeons in making more precise de-cisions.By tailoring treatment regimens to each patient's unique needs,AI enhances both the effectiveness of treatment and the patient experience.Overall,AI in medical imaging promises a bright future,significantly enhancing diagnostic precision and therapeutic efficacy,and ultimately delivering higher-quality medical care to patients.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
基金supported by the National Natural Science Foundation of China(Grant Nos.82272955 and 22203057)the Natural Science Foundation of Fujian Province(Grant No.2021J011361).
文摘The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.
文摘BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm^(3))being significantly lower than before correction(825.632 cm^(3),P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.
基金funded by the National Key R&D Program of China(No.2020YFC150071)partly supported by the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team(2022)and the Research Funds for the Interdisciplinary Projects,CHU(No.300104240914)。
文摘0 INTRODUCTION.According to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 a.m.local time.The epicenter is located at 28.5°N,87.45°E,with a depth of~10 km.
基金supported by the National Key R&D Program of China 2022YFF0503002the National Natural Science Foundation of China(NSFC,Grant Nos.12333010 and 12233012)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.
基金supported by the Research Project of the Shanghai Health Commission,No.2020YJZX0111(to CZ)the National Natural Science Foundation of China,Nos.82021002(to CZ),82272039(to CZ),82171252(to FL)+1 种基金a grant from the National Health Commission of People’s Republic of China(PRC),No.Pro20211231084249000238(to JW)Medical Innovation Research Project of Shanghai Science and Technology Commission,No.21Y11903300(to JG).
文摘Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.
基金supported by the Medical Innovation Research Special Project of Science and Technology Commission of Shanghai Municipality(Grant/Award Number:23Y11907800)Fundamental Research Funds for the Central Universities(Grant/Award Number:YG2023ZD22)Shanghai Key Laboratory of Child Brain and Development(Grant/Award Number:24dz2260100).
文摘Fetal intracranial tumors are rare,accounting for approximately 0.5%–1.9%of all pediatric tumors,though the true incidence may be underestimated.These tumors often present with distinct histopathological features,imaging characteristics,and clinical behavior compared to their postnatal counterparts.This review summarizes the current understanding of the prenatal diagnosis and characterization of fetal brain tumors,with a particular focus on the role of fetal magnetic resonance imaging(MRI).We discuss the advantages of advanced MR sequences in enhancing lesion detection and anatomical delineation following suspicious findings on obstetric ultrasound.Common tumor types encountered in utero—including teratomas,as-trocytomas,medulloblastomas,choroid plexus papillomas,and craniopharyngiomas—are reviewed in terms of imaging fea-tures,differential diagnosis,and clinical implications.Furthermore,the review addresses the diagnostic challenges,prognostic considerations,and the potential role of fetal MRI in guiding perinatal management and parental counseling.
基金upported by the National Natural Science Foundation of China(Grant No.62305184)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.
文摘Background:Congenital hepatic hemangioma(CHH)is a rare benign vascular tumor that occurs prenatally.However,only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH,and no studies have conducted long-term follow-up on it.This study aimed to explore the ultrasound and magnetic resonance features,growth patterns,and clinical outcomes of CHH.Methods:Thirty-six pregnancies with a prenatal fetal diagnosis and postnatal diagnosis of CHH were studied.CHHs were grouped into those with a diameter≥4 cm and those with a diameter<4 cm according to the largest diameter.Fisher's exact test was used to compare the imaging characteristics between the groups.The volume of CHHs was measured at each follow-up visit to plot the growth pattern of the tumors,and the volume of CHHs was compared before and after birth using a rank sum test analysis.Results:Thirty-three cases of CHHs were confirmed by postnatal imaging,and three were confirmed by a biopsy.Mixed echoes were more common in the diameter≥4 cm group than in the diameter<4 cm group(p=0.026).Complications were more likely to occur in the large-diameter group.Eighteen(54.5%)cases were classified as rapidly involuting congenital hemangioma,nine(27.3%)as partially involuting congenital hemangioma,and two(6.1%)as noninvoluting congenital hemangioma.A new type of CHH was identified in which four(12.1%)cases continued to proliferate after birth and spontaneously subsided in subsequent months.The CHH volume decreased with age and was significantly decreased at 9 months postnatal compared to birth(p=0.001).Conclusion:This study showed the imaging features of CHH were associated with the lesion size.Based on postnatal follow-up,a new type of CHH was identified.If there are no complications at birth in CHH cases,a good prognosis is indicated.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
文摘Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
基金supported by the National Nature Science Foundation of China(Nos.62075079,62305127,61975200)the Natural Science Foundation of Jilin Province(20230508135RC)the Science and Technology Development Foundation of Changchun City(23GZZ15).
文摘The fluorescence imaging (FLI) in the second near-infrared window (NIR-II, 1000–1700nm) has attracted considerable attention in the past decade. In contrast to conventional NIR-I window excitation (808nm/980nm), FLI with NIR-II window excitation (1064nm/other wavelength beyond 1000nm) can afford deeper tissue penetration depth with high clarity due to the merits of suppressed photon scattering and diminished autofluorescence. In this review, we have summarized NIR-II window excitable/emissive organic/polymeric fluorophores recently developed. The characteristics of these fluorophores such as chemical structures and photophysical properties have also been critically discussed. Furthermore, the latest development of noninvasive in vivo FLI with NIR-II excitation was highlighted. The ideal imaging results emphasized the importance of NIR-II excitation of these fluorophores in enabling deep tissue penetration and high-resolution imaging. Finally, a perspective on the challenges and prospects of NIR-II excitable/emissive organic/polymeric fluorophores was also discussed. We expected this review will be served as a source of inspiration for researchers, stimulating the creation of novel NIR-II excitable fluorophores and fostering the development of bioimaging applications.
文摘BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
