This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, o...This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for downsampling.These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution.To overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.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
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
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
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
To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints ...To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.展开更多
BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To...BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.METHODS We examined 23 anxious MDD patients,30 nonanxious MDD patients,and 28 healthy controls(HCs)using the DC for data analysis.RESULTS Compared with HCs,the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus(FFG)and inferior occipital gyrus,whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus.The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX,right precuneus,and opercular part of the inferior frontal gyrus.Unlike the nonanxious MDD group,the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine(CAL).Besides,declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.CONCLUSION This study shows that abnormal DC patterns in MDD,especially in the left CAL,can distinguish MDD from its anxiety subtype,indicating a potential neuroimaging biomarker.展开更多
Historically,psychiatric diagnoses have been made based on patient’s reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders.The utilization of neuroimaging or biomarkers to...Historically,psychiatric diagnoses have been made based on patient’s reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders.The utilization of neuroimaging or biomarkers to make the diagnosis and manage psychiatric disorders remains a distant goal.There have been several studies that examine brain imaging in psychiatric disorders,but more work is needed to elucidate the complexities of the human brain.In this editorial,we examine two articles by Xu et al and Stoyanov et al,that show developments in the direction of using neuroimaging to examine the brains of people with schizo-phrenia and depression.Xu et al used magnetic resonance imaging to examine the brain structure of patients with schizophrenia,in addition to examining neurotransmitter levels as biomarkers.Stoyanov et al used functional magnetic resonance imaging to look at modulation of different neural circuits by diagnostic-specific scales in patients with schizophrenia and depression.These two studies provide crucial evidence in advancing our understanding of the brain in prevalent psychiatric disorders.展开更多
Angiomyolipomas(AMLs)represent the most common benign solid renal tumors.The frequency of their detection in the general population is increasing owing to advances in imaging technology.The objective of this review is...Angiomyolipomas(AMLs)represent the most common benign solid renal tumors.The frequency of their detection in the general population is increasing owing to advances in imaging technology.The objective of this review is to discuss computed tomography(CT)and magnetic resonance imaging findings for both typical and atypical renal AMLs,along with their associated complications.AMLs are typically defined as solid triphasic tumors composed of varying amounts of dysmorphic and tortuous blood vessels,smooth muscle components and adipose tissue.In an adult,a classical renal AML appears as a solid,heterogeneous renal cortical mass with macroscopic fat.However,up to 5%of AMLs contain minimal fat and cannot be reliably diagnosed by imaging.Fat-poor AMLs can appear as hyperattenuating masses on unenhanced CT and as hypointense masses on T2WI;other AMLs may be isodense or exhibit cystic components.Hemorrhage is the most common complication,and AMLs with hemorrhage can mimic other tumors,making their diagnosis challenging.Understanding the variable and heterogeneous nature of this neoplasm to correctly classify renal AMLs and to avoid misdiagnosis of other renal lesions is crucial.展开更多
文摘This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for downsampling.These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution.To overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
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.
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.
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.
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)。
文摘To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.
基金Supported by Hubei Provincial Department of Science and Technology Natural Fund,No.2024AFC056the Open Fund of the Mental Health Research Institute at Three Gorges University,No.YCXL-23-11.
文摘BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.METHODS We examined 23 anxious MDD patients,30 nonanxious MDD patients,and 28 healthy controls(HCs)using the DC for data analysis.RESULTS Compared with HCs,the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus(FFG)and inferior occipital gyrus,whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus.The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX,right precuneus,and opercular part of the inferior frontal gyrus.Unlike the nonanxious MDD group,the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine(CAL).Besides,declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.CONCLUSION This study shows that abnormal DC patterns in MDD,especially in the left CAL,can distinguish MDD from its anxiety subtype,indicating a potential neuroimaging biomarker.
文摘Historically,psychiatric diagnoses have been made based on patient’s reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders.The utilization of neuroimaging or biomarkers to make the diagnosis and manage psychiatric disorders remains a distant goal.There have been several studies that examine brain imaging in psychiatric disorders,but more work is needed to elucidate the complexities of the human brain.In this editorial,we examine two articles by Xu et al and Stoyanov et al,that show developments in the direction of using neuroimaging to examine the brains of people with schizo-phrenia and depression.Xu et al used magnetic resonance imaging to examine the brain structure of patients with schizophrenia,in addition to examining neurotransmitter levels as biomarkers.Stoyanov et al used functional magnetic resonance imaging to look at modulation of different neural circuits by diagnostic-specific scales in patients with schizophrenia and depression.These two studies provide crucial evidence in advancing our understanding of the brain in prevalent psychiatric disorders.
文摘Angiomyolipomas(AMLs)represent the most common benign solid renal tumors.The frequency of their detection in the general population is increasing owing to advances in imaging technology.The objective of this review is to discuss computed tomography(CT)and magnetic resonance imaging findings for both typical and atypical renal AMLs,along with their associated complications.AMLs are typically defined as solid triphasic tumors composed of varying amounts of dysmorphic and tortuous blood vessels,smooth muscle components and adipose tissue.In an adult,a classical renal AML appears as a solid,heterogeneous renal cortical mass with macroscopic fat.However,up to 5%of AMLs contain minimal fat and cannot be reliably diagnosed by imaging.Fat-poor AMLs can appear as hyperattenuating masses on unenhanced CT and as hypointense masses on T2WI;other AMLs may be isodense or exhibit cystic components.Hemorrhage is the most common complication,and AMLs with hemorrhage can mimic other tumors,making their diagnosis challenging.Understanding the variable and heterogeneous nature of this neoplasm to correctly classify renal AMLs and to avoid misdiagnosis of other renal lesions is crucial.
