The measurement of electron density is important for medical diagnosis and charged particle radiotherapy treatment planning.Traditionally,electron density is obtained by CT imaging using the relationship between CT-nu...The measurement of electron density is important for medical diagnosis and charged particle radiotherapy treatment planning.Traditionally,electron density is obtained by CT imaging using the relationship between CT-number and electron densities established beforehand.However,the measurement is not accurate due to the beam hardening effect.In this paper,we propose a simple and practical electron density acquisition method based on dual-energy CT technique.For each sample,the CT imaging is conducted using two selected X-ray energy from synchrotron radiation.A post-processing dual-energy reconstruction method is used.Linear attenuation coefficients of the scanned samples are obtained by FBP reconstruction.The effective atomic number and electron density are got by solving the dual-energy simultaneous equations.Different phantoms and breast tissues were scanned in this experimental study under 10 keV and 30 keV monochromatic X-rays.The distribution of effective atomic numbers and electron densities of the scanned phantoms were obtained by Dual-energy CT image reconstruction,which agrees well with the theoretical values.Compared with conventional methods,the measurement accuracy is greatly improved, and the measurement error is reduced to about 1%.This experimental study demonstrates that DECT imaging based on synchrotron radiation source is applicable to medical diagnosis for quantitative measurement with high accuracy.展开更多
The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment p...The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging.展开更多
In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.De...In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.展开更多
Mesenchymal stem cells(MSCs)have emerged as promising candidates for idiopathic pulmonary fibrosis(IPF)therapy.Increasing the MSC survival rate and deepening the understanding of the behavior of transplanted MSCs are ...Mesenchymal stem cells(MSCs)have emerged as promising candidates for idiopathic pulmonary fibrosis(IPF)therapy.Increasing the MSC survival rate and deepening the understanding of the behavior of transplanted MSCs are of great significance for improving the efficacy of MSC-based IPF treatment.Therefore,dual-functional Au-based nanoparticles(Au@PEG@PEI@TAT NPs,AuPPT)were fabricated by sequential modification of cationic polymer polyetherimide(PEI),polyethylene glycol(PEG),and transactivator of transcription(TAT)penetration peptide on AuNPs,to co-deliver retinoic acid(RA)and microRNA(miRNA)for simultaneously enhancing MSC survive and real-time imaging tracking of MSCs during IPF treatment.AuPPT NPs,with good drug loading and cellular uptake abilities,could efficiently deliver miRNA and RA to protect MSCs from reactive oxygen species and reduce their expression of apoptosis executive protein Caspase 3,thus prolonging the survival time of MSC after transplantation.In themeantime,the intracellular accumulation of AuPPT NPs enhanced the computed tomography imaging contrast of transplantedMSCs,allowing them to be visually tracked in vivo.This study establishes an Au-based dual-functional platform for drug delivery and cell imaging tracking,which provides a new strategy for MSC-related IPF therapy.展开更多
Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with s...Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with severe COVID-19 who tested positive by the nucleic acid test in our hospital were selected,mainly focusing on the morphology,distribution characteristics,and dynamic changes of the first CT findings.Results:3 patients with severe pneumonia were older,with one aged 80.The first chest CT examination for all 3 patients differed.Imaging showed a leafy distribution of consolidation,primarily affecting the lower lobes of both lungs and extending subpleurally.A grid-like pattern was observed,along with changes in the consolidation and air bronchogram.These changes had slower absorption,especially in patients with underlying diseases.Conclusion:CT manifestations of severe COVID-19 have specific characteristics and the analysis of their characteristics and dynamic changes provide valuable insights for clinical treatment.展开更多
Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admi...Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.展开更多
Imaging technologies are utilized to study the brain morphology and the functions of rat models of Parkinson disease （PD）. Magnetic resonance imaging （MRI） and magnetic resonance angiography （MRA） are used to ob...Imaging technologies are utilized to study the brain morphology and the functions of rat models of Parkinson disease （PD）. Magnetic resonance imaging （MRI） and magnetic resonance angiography （MRA） are used to obtain morphological imaging data. Functional imaging data, such as the spectrum and blood flow changes are obtained by proton magnetic resonance spectroscopy （1H-MRS） and CT perfusion （CTP）. Results show that PD rat models have no characteristic morphological imaging abnormalities, but exist regional cerebral blood flow （CBF） reductions and spectral changes in the striatum.展开更多
基金supported by National Key Technology R&D Program of the Ministry of Science and Technology(No.2012BA107B05)
文摘The measurement of electron density is important for medical diagnosis and charged particle radiotherapy treatment planning.Traditionally,electron density is obtained by CT imaging using the relationship between CT-number and electron densities established beforehand.However,the measurement is not accurate due to the beam hardening effect.In this paper,we propose a simple and practical electron density acquisition method based on dual-energy CT technique.For each sample,the CT imaging is conducted using two selected X-ray energy from synchrotron radiation.A post-processing dual-energy reconstruction method is used.Linear attenuation coefficients of the scanned samples are obtained by FBP reconstruction.The effective atomic number and electron density are got by solving the dual-energy simultaneous equations.Different phantoms and breast tissues were scanned in this experimental study under 10 keV and 30 keV monochromatic X-rays.The distribution of effective atomic numbers and electron densities of the scanned phantoms were obtained by Dual-energy CT image reconstruction,which agrees well with the theoretical values.Compared with conventional methods,the measurement accuracy is greatly improved, and the measurement error is reduced to about 1%.This experimental study demonstrates that DECT imaging based on synchrotron radiation source is applicable to medical diagnosis for quantitative measurement with high accuracy.
基金supported by Scientific Research Deanship at University of Ha’il,Saudi Arabia through project number RG-23137.
文摘The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging.
基金the Deanship for Research Innovation,Ministry of Education in Saudi Arabia,for funding this research work through project number IFKSUDR-H122.
文摘In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.
基金supported by the National Natural Science Foundation of China(Grant No.32171367)Natural Science Foundation of Jiangsu Province(Grant No.BK20230236)+1 种基金Science and Technology Project of Suzhou(Grant No.SS202135)CAS-VPST Silk Road Science Fund 2021(Grant No.121E32KYSB20200021).
文摘Mesenchymal stem cells(MSCs)have emerged as promising candidates for idiopathic pulmonary fibrosis(IPF)therapy.Increasing the MSC survival rate and deepening the understanding of the behavior of transplanted MSCs are of great significance for improving the efficacy of MSC-based IPF treatment.Therefore,dual-functional Au-based nanoparticles(Au@PEG@PEI@TAT NPs,AuPPT)were fabricated by sequential modification of cationic polymer polyetherimide(PEI),polyethylene glycol(PEG),and transactivator of transcription(TAT)penetration peptide on AuNPs,to co-deliver retinoic acid(RA)and microRNA(miRNA)for simultaneously enhancing MSC survive and real-time imaging tracking of MSCs during IPF treatment.AuPPT NPs,with good drug loading and cellular uptake abilities,could efficiently deliver miRNA and RA to protect MSCs from reactive oxygen species and reduce their expression of apoptosis executive protein Caspase 3,thus prolonging the survival time of MSC after transplantation.In themeantime,the intracellular accumulation of AuPPT NPs enhanced the computed tomography imaging contrast of transplantedMSCs,allowing them to be visually tracked in vivo.This study establishes an Au-based dual-functional platform for drug delivery and cell imaging tracking,which provides a new strategy for MSC-related IPF therapy.
基金Qinghai Provincial Health Commission Medical and Health Science and Technology Project Guiding Topics“Analysis of Dynamic Changes in Chest Imaging of New Coronavirus Pneumonia in Qinghai Province”(2022-wjzdx-63)。
文摘Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with severe COVID-19 who tested positive by the nucleic acid test in our hospital were selected,mainly focusing on the morphology,distribution characteristics,and dynamic changes of the first CT findings.Results:3 patients with severe pneumonia were older,with one aged 80.The first chest CT examination for all 3 patients differed.Imaging showed a leafy distribution of consolidation,primarily affecting the lower lobes of both lungs and extending subpleurally.A grid-like pattern was observed,along with changes in the consolidation and air bronchogram.These changes had slower absorption,especially in patients with underlying diseases.Conclusion:CT manifestations of severe COVID-19 have specific characteristics and the analysis of their characteristics and dynamic changes provide valuable insights for clinical treatment.
文摘Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.
基金Supported by the National Natural Science Foundation of China (30671997)~~
文摘Imaging technologies are utilized to study the brain morphology and the functions of rat models of Parkinson disease （PD）. Magnetic resonance imaging （MRI） and magnetic resonance angiography （MRA） are used to obtain morphological imaging data. Functional imaging data, such as the spectrum and blood flow changes are obtained by proton magnetic resonance spectroscopy （1H-MRS） and CT perfusion （CTP）. Results show that PD rat models have no characteristic morphological imaging abnormalities, but exist regional cerebral blood flow （CBF） reductions and spectral changes in the striatum.
