Abstract In this paper, the capabilities of laser-induced breakdown spectroscopy （LIBS） for rapid analysis to multi-component plant are illustrated using a 1064 nm laser focused onto the surface of folium lycii. Bas...Abstract In this paper, the capabilities of laser-induced breakdown spectroscopy （LIBS） for rapid analysis to multi-component plant are illustrated using a 1064 nm laser focused onto the surface of folium lycii. Based on homogeneous plasma assumption, nine of essential micronutrients in folium lycii are identified. Using Saha equation and Boltzmann plot method electron density and plasma temperature are obtained, and their relative concentration （Ca, Mg, A1, Si, Ti, Na, K, Li, and Sr） are obtained employing a semi-quantitative method.展开更多
Heavy oil is an important resource in current petroleum exploitation, and the chemical composition information of heavy oil is crucial for revealing its viscosity-inducing mechanism and solving practical exploitation ...Heavy oil is an important resource in current petroleum exploitation, and the chemical composition information of heavy oil is crucial for revealing its viscosity-inducing mechanism and solving practical exploitation issues. In this study, the techniques of high-temperature gas chromatography and high-resolution mass spectrometry equipped with an electrospray ionization source were applied to reveal the chemical composition of typical heavy oils from western, central, and eastern China. The results indicate that these heavy oils display significant variations in their bulk properties, with initial boiling points all above 200℃. Utilizing pre-treatment and ESI high-resolution mass spectrometry, an analysis of the molecular composition of saturated hydrocarbons, aromatic hydrocarbons, acidic oxygen compounds, sulfur compounds, basic nitrogen compounds, and neutral nitrogen compounds within the heavy oil was conducted. Ultimately, a semi-quantitative analysis of the molecular composition of the heavy oil was achieved by integrating the elemental content. The semi-quantitative analysis results of Shengli-J8 heavy oil and a conventional Shengli crude oil show that Shengli-J8 heavy oil lacks alkanes and low molecular weight aromatic hydrocarbons, which contributes to its high viscosity. Additionally,characteristic molecular sets for different heavy oils were identified based on the semi-quantitative analysis of molecular composition. The semi-quantitative analysis of molecular composition in heavy oils may provide valuable reference data for establishing theoretical models on the viscosity-inducing mechanism in heavy oils and designing viscosity-reducing agents for heavy oil exploitation.展开更多
The content of berberine hydrochloride(BH)in compound berberine tablets(CBTs)is subject to strict requirements.Its content is usually measured based on chemical analysis.In this paper,the fluorescence spectral imaging...The content of berberine hydrochloride(BH)in compound berberine tablets(CBTs)is subject to strict requirements.Its content is usually measured based on chemical analysis.In this paper,the fluorescence spectral imaging method was used to study the relative content of BH from a physics perspective.By comparing the relative fluorescence intensity of self-made CBTs with di®erent mass percentages of BH,a linear positive relationship was observed between the BH content and the relative fluorescence intensity,and accordingly the quality of CBTs of different brands was evaluated.The results indicate that the fluorescence spectral imaging method can be a simple,fast and nondestructive semi-quantitative analysis method to determine the content of BH in CBTs,and this method has great potential in the quality control of CBTs.展开更多
Objective: To investigate the value of whole-lesion texture analysis on preoperative gadoxetic acid enhanced magnetic resonance imaging(MRI) for predicting tumor Ki-67 status after curative resection in patients with ...Objective: To investigate the value of whole-lesion texture analysis on preoperative gadoxetic acid enhanced magnetic resonance imaging(MRI) for predicting tumor Ki-67 status after curative resection in patients with hepatocellular carcinoma(HCC).Methods: This study consisted of 89 consecutive patients with surgically confirmed HCC. Texture features were extracted from multiparametric MRI based on whole-lesion regions of interest. The Ki-67 status was immunohistochemical determined and classified into low Ki-67(labeling index ≤15%) and high Ki-67(labeling index >15%) groups. Least absolute shrinkage and selection operator(LASSO) and multivariate logistic regression were applied for generating the texture signature, clinical nomogram and combined nomogram. The discrimination power, calibration and clinical usefulness of the three models were evaluated accordingly. Recurrence-free survival(RFS) rates after curative hepatectomy were also compared between groups.Results: A total of 13 texture features were selected to construct a texture signature for predicting Ki-67 status in HCC patients(C-index: 0.878, 95% confidence interval: 0.791-0.937). After incorporating texture signature to the clinical nomogram which included significant clinical variates(AFP, BCLC-stage, capsule integrity, tumor margin,enhancing capsule), the combined nomogram showed higher discrimination ability(C-index: 0.936 vs. 0.795,P<0.001), good calibration(P>0.05 in Hosmer-Lemeshow test) and higher clinical usefulness by decision curve analysis. RFS rate was significantly lower in the high Ki-67 group compared with the low Ki-67 group after curative surgery(63.27% vs. 85.00%, P<0.05).Conclusions: Texture analysis on gadoxetic acid enhanced MRI can serve as a noninvasive approach to preoperatively predict Ki-67 status of HCC after curative resection. The combination of texture signature and clinical factors demonstrated the potential to further improve the prediction performance.展开更多
Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative asse...Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)- averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histological-cytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.展开更多
The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier en...The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-clas- sifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classitiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.展开更多
目的分析小儿神经性厌食症患儿的MRI体素形态学特征。方法选取2021年2月至2023年1月于九江市妇幼保健院接受治疗的30例小儿神经性厌食症患儿作为研究组,另选择同期于本院体检的30名健康儿童作为对照组。两组均接受头部MRI检查,比较两组...目的分析小儿神经性厌食症患儿的MRI体素形态学特征。方法选取2021年2月至2023年1月于九江市妇幼保健院接受治疗的30例小儿神经性厌食症患儿作为研究组,另选择同期于本院体检的30名健康儿童作为对照组。两组均接受头部MRI检查,比较两组基于体素形态学测量的分数各向异性(fractional anisotropy,FA)值,分析研究组患儿脑组织结构改变与体重指数(body mass index,BMI)的相关性。结果研究组两侧岛叶、左侧丘脑、左侧额下回后部、左侧额叶中回、左侧前扣带回、左内侧额叶回、左侧额上回的FA值均低于对照组,差异有统计学意义(P<0.05)。研究组左侧额上回、丘脑、脑岛的FA值与BMI均呈正相关(r>0,P<0.05)。结论基于体素形态学分析的MRI检查,可较好地反映神经性厌食症患儿的大脑白质结构损伤,借助脑组织结构改变能反映厌食症的病情严重程度,对于揭示神经性厌食症的发病机制具有重要价值。展开更多
In order to detect the molecular mechanism of heterosis in pigs, the mRNA differential display technique was performed to investigate the differences of gene expression in the Longissimus dorsi tissue from Meishan, ...In order to detect the molecular mechanism of heterosis in pigs, the mRNA differential display technique was performed to investigate the differences of gene expression in the Longissimus dorsi tissue from Meishan, Meishan × Large White hybrid and Large White pigs with nine 3'-end anchored primers in combination with ten 5'-end arbitrary primers and nearly 3000 reproducible bands were examined. One novel expressed sequence tag (EST4, GenBank accession number: AY553914) that was differentially expressed in Meishan, Meishan× Large White hybrid and Large White pigs was isolated from the Longissimus dorsi muscle tissue and identified through semi-quantitative RT-PCR. BLAST analysis revealed that the 350 bp long EST (EST4) was not homologous to any of the known porcine genes. Tissue expression profile analyses showed that the EST4 was expressed in most of tissues.LIU Yong-gang, Ph D candidate展开更多
[ Objective ] The aim was to explore the relationship between the Dorsal and diapause development of Delia araiqua. [ Method ] The full-length cDNA of Dorsal in D. antiqua was cloned through RACE. The similarity among...[ Objective ] The aim was to explore the relationship between the Dorsal and diapause development of Delia araiqua. [ Method ] The full-length cDNA of Dorsal in D. antiqua was cloned through RACE. The similarity among deduced amino acid sequence of Dorsal cDNA and the Dorsals of other 14 insect species were compared, and the phylogenetic analysis of these Dorsals was conducted. The expression level of Dorsal gene in winter-, summer- and non-diapausing pupae was analyzed. [ Result] The full-length of Dorsal cDNA sequence, 2 412 bp, was obtained with ORF 1974 bp, which coded 657 amino acids with predicted Mw 72.9 kDa and PI 8.5. The result of similarity comparison indicated that the DaDorsal was most related to those of Drosophlia melanogaster and Drosophlia pseudoobscura. The result of semi-quantitative RT-PCR analysis showed the expression level of Dorsal gene increased in characteristic duration of winter-, summer- and non- diapansing pupae, especially at the late diapanse, which might imply its relationship to D. antiqua diapause and development. [ Conclusion] The study lays the foundation for further study on gene function of Dorsal in insect diapause and development.展开更多
Purpose: To determine the total direct costs (fixed and variable costs) of diffusion tensor imaging (DTI) and MR tractography reconstruction of the brain. Materials and Methods: The direct fixed and variable costs of ...Purpose: To determine the total direct costs (fixed and variable costs) of diffusion tensor imaging (DTI) and MR tractography reconstruction of the brain. Materials and Methods: The direct fixed and variable costs of DTI with MR tractography were determined prospectively with time and motion analysis in a 1.5-Tesla MR scanner using 15 encoding directions. Seventeen patients with seizure disorders, 9 males & 8 females, with mean age of 13 years (age range 2 - 33 years) were studied. Total direct costs were calculated from all direct fixed and variable costs. Sensitivity analyses between 1.5 versus a 3-Tesla MR system, and 15 versus 32 encoding directions were done. Results: The total direct costs of DTI and MR tractography for a 1.5-T system with 15 encoding directions were US $97. Variable cost was $76.80 and fixed cost was $20.20. Total direct costs for a 3-T system with 15 directions decreased to US $94.5 because of the shorter scan time despite the higher cost of the 3-T system. The most costly component of the direct cost was post-processing analysis at US $46.00. Conclusion: DTI with MR tractography has important total direct costs with variable costs higher than the fixed costs. The post processing variable cost is the most expensive component. Developing more accurate automated post-processing software for DTI and MR tractography is important to decrease this variable labor cost. Given the added value of DTI-MR tractography and the costs involved reimbursement codes should be considered.展开更多
The purpose of this study was to demonstrate a simple and fast method for solving the time-dependent Bloch-McConnell equations describing the behavior of magnetization in magnetic resonance imaging (MRI) in the presen...The purpose of this study was to demonstrate a simple and fast method for solving the time-dependent Bloch-McConnell equations describing the behavior of magnetization in magnetic resonance imaging (MRI) in the presence of multiple chemical exchange pools. First, the time-dependent Bloch- McConnell equations were reduced to a homogeneous linear differential equation, and then a simple equation was derived to solve it using a matrix operation and Kronecker tensor product. From these solutions, the longitudinal relaxation rate (R1ρ) and transverse relaxation rate in the rotating frame (R2ρ) and Z-spectra were obtained. As illustrative examples, the numerical solutions for linear and star-type three-pool chemical exchange models and linear, star- type, and kite-type four-pool chemical exchange models were presented. The effects of saturation time (ST) and radiofrequency irradiation power (ω1) on the chemical exchange saturation transfer (CEST) effect in these models were also investigated. Although R1ρ and R2ρ were not affected by the ST, the CEST effect observed in the Z-spectra increased and saturated with increasing ST. When ω1 was varied, the CEST effect increased with increasing ω1 in R1ρ, R2ρ, and Z-spectra. When ω1 was large, however, the spillover effect due to the direct saturation of bulk water protons also increased, suggesting that these parameters must be determined in consideration of both the CEST and spillover effects. Our method will be useful for analyzing the complex CEST contrast mechanism and for investigating the optimal conditions for CEST MRI in the presence of multiple chemical exchange pools.展开更多
The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study pre...The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.展开更多
Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intell...Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.展开更多
Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a co...Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.展开更多
Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors pr...Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.展开更多
基金supported by National Natural Science Foundation of China (Nos.10774122, 10876028)the specialized Research Fund for the Doctoral Program of Higher Education of China (No.20070736001)the Foundation of Northwest Normal University of China (NWNU-KJCXGC-03-21)
文摘Abstract In this paper, the capabilities of laser-induced breakdown spectroscopy （LIBS） for rapid analysis to multi-component plant are illustrated using a 1064 nm laser focused onto the surface of folium lycii. Based on homogeneous plasma assumption, nine of essential micronutrients in folium lycii are identified. Using Saha equation and Boltzmann plot method electron density and plasma temperature are obtained, and their relative concentration （Ca, Mg, A1, Si, Ti, Na, K, Li, and Sr） are obtained employing a semi-quantitative method.
基金supported by the National Key R&D Program of China (2018YFA0702400)the Science Foundation of China University of Petroleum, Beijing (2462023QNXZ017)。
文摘Heavy oil is an important resource in current petroleum exploitation, and the chemical composition information of heavy oil is crucial for revealing its viscosity-inducing mechanism and solving practical exploitation issues. In this study, the techniques of high-temperature gas chromatography and high-resolution mass spectrometry equipped with an electrospray ionization source were applied to reveal the chemical composition of typical heavy oils from western, central, and eastern China. The results indicate that these heavy oils display significant variations in their bulk properties, with initial boiling points all above 200℃. Utilizing pre-treatment and ESI high-resolution mass spectrometry, an analysis of the molecular composition of saturated hydrocarbons, aromatic hydrocarbons, acidic oxygen compounds, sulfur compounds, basic nitrogen compounds, and neutral nitrogen compounds within the heavy oil was conducted. Ultimately, a semi-quantitative analysis of the molecular composition of the heavy oil was achieved by integrating the elemental content. The semi-quantitative analysis results of Shengli-J8 heavy oil and a conventional Shengli crude oil show that Shengli-J8 heavy oil lacks alkanes and low molecular weight aromatic hydrocarbons, which contributes to its high viscosity. Additionally,characteristic molecular sets for different heavy oils were identified based on the semi-quantitative analysis of molecular composition. The semi-quantitative analysis of molecular composition in heavy oils may provide valuable reference data for establishing theoretical models on the viscosity-inducing mechanism in heavy oils and designing viscosity-reducing agents for heavy oil exploitation.
基金The authors would like to acknowledge the support of the Ph.D.research startup foundation of Guangdong Medical University (2XB14006).
文摘The content of berberine hydrochloride(BH)in compound berberine tablets(CBTs)is subject to strict requirements.Its content is usually measured based on chemical analysis.In this paper,the fluorescence spectral imaging method was used to study the relative content of BH from a physics perspective.By comparing the relative fluorescence intensity of self-made CBTs with di®erent mass percentages of BH,a linear positive relationship was observed between the BH content and the relative fluorescence intensity,and accordingly the quality of CBTs of different brands was evaluated.The results indicate that the fluorescence spectral imaging method can be a simple,fast and nondestructive semi-quantitative analysis method to determine the content of BH in CBTs,and this method has great potential in the quality control of CBTs.
基金supported by Science and Technology Support Program of Sichuan Province (No. 2017SZ0003)Research Grant of National Nature Science Foundation of China (No. 81471658)
文摘Objective: To investigate the value of whole-lesion texture analysis on preoperative gadoxetic acid enhanced magnetic resonance imaging(MRI) for predicting tumor Ki-67 status after curative resection in patients with hepatocellular carcinoma(HCC).Methods: This study consisted of 89 consecutive patients with surgically confirmed HCC. Texture features were extracted from multiparametric MRI based on whole-lesion regions of interest. The Ki-67 status was immunohistochemical determined and classified into low Ki-67(labeling index ≤15%) and high Ki-67(labeling index >15%) groups. Least absolute shrinkage and selection operator(LASSO) and multivariate logistic regression were applied for generating the texture signature, clinical nomogram and combined nomogram. The discrimination power, calibration and clinical usefulness of the three models were evaluated accordingly. Recurrence-free survival(RFS) rates after curative hepatectomy were also compared between groups.Results: A total of 13 texture features were selected to construct a texture signature for predicting Ki-67 status in HCC patients(C-index: 0.878, 95% confidence interval: 0.791-0.937). After incorporating texture signature to the clinical nomogram which included significant clinical variates(AFP, BCLC-stage, capsule integrity, tumor margin,enhancing capsule), the combined nomogram showed higher discrimination ability(C-index: 0.936 vs. 0.795,P<0.001), good calibration(P>0.05 in Hosmer-Lemeshow test) and higher clinical usefulness by decision curve analysis. RFS rate was significantly lower in the high Ki-67 group compared with the low Ki-67 group after curative surgery(63.27% vs. 85.00%, P<0.05).Conclusions: Texture analysis on gadoxetic acid enhanced MRI can serve as a noninvasive approach to preoperatively predict Ki-67 status of HCC after curative resection. The combination of texture signature and clinical factors demonstrated the potential to further improve the prediction performance.
文摘Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)- averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histological-cytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.
文摘The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-clas- sifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classitiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.
文摘目的分析小儿神经性厌食症患儿的MRI体素形态学特征。方法选取2021年2月至2023年1月于九江市妇幼保健院接受治疗的30例小儿神经性厌食症患儿作为研究组,另选择同期于本院体检的30名健康儿童作为对照组。两组均接受头部MRI检查,比较两组基于体素形态学测量的分数各向异性(fractional anisotropy,FA)值,分析研究组患儿脑组织结构改变与体重指数(body mass index,BMI)的相关性。结果研究组两侧岛叶、左侧丘脑、左侧额下回后部、左侧额叶中回、左侧前扣带回、左内侧额叶回、左侧额上回的FA值均低于对照组,差异有统计学意义(P<0.05)。研究组左侧额上回、丘脑、脑岛的FA值与BMI均呈正相关(r>0,P<0.05)。结论基于体素形态学分析的MRI检查,可较好地反映神经性厌食症患儿的大脑白质结构损伤,借助脑组织结构改变能反映厌食症的病情严重程度,对于揭示神经性厌食症的发病机制具有重要价值。
文摘In order to detect the molecular mechanism of heterosis in pigs, the mRNA differential display technique was performed to investigate the differences of gene expression in the Longissimus dorsi tissue from Meishan, Meishan × Large White hybrid and Large White pigs with nine 3'-end anchored primers in combination with ten 5'-end arbitrary primers and nearly 3000 reproducible bands were examined. One novel expressed sequence tag (EST4, GenBank accession number: AY553914) that was differentially expressed in Meishan, Meishan× Large White hybrid and Large White pigs was isolated from the Longissimus dorsi muscle tissue and identified through semi-quantitative RT-PCR. BLAST analysis revealed that the 350 bp long EST (EST4) was not homologous to any of the known porcine genes. Tissue expression profile analyses showed that the EST4 was expressed in most of tissues.LIU Yong-gang, Ph D candidate
基金Supported by National Natural Science Foundation of China (30870340,31071968)Science and Technology Research Projects of Chongqing Education Commission (KJ100620 )Key Project of School Fund of Chongqing Normal University (2011XLZ12)
文摘[ Objective ] The aim was to explore the relationship between the Dorsal and diapause development of Delia araiqua. [ Method ] The full-length cDNA of Dorsal in D. antiqua was cloned through RACE. The similarity among deduced amino acid sequence of Dorsal cDNA and the Dorsals of other 14 insect species were compared, and the phylogenetic analysis of these Dorsals was conducted. The expression level of Dorsal gene in winter-, summer- and non-diapausing pupae was analyzed. [ Result] The full-length of Dorsal cDNA sequence, 2 412 bp, was obtained with ORF 1974 bp, which coded 657 amino acids with predicted Mw 72.9 kDa and PI 8.5. The result of similarity comparison indicated that the DaDorsal was most related to those of Drosophlia melanogaster and Drosophlia pseudoobscura. The result of semi-quantitative RT-PCR analysis showed the expression level of Dorsal gene increased in characteristic duration of winter-, summer- and non- diapansing pupae, especially at the late diapanse, which might imply its relationship to D. antiqua diapause and development. [ Conclusion] The study lays the foundation for further study on gene function of Dorsal in insect diapause and development.
文摘Purpose: To determine the total direct costs (fixed and variable costs) of diffusion tensor imaging (DTI) and MR tractography reconstruction of the brain. Materials and Methods: The direct fixed and variable costs of DTI with MR tractography were determined prospectively with time and motion analysis in a 1.5-Tesla MR scanner using 15 encoding directions. Seventeen patients with seizure disorders, 9 males & 8 females, with mean age of 13 years (age range 2 - 33 years) were studied. Total direct costs were calculated from all direct fixed and variable costs. Sensitivity analyses between 1.5 versus a 3-Tesla MR system, and 15 versus 32 encoding directions were done. Results: The total direct costs of DTI and MR tractography for a 1.5-T system with 15 encoding directions were US $97. Variable cost was $76.80 and fixed cost was $20.20. Total direct costs for a 3-T system with 15 directions decreased to US $94.5 because of the shorter scan time despite the higher cost of the 3-T system. The most costly component of the direct cost was post-processing analysis at US $46.00. Conclusion: DTI with MR tractography has important total direct costs with variable costs higher than the fixed costs. The post processing variable cost is the most expensive component. Developing more accurate automated post-processing software for DTI and MR tractography is important to decrease this variable labor cost. Given the added value of DTI-MR tractography and the costs involved reimbursement codes should be considered.
文摘The purpose of this study was to demonstrate a simple and fast method for solving the time-dependent Bloch-McConnell equations describing the behavior of magnetization in magnetic resonance imaging (MRI) in the presence of multiple chemical exchange pools. First, the time-dependent Bloch- McConnell equations were reduced to a homogeneous linear differential equation, and then a simple equation was derived to solve it using a matrix operation and Kronecker tensor product. From these solutions, the longitudinal relaxation rate (R1ρ) and transverse relaxation rate in the rotating frame (R2ρ) and Z-spectra were obtained. As illustrative examples, the numerical solutions for linear and star-type three-pool chemical exchange models and linear, star- type, and kite-type four-pool chemical exchange models were presented. The effects of saturation time (ST) and radiofrequency irradiation power (ω1) on the chemical exchange saturation transfer (CEST) effect in these models were also investigated. Although R1ρ and R2ρ were not affected by the ST, the CEST effect observed in the Z-spectra increased and saturated with increasing ST. When ω1 was varied, the CEST effect increased with increasing ω1 in R1ρ, R2ρ, and Z-spectra. When ω1 was large, however, the spillover effect due to the direct saturation of bulk water protons also increased, suggesting that these parameters must be determined in consideration of both the CEST and spillover effects. Our method will be useful for analyzing the complex CEST contrast mechanism and for investigating the optimal conditions for CEST MRI in the presence of multiple chemical exchange pools.
基金Prince Sattambin Abdulaziz University for funding this research work through the project number(PSAU/2025/R/1446).
文摘The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.
基金supported by the European University of Atlantic.
文摘Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.
基金supported by Gansu Natural Science Foundation Programme(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。
文摘Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.
基金the National Science and Technology Council(NSTC)of the Republic of China,Taiwan,for financially supporting this research under Contract No.NSTC 112-2637-M-131-001.
文摘Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.
