Magnetic resonance imaging(MRI)inherently requires considerable time for data acquisition,but obtaining multi-contrast MRI data further prolongs this process,thereby increasing susceptibility to motion artifacts.It is...Magnetic resonance imaging(MRI)inherently requires considerable time for data acquisition,but obtaining multi-contrast MRI data further prolongs this process,thereby increasing susceptibility to motion artifacts.It is worth noting that the multi-contrast MR images have both structural similarities and unique contrast information.Therefore,to take advantage of their similarities while preserving their distinctive characteristics,we proposed a new method called high-dimensional subsets embedding(HDSE).This novel approach is based on the frame of low-rank modeling of local k-space neighborhoods with parallel imaging(P-LORAKS).Specifically,our approach utilizes the structural similarity of multi-contrast MR images to process different k-space data through two independent channels.In one channel,we individually separate the complementary T_(1)-T_(2)k-space data and directly construct a new subset of local k-space,allowing the model to better capture structural correlations between multiple contrasts.In another channel,we provide global under-sampled T_(2)-weighted k-space data further constrain image acquisition in highdimensional space to maintain image consistency and reduce noise amplification.These two different channels information is fused together to form high-dimensional feature objects.Besides,we embed the constructed objects into P-LORAKS in various ways to enhance the reconstruction performance.Experimental results demonstrated that the aided reconstruction of local subsets fusion and the high-dimensional reconstruction of adaptive global constraints can improve the accuracy of image reconstruction and enhance the robustness of the model.展开更多
Wave propagation in the viscoacoustic media is physically dispersive and dissipated.Completely excluding the numerical dispersion error from the physical dispersion in the viscoacoustic wave simu-lation is indispensab...Wave propagation in the viscoacoustic media is physically dispersive and dissipated.Completely excluding the numerical dispersion error from the physical dispersion in the viscoacoustic wave simu-lation is indispensable to understanding the intrinsic property of the wave propagation in attenuated media for the petroleum exploration geophysics.In recent years,a viscoacoustic wave equation char-acterized by fractional Laplacian gains wide attention in geophysical community.However,the first-order form of the viscoacoustic wave equation,often solved by the conventional staggered-grid pseu-dospectral method,suffers from the numerical dispersion error in time due to the low-order finite-difference approximation.It is challenging to completely eliminate the error because the viscoacoustic wave equation contains two temporal derivatives,which stem from the time stepping and the amplitude attenuation terms,respectively.To tackle the issue,we derive two exact first-order k-space viscoacoustic formulations that can fully exclude the numerical error from the physical dispersion.For the homoge-neous case,two formulations agree with the viscoacoustic analytical solution very well and have the same efficiency.For the heterogeneous case,our second k-space formulation is more efficient than the first one because the second formulation significantly reduces the number of the wavenumber-space mixed-domain operators,which are the expensive part of the viscoacoustic k-space simulation.Nu-merical cases validate that the two first-order k-space formulations are effective and efficient alternatives to the current staggered-grid pseudospectral formulation for the viscoacoustic wave simulation.展开更多
The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is implemented with non-cartesian sampled k-space trajectories in this paper. SENSE has the special capability to re...The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is implemented with non-cartesian sampled k-space trajectories in this paper. SENSE has the special capability to reduce the scanning time for MRI experiments while maintaining the image resolution with under-sampling data sets. In this manner, it has become an increasingly popular technique for multiple MRI data acquisition and image reconstruction schemes. The gridding algorithm is also implemented with SENSE due to its ability in evaluating forward and adjoin operator with non-cartesian sampled data. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct unaliased images from under-sampled data. The performance of SENSE with real data set identifies the computational issues to be improved for researched.展开更多
Protein succinylation is a biochemical reaction in which a succinyl group(-CO-CH2-CH2-CO-)is attached to the lysine residue of a protein molecule.Lysine succinylation plays important regulatory roles in living cells.H...Protein succinylation is a biochemical reaction in which a succinyl group(-CO-CH2-CH2-CO-)is attached to the lysine residue of a protein molecule.Lysine succinylation plays important regulatory roles in living cells.However,studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation.To facilitate this process,several tools have been proposed for the computational identification of succinylated lysine sites.In this study,we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition.Using experimentally verified lysine succinylated sites collected from public resources,the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program.These findings enabled the adoption of an effective machine learning method,support vector machine,to train a predictive model with not only the amino acid composition,but also the composition of k-spaced amino acid pairs.After the selection of the best model using a ten-fold crossvalidation approach,the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles.Finally,the selected model was used to develop a web-based tool,SuccSite,to aid the study of protein succinylation.Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites.We will regularly update SuccSite by integrating more experimental datasets.SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.展开更多
Every complete metric linear space is a K-space.A non-completenormed space can be a K-space.And non-metrizable K-spaces exist.A series ofbasic results on complete metric linear spaces Can be generalized to K-spaces.Fo...Every complete metric linear space is a K-space.A non-completenormed space can be a K-space.And non-metrizable K-spaces exist.A series ofbasic results on complete metric linear spaces Can be generalized to K-spaces.Forexample,every bounded linear operator from a K-space into a locally convex spaceis sequentially continuous;every bounded semi-norm on a K-space is sequentiallycontinuous and,therefore,piecewise separable;a.C-sequeatial K-space is both bar-relled and bornological;the family of sequentially continuous linear functionals ona K-space is weak sequentially complete.展开更多
Let K be a class of spaces which are eigher a pseudo-open s-image of a metric space or a k-space having a compact-countable closed k-network. Let K′ be a class of spaces which are either a Fréchet space with a p...Let K be a class of spaces which are eigher a pseudo-open s-image of a metric space or a k-space having a compact-countable closed k-network. Let K′ be a class of spaces which are either a Fréchet space with a point-countable k-network or a point-G_δ k-space having a compact-countable k-network. In this paper, we obtain some sufficient and necessary conditions that the products of finitely or countably many spaces in the class K or K′ are a k-space. The main results are that Theorem A If X, Y ∈ K. Then X x Y is a k-space if and only if (X, Y) has the Tanaka’s condition. Theorem B The following are equivalent: (a) BF(w2) is false. (b) For each X, Y ∈ K′, X x Y is a k-space if and only if (X, Y) has the Tanaka’s condition.展开更多
目的优化STAR-VIBE序列,探索自由呼吸状态下肝脏多期动态增强扫描的临床应用价值。方法纳入2022年3月~2024年7月武汉中心医院96例肝脏增强磁共振扫描患者,根据患者是否能屏气15 s将患者分为屏气功能正常组与屏气障碍组,48例/组。两组均...目的优化STAR-VIBE序列,探索自由呼吸状态下肝脏多期动态增强扫描的临床应用价值。方法纳入2022年3月~2024年7月武汉中心医院96例肝脏增强磁共振扫描患者,根据患者是否能屏气15 s将患者分为屏气功能正常组与屏气障碍组,48例/组。两组均接受STAR-VIBE自由呼吸动态增强扫描及延迟期扫描,并行常规T1-VIBE屏气延迟增强扫描。通过双盲法评估两种序列动态增强各期相的显影准确性;采用5分量表对延迟期图像质量进行定量评估,在两组延迟期STAR-VIBE及T1-VIBE图像同一层面避开血管及病灶测量肝实质、竖脊肌及背景噪声信号强度值(SI),分别计算肝脏信噪比(SNR)与对比噪声比(CNR)。结果研究队列中2例患者动脉期和门静脉期分期不明显,总体分期准确率98%。屏气正常组中,STAR-VIBE及T1-VIBE延迟期图像质量评分无统计学意义(P>0.05),均达到良好水平(STAR-VIBE:4.66±0.59 vs T1-VIBE:4.83±0.37),满足诊断需求;屏气障碍组两序列间评分差异有统计学意义(P<0.05)。两组间STAR-VIBE延迟图像质量评分均获得了良好,差异无统计学意义(P>0.05)。SNR与CNR分析显示,屏气正常组STAR-VIBE及T1-VIBE延迟间SNR/CNR的差异无统计学意义(P>0.05),而屏气障碍组的差异有统计学意义(P<0.05)。两组间SNR、CNR的差异无统计学意义(P>0.05)。结论经优化的STAR-VIBE序列能够获取符合诊断要求的自由呼吸状态下肝脏多期动态增强影像,为屏气功能受限患者提供了有效的影像学评估方案。展开更多
基金supported by National Natural Science Foundation under 62122033.
文摘Magnetic resonance imaging(MRI)inherently requires considerable time for data acquisition,but obtaining multi-contrast MRI data further prolongs this process,thereby increasing susceptibility to motion artifacts.It is worth noting that the multi-contrast MR images have both structural similarities and unique contrast information.Therefore,to take advantage of their similarities while preserving their distinctive characteristics,we proposed a new method called high-dimensional subsets embedding(HDSE).This novel approach is based on the frame of low-rank modeling of local k-space neighborhoods with parallel imaging(P-LORAKS).Specifically,our approach utilizes the structural similarity of multi-contrast MR images to process different k-space data through two independent channels.In one channel,we individually separate the complementary T_(1)-T_(2)k-space data and directly construct a new subset of local k-space,allowing the model to better capture structural correlations between multiple contrasts.In another channel,we provide global under-sampled T_(2)-weighted k-space data further constrain image acquisition in highdimensional space to maintain image consistency and reduce noise amplification.These two different channels information is fused together to form high-dimensional feature objects.Besides,we embed the constructed objects into P-LORAKS in various ways to enhance the reconstruction performance.Experimental results demonstrated that the aided reconstruction of local subsets fusion and the high-dimensional reconstruction of adaptive global constraints can improve the accuracy of image reconstruction and enhance the robustness of the model.
文摘Wave propagation in the viscoacoustic media is physically dispersive and dissipated.Completely excluding the numerical dispersion error from the physical dispersion in the viscoacoustic wave simu-lation is indispensable to understanding the intrinsic property of the wave propagation in attenuated media for the petroleum exploration geophysics.In recent years,a viscoacoustic wave equation char-acterized by fractional Laplacian gains wide attention in geophysical community.However,the first-order form of the viscoacoustic wave equation,often solved by the conventional staggered-grid pseu-dospectral method,suffers from the numerical dispersion error in time due to the low-order finite-difference approximation.It is challenging to completely eliminate the error because the viscoacoustic wave equation contains two temporal derivatives,which stem from the time stepping and the amplitude attenuation terms,respectively.To tackle the issue,we derive two exact first-order k-space viscoacoustic formulations that can fully exclude the numerical error from the physical dispersion.For the homoge-neous case,two formulations agree with the viscoacoustic analytical solution very well and have the same efficiency.For the heterogeneous case,our second k-space formulation is more efficient than the first one because the second formulation significantly reduces the number of the wavenumber-space mixed-domain operators,which are the expensive part of the viscoacoustic k-space simulation.Nu-merical cases validate that the two first-order k-space formulations are effective and efficient alternatives to the current staggered-grid pseudospectral formulation for the viscoacoustic wave simulation.
文摘The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is implemented with non-cartesian sampled k-space trajectories in this paper. SENSE has the special capability to reduce the scanning time for MRI experiments while maintaining the image resolution with under-sampling data sets. In this manner, it has become an increasingly popular technique for multiple MRI data acquisition and image reconstruction schemes. The gridding algorithm is also implemented with SENSE due to its ability in evaluating forward and adjoin operator with non-cartesian sampled data. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct unaliased images from under-sampled data. The performance of SENSE with real data set identifies the computational issues to be improved for researched.
基金the Warshel Institute for Computational Biology,School of Life and Health Sciences,The Chinese University of Hong Kong,Shenzhen,China for financially supporting this research
文摘Protein succinylation is a biochemical reaction in which a succinyl group(-CO-CH2-CH2-CO-)is attached to the lysine residue of a protein molecule.Lysine succinylation plays important regulatory roles in living cells.However,studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation.To facilitate this process,several tools have been proposed for the computational identification of succinylated lysine sites.In this study,we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition.Using experimentally verified lysine succinylated sites collected from public resources,the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program.These findings enabled the adoption of an effective machine learning method,support vector machine,to train a predictive model with not only the amino acid composition,but also the composition of k-spaced amino acid pairs.After the selection of the best model using a ten-fold crossvalidation approach,the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles.Finally,the selected model was used to develop a web-based tool,SuccSite,to aid the study of protein succinylation.Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites.We will regularly update SuccSite by integrating more experimental datasets.SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.
文摘Every complete metric linear space is a K-space.A non-completenormed space can be a K-space.And non-metrizable K-spaces exist.A series ofbasic results on complete metric linear spaces Can be generalized to K-spaces.Forexample,every bounded linear operator from a K-space into a locally convex spaceis sequentially continuous;every bounded semi-norm on a K-space is sequentiallycontinuous and,therefore,piecewise separable;a.C-sequeatial K-space is both bar-relled and bornological;the family of sequentially continuous linear functionals ona K-space is weak sequentially complete.
基金Project supported by the Mathematical Tianyuan Foundation of China
文摘Let K be a class of spaces which are eigher a pseudo-open s-image of a metric space or a k-space having a compact-countable closed k-network. Let K′ be a class of spaces which are either a Fréchet space with a point-countable k-network or a point-G_δ k-space having a compact-countable k-network. In this paper, we obtain some sufficient and necessary conditions that the products of finitely or countably many spaces in the class K or K′ are a k-space. The main results are that Theorem A If X, Y ∈ K. Then X x Y is a k-space if and only if (X, Y) has the Tanaka’s condition. Theorem B The following are equivalent: (a) BF(w2) is false. (b) For each X, Y ∈ K′, X x Y is a k-space if and only if (X, Y) has the Tanaka’s condition.
文摘目的优化STAR-VIBE序列,探索自由呼吸状态下肝脏多期动态增强扫描的临床应用价值。方法纳入2022年3月~2024年7月武汉中心医院96例肝脏增强磁共振扫描患者,根据患者是否能屏气15 s将患者分为屏气功能正常组与屏气障碍组,48例/组。两组均接受STAR-VIBE自由呼吸动态增强扫描及延迟期扫描,并行常规T1-VIBE屏气延迟增强扫描。通过双盲法评估两种序列动态增强各期相的显影准确性;采用5分量表对延迟期图像质量进行定量评估,在两组延迟期STAR-VIBE及T1-VIBE图像同一层面避开血管及病灶测量肝实质、竖脊肌及背景噪声信号强度值(SI),分别计算肝脏信噪比(SNR)与对比噪声比(CNR)。结果研究队列中2例患者动脉期和门静脉期分期不明显,总体分期准确率98%。屏气正常组中,STAR-VIBE及T1-VIBE延迟期图像质量评分无统计学意义(P>0.05),均达到良好水平(STAR-VIBE:4.66±0.59 vs T1-VIBE:4.83±0.37),满足诊断需求;屏气障碍组两序列间评分差异有统计学意义(P<0.05)。两组间STAR-VIBE延迟图像质量评分均获得了良好,差异无统计学意义(P>0.05)。SNR与CNR分析显示,屏气正常组STAR-VIBE及T1-VIBE延迟间SNR/CNR的差异无统计学意义(P>0.05),而屏气障碍组的差异有统计学意义(P<0.05)。两组间SNR、CNR的差异无统计学意义(P>0.05)。结论经优化的STAR-VIBE序列能够获取符合诊断要求的自由呼吸状态下肝脏多期动态增强影像,为屏气功能受限患者提供了有效的影像学评估方案。
