Background Structural imaging holds great potential for precise targeting and stimulation for deep brain stimulation(DBS).The anatomical information it provides may serve as potential biomarkers for predicting the eff...Background Structural imaging holds great potential for precise targeting and stimulation for deep brain stimulation(DBS).The anatomical information it provides may serve as potential biomarkers for predicting the efficacy of DBS in treatment-resistant depression(TRD).Aims The primary aim is to identify preoperative imaging biomarkers that correlate with the efficacy of DBS in patients with TRD.Methods Preoperative imaging parameters were estimated and correlated with the 6-month clinical outcome of patients with TRD receiving combined bed nucleus of the stria terminalis(BNST)-nucleus accumbens(NAc)DBS.White matter(WM)properties were extracted and compared between the response/non-response and remission/non-remission groups.Structural connectome was constructed and analysed using graph theory.Distances of the volume of activated tissue(VAT)to the main modulating tracts were also estimated to evaluate the correlations.Results Differences in fibre bundle properties of tracts,including superior thalamic radiation and reticulospinal tract,were observed between the remission and nonremission groups.Distance of the centre of the VAT to tracts connecting the ventral tegmental area and the anterior limb of internal capsule on the left side varied between the remission and non-remission groups(p=0.010,t=3.07).The normalised clustering coefficient(γ)and the small-world property(σ)in graph analysis correlated with the symptom improvement after the correction of age.Conclusions Presurgical structural alterations in WM tracts connecting the frontal area with subcortical regions,as well as the distance of the VAT to the modulating tracts,may influence the clinical outcome of BNST-NAc DBS.These findings provide potential imaging biomarkers for the DBS treatment for patients with TRD.展开更多
Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under differen...Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.展开更多
Nuclear reaction rate A is a significant factor in processes of nucleosyntheses.A multi-layer directed-weighted nuclear reaction network,in which the reaction rate is taken as the weight,and neutron,proton,4 He and th...Nuclear reaction rate A is a significant factor in processes of nucleosyntheses.A multi-layer directed-weighted nuclear reaction network,in which the reaction rate is taken as the weight,and neutron,proton,4 He and the remainder nuclei as the criteria for different reaction layers,is for the first time built based on all thermonuclear reactions in the JINA REACLIB database.Our results show that with the increase in the stellar temperature T9,the distribution of nuclear reaction rates on the R-layer network demonstrates a transition from unimodal to bimodal distributions.Nuclei on the R-layer in the region of A=[1,2.5×101]have a more complicated out-going degree distribution than that in the region of A=[1011,1013],and the number of involved nuclei at T9=1 is very different from the one at T9=3.The redundant nuclei in the region of A=[1,2.5×101]at T9=3 prefer(γ,p)and(γ,α)reactions to the ones at T9=1,which produce nuclei around theβstable line.This work offers a novel way to the big-data analysis on the nuclear reaction network at stellar temperatures.展开更多
基金supported by an unrestricted,investigator-initiated research grant by Scenery(BS),which provided the devices used.The project was sponsored by SJTU Trans-med Awards Research(2019015 to BS)Shanghai Clinical Research Centre for Mental Health(19MC191100 to BS)+3 种基金sponsored by the National Natural Science Foundation of China(81771482)supported by the Guangci Professorship Programme of Ruijin Hospital(N/A)and a Medical Research Council Senior Clinical Fellowship(MR/P008747/1)sponsored by the National Natural Science Foundation of China(82101546)the Shanghai Sailing Program(21YF1426700).The funding sources were not involved in the design and conduct of the study。
文摘Background Structural imaging holds great potential for precise targeting and stimulation for deep brain stimulation(DBS).The anatomical information it provides may serve as potential biomarkers for predicting the efficacy of DBS in treatment-resistant depression(TRD).Aims The primary aim is to identify preoperative imaging biomarkers that correlate with the efficacy of DBS in patients with TRD.Methods Preoperative imaging parameters were estimated and correlated with the 6-month clinical outcome of patients with TRD receiving combined bed nucleus of the stria terminalis(BNST)-nucleus accumbens(NAc)DBS.White matter(WM)properties were extracted and compared between the response/non-response and remission/non-remission groups.Structural connectome was constructed and analysed using graph theory.Distances of the volume of activated tissue(VAT)to the main modulating tracts were also estimated to evaluate the correlations.Results Differences in fibre bundle properties of tracts,including superior thalamic radiation and reticulospinal tract,were observed between the remission and nonremission groups.Distance of the centre of the VAT to tracts connecting the ventral tegmental area and the anterior limb of internal capsule on the left side varied between the remission and non-remission groups(p=0.010,t=3.07).The normalised clustering coefficient(γ)and the small-world property(σ)in graph analysis correlated with the symptom improvement after the correction of age.Conclusions Presurgical structural alterations in WM tracts connecting the frontal area with subcortical regions,as well as the distance of the VAT to the modulating tracts,may influence the clinical outcome of BNST-NAc DBS.These findings provide potential imaging biomarkers for the DBS treatment for patients with TRD.
基金supported by National Natural Science Foundation of China (No.62101136)Shanghai Sailing Program (No.21YF1402800)+3 种基金Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01)ZJLab,Shanghai Municipal of Science and Technology Project (No.20JC1419500)Natural Science Foundation of Chongqing (No.CSTB2022NSCQ-MSX0360)Shanghai Center for Brain Science and Brain-inspired Technology.
文摘Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11890714,11421505,11875133,and 11075057)the National Key R&D Program of China(Grant No.2018YFB2101302)+1 种基金the Key Research Program of Frontier Sciences of the CAS(Grant No.QYZDJ-SSW-SLH002)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB34030200)。
文摘Nuclear reaction rate A is a significant factor in processes of nucleosyntheses.A multi-layer directed-weighted nuclear reaction network,in which the reaction rate is taken as the weight,and neutron,proton,4 He and the remainder nuclei as the criteria for different reaction layers,is for the first time built based on all thermonuclear reactions in the JINA REACLIB database.Our results show that with the increase in the stellar temperature T9,the distribution of nuclear reaction rates on the R-layer network demonstrates a transition from unimodal to bimodal distributions.Nuclei on the R-layer in the region of A=[1,2.5×101]have a more complicated out-going degree distribution than that in the region of A=[1011,1013],and the number of involved nuclei at T9=1 is very different from the one at T9=3.The redundant nuclei in the region of A=[1,2.5×101]at T9=3 prefer(γ,p)and(γ,α)reactions to the ones at T9=1,which produce nuclei around theβstable line.This work offers a novel way to the big-data analysis on the nuclear reaction network at stellar temperatures.
