Semantic image synthesis aims to generate highquality images given semantic conditions,i.e.,segmentation masks and style reference images.Existing methods widely adopt generative adversarial networks(GANs).GANs take a...Semantic image synthesis aims to generate highquality images given semantic conditions,i.e.,segmentation masks and style reference images.Existing methods widely adopt generative adversarial networks(GANs).GANs take all conditional inputs and directly synthesize images in a single forward step.In this paper,semantic image synthesis is treated as an image denoising task and is handled with a novel image-to-image diffusion model(IIDM).展开更多
BACKGROUND Spinal cord injury can lead to long-term disability,but current imaging methods are limited in predicting outcomes.Rapid diffusion tensor imaging(DTI)has shown promise,yet its clinical utility remains under...BACKGROUND Spinal cord injury can lead to long-term disability,but current imaging methods are limited in predicting outcomes.Rapid diffusion tensor imaging(DTI)has shown promise,yet its clinical utility remains underexplored.AIM To evaluate the potential applications of a short DTI sequence,incorporated into a cervical spine magnetic resonance imaging(MRI)protocol,for characterizing a range of symptomatic spinal cord pathologies.We propose that cervical spine tractography can provide essential diagnostic information beyond what is currently available from conventional MRI.METHODS We utilized a quick DTI sequence to create tractography models of the cervical spinal cord in four patients with distinct pathologies of various etiologies:Cord contusion,metastasis,myelopathy,and multiple sclerosis.We used DSI Studio software for post-processing of tractography cases.Fiber tract findings for each pathology case were compared to five control cases from the same scanner by looking for individual differences in white matter tract integrity based on the fractional anisotropy(FA)and mean diffusivity(MD)of the regions of interest from controls.These correlated with clinical presentations and conventional MRI findings.RESULTS Control cases showed consistent and intact tract patterns with stable FA and MD values.In pathological cases,abnormalities in fiber orientation and tract continuity correlated with clinical symptoms and lesion locations.CONCLUSION The tractography models can provide additional information on white matter disruption that was not discernible on standard MRI sequences.However,its clinical use remains limited due to the need for specialized imaging protocols and complex post-processing,restricting its use to mostly academic settings.展开更多
BACKGROUND The differential diagnosis between hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC)is crucial.The individual differences of patients increase the complexity of diagnosis.Currently,imagi...BACKGROUND The differential diagnosis between hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC)is crucial.The individual differences of patients increase the complexity of diagnosis.Currently,imaging diagnosis mainly relies on conventional computed tomography and magnetic resonance imaging(MRI),but few studies have investigated MRI functional imaging.This study combined MRI functional imaging including intravoxel incoherent motion(IVIM)and diffusion kurtosis imaging(DKI),facilitating differential diagnosis.AIM To explore the differential diagnostic value of IVIM imaging and DKI in differentiating between HCC and ICC.METHODS A total of 58 patients who underwent multi-b-value diffusion weighted imaging(DWI)on a 3.0 T magnetic MRI scanner were enrolled in this study.Standard apparent diffusion coefficient(SADC),IVIM quantitative parameters,including pure diffusion coefficient(D),pseudo diffusion coefficient(Dstar),and perfusion fraction(f),as well as the DKI quantitative parameters mean diffusion coefficient(MD)and mean kurtosis coefficient(MK)were computed by multi-b DWI images.Theχ2 test was used for classified data,and a one-way analysis of variance was performed for counted data.P<0.05 indicated statistical significance.The diagnostic value of parameters in HCC and ICC was analyzed using the receiver operating characteristic(ROC)curve.RESULTS The SADC,D,and MD values were significantly lower in the HCC group compared to the ICC group,whereas MK was significantly higher in the HCC group than in the ICC group(P<0.05).No significant difference in Dstar and f was observed between the HCC group and the ICC group(P>0.05).The optimal cutoff levels of the total values of SADC,D,MK,MD and all associated parameters were 1.25×10^(-3)mm^(2)/second,1.32×10^(-3)mm^(2)/second,650.2×10^(-3)mm^(2)/second,1.41×10^(-3)mm^(2)/second and 0.46×10^(-3)mm^(2)/second,respectively.The sensitivity of diagnosis was 95%,80%,90%,100%,and 70%,respectively,the specificity of diagnosis was 67.39%,69.57%,67.39%,43.48%,and 93.48%,respectively,and the area under the ROC curve was 0.874,0.793,0.733,0.757,and 0.895,respectively.CONCLUSION SADC,D,MK,and MD could be used to distinguish HCC from ICC,with the diagnostic value reaching a maximum after establishing a joint model.展开更多
Three high dimensional spatial standardization algorithms are used for diffusion tensor image(DTI)registration,and seven kinds of methods are used to evaluate their performances.Firstly,the template used in this paper...Three high dimensional spatial standardization algorithms are used for diffusion tensor image(DTI)registration,and seven kinds of methods are used to evaluate their performances.Firstly,the template used in this paper was obtained by spatial transformation of 16 subjects by means of tensor-based standardization.Then,high dimensional standardization algorithms for diffusion tensor images,including fractional anisotropy(FA)based diffeomorphic registration algorithm,FA based elastic registration algorithm and tensor-based registration algorithm,were performed.Finally,7 kinds of evaluation methods,including normalized standard deviation,dyadic coherence,diffusion cross-correlation,overlap of eigenvalue-eigenvector pairs,Euclidean distance of diffusion tensor,and Euclidean distance of the deviatoric tensor and deviatoric of tensors,were used to qualitatively compare and summarize the above standardization algorithms.Experimental results revealed that the high-dimensional tensor-based standardization algorithms perform well and can maintain the consistency of anatomical structures.展开更多
Objective To assess the reproducibility of whole-body diffusion weighted imaging(WB-DWI) technique in healthy volunteers under normal breathing with background body signal suppression.Methods WB-DWI was performed on 3...Objective To assess the reproducibility of whole-body diffusion weighted imaging(WB-DWI) technique in healthy volunteers under normal breathing with background body signal suppression.Methods WB-DWI was performed on 32 healthy volunteers twice within two-week period using short TI inversion-recovery diffusion-weighted echo-planar imaging sequence and built-in body coil.The volunteers were scanned across six stations continuously covering the entire body from the head to the feet under normal breathing.The bone apparent diffusion coefficient(ADC) and exponential ADC(eADC) of regions of interest(ROIs) were measured.We analyzed correlation of the results using paired-t-test to assess the reproducibility of the WB-DWI technique.Results We were successful in collecting and analyzing data of 64 WB-DWI images.There was no significant difference in bone ADC and eADC of 824 ROIs between the paired observers and paired scans(P>0.05).Most of the images from all stations were of diagnostic quality.Conclusion The measurements of bone ADC and eADC have good reproducibility.WB-DWI technique under normal breathing with background body signal suppression is adequate.展开更多
Increasingly,attention is being directed towards time-dependent diffusion magnetic resonance imaging(TDDMRI),a method that reveals time-related changes in the diffusional behavior of water molecules in biological tiss...Increasingly,attention is being directed towards time-dependent diffusion magnetic resonance imaging(TDDMRI),a method that reveals time-related changes in the diffusional behavior of water molecules in biological tissues,thereby enabling us to probe related microstructure events.With ongoing improvements in hardware and advanced pulse sequences,significant progress has been made in applying TDDMRI to clinical research.The development of accurate mathematical models and computational methods has bolstered theoretical support for TDDMRI and elevated our understanding of molecular diffusion.In this review,we introduce the concept and basic physics of TDDMRI,and then focus on the measurement strategies and modeling approaches in short-and long-diffusion-time domains.Finally,we discuss the challenges in this field,including the requirement for efficient scanning and data processing technologies,the development of more precise models depicting time-dependent molecular diffusion,and critical clinical applications.展开更多
BACKGROUND About 10%-31% of colorectal liver metastases(CRLM)patients would concomitantly show hepatic lymph node metastases(LNM),which was considered as sign of poor biological behavior and a relative contraindicatio...BACKGROUND About 10%-31% of colorectal liver metastases(CRLM)patients would concomitantly show hepatic lymph node metastases(LNM),which was considered as sign of poor biological behavior and a relative contraindication for liver resection.Up to now,there’s still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM,except for pathology examination of lymph node after resection.AIM To compare the ability of mono-exponential,bi-exponential,and stretchedexponential diffusion-weighted imaging(DWI)models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.METHODS In this retrospective study,97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging,including DWI with ten b values before and after chemotherapy.Various parameters,such as the apparent diffusion coefficient from the mono-exponential model,and the true diffusion coefficient,the pseudo-diffusion coefficient,and the perfusion fraction derived from the intravoxel incoherent motion model,along with distributed diffusion coefficient(DDC)andαfrom the stretched-exponential model(SEM),were measured.The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups.A nomogram was constructed to predict the hepatic lymph node status.The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.RESULTS Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes.A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients,with an area under the curve of 0.873.Furthermore,parameters from SEM showed substantial repeatability.CONCLUSION The developed nomogram,incorporating the pre-treatment DDC and the short axis of the largest lymph node,can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery.This nomogram was proven to be more valuable,exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI.The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.展开更多
To denoise the diffusion weighted images （DWls） featured as multi-boundary, which was very important for the calculation of accurate DTIs （diffusion tensor magnetic resonance imaging）, a modified Wiener filter was...To denoise the diffusion weighted images （DWls） featured as multi-boundary, which was very important for the calculation of accurate DTIs （diffusion tensor magnetic resonance imaging）, a modified Wiener filter was proposed. Through analyzing the widely accepted adaptive Wiener filter in image denoising fields, which suffered from annoying noise around the edges of DWIs and in turn greatly affected the denoising effect of DWIs, a local-shift method capable of overcoming the defect of the adaptive Wiener filter was proposed to help better denoising DWIs and the modified Wiener filter was constructed accordingly. To verify the denoising effect of the proposed method, the modified Wiener filter and adaptive Wiener filter were performed on the noisy DWI data, respectively, and the results of different methods were analyzed in detail and put into comparison. The experimental data show that, with the modified Wiener method, more satisfactory results such as lower non-positive tensor percentage and lower mean square errors of the fractional anisotropy map and trace map are obtained than those with the adaptive Wiener method, which in turn helps to produce more accurate DTIs.展开更多
The existence of a global minimizer for a variational problem arising in registration of diffusion tensor images is proved, which ensures that there is a regular spatial transformation for the registration of diffusio...The existence of a global minimizer for a variational problem arising in registration of diffusion tensor images is proved, which ensures that there is a regular spatial transformation for the registration of diffusion tensor images.展开更多
BACKGROUND： Limbic encephalitis is a rare syndrome that specifically affects the limbic system. Magnetic resonance imaging （MRI） has been typically used to detect brain changes in this disease. However, the mechani...BACKGROUND： Limbic encephalitis is a rare syndrome that specifically affects the limbic system. Magnetic resonance imaging （MRI） has been typically used to detect brain changes in this disease. However, the mechanisms of limbic encephalitis-related white matter damage remain poorly understood. OBJECTIVE： To characterize white matter connectivity changes secondary to injuries of the limbic system in limbic encephalitis through combined application of diffusion tensor imaging （DTI） and voxel-based morphometry. DESIGN, TIME AND SETTING： A non-randomized, controlled, clinical, neuroimaging, DTI study was performed at the Department of Radiology, West China Hospital in December 2008. PARTICIPANTS： A male, 46-year-old, limbic encephalitis patient, as well as 11 age- and gender-matched healthy volunteers, were enrolled in the present study. METHODS： MRI was performed on the limbic encephalitis patient using a 3.0T MR scanner. Three-dimensional SPGR Tl-weighted images and DTI were acquired in the patient and controls. Data were analyzed using Matlab 7.0 and SPM2 software. MAIN OUTCOME MEASURES： Results from routine MRI scan with contrast enhancement of patient, as well as fractional anisotropy and mean diffusivity value map differences between patient and controls. RESULTS： Significant symmetric MRI signal intensity abnormalities were observed with routine MRI Affected bilateral hippocampi and amygdala exhibited hypointense signals in TIWI and hyperintense signals in T2 images. The DTI study revealed decreased fractional anisotropy values in the bilateral alveus and fimbria of the hippocampus, bilateral internal and external capsules, white matter of the right prefrontal area, and left corona radiate in the patient compared with normal controls （P 〈 0.001） Significantly increased fractional anisotropy, mean diffusivity, or decreased mean diffusivity were not observed in the patient, compared with controls. CONCLUSION： Secondary white matter damage to the hippocampal afveus and fimbria was apparent in the limbic encephalitis patient. In addition, other white matter fiber injuries surrounded the limbic structures, which were not attributed to secondary limbic system injuries.展开更多
The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review inve...The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following:Technical challenges and corresponding strategies:Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities.Advanced techniques including high‐performance gradient systems,parallel imaging,multi‐shot acquisition,and parallel transmission can mitigate these issues.Comparison of 3‐Tesla and 7T diffusion imaging:Technologies such as multiplexed sensitivity encoding and deep learning reconstruction(DLR)have been developed to mitigate artifacts and improve image quality.This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system,facilitating enhanced visualization of microstructural changes.Despite greater geometric distortions and signal inhomogeneity at 7T,the system shows clear advantages in high b‐value imaging and highresolution diffusion tensor imaging.Additionally,multiplexed sensitivity encoding significantly reduces image blurring and distortion,and DLR substantially improves the signal‐to‐noise ratio and image sharpness.7T diffusion applications in structural analysis and disease characterization:This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.展开更多
The human brain is known to contain a maximum of eight cholinergic nuclei： the basal forebrain region： the medial septal nucleus （Ch 1）, the vertical nucleus of the diagonal band （Ch 2）, the horizontal limb of t...The human brain is known to contain a maximum of eight cholinergic nuclei： the basal forebrain region： the medial septal nucleus （Ch 1）, the vertical nucleus of the diagonal band （Ch 2）, the horizontal limb of the diago- nal band （Ch 3）, and the nucleus basalis of Meynert （Ch 4）; the brainstem： the pedunculopontine nucleus （Ch 5）, the laterodorsal tegmental nucleus （Ch 6）, and the para- bigeminal nucleus （Ch 8）; and the thalamus： the medial habenular nucleus （Ch 7） （Nieuwenhuys et al., 2008; Naidich and Duvernoy, 2009）. The cingulum is the neu- ral tract extending from the orbitofrontal cortex to the medial temporal lobe （Mufson and Pandya, 1984）. The cingulum plays an important role in memory because it is a passage of the medial cholinergic pathway, which pro- vides cholinergic innervations to the cerebral cortex after originating from Ch 1 and Ch 2 as well as Ch 4 （mainly） （Selden et al., 1998; Nieuwenhuys et al., 2008; Hong and lang, 2010）.展开更多
The Rician noise introduced into the diffusion tensor images （DTIs） can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the wavelet...The Rician noise introduced into the diffusion tensor images （DTIs） can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the wavelet-based diffusion method to denoise multiehannel typed diffusion weighted （DW） images. The presented smoothing strategy, which utilizes anisotropic nonlinear diffusion in wavelet domain, successfully removes noise while preserving both texture and edges. To evaluate quantitatively the efficiency of the presented method in accounting for the Rician noise introduced into the DW images, the peak-to-peak signal-to-noise ratio （PSNR） and signal-to-mean squared error ratio （SMSE） metrics are adopted. Based on the synthetic and real data, we calculated the ap- parent diffusion coefficient （ADC） and tracked the fibers. We made comparisons between the presented model, the wave shrinkage and regularized nonlinear diffusion smoothing method. All the experiment results prove quantitatively and visually the better performance of the presented filter.展开更多
To evaluate the effect of the positive-indefinite matrix on the diffusion tensor-derived parameters, a modified algorithm is proposed for calculating these parameters. Magnetic resonance （MR） diffusion tensor images...To evaluate the effect of the positive-indefinite matrix on the diffusion tensor-derived parameters, a modified algorithm is proposed for calculating these parameters. Magnetic resonance （MR） diffusion tensor images of five healthy volunteers are collected. The diffusion sensitive gradient magnetic fields are applied along 25 directions and the diffusion weighting value is 1 000 s/mm^2. Many positive-indefinite diffusion tensors can be found in the white matter area, such as the genu and the splenium of corpus callosum. Due to the positive-indefinite matrix, the mean diffusivity （MD） and the fractional anisotropy （FA） are under-estimated and over-estimated by using the conventional algorithm. Thus, the conventional algorithm is modified by using the absolute values of all eigenvalues. Results show that both the robustness and the reliability for deriving these parameters are improved by the modified algorithm.展开更多
BACKGROUND Advanced esophageal squamous cell carcinoma(ESCC)has an extremely poor prognosis.Preoperative chemoradiotherapy(CRT)can significantly prolong survival,especially in those who achieve pathological complete r...BACKGROUND Advanced esophageal squamous cell carcinoma(ESCC)has an extremely poor prognosis.Preoperative chemoradiotherapy(CRT)can significantly prolong survival,especially in those who achieve pathological complete response(pCR).However,the pretherapeutic prediction of pCR remains challenging.AIM To predict pCR and survival in ESCC patients undergoing CRT using an artificial intelligence(AI)-based diffusion-weighted magnetic resonance imaging(DWI-MRI)radiomics model.METHODS We retrospectively analyzed 70 patients with ESCC who underwent curative surgery following CRT.For each patient,pre-treatment tumors were semi-automatically segmented in three dimensions from DWI-MRI images(b=0,1000 second/mm^(2)),and a total of 76 radiomics features were extracted from each segmented tumor.Using these features as explanatory variables and pCR as the objective variable,machine learning models for predicting pCR were developed using AutoGluon,an automated machine learning library,and validated by stratified double cross-validation.RESULTS pCR was achieved in 15 patients(21.4%).Apparent diffusion coefficient skewness demonstrated the highest predictive performance[area under the curve(AUC)=0.77].Gray-level co-occurrence matrix(GLCM)entropy(b=1000 second/mm²)was an independent prognostic factor for relapse-free survival(RFS)(hazard ratio=0.32,P=0.009).In Kaplan-Meier analysis,patients with high GLCM entropy showed significantly better RFS(P<0.001,log-rank).The best-performing machine learning model achieved an AUC of 0.85.The predicted pCR-positive group showed significantly better RFS than the predicted pCR-negative group(P=0.007,log-rank).CONCLUSION AI-based radiomics analysis of DWI-MRI images in ESCC has the potential to accurately predict the effect of CRT before treatment and contribute to constructing optimal treatment strategies.展开更多
Background While Nordic hamstring exercise(NHE)training has been shown to reduce hamstring strains,the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear.This study investigates architectural an...Background While Nordic hamstring exercise(NHE)training has been shown to reduce hamstring strains,the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear.This study investigates architectural and microstructural adaptations of the biceps femoris short head(BFsh),biceps femoris long head(BFlh),semitendinosus(ST),and semimembranosus(SM)in response to an NHE intervention.Methods Eleven subjects completed 9 weeks of supervised NHE training followed by 3 weeks of detraining.Magnetic resonance imaging was performed at pre-training,post-training,and detraining to assess architectural(volume,fiber tract length,and fiber tract angle)and microstructural(axial(AD),mean(MD),radial(RD)diffusivities,and fractional anisotropy(FA))parameters of the 4 hamstrings.Results NHE training induced significant but non-uniform hamstring muscle hypertrophy(BFsh:22%,BFlh:9%,ST:26%,SM:6%)and fiber tract length increase(BFsh:11%,BFlh:7%,ST:18%,SM:10%).AD(5%),MD(4%),and RD(5%)showed significant increases,but fiber tract angle and FA remained unchanged.After detraining,only ST showed a significant reduction(8%)in volume,which remained higher than the pre-training value.While fiber tract lengths returned to baseline,AD,MD,and RD remained higher than pre-training levels for all hamstrings.Conclusion The 9-week NHE training substantially increased hamstring muscle volume with greater hypertrophy in ST and BFsh.Hypertrophy was accompanied by increases in fiber tract lengths and cross-sections(increased RD).After 3 weeks of detraining,fiber tract length gains across all hamstrings declined,emphasizing the importance of sustained training to maintain all the protective adaptations.展开更多
Background:Platinum can cause chemotherapy-related cognitive impairment.Low-intensity focused ultrasound(LIFUS)is a promising noninvasive physical stimulation method with a unique advantage in neurological rehabilitat...Background:Platinum can cause chemotherapy-related cognitive impairment.Low-intensity focused ultrasound(LIFUS)is a promising noninvasive physical stimulation method with a unique advantage in neurological rehabilitation.We aimed to investigate whether LIFUS can alleviate cisplatin-induced cognitive impairment in rats and explore the related neuropatho-logical mechanisms.Methods:After confirming the target position for LIFUS treatment in 18 rats,64 rats were randomly divided into four groups:control,model,sham,and LIFUS groups.Before and after LIFUS treatment,detailed biological behavioral assessments and magnetic resonance imaging were performed.Finally,the rats were euthanized,and relevant histopathological and molecular biological experiments were conducted and analyzed.Results:In the Morris water maze,the model group showed fewer platform crossings(1.250.93 vs.5.691.58),a longer escape latency(41.6536.55 s vs.6.382.11 s),and a lower novel object recognition index(29.7711.83 vs.83.695.67)than the control group.LIFUS treatment improved these metrics,with more platform crossings(3.130.34),a higher recognition index(65.588.71),and a shorter escape latency(6.452.27 s).Longitudinal analysis of the LIFUS group further confirmed these improvements.Neuroimaging revealed significant differences in diffusion tensor imaging metrics of specific brain regions pre-and post-LIFUS.Moreover,neuropathology showed higher dendritic spine density,less myelin loss,fewer apoptotic cells,more synapses,and less mitochondrial autophagy after LIFUS treatment.The neuroimaging indicators were correlated with behavioral improvements,highlighting the potential of LIFUS for alleviating cognitive impairment(as demonstrated through imaging and analysis).Our investigation of the molecular biological mechanisms revealed distinct protein expression patterns in the hippocampus and its subregions.In the model group,glial fibrillary acidic protein(GFAP)and ionized calcium-binding adaptor molecule 1(IBA1)expression levels were elevated across the hippocampus,whereas neuronal nuclei(NeuN)expression was reduced.Subregional analysis revealed higher GFAP and IBA1 and lower NeuN,especially in the dentate gyrus subregion.Moreover,positive cell areas were larger in the cornu ammonis(CA)1,CA2,CA3,and dentate gyrus regions.In the CA2 and CA3,significant differences among the groups were observed in GFAP-positive cell counts and areas,and there were variations in NeuN expression.Conclusions:Our results suggest that LIFUS can reverse cisplatin-induced cognitive impairments.The neuroimaging findings were consistent with the behavioral and histological results and suggest a neuropathological basis that supports further research into the clinical applications of LIFUS.Furthermore,LIFUS appeared to enhance the plasticity of neuronal synapses in the rat hippocampus and reduce hippocampal inflammation.These findings highlight the clinical potential of LIFUS as an effective,noninvasive therapeutic strategy and monitoring tool for chemotherapy-induced cognitive deficits.展开更多
Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human braintissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidlyest...Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human braintissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidlyestimate diffusion tensors (DTs) using only a small quantity of diffusion-weighted (DW) images. However, thesemethods typically use the DW images obtained with fixed q-space sampling schemes as the training data, limitingthe application scenarios of such methods. To address this issue, we develop a new deep neural network calledq-space-coordinate-guided diffusion tensor imaging (QCG-DTI), which can efficiently and correctly estimate DTsunder flexible q-space sampling schemes. First, we propose a q-space-coordinate-embedded feature consistencystrategy to ensure the correspondence between q-space-coordinates and their respective DW images. Second, aq-space-coordinate fusion (QCF) module is introduced which eficiently embeds q-space-coordinates into multiscalefeatures of the corresponding DW images by linearly adjusting the feature maps along the channel dimension,thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense(MRD) module is proposed which enhances the network's feature extraction and image reconstruction capabilitiesby using dual-branch convolutions with different kernel sizes to extract features at diferent scales. Compared tostate-of-the-art methods that rely on a fixed sampling scheme, the proposed network can obtain high-quality diffusiontensors and derived parameters even using DW images acquired with flexible q-space sampling schemes. Comparedto state-of-the-art deep learning methods, QCG-DTI reduces the mean absolute error by approximately 15% onfractional anisotropy and around 25% on mean diffusivity.展开更多
BACKGROUND Anoxic brain injury is a potentially lethal condition characterized by cerebral hypoperfusion and irreversible neuronal injury.Arterial spin-labeling(ASL)perfusion and diffusion-weighted imaging(DWI)magneti...BACKGROUND Anoxic brain injury is a potentially lethal condition characterized by cerebral hypoperfusion and irreversible neuronal injury.Arterial spin-labeling(ASL)perfusion and diffusion-weighted imaging(DWI)magnetic resonance imaging(MRI)have been proposed as tools to detect cerebral ischemic changes and may aid in the assessment of anoxic injury.AIM To explore the relationship between regional ASL perfusion patterns and clinical outcomes following cardiac arrest.METHODS We performed a retrospective review to identify patients with clinical suspicion of anoxic brain injury who underwent MRI within 15 days of cardiac arrest.Receiver operator characteristic(ROC)analysis and univariate logistic regression were used to evaluate associations between ASL perfusion scores,DWI signal intensity,and the following clinical features:(1)Myoclonus status epilepticus(MSE)within 24 hours;(2)Absent extensor or motor reflexes(EMR)at day 3 post-arrest;and(3)Absent brainstem reflexes(BSR)within 15 days.RESULTS Twenty-eight patients met inclusion criteria.Increased ASL signal in the left occipital lobe was significantly associated with MSE(P=0.038),while a trend was observed between right frontal ASL signal and EMR(P=0.078).ROC analysis showed that ASL scores≥7 were associated with higher odds of absent BSR(OR 2.14,P=0.53),though this did not reach statistical significance.DWI signal intensity did not show significant associations with clinical outcomes.The overall discriminatory performance of ASL for predicting outcomes was limited(AUC≈0.52).CONCLUSION This exploratory study suggests that regional ASL hyperperfusion,particularly in the left occipital and right frontal lobes,may be associated with adverse clinical signs following cardiac arrest.However,most findings did not reach statistical significance,and the study was underpowered to detect small-to-moderate effects.These preliminary results should be interpreted with caution and considered hypothesis-generating.Larger,prospective studies are warranted to clarify the prognostic value of ASL perfusion imaging in anoxic brain injury.展开更多
BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairmen...BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairment,is critical for clinical intervention,yet it remains elusive and challenging to identify.AIM To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.METHODS Using diffusion tensor imaging(DTI),we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls.Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.RESULTS T2DM patients exhibited reduced global/local efficiency and small-worldness,alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections,suggesting compensatory mechanisms.A classification model leveraging 18 connectivity features achieved 92.5%accuracy in distinguishing T2DM brains.Structural connectivity patterns further predicted disease onset with an error of±1.9 years.CONCLUSION Our findings reveal early-stage brain network reorganization in T2DM,highlighting subcortical-frontal connectivity as a compensatory biomarker.The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.展开更多
基金supported by the National Natural Science Foundation for Young Scientists of China Award(No.62106289).
文摘Semantic image synthesis aims to generate highquality images given semantic conditions,i.e.,segmentation masks and style reference images.Existing methods widely adopt generative adversarial networks(GANs).GANs take all conditional inputs and directly synthesize images in a single forward step.In this paper,semantic image synthesis is treated as an image denoising task and is handled with a novel image-to-image diffusion model(IIDM).
文摘BACKGROUND Spinal cord injury can lead to long-term disability,but current imaging methods are limited in predicting outcomes.Rapid diffusion tensor imaging(DTI)has shown promise,yet its clinical utility remains underexplored.AIM To evaluate the potential applications of a short DTI sequence,incorporated into a cervical spine magnetic resonance imaging(MRI)protocol,for characterizing a range of symptomatic spinal cord pathologies.We propose that cervical spine tractography can provide essential diagnostic information beyond what is currently available from conventional MRI.METHODS We utilized a quick DTI sequence to create tractography models of the cervical spinal cord in four patients with distinct pathologies of various etiologies:Cord contusion,metastasis,myelopathy,and multiple sclerosis.We used DSI Studio software for post-processing of tractography cases.Fiber tract findings for each pathology case were compared to five control cases from the same scanner by looking for individual differences in white matter tract integrity based on the fractional anisotropy(FA)and mean diffusivity(MD)of the regions of interest from controls.These correlated with clinical presentations and conventional MRI findings.RESULTS Control cases showed consistent and intact tract patterns with stable FA and MD values.In pathological cases,abnormalities in fiber orientation and tract continuity correlated with clinical symptoms and lesion locations.CONCLUSION The tractography models can provide additional information on white matter disruption that was not discernible on standard MRI sequences.However,its clinical use remains limited due to the need for specialized imaging protocols and complex post-processing,restricting its use to mostly academic settings.
基金Supported by Chutian Talents of Hubei,No.CTYC001Talent Project of Hubei Cancer Hospital,No.2025HBCHLHRC001Clinical Medical Science and Technology of Jinan,No.202134053.
文摘BACKGROUND The differential diagnosis between hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC)is crucial.The individual differences of patients increase the complexity of diagnosis.Currently,imaging diagnosis mainly relies on conventional computed tomography and magnetic resonance imaging(MRI),but few studies have investigated MRI functional imaging.This study combined MRI functional imaging including intravoxel incoherent motion(IVIM)and diffusion kurtosis imaging(DKI),facilitating differential diagnosis.AIM To explore the differential diagnostic value of IVIM imaging and DKI in differentiating between HCC and ICC.METHODS A total of 58 patients who underwent multi-b-value diffusion weighted imaging(DWI)on a 3.0 T magnetic MRI scanner were enrolled in this study.Standard apparent diffusion coefficient(SADC),IVIM quantitative parameters,including pure diffusion coefficient(D),pseudo diffusion coefficient(Dstar),and perfusion fraction(f),as well as the DKI quantitative parameters mean diffusion coefficient(MD)and mean kurtosis coefficient(MK)were computed by multi-b DWI images.Theχ2 test was used for classified data,and a one-way analysis of variance was performed for counted data.P<0.05 indicated statistical significance.The diagnostic value of parameters in HCC and ICC was analyzed using the receiver operating characteristic(ROC)curve.RESULTS The SADC,D,and MD values were significantly lower in the HCC group compared to the ICC group,whereas MK was significantly higher in the HCC group than in the ICC group(P<0.05).No significant difference in Dstar and f was observed between the HCC group and the ICC group(P>0.05).The optimal cutoff levels of the total values of SADC,D,MK,MD and all associated parameters were 1.25×10^(-3)mm^(2)/second,1.32×10^(-3)mm^(2)/second,650.2×10^(-3)mm^(2)/second,1.41×10^(-3)mm^(2)/second and 0.46×10^(-3)mm^(2)/second,respectively.The sensitivity of diagnosis was 95%,80%,90%,100%,and 70%,respectively,the specificity of diagnosis was 67.39%,69.57%,67.39%,43.48%,and 93.48%,respectively,and the area under the ROC curve was 0.874,0.793,0.733,0.757,and 0.895,respectively.CONCLUSION SADC,D,MK,and MD could be used to distinguish HCC from ICC,with the diagnostic value reaching a maximum after establishing a joint model.
基金Supported by the National Key Research and Development Program of China(2016YFC0100300)the National Natural Science Foundation of China(61402371,61771369)+1 种基金the Natural Science Basic Research Plan in Shaanxi Province of China(2017JM6008)the Fundamental Research Funds for the Central Universities of China(3102017zy032,3102018zy020)
文摘Three high dimensional spatial standardization algorithms are used for diffusion tensor image(DTI)registration,and seven kinds of methods are used to evaluate their performances.Firstly,the template used in this paper was obtained by spatial transformation of 16 subjects by means of tensor-based standardization.Then,high dimensional standardization algorithms for diffusion tensor images,including fractional anisotropy(FA)based diffeomorphic registration algorithm,FA based elastic registration algorithm and tensor-based registration algorithm,were performed.Finally,7 kinds of evaluation methods,including normalized standard deviation,dyadic coherence,diffusion cross-correlation,overlap of eigenvalue-eigenvector pairs,Euclidean distance of diffusion tensor,and Euclidean distance of the deviatoric tensor and deviatoric of tensors,were used to qualitatively compare and summarize the above standardization algorithms.Experimental results revealed that the high-dimensional tensor-based standardization algorithms perform well and can maintain the consistency of anatomical structures.
文摘Objective To assess the reproducibility of whole-body diffusion weighted imaging(WB-DWI) technique in healthy volunteers under normal breathing with background body signal suppression.Methods WB-DWI was performed on 32 healthy volunteers twice within two-week period using short TI inversion-recovery diffusion-weighted echo-planar imaging sequence and built-in body coil.The volunteers were scanned across six stations continuously covering the entire body from the head to the feet under normal breathing.The bone apparent diffusion coefficient(ADC) and exponential ADC(eADC) of regions of interest(ROIs) were measured.We analyzed correlation of the results using paired-t-test to assess the reproducibility of the WB-DWI technique.Results We were successful in collecting and analyzing data of 64 WB-DWI images.There was no significant difference in bone ADC and eADC of 824 ROIs between the paired observers and paired scans(P>0.05).Most of the images from all stations were of diagnostic quality.Conclusion The measurements of bone ADC and eADC have good reproducibility.WB-DWI technique under normal breathing with background body signal suppression is adequate.
基金supported by the Ministry of Science and Technology of the People’s Republic of China(No.2021ZD0200202)the National Natural Science Foundation of China(No.82122032)the Science and Technology Department of Zhejiang Province(Nos.202006140 and 2022C03057).
文摘Increasingly,attention is being directed towards time-dependent diffusion magnetic resonance imaging(TDDMRI),a method that reveals time-related changes in the diffusional behavior of water molecules in biological tissues,thereby enabling us to probe related microstructure events.With ongoing improvements in hardware and advanced pulse sequences,significant progress has been made in applying TDDMRI to clinical research.The development of accurate mathematical models and computational methods has bolstered theoretical support for TDDMRI and elevated our understanding of molecular diffusion.In this review,we introduce the concept and basic physics of TDDMRI,and then focus on the measurement strategies and modeling approaches in short-and long-diffusion-time domains.Finally,we discuss the challenges in this field,including the requirement for efficient scanning and data processing technologies,the development of more precise models depicting time-dependent molecular diffusion,and critical clinical applications.
基金Supported by Beijing Hospitals Authority Youth Program,No.QML20231103Beijing Hospitals Authority Ascent Plan,No.DFL20191103National Key R&D Program of China,No.2023YFC3402805.
文摘BACKGROUND About 10%-31% of colorectal liver metastases(CRLM)patients would concomitantly show hepatic lymph node metastases(LNM),which was considered as sign of poor biological behavior and a relative contraindication for liver resection.Up to now,there’s still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM,except for pathology examination of lymph node after resection.AIM To compare the ability of mono-exponential,bi-exponential,and stretchedexponential diffusion-weighted imaging(DWI)models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.METHODS In this retrospective study,97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging,including DWI with ten b values before and after chemotherapy.Various parameters,such as the apparent diffusion coefficient from the mono-exponential model,and the true diffusion coefficient,the pseudo-diffusion coefficient,and the perfusion fraction derived from the intravoxel incoherent motion model,along with distributed diffusion coefficient(DDC)andαfrom the stretched-exponential model(SEM),were measured.The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups.A nomogram was constructed to predict the hepatic lymph node status.The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.RESULTS Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes.A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients,with an area under the curve of 0.873.Furthermore,parameters from SEM showed substantial repeatability.CONCLUSION The developed nomogram,incorporating the pre-treatment DDC and the short axis of the largest lymph node,can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery.This nomogram was proven to be more valuable,exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI.The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.
基金Project(2009AA04Z214) supported by the National High Technology Research and Development Program of ChinaProject(07JJ6133) supported by the Natural Science Foundation of Hunan Province, China
文摘To denoise the diffusion weighted images （DWls） featured as multi-boundary, which was very important for the calculation of accurate DTIs （diffusion tensor magnetic resonance imaging）, a modified Wiener filter was proposed. Through analyzing the widely accepted adaptive Wiener filter in image denoising fields, which suffered from annoying noise around the edges of DWIs and in turn greatly affected the denoising effect of DWIs, a local-shift method capable of overcoming the defect of the adaptive Wiener filter was proposed to help better denoising DWIs and the modified Wiener filter was constructed accordingly. To verify the denoising effect of the proposed method, the modified Wiener filter and adaptive Wiener filter were performed on the noisy DWI data, respectively, and the results of different methods were analyzed in detail and put into comparison. The experimental data show that, with the modified Wiener method, more satisfactory results such as lower non-positive tensor percentage and lower mean square errors of the fractional anisotropy map and trace map are obtained than those with the adaptive Wiener method, which in turn helps to produce more accurate DTIs.
基金supported by NSFC under grant No.11471331partially supported by National Center for Mathematics and Interdisciplinary Sciences
文摘The existence of a global minimizer for a variational problem arising in registration of diffusion tensor images is proved, which ensures that there is a regular spatial transformation for the registration of diffusion tensor images.
基金the National Natural Science Foundation of China,No.30800263
文摘BACKGROUND： Limbic encephalitis is a rare syndrome that specifically affects the limbic system. Magnetic resonance imaging （MRI） has been typically used to detect brain changes in this disease. However, the mechanisms of limbic encephalitis-related white matter damage remain poorly understood. OBJECTIVE： To characterize white matter connectivity changes secondary to injuries of the limbic system in limbic encephalitis through combined application of diffusion tensor imaging （DTI） and voxel-based morphometry. DESIGN, TIME AND SETTING： A non-randomized, controlled, clinical, neuroimaging, DTI study was performed at the Department of Radiology, West China Hospital in December 2008. PARTICIPANTS： A male, 46-year-old, limbic encephalitis patient, as well as 11 age- and gender-matched healthy volunteers, were enrolled in the present study. METHODS： MRI was performed on the limbic encephalitis patient using a 3.0T MR scanner. Three-dimensional SPGR Tl-weighted images and DTI were acquired in the patient and controls. Data were analyzed using Matlab 7.0 and SPM2 software. MAIN OUTCOME MEASURES： Results from routine MRI scan with contrast enhancement of patient, as well as fractional anisotropy and mean diffusivity value map differences between patient and controls. RESULTS： Significant symmetric MRI signal intensity abnormalities were observed with routine MRI Affected bilateral hippocampi and amygdala exhibited hypointense signals in TIWI and hyperintense signals in T2 images. The DTI study revealed decreased fractional anisotropy values in the bilateral alveus and fimbria of the hippocampus, bilateral internal and external capsules, white matter of the right prefrontal area, and left corona radiate in the patient compared with normal controls （P 〈 0.001） Significantly increased fractional anisotropy, mean diffusivity, or decreased mean diffusivity were not observed in the patient, compared with controls. CONCLUSION： Secondary white matter damage to the hippocampal afveus and fimbria was apparent in the limbic encephalitis patient. In addition, other white matter fiber injuries surrounded the limbic structures, which were not attributed to secondary limbic system injuries.
文摘The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following:Technical challenges and corresponding strategies:Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities.Advanced techniques including high‐performance gradient systems,parallel imaging,multi‐shot acquisition,and parallel transmission can mitigate these issues.Comparison of 3‐Tesla and 7T diffusion imaging:Technologies such as multiplexed sensitivity encoding and deep learning reconstruction(DLR)have been developed to mitigate artifacts and improve image quality.This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system,facilitating enhanced visualization of microstructural changes.Despite greater geometric distortions and signal inhomogeneity at 7T,the system shows clear advantages in high b‐value imaging and highresolution diffusion tensor imaging.Additionally,multiplexed sensitivity encoding significantly reduces image blurring and distortion,and DLR substantially improves the signal‐to‐noise ratio and image sharpness.7T diffusion applications in structural analysis and disease characterization:This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology,No.2012R1A1A4A01001873
文摘The human brain is known to contain a maximum of eight cholinergic nuclei： the basal forebrain region： the medial septal nucleus （Ch 1）, the vertical nucleus of the diagonal band （Ch 2）, the horizontal limb of the diago- nal band （Ch 3）, and the nucleus basalis of Meynert （Ch 4）; the brainstem： the pedunculopontine nucleus （Ch 5）, the laterodorsal tegmental nucleus （Ch 6）, and the para- bigeminal nucleus （Ch 8）; and the thalamus： the medial habenular nucleus （Ch 7） （Nieuwenhuys et al., 2008; Naidich and Duvernoy, 2009）. The cingulum is the neu- ral tract extending from the orbitofrontal cortex to the medial temporal lobe （Mufson and Pandya, 1984）. The cingulum plays an important role in memory because it is a passage of the medial cholinergic pathway, which pro- vides cholinergic innervations to the cerebral cortex after originating from Ch 1 and Ch 2 as well as Ch 4 （mainly） （Selden et al., 1998; Nieuwenhuys et al., 2008; Hong and lang, 2010）.
基金National‘973’ProjectGrant number:2003 CB716103+1 种基金Shanghai Normal University ProjectGrant number:SK200734
文摘The Rician noise introduced into the diffusion tensor images （DTIs） can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the wavelet-based diffusion method to denoise multiehannel typed diffusion weighted （DW） images. The presented smoothing strategy, which utilizes anisotropic nonlinear diffusion in wavelet domain, successfully removes noise while preserving both texture and edges. To evaluate quantitatively the efficiency of the presented method in accounting for the Rician noise introduced into the DW images, the peak-to-peak signal-to-noise ratio （PSNR） and signal-to-mean squared error ratio （SMSE） metrics are adopted. Based on the synthetic and real data, we calculated the ap- parent diffusion coefficient （ADC） and tracked the fibers. We made comparisons between the presented model, the wave shrinkage and regularized nonlinear diffusion smoothing method. All the experiment results prove quantitatively and visually the better performance of the presented filter.
基金Supported by the Research Project of Dongguan Higher Education (200910815252)the Beijing Natural Science Foundation(7102102)the Scientific Research Key Program of Beijing Municipal Commission of Ed-ucation(KZ200810025011)~~
文摘To evaluate the effect of the positive-indefinite matrix on the diffusion tensor-derived parameters, a modified algorithm is proposed for calculating these parameters. Magnetic resonance （MR） diffusion tensor images of five healthy volunteers are collected. The diffusion sensitive gradient magnetic fields are applied along 25 directions and the diffusion weighting value is 1 000 s/mm^2. Many positive-indefinite diffusion tensors can be found in the white matter area, such as the genu and the splenium of corpus callosum. Due to the positive-indefinite matrix, the mean diffusivity （MD） and the fractional anisotropy （FA） are under-estimated and over-estimated by using the conventional algorithm. Thus, the conventional algorithm is modified by using the absolute values of all eigenvalues. Results show that both the robustness and the reliability for deriving these parameters are improved by the modified algorithm.
文摘BACKGROUND Advanced esophageal squamous cell carcinoma(ESCC)has an extremely poor prognosis.Preoperative chemoradiotherapy(CRT)can significantly prolong survival,especially in those who achieve pathological complete response(pCR).However,the pretherapeutic prediction of pCR remains challenging.AIM To predict pCR and survival in ESCC patients undergoing CRT using an artificial intelligence(AI)-based diffusion-weighted magnetic resonance imaging(DWI-MRI)radiomics model.METHODS We retrospectively analyzed 70 patients with ESCC who underwent curative surgery following CRT.For each patient,pre-treatment tumors were semi-automatically segmented in three dimensions from DWI-MRI images(b=0,1000 second/mm^(2)),and a total of 76 radiomics features were extracted from each segmented tumor.Using these features as explanatory variables and pCR as the objective variable,machine learning models for predicting pCR were developed using AutoGluon,an automated machine learning library,and validated by stratified double cross-validation.RESULTS pCR was achieved in 15 patients(21.4%).Apparent diffusion coefficient skewness demonstrated the highest predictive performance[area under the curve(AUC)=0.77].Gray-level co-occurrence matrix(GLCM)entropy(b=1000 second/mm²)was an independent prognostic factor for relapse-free survival(RFS)(hazard ratio=0.32,P=0.009).In Kaplan-Meier analysis,patients with high GLCM entropy showed significantly better RFS(P<0.001,log-rank).The best-performing machine learning model achieved an AUC of 0.85.The predicted pCR-positive group showed significantly better RFS than the predicted pCR-negative group(P=0.007,log-rank).CONCLUSION AI-based radiomics analysis of DWI-MRI images in ESCC has the potential to accurately predict the effect of CRT before treatment and contribute to constructing optimal treatment strategies.
基金financial support from the general electric (GE) healthcareAustralian Research Council Discovery Project (DP200101476)+5 种基金in parts by National Institutes of Health (NIH) grants (R01 AR077604, R01 EB002524, R01 AR079431, P41 EB02706)Stanford Graduate FellowshipThe University of Queensland Graduate ScholarshipNational Health and Medical Research Council of Australia Fellowship (#1194937)Wu Tsai Human Performance Alliance at Stanford Universitythe Joe and Clara Tsai Foundation
文摘Background While Nordic hamstring exercise(NHE)training has been shown to reduce hamstring strains,the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear.This study investigates architectural and microstructural adaptations of the biceps femoris short head(BFsh),biceps femoris long head(BFlh),semitendinosus(ST),and semimembranosus(SM)in response to an NHE intervention.Methods Eleven subjects completed 9 weeks of supervised NHE training followed by 3 weeks of detraining.Magnetic resonance imaging was performed at pre-training,post-training,and detraining to assess architectural(volume,fiber tract length,and fiber tract angle)and microstructural(axial(AD),mean(MD),radial(RD)diffusivities,and fractional anisotropy(FA))parameters of the 4 hamstrings.Results NHE training induced significant but non-uniform hamstring muscle hypertrophy(BFsh:22%,BFlh:9%,ST:26%,SM:6%)and fiber tract length increase(BFsh:11%,BFlh:7%,ST:18%,SM:10%).AD(5%),MD(4%),and RD(5%)showed significant increases,but fiber tract angle and FA remained unchanged.After detraining,only ST showed a significant reduction(8%)in volume,which remained higher than the pre-training value.While fiber tract lengths returned to baseline,AD,MD,and RD remained higher than pre-training levels for all hamstrings.Conclusion The 9-week NHE training substantially increased hamstring muscle volume with greater hypertrophy in ST and BFsh.Hypertrophy was accompanied by increases in fiber tract lengths and cross-sections(increased RD).After 3 weeks of detraining,fiber tract length gains across all hamstrings declined,emphasizing the importance of sustained training to maintain all the protective adaptations.
基金supported by the National Natural Science Foundation of China(82171908 and 82102015)the General Project of the Nanjing Medical Science and Technology Development Program(YKK21075)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515140030).
文摘Background:Platinum can cause chemotherapy-related cognitive impairment.Low-intensity focused ultrasound(LIFUS)is a promising noninvasive physical stimulation method with a unique advantage in neurological rehabilitation.We aimed to investigate whether LIFUS can alleviate cisplatin-induced cognitive impairment in rats and explore the related neuropatho-logical mechanisms.Methods:After confirming the target position for LIFUS treatment in 18 rats,64 rats were randomly divided into four groups:control,model,sham,and LIFUS groups.Before and after LIFUS treatment,detailed biological behavioral assessments and magnetic resonance imaging were performed.Finally,the rats were euthanized,and relevant histopathological and molecular biological experiments were conducted and analyzed.Results:In the Morris water maze,the model group showed fewer platform crossings(1.250.93 vs.5.691.58),a longer escape latency(41.6536.55 s vs.6.382.11 s),and a lower novel object recognition index(29.7711.83 vs.83.695.67)than the control group.LIFUS treatment improved these metrics,with more platform crossings(3.130.34),a higher recognition index(65.588.71),and a shorter escape latency(6.452.27 s).Longitudinal analysis of the LIFUS group further confirmed these improvements.Neuroimaging revealed significant differences in diffusion tensor imaging metrics of specific brain regions pre-and post-LIFUS.Moreover,neuropathology showed higher dendritic spine density,less myelin loss,fewer apoptotic cells,more synapses,and less mitochondrial autophagy after LIFUS treatment.The neuroimaging indicators were correlated with behavioral improvements,highlighting the potential of LIFUS for alleviating cognitive impairment(as demonstrated through imaging and analysis).Our investigation of the molecular biological mechanisms revealed distinct protein expression patterns in the hippocampus and its subregions.In the model group,glial fibrillary acidic protein(GFAP)and ionized calcium-binding adaptor molecule 1(IBA1)expression levels were elevated across the hippocampus,whereas neuronal nuclei(NeuN)expression was reduced.Subregional analysis revealed higher GFAP and IBA1 and lower NeuN,especially in the dentate gyrus subregion.Moreover,positive cell areas were larger in the cornu ammonis(CA)1,CA2,CA3,and dentate gyrus regions.In the CA2 and CA3,significant differences among the groups were observed in GFAP-positive cell counts and areas,and there were variations in NeuN expression.Conclusions:Our results suggest that LIFUS can reverse cisplatin-induced cognitive impairments.The neuroimaging findings were consistent with the behavioral and histological results and suggest a neuropathological basis that supports further research into the clinical applications of LIFUS.Furthermore,LIFUS appeared to enhance the plasticity of neuronal synapses in the rat hippocampus and reduce hippocampal inflammation.These findings highlight the clinical potential of LIFUS as an effective,noninvasive therapeutic strategy and monitoring tool for chemotherapy-induced cognitive deficits.
基金Project supported by the National Natural Science Foundation of China(No.62062023)。
文摘Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human braintissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidlyestimate diffusion tensors (DTs) using only a small quantity of diffusion-weighted (DW) images. However, thesemethods typically use the DW images obtained with fixed q-space sampling schemes as the training data, limitingthe application scenarios of such methods. To address this issue, we develop a new deep neural network calledq-space-coordinate-guided diffusion tensor imaging (QCG-DTI), which can efficiently and correctly estimate DTsunder flexible q-space sampling schemes. First, we propose a q-space-coordinate-embedded feature consistencystrategy to ensure the correspondence between q-space-coordinates and their respective DW images. Second, aq-space-coordinate fusion (QCF) module is introduced which eficiently embeds q-space-coordinates into multiscalefeatures of the corresponding DW images by linearly adjusting the feature maps along the channel dimension,thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense(MRD) module is proposed which enhances the network's feature extraction and image reconstruction capabilitiesby using dual-branch convolutions with different kernel sizes to extract features at diferent scales. Compared tostate-of-the-art methods that rely on a fixed sampling scheme, the proposed network can obtain high-quality diffusiontensors and derived parameters even using DW images acquired with flexible q-space sampling schemes. Comparedto state-of-the-art deep learning methods, QCG-DTI reduces the mean absolute error by approximately 15% onfractional anisotropy and around 25% on mean diffusivity.
文摘BACKGROUND Anoxic brain injury is a potentially lethal condition characterized by cerebral hypoperfusion and irreversible neuronal injury.Arterial spin-labeling(ASL)perfusion and diffusion-weighted imaging(DWI)magnetic resonance imaging(MRI)have been proposed as tools to detect cerebral ischemic changes and may aid in the assessment of anoxic injury.AIM To explore the relationship between regional ASL perfusion patterns and clinical outcomes following cardiac arrest.METHODS We performed a retrospective review to identify patients with clinical suspicion of anoxic brain injury who underwent MRI within 15 days of cardiac arrest.Receiver operator characteristic(ROC)analysis and univariate logistic regression were used to evaluate associations between ASL perfusion scores,DWI signal intensity,and the following clinical features:(1)Myoclonus status epilepticus(MSE)within 24 hours;(2)Absent extensor or motor reflexes(EMR)at day 3 post-arrest;and(3)Absent brainstem reflexes(BSR)within 15 days.RESULTS Twenty-eight patients met inclusion criteria.Increased ASL signal in the left occipital lobe was significantly associated with MSE(P=0.038),while a trend was observed between right frontal ASL signal and EMR(P=0.078).ROC analysis showed that ASL scores≥7 were associated with higher odds of absent BSR(OR 2.14,P=0.53),though this did not reach statistical significance.DWI signal intensity did not show significant associations with clinical outcomes.The overall discriminatory performance of ASL for predicting outcomes was limited(AUC≈0.52).CONCLUSION This exploratory study suggests that regional ASL hyperperfusion,particularly in the left occipital and right frontal lobes,may be associated with adverse clinical signs following cardiac arrest.However,most findings did not reach statistical significance,and the study was underpowered to detect small-to-moderate effects.These preliminary results should be interpreted with caution and considered hypothesis-generating.Larger,prospective studies are warranted to clarify the prognostic value of ASL perfusion imaging in anoxic brain injury.
基金Supported by National Natural Science Foundation of China,No.82104698,No.82330058,No.T2341014,and No.32200923.
文摘BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairment,is critical for clinical intervention,yet it remains elusive and challenging to identify.AIM To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.METHODS Using diffusion tensor imaging(DTI),we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls.Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.RESULTS T2DM patients exhibited reduced global/local efficiency and small-worldness,alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections,suggesting compensatory mechanisms.A classification model leveraging 18 connectivity features achieved 92.5%accuracy in distinguishing T2DM brains.Structural connectivity patterns further predicted disease onset with an error of±1.9 years.CONCLUSION Our findings reveal early-stage brain network reorganization in T2DM,highlighting subcortical-frontal connectivity as a compensatory biomarker.The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.
