Tongtong Che

Bio: Tongtong Che is an academic researcher from Beihang University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 9 publications receiving 26 citations. Previous affiliations of Tongtong Che include Shandong Normal University.

Deep Group-Wise Registration for Multi-Spectral Images From Fundus Images

Abstract: Multi-spectral imaging (MSI) is a novel non-invasive tool for visualizing the entire span of the eye, from the internal limiting membrane to the choroid. However, spatial misalignments can be frequently observed in sequential MSI images because the eye saccade movement is usually faster than the MSI image acquisition speed. Therefore, registering MSI images is necessary for computer-based analysis of retinal degeneration via MSI. In this paper, we propose an early deep learning framework for achieving an accurate registration of MSI images in a group-wise fashion. The framework contains three parts: a template construction based on principal component analysis, a deformation field calculation, and a spatial transformation. The framework is uniquely capable of resolving two key challenges, i.e., the “multi-modal” characteristics in MSI images for the acquisition with different spectra and the requirement of joint registration of the sequential images. Our experimental results demonstrate the superior performance of our framework compared to several representative state-of-the-art techniques in both speed and accuracy.

A Flexible Lightweight Triboelectric Nanogenerator for Protector and Scoring System in Taekwondo Competition Monitoring

Abstract: Nowadays, the applications of the triboelectric nanogenerator in sensing and monitoring sports experience a blooming prosperity. Here, we report a flexible and lightweight triboelectric nanogenerator (FL-TENG) made of hydrogel electrodes, polytetrafluoroethylene (PTFE), PDMS, and polyurethane (PU). Based on the triboelectric effect, the FL-TENG can work as a self-powered sensor attaching to taekwondo protective gear, which can be used to monitor athletes’ competition performance and improve the fairness of the competition. In addition, the FL-TENG can drive micro-wireless devices for wireless transmitting sports data during the competition in real time. This kind of sustainable green self-powered sensor provides a new path for the field of sports competition monitoring.

Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment

Abstract: Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual‐level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients’ R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into “similar to the pattern of NCs” (N‐CI, N = 252) and “similar to the pattern of AD” (A‐CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A‐CI and 21.77% for N‐CI) within three years; 4) enriched genes for potassium‐ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.

Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study.

Abstract: Growing evidence indicates that amyloid-beta (Aβ) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aβ positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aβ PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aβ, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aβ PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.

DGR-Net: Deep Groupwise Registration of Multispectral Images

Abstract: Groupwise registration of multispectral images (MSI) is clinically essential to facilitate accurate information fusion across different modalities. However, the groupwise registration of multispectral images is a challenging task because multiple different imaging modalities makes it difficult to jointly optimize the deformation. In this work, we propose an unbiased deep groupwise registration framework, DGR-Net, which takes a complete consideration of the information aggregated by calculating the deformation of the sequence image. Our framwork guided by principal component analysis (PCA) image. Network optimization is accelerated by combining internal smoothing and external correlation of the deformation fields. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging multi-modality groupwise registration task and also outperforms the state-of-the-art approaches.

Standardizing workflows in imaging transcriptomics with the abagen toolbox

Abstract: Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as {rho} [≥] 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

REMPE: Registration of Retinal Images Through Eye Modelling and Pose Estimation

Abstract: Objective: In-vivo assessment of small vessels can promote accurate diagnosis and monitoring of diseases related to vasculopathy, such as hypertension and diabetes. The eye provides a unique, open, and accessible window for directly imaging small vessels in the retina with non-invasive techniques, such as fundoscopy. In this context, accurate registration of retinal images is of paramount importance in the comparison of vessel measurements from original and follow-up examinations, which is required for monitoring the disease and its treatment. At the same time, retinal registration exhibits a range of challenges due to the curved shape of the retina and the modification of imaged tissue across examinations. Thereby, the objective is to improve the state-of-the-art in the accuracy of retinal image registration. Method: In this work, a registration framework that simultaneously estimates eye pose and shape is proposed. Corresponding points in the retinal images are utilized to solve the registration as a 3D pose estimation. Results: The proposed framework is evaluated quantitatively and shown to outperform state-of-the-art methods in retinal image registration for fundoscopy images. Conclusion: Retinal image registration methods based on eye modelling allow to perform more accurate registration than conventional methods. Significance: This is the first method to perform retinal image registration combined with eye modelling. The method improves the state-of-the-art in accuracy of retinal registration for fundoscopy images, quantitatively evaluated in benchmark datasets annotated with ground truth. The implementation of registration method has been made publicly available.

Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment

Abstract: Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual‐level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients’ R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into “similar to the pattern of NCs” (N‐CI, N = 252) and “similar to the pattern of AD” (A‐CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A‐CI and 21.77% for N‐CI) within three years; 4) enriched genes for potassium‐ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.