PGDPNN: prior-information generation and distribution prediction neural network for fluorescence molecular tomography reconstruction.

Fluorescence molecular tomography (FMT) is a promising and high sensitivity imaging modality that reconstructs the three-dimensional distribution of interior fluorescent sources. However, FMT reconstruction suffers from limited spatial resolution due to the simplifications in the forward model and the severely ill-posed nature of the inverse problem. In this study, we perform a clustering analysis using the radiomic features of the surface signal distribution.

Deep system prior based graph convolution network for NIR-II fluorescence molecular tomography.

Background And Objective: Fluorescence molecular tomography (FMT) is a promising imaging technique that can quantify the internal distribution of tumor in the early stage. However, due to the ill-posed inverse problem caused by the severe photon scattering effect, the promotion of efficiency and accuracy is still an issue for FMT and the reconstruction of the morphological performance is still difficult to meet the practical requirement.

Methods: In this paper, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering to alleviated ill-posedness, and a deep system prior based graph convolution network (DSPGN) was proposed for FMT, which fully takes the morphology represented by graph-structure into the reconstruction process to improve the morphological performance of FMT.

FMT-ReconNet: Fluorescence Molecular Tomography Reconstruction using Prior Knowledge and Deformation Neural Network.

Fluorescence molecular tomography (FMT) is a powerful imaging technique for 3D reconstruction of internal fluorescent sources. However, its spatial resolution is limited by a simplified forward model and an ill-posed inverse problem. To address this, we introduce FMT-ReconNet, a deep neural network comprising a spatial transformer network (STN) for source transformation and a V-Net for reconstruction.

Monotone Accelerated Proximal Gradient Network For Bioluminescence Tomography Reconstruction.

Bioluminescence tomography (BLT) is a noninvasive technique designed to enable three-dimensional (3D) visualization and quantification of viable tumor cells in living organisms. However, despite the excellent sensitivity and specificity of bioluminescence imaging (BLI), BLT is limited by the photon scattering effect and ill-posed inverse problem. To overcome this problem, regularization algorithms have been widely studied and achieved impressive results.

GAICN: Graph Attention Iterative Contraction Network for Bioluminescence Tomography.

Bioluminescence tomography (BLT) can provide non-invasive quantitative three-dimensional tumor information which has been widely applied in pre-clinical studies. Meanwhile, in recent years, deep learning methods have significantly improved the reconstruction resolution and speed by establishing a non-linear mapping relationship between surface-measured bioluminescence and light source distribution. However, this mapping relationship only works for specific biological tissues and light transmission processes under fixed wavelengths, resulting in poor stability and generalizability.

Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model.

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography.

Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems.

Two-step reconstruction framework of fluorescence molecular tomography based on energy statistical probability.

Fluorescence molecular tomography (FMT), as a promising technique for early tumor detection, can non-invasively visualize the distribution of fluorescent marker probe three-dimensionally. However, FMT reconstruction is a severely ill-posed problem, which remains an obstacle to wider application of FMT. In this paper, a two-step reconstruction framework was proposed for FMT based on the energy statistical probability.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects.

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring.

3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.

Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction.

Preparation facilitates response inhibition based on a stop-signal task.

This study demonstrated that the higher stop-signal probability condition showed a longer go reaction time and shorter stop-signal reaction time (SSRT) compared with the lower stop-signal probability condition. In addition, preparation cost was correlated with SSRT. These results suggest that preparation facilitates response inhibition.

The relationship between schizotypal personality features and mind wandering among college students during COVID-19 pandemic: A moderator of depression.

Introduction: Although the impact of the COVID-19 pandemic on people's mental health has been well documented in many studies, the schizotypal personality features in the general population have not received sufficient attention.

Methods: Study 1 is a longitudinal study tracking changes in schizotypal personality features among college students during the COVID-19 pandemic. A total of 153 Chinese college students were assessed using the Schizotypal Personality Questionnaire.

The relationship between perceived social support with anxiety, depression, and insomnia among Chinese college students during the COVID-19 pandemic: The mediating role of self-control.

Object: We aimed to investigate the associations between perceived social support and anxiety, depression, and sleep disturbance self-control among Chinese college students during the COVID-19 pandemic.

Materials And Methods: The Perceived Social Support Scale, Self-control Scale, Self-rating Anxiety Scale, Self-rating Depression Scale, and Insomnia Severity Index Scale were used to survey 1,997 college students during the COVID-19 pandemic, who submitted valid questionnaires ( = 19.93, = 1.

BIG Modulates Stem Cell Niche and Meristem Development via SCR/SHR Pathway in Arabidopsis Roots.

BIG, a regulator of polar auxin transport, is necessary to regulate the growth and development of Arabidopsis. Although mutations in the gene cause severe root developmental defects, the exact mechanism remains unclear. Here, we report that disruption of the gene resulted in decreased quiescent center (QC) activity and columella cell numbers, which was accompanied by the downregulation of () gene expression.

VoxDMRN: a voxelwise deep max-pooling residual network for bioluminescence tomography reconstruction.

Bioluminescence tomography (BLT) has extensive applications in preclinical studies for cancer research and drug development. However, the spatial resolution of BLT is inadequate because the numerical methods are limited for solving the physical models of photon propagation and the restriction of using tetrahedral meshes for reconstruction. We conducted a series of theoretical derivations and divided the BLT reconstruction process into two steps: feature extraction and nonlinear mapping.

L-L Minimization Via A Proximal Operator For Fluorescence Molecular Tomography.

Fluorescent Molecular Tomography (FMT) is a highly sensitive and noninvasive imaging method that provides three-dimensional distribution of biomarkers by noninvasive detection of fluorescent marker probes. However, due to the light scattering effect and ill-posedness of inverse problems, it is challenging to develop an efficient construction method that can provide the exact location and morphology of the fluorescence distribution. In this paper, we proposed L-L norm regularization to improve FMT reconstruction.

End-To-End Bioluminescence Tomography Reconstruction Based On Convolution Neural Network Scheme.

Bioluminescence tomography (BLT) has received a lot of attention as an important technique in bio-optical imaging. Compared with traditional methods, neural network methods have the advantages of fast reconstruction speed and support for batch processing. In this paper, we propose a end-to-end BLT reconstruction based on convolution neural networks scheme.

BIG regulates stomatal immunity and jasmonate production in Arabidopsis.

Plants have evolved an array of responses that provide them with protection from attack by microorganisms and other predators. Many of these mechanisms depend upon interactions between the plant hormones jasmonate (JA) and ethylene (ET). However, the molecular basis of these interactions is insufficiently understood.