Yong Long
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View article: A two-component system MechNtrB/MechNtrC related to nitrogen metabolism regulation in Micromonospora echinospora DSM43816
A two-component system MechNtrB/MechNtrC related to nitrogen metabolism regulation in Micromonospora echinospora DSM43816 Open
The NtrB/NtrC two-component signal transduction system (TCS), predominantly found in Gram-negative bacteria, plays a crucial role in nitrogen metabolism regulation. Through BLASTP analysis of the complete proteome of Micromonospora echinos…
View article: SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing Open
Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two het…
View article: SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing Open
Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two het…
View article: Proton ARC based LATTICE radiation therapy: feasibility study, energy layer optimization and LET optimization
Proton ARC based LATTICE radiation therapy: feasibility study, energy layer optimization and LET optimization Open
Objective. LATTICE, a spatially fractionated radiation therapy (SFRT) modality, is a 3D generalization of GRID and delivers highly modulated peak-valley spatial dose distribution to tumor targets, characterized by peak-to-valley dose ratio…
View article: Multi‐layer clustering‐based residual sparsifying transform for low‐dose CT image reconstruction
Multi‐layer clustering‐based residual sparsifying transform for low‐dose CT image reconstruction Open
Purpose The recently proposed sparsifying transform (ST) models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure reveal great potential for learning features in dif…
View article: Beam angle optimization for proton therapy via group‐sparsity based angle generation method
Beam angle optimization for proton therapy via group‐sparsity based angle generation method Open
Background In treatment planning, beam angle optimization (BAO) refers to the selection of a subset with a given number of beam angles from all available angles that provides the best plan quality. BAO is a NP‐hard combinatorial problem. A…
View article: A treatment plan optimization method with direct minimization of number of energy jumps for proton arc therapy
A treatment plan optimization method with direct minimization of number of energy jumps for proton arc therapy Open
Objective . The optimization of energy layer distributions is crucial to proton arc therapy: on one hand, a sufficient number of energy layers is needed to ensure the plan quality; on the other hand, an excess number of energy jumps (NEJ) …
View article: Combining deep learning and adaptive sparse modeling for low-dose CT reconstruction
Combining deep learning and adaptive sparse modeling for low-dose CT reconstruction Open
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addr…
View article: Energy layer optimization via energy matrix regularization for proton spot‐scanning arc therapy
Energy layer optimization via energy matrix regularization for proton spot‐scanning arc therapy Open
Purpose Spot‐scanning arc therapy (SPArc) is an emerging proton modality that can potentially offer a combination of advantages in plan quality and delivery efficiency, compared with traditional IMPT of a few beam angles. Unlike IMPT, freq…
View article: Multilayer residual sparsifying transform (MARS) model for low‐dose CT image reconstruction
Multilayer residual sparsifying transform (MARS) model for low‐dose CT image reconstruction Open
Purpose Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been high…
View article: Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction Open
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings. Model-based image reconstruction methods have been proven to be effective in removing artifacts in LDC…
View article: Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction Open
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typically involve supervised learning or unsupervised learning…
View article: Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction Open
Signal models based on sparse representation have received considerable attention in recent years. Compared to synthesis dictionary learning, sparsifying transform learning involves highly efficient sparse coding and operator update steps.…
View article: Momentum-Net for Low-Dose CT Image Reconstruction
Momentum-Net for Low-Dose CT Image Reconstruction Open
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At each layer of the proposed Momentum-Net, the model-based im…
View article: SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction Open
Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized into supervised learning methods and unsupervised or model-ba…
View article: DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering Open
Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, …
View article: Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction Open
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computational imaging applications. In particular, sparsifying transfo…
View article: DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering Open
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, …
View article: BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization
BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization Open
View article: Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT
Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT Open
Statistical image reconstruction (SIR) methods for X-ray CT produce high-quality and accurate images, while greatly reducing patient exposure to radiation. When further reducing X-ray dose to an ultra-low level by lowering the tube current…
View article: Sparse-View X-Ray CT Reconstruction Using 𝓵 1 Prior with Learned Transform.
Sparse-View X-Ray CT Reconstruction Using 𝓵 1 Prior with Learned Transform. Open
A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce the number of projection views (sparse-view CT); however, i…
View article: Statistical image‐domain multimaterial decomposition for dual‐energy <scp>CT</scp>
Statistical image‐domain multimaterial decomposition for dual‐energy <span>CT</span> Open
Purpose Dual‐energy CT ( DECT ) enhances tissue characterization because of its basis material decomposition capability. In addition to conventional two‐material decomposition from DECT measurements, multimaterial decomposition ( MMD ) is …
View article: Action Recognition of Human’s Lower Limbs Based on a Human Joint
Action Recognition of Human’s Lower Limbs Based on a Human Joint Open
In order to recognize the actions of human’s lower limbs, a novel action recognition method based on a human joint was proposed. Firstly, hip joint was chosen as the recognition object, its y coordinates were as recognition parameter, and …
View article: Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms
Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms Open
For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and mo…