Search Results for author:
Found 19 papers, 2 papers with code
Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning
In our experiments, we study combinations of supervised deep network reconstructors and MBIR solver with learned sparse representation-based priors or analytical priors.
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
We propose a new INN architecture for DECT material decomposition.
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings.
Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction
In this work, we develop a new image reconstruction approach based on a novel multi-layer model learned in an unsupervised manner by combining both sparse representations and deep models.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Signal models based on sparse representation have received considerable attention in recent years.
Momentum-Net for Low-Dose CT Image Reconstruction
We also investigated the spectral normalization technique that applies to image refining NN learning to satisfy the nonexpansive NN property; however, experimental results show that this does not improve the image reconstruction performance of Momentum-Net.
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms.
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net.
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
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.
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.
SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
Signal Processing Image and Video Processing Optimization and Control Medical Physics
Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform
Our results with the extended cardiac-torso (XCAT) phantom data and clinical chest data show that, for sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST-$\ell_1$ improves the quality of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ prior with learned ST.
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images.
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.
