Smoothed L0-Constraint Dictionary Learning for Low-Dose X-Ray CT Reconstruction

Komolafe, Temitope Emmanuel; Wang, Kang; Du, Qiang; Hu, Tao; Yuan, Gang; Zheng, Jian; Cheng, Zhang; Li, Ming; Yang, Xiaodong

doi:10.1109/access.2020.3004174

Cited by 15 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convex relaxations have better theoretical guarantees and recoverability but are more time-consuming [32]. Due to computational considerations, we use the smoothed 0 ℓ (SL0) algorithm [33] for data retrieval in this paper.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Fourier-domain image reconstruction in near-field microwave imaging using a dynamic metasurface antenna: a sparse-sampling-based approach

Molaei¹,

Skouroliakou²,

Fusco³

et al. 2023

Radar Sensor Technology XXVII

View full text Add to dashboard Cite

In recent years, dynamic metasurface antennas (DMAs) have been proposed as an efficient alternative platform for computational imaging, which can drastically simplify the hardware architecture. In this paper, we first mathematically describe the existing solution to be able to convert raw measurements obtained by a DMA in the frequency-space domain into raw data on Fourier bases. Next, an optimization problem based on compressive sensing theory is defined, through which only a limited share of the total frequency/spatial data will be needed. The converted/retrieved data are used to reconstruct the image in the Fourier domain. The performance of the corresponding image reconstruction techniques (with/without Stolt interpolation operation) is evaluated in terms of the quality of the reconstructed image (both visually and quantitatively) and computational time with computer simulations.

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

Fourier-domain image reconstruction in near-field microwave imaging using a dynamic metasurface antenna: a sparse-sampling-based approach

Molaei¹,

Skouroliakou²,

Fusco³

et al. 2023

Radar Sensor Technology XXVII

View full text Add to dashboard Cite

show abstract

“…Other attempts to improve the performance of dictionary learning include: using l 1 for misfit between image and its dictionary representation [76], smoothing intermediate image updates to remove artifacts [43], using dictionary learning in combination with total variation (TV) [77] and clustering patches and learning dictionaries for each class separately [37].…”

Section: Dictionary Based Ct Reconstructionmentioning

confidence: 99%

Deep learning based dictionary learning and tomographic image reconstruction

Rudzusika¹,

Kœhler²,

Öktem³

2021

Preprint

View full text Add to dashboard Cite

This work presents an approach for image reconstruction in clinical low-dose tomography that combines principles from sparse signal processing with ideas from deep learning. First, we describe sparse signal representation in terms of dictionaries from a statistical perspective and interpret dictionary learning as a process of aligning distribution that arises from a generative model with empirical distribution of true signals. As a result we can see that sparse coding with learned dictionaries resembles a specific variational autoencoder, where the decoder is a linear function and the encoder is a sparse coding algorithm. Next, we show that dictionary learning can also benefit from computational advancements introduced in the context of deep learning, such as parallelism and as stochastic optimization. Finally, we show that regularization by dictionaries achieves competitive performance in computed tomography (CT) reconstruction comparing to state-of-the-art model based and data driven approaches.

show abstract

“…Examples of such filters include the noise-adaptive bilateral [ 9 ] and structure-adaptive sinogram filters [ 10 ]. Iterative reconstruction methods are widely used [ 11 , 12 ]. These methods combine prior information from the image domain with the data characteristics of the projection domain to reconstruct high-quality CT images.…”

Section: Introductionmentioning

confidence: 99%

Parallel processing model for low-dose computed tomography image denoising

Yao,

Wang,

et al. 2024

Vis. Comput. Ind. Biomed. Art

View full text Add to dashboard Cite

Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise and artifacts, negatively impacting the radiologists’ ability to accurately diagnose. To address this issue, many studies have focused on denoising LDCT images using deep learning (DL) methods. However, these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources, which adversely affects the performance of current denoising models. In this study, we propose a parallel processing model, the multi-encoder deep feature transformation network (MDFTN), which is designed to enhance the performance of LDCT imaging for multisource data. Unlike traditional network structures, which rely on continual learning to process multitask data, the approach can simultaneously handle LDCT images within a unified framework from various imaging sources. The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module (DFTM). During forward propagation in network training, each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space. Subsequently, each decoder performs an inverse operation for multisource loss estimation. Through collaborative training, the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization. Numerous experiments were conducted on two public datasets and one local dataset, which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures. The source code is available at https://github.com/123456789ey/MDFTN.

show abstract

Smoothed L0-Constraint Dictionary Learning for Low-Dose X-Ray CT Reconstruction

Cited by 15 publications

References 31 publications

Fourier-domain image reconstruction in near-field microwave imaging using a dynamic metasurface antenna: a sparse-sampling-based approach

Fourier-domain image reconstruction in near-field microwave imaging using a dynamic metasurface antenna: a sparse-sampling-based approach

Deep learning based dictionary learning and tomographic image reconstruction

Parallel processing model for low-dose computed tomography image denoising

Contact Info

Product

Resources

About