Classical total variation (TV) based iterative reconstruction algorithms assume that the signal is piecewise smooth, which causes reconstruction results to suffer from the over-smoothing effect. To address this problem, this work presents a novel computed tomography (CT) reconstruction method for the few-view problem called the group-sparsity regularization-based simultaneous algebraic reconstruction technique (GSR-SART). Group-based sparse representation, which utilizes the concept of a group as the basic unit of sparse representation instead of a patch, is introduced as the image domain prior regularization term to eliminate the over-smoothing effect. By grouping the nonlocal patches into different clusters with similarity measured by Euclidean distance, the sparsity and nonlocal similarity in a single image are simultaneously explored. The split Bregman iteration algorithm is applied to obtain the numerical scheme. Experimental results demonstrate that our method both qualitatively and quantitatively outperforms several existing reconstruction methods, including filtered back projection, expectation maximization, SART, and TV-based projections onto convex sets.

Few-View CT Reconstruction with Group-Sparsity Regularization

Few-view or sparse-view computed tomography has been recently introduced as a great potential to speed up data acquisition and alleviate the amount of patient radiation dose. This study aims to present a method for high-quality ultra-fast image reconstruction from sparsely sampled projections to overcome problems of previous methods, missing and blurring tissue boundaries, low-contrast objects, variations in shape and texture between the images of different individuals, and their outcomes. To this end, a new deep learning (DL) framework based on convolution neural network (CNN) models is proposed to solve the problem of CT reconstruction under sparsely sampled data, named the multi-receptive field densely connected CNN (MRDC-CNN). MRDC-CNN benefits from an encoder–decoder structure by proposing dense skip connections to recover the missing information, multi-receptive field modules to enlarge the receptive field, and having no batch normalization layers to boost the performance. The MRDC-CNN with a hybrid loss function format introduces several auxiliary losses combined with the main loss to accelerate convergence rate and alleviate the gradient vanishing problem during network training and maximize its performance. Results have shown that MRDC-CNN is 4–6times faster than the state-of-the-art methods, with fewer memory requirements, better performance in objective quality evaluations, and improved visual quality. The results indicated the superiority of our proposed method compared to the latest algorithms. In conclusion, the proposed method could lead to high-quality CT imaging with quicker imaging speed and lower radiation dose. The source code is available on a GitHub repository at this link. 

A deep learning method for high-quality ultra-fast CT image reconstruction from sparsely sampled projections

A review on the current progress of layered double hydroxide application in biomedical sectors

With accelerated technologies, different kinds of health technology devices have been provided to customers that continuously record bio and vital signals. Some of these products are wearable that can be used all day long and during sleeping time. Due to the wearability feature and continuous recording, a vast amount of data can be achieved and analyzed. The recorded data are usually shared with a cloud to implement comprehensive analysis methods where deep and machine learning algorithms play the main role. Finally, they can assess some health factors of the customer and most likely predict future health risks. This chapter shall review the role of the clinical scanners and their valuable data in risk detection, more portable modalities, home-used commercial devices, and emerging techniques which are so potent for future home-used health risks analysis. In the end, we conclude the state-of-the-art and provide our vision about the future of health risk analysis. 

https://www.intechopen.com/citation-pdf-url/85982

Health Risk Measurement and Assessment Technology: Current State and Future Prospect

Limited-angle and sparse-view computed tomography have been widely used to shorten the acquisition time in medical imaging and to offer the possibility of scanning large objects. However, this is a severely ill-posed inverse problem due to missing data. In these scenarios, the well-known filtered back-projection reconstruction technique exhibits severe artifacts and degradation. Recently, deep learning methods have demonstrated impressive performance in computer vision (denoising, classification, etc.) but it frequently fails to solve both limited-angle and sparse-view reconstruction. Inspired by the high performance of GAN-based image-to-image translation methods, we investigate a patchGAN as a solution to the reconstruction problem mapping data (Radon space) into the image domain. The generator is made of a V-net where the reconstruction in the sense of a least-squares minimization is carried out at different scales in the encoder path and linked with the decoder path by skip connections. The discriminator uses both information from the image and projection data domains. The proposed method gives promising reconstruction results from data acquired with a limited angular range covering only 110 degrees (instead of 180 degrees), as well as for sparse-view data with 10 degrees of sampling step. Moreover, different reconstruction results show that the method is able to reconstruct images from sparse and limited angular range data at the same time. 

Tomographic reconstruction from sparse-view and limited-angle data using a generative adversarial network

Few-view computed tomography (CT) is able to provide the internal information of an object for diagnosis, while reducing radiation dose and decreasing side effects. In spite of these benefits, few-view CT images with conventional reconstruction algorithms are suffer from structural distortion and artifacts due to insufficient projections. In this study, a deep learning-based network was proposed for reconstructing few-view CT images and improving image quality. The proposed network consisted of fully connected, convolution and deconvolution layers, and the network training was implemented to directly transform few-view projections into reconstructed images without expert reconstruction knowledge. The quality of the output images obtained from the trained network was compared with those reconstructed by using the filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) algorithms. The results showed that the proposed network is able to improve the quantitative accuracy and noise property of reconstructed images, and the proposed network is more suitable for few-view CT reconstruction than the other algorithms. In conclusion, the proposed network can be potentially used to improve few-view CT image quality and considered as an alternative for few-view CT reconstruction. The source code is available on a GitHub repository at https://github.com/ YIMDOBIN/CT_reconstruction_via_DL .

Dahye Lee

Papers

Deep learning-based image reconstruction for few-view computed tomography

Analysis of Image Quality According to Imaging Parameters in Digital Tomosynthesis

Image Quality Evaluation in Computed Tomography Using Super-resolution Convolutional Neural Network