Extra projection data identification method for fast-continuous-rotation industrial cone-beam CT

Mao

et al. 2016

Inverse Problems

Standard computed tomography (CT) cannot reproduce spectral information of an object. Hardware solutions include dual-energy CT which scans the object twice in different x-ray energy levels, and energy-discriminative detectors which can separate lower and higher energy levels from a single x-ray scan. In this paper, we propose a software solution and give an iterative algorithm that reconstructs an image with spectral information from just one scan with a standard energy-integrating detector. The spectral information obtained can be used to produce color CT images, spectral curves of the attenuation coefficient μ(r, E)at points inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. Our software solution requires no change on hardware of a CT machine. With the Shepp–Logan phantom, we have found that although the photoelectric and Compton components were not perfectly reconstructed, their composite effect was very accurately reconstructed as compared to the ground truth and the dual-energy CT counterpart. This means that our proposed method has an intrinsic benefit in beam hardening correction and metal artifact reduction. The algorithm is based on a nonlinear polychromatic acquisition model for x-ray CT. The key technique is a sparse representation of iterations in a framelet system. Convergence of the algorithm is studied. This is believed to be the first application of framelet imaging tools to a nonlinear inverse problem.

Section: Experiments On Real-world Datamentioning

confidence: 99%

A framelet-based iterative maximum-likelihood reconstruction algorithm for spectral CT

Mao

et al. 2016

Inverse Problems

“…Despite the imaging quality of CT being outstanding, the inspection speed of traditional CT is generally slow. Several works have been conducted to elevate the imaging speed [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement

Zheng

2023

Electronics

Industrial CT is useful for defect detection, dimensional inspection and geometric analysis, while it does not meet the needs of industrial mass production because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT-reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor and observe the objects in different angles of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time this source-detector layout strategy has been adopted. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. Our experiment shows that the reconstruction resulting in undersampled projections is highly enhanced using a deep-learning neural network which meets the demand of non-destructive testing. Meanwhile, our proposed system structure can perform quick scans and reconstructions on larger objects. It solves the pain points of limited scan size and slow scanning speed of existing industrial CT scans.

“…Despite that the imaging quality of CT is outstanding, the inspection speed of traditional CT is generally slow. Several works have been done to elevate the imaging speed: [3][4] [5][6] [7].…”

Section: Introductionmentioning

confidence: 99%

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement

Zheng

2023

Preprint

Industrial CT is useful for defect detection, dimensional inspection and geometric analysis. While it does not meet the needs of industrial mass production, because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor, and observe the objects in different angle of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time to adopt this source-detector layout strategy. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency.