Image reconstruction and enhanced resolution imaging from irregular samples

Resolution in ART: An experimental investigation of the resolving power of an algebraic picture reconstruction technique

To investigate algebraic reconstruction technique (ART) for parallel imaging reconstruction of radial data, applied to accelerated cardiac cine. A graphics processing unit (GPU)-accelerated ART reconstruction was implemented and applied to simulations, point spread functions and in 12 subjects imaged with radial cardiac cine acquisitions. Cine images were reconstructed with radial ART at multiple undersampling levels (192 Nr × Np = 96 to 16). Images were qualitatively and quantitatively analyzed for sharpness and artifacts, and compared to filtered back-projection, and conjugate gradient SENSE. Radial ART provided reduced artifacts and mainly preserved spatial resolution, for both simulations and in vivo data. Artifacts were qualitatively and quantitatively less with ART than filtered back-projection using 48, 32, and 24 Np , although filtered back-projection provided quantitatively sharper images at undersampling levels of 48-24 Np (all P < 0.05). Use of undersampled radial data for generating auto-calibrated coil-sensitivity profiles resulted in slightly reduced quality. ART was comparable to conjugate gradient SENSE. GPU-acceleration increased ART reconstruction speed 15-fold, with little impact on the images. GPU-accelerated ART is an alternative approach to image reconstruction for parallel radial MR imaging, providing reduced artifacts while mainly maintaining sharpness compared to filtered back-projection, as shown by its first application in cardiac studies.

Fluorescence diffuse optical tomography (fDOT) is a noninvasive imaging technique that makes it possible to quantify the spatial distribution of fluorescent tracers in small animals. fDOT image reconstruction is commonly performed by means of iterative methods such as the algebraic reconstruction technique (ART). The useful results yielded by more advanced l1-regularized techniques for signal recovery and image reconstruction, together with the recent publication of Split Bregman (SB) procedure, led us to propose a new approach to the fDOT inverse problem, namely, ART-SB. This method alternates a cost-efficient reconstruction step (ART iteration) with a denoising filtering step based on minimization of total variation of the image using the SB method, which can be solved efficiently and quickly. We applied this method to simulated and experimental fDOT data and found that ART-SB provides substantial benefits over conventional ART.

BackgroundAfter the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems.MethodsIn this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV.ResultsA tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV.ConclusionsRMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

Tomography is a method for reconstructing an image of some object cross-section using emitted electromagnetics waves. Some techniques frequently applied for tomography covers backprojection, Algebraic Reconstruction Technique (ART), and backpropagation. In this paper, frequency selection and image reconstruction techniques for tomography are investigated using parallel-beam projection at microwave frequency. The use of some microwave frequencies of 1.5GHz, 3GHz, and 4.5GHz is intended to determine the characteristics and effect of selected frequency in producing the reconstructed image. A perforated wood is used as a target which is represented by a wood of tree trunk for object measurement. Here, the filtered backprojection and backpropagation techniques are implemented for reconstructing the image of measured object. From the result, it shows that the reconstructed image using backpropagation technique at the frequency of 3GHz has better image quality compared to the ones using filtered backprojection technique.

The aim of this study was to conduct a comprehensive assessment of image quality in coronary computed tomography angiography (CCTA) reconstructed using deep learning image reconstruction (DLIR) technique. Objective and subjective assessment methods were utilized to analyze intraindividual image quality and to compare it with conventionally used hybrid iterative reconstruction algorithm (ASiR-V) regarding their dependency to heart rate, the main cause of motion blurring in CCTA. A total of 500 consecutive patients scheduled to undergo clinically indicated CCTA under prospective electrocardiography-triggering on a 256-slice computed tomography scanner were included. The image data were reconstructed using two different reconstruction techniques (ASiR-V and TrueFidelity), enabling a direct comparison. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and vessel contrast were used to determine objective image quality. Subjective image quality was assessed using a 4-point Likert scale. DLIR exhibited significantly better SNR in the aortic root, and this difference intensified with increasing heart rate. In contrast, SNR deteriorated with increasing heart rate for ASiR-V. The subjective comparison by two radiologists revealed significantly better ratings for TrueFidelity, both depending on heart rate and independent of it. For the left coronary artery, no significant differences were observed between the two algorithms in terms of SNR and CNR. In addition, vascular contrast did not differ significantly between DLIR and ASiR-V. Artificial intelligence in the form of DLIR offers significant advantages over manually created ASiR-V, objectively and subjectively evaluated, depending on and independent of heart rate. However, this study also identified drawbacks regarding vessel contrast and SNR, suggesting potential for further improvements.

ULTRASONIC TOMOGRAPHY IN COMPOSITE MATERIALS

Image reconstruction in electrical impedance tomography using the sensitivity theorem is generally based on the assumption that the initial conductivity distribution of the body being imaged is uniform. The technique of image reconstruction using this method is described and reconstructed images are presented. Improvements in image quality and accuracy are demonstrated when accurate a priori `anatomical' information, in the form of a model of the distribution of conductivity within the region to be imaged, is used to construct the sensitivity matrix. In practice correct a priori information is not available, for example the conductivity values within the various anatomical regions will not be known. An iterative algorithm is presented which allows the conductivity parameters of the a priori model to be determined during reconstruction. Keywords: electrical impedance tomography, image reconstruction, a priori information

To obtain useful knowledge and experience of CCERT under different excitation patterns, the image reconstruction performance of a 12-electrode CCERT sensor under five different excitation patterns (1-electrode excitation pattern, 2-electrode excitation pattern, 3-electrode excitation pattern, 4-electrode excitation pattern, and 5-electrode excitation pattern) are investigated and compared. A 12-electrode CCERT prototype, which can implement different excitation patterns, is developed to obtain the practical experiment data. Four typical image reconstruction algorithms (Linear Back Projection (LBP) algorithm, Landweber’s iteration algorithm, Algebraic Reconstruction Technique (ART) algorithm, and Simultaneous Iterative Reconstruction Technique (SIRT) algorithm) are introduced to investigate and compare the image reconstruction performance with the experimental data obtained from the CCERT prototype. Research results show that the 1-electrode excitation pattern and the 2-electrode excitation pattern have better global image reconstruction performance. These two excitation patterns are better choices, while the 3-electrode excitation pattern, the 4-electrode excitation pattern, and the 5-electrode excitation pattern are not recommended.

Limited-data tomography, to which electromagnetic geotomography belongs, is analyzed in this paper. In this technique, a discrete forward projection model may be expressed by a rank-deficient system of linear equations whose the nullspace is non-trivial. This means that some image components may fall into the nullspace, and hence the minimal-norm least-square solution, to which many image reconstructions methods converge, may be different from the true one. The Algebraic Reconstruction Technique (ART), Simultaneous Iterative Reconstruction Technique (SIRT), or Conjugate Gradients Least Squares (CGLS) are examples of such methods. In this paper, we deal with the question of how to partially recover the missing image components. First, we analyze the advantages of using the iterative Tikhonov regularization and the Maximum A Posteriori (MAP) algorithm with Gibbs prior. Then, we conclude that the missing (nullspace) image components can be partially recovered if the MAP algorithm is implemented through a multigrid technique. The results, which are presented for synthetic noise-free and noisy data, demonstrate the validity of our assumption. The problem of estimating the regularization and scaling parameters in the MAP algorithm is also addressed.

In this paper, we developed an accelerated algorithm for the classical simultaneous iterative reconstruction technique (SIRT) method applied in CT image reconstruction. The proposed algorithm possesses the following two features. First, it can flexibly handle the image reconstruction problem where projection data is contaminated by Poisson noise. This property makes it successful in compensating the disadvantage of the typical algebraic reconstruction technique (ART) method. Second, we utilize Passty's proximal splitting framework to construct a row-action type accelerated iterative algorithm to minimize the cost function. The accelerating strategy makes it successful in compensating the disadvantage of the famous SIRT method. We proved that the new algorithm can achieve significant image quality with noise reduction in less than 10 iterations, while SIRT needs more than 200 iterations. Both digital phantom and clinical abdominal CT image were reconstructed for demonstrating the efficiency of the proposed method.

A new algebraic reconstruction technique (ART), nonlinear auto-adjusting iterative reconstruction technique (NAIRT), is proposed and applied to reconstruct a section of an actual thermal air flow field. With numerical simulation, NAIRT was tested to reconstruct a complicated field to demonstrate its superior reconstructive capability. In contrast, three typical ARTs, the basic ART, simultaneous ART (SART), and a modified SART (MSART), were simulated to demonstrate the reconstructive capability improvement attained through the use of the proposed NAIRT. The calculated results were discussed with mean square error (MSE) and peak error (PE). A thermal air flow field was produced with an alcohol burner and was detected by a laser beam. With laser beam projections, a cross-section of the field was reconstructed by NAIRT. As a result, the reconstructive capability was improved much by NAIRT. The MSE decreased by 95.5%, and PE by 97.2% from that of the basic ART. Only NAIRT converged without filters while its reconstructive accuracy improved. By increasing the projections from 42 to 84, the accuracy of NAIRT without filters was improved significantly. NAIRT could effectively reconstruct the section of the thermal field. The proposed NAIRT needed no filter for its convergence and it had the highest reconstructive accuracy and simplest iterative expression of those analyzed.

Underwater computer vision has attracted increasing attention in the research community due to the recent advances in underwater platforms such as of rovers, gliders, autonomous underwater vehicles (AUVs) , and the like, that now make possible the acquisition of vast amounts of imagery and video for applications such as biodiversity assessment, environmental monitoring, and search and rescue. Despite growing interest, underwater computer vision is still a relatively under-researched area, where the attention in the literature has been paid to the use of computer vision techniques for image restoration and reconstruction, where image formation models and image processing methods are used to recover colour corrected or enhanced images. This is due to the notion that these methods can be used to achieve photometric invariants to perform higher-level vision tasks such as shape recovery and recognition under the challenging and widely varying imaging conditions that apply to underwater scenes. In this paper, we review underwater computer vision techniques for image reconstruction, restoration, recognition, depth, and shape recovery. Further, we review current applications such as biodiversity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others. We also delve upon the current trends in the field and examine the challenges and opportunities in the area.

Parallel imaging reconstructions result in spatially varying noise amplification characterized by the g-factor, precluding conventional measurements of noise from the final image. A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal-to-noise ratio and g-factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment. Only a simple "prescan" measurement of noise amplitude and correlation in the phased-array receiver, and a single accelerated image acquisition are required, allowing robust assessment of signal-to-noise ratio and g-factor. The "pseudo multiple replica" method has been rigorously validated in phantoms and in vivo, showing excellent agreement with true multiple replica and analytical methods. This method is universally applicable to the parallel imaging reconstruction techniques used in clinical applications and will allow pixel-by-pixel image noise measurements for all parallel imaging strategies, allowing quantitative comparison between arbitrary k-space trajectories, image reconstruction, or noise conditioning techniques.

In this paper, we propose an algebraic reconstruction technique (ART) based discrete tomography method to reconstruct an image accurately using projections from a few views. We specifically consider the problem of reconstructing an image of bottles filled with various types of liquids from X-ray projections. By exploiting the fact that bottles are usually filled with homogeneous material, we show that it is possible to obtain accurate reconstruction with only a few projections by an ART based algorithm. In order to deal with various types of liquids in our problem, we first introduce our discrete steering method which is a generalization of the binary steering approach for our proposed multi-valued discrete reconstruction. The main idea of the steering approach is to use slowly varying thresholds instead of fixed thresholds. We further improve reconstruction accuracy by reducing the number of variables in ART by combining our discrete steering with the discrete ART (DART) that fixes the values of interior pixels of segmented regions considered as reliable. By simulation studies, we show that our proposed discrete steering combined with DART yields superior reconstruction than both discrete steering only and DART only cases. The resulting reconstructions are quite accurate even with projections using only four views.