Proposed Image Reconstruction Method Research Articles

Image reconstruction method based on wavelet fusion in electrical capacitance tomography with rotatable electrode sensor

In this paper, an image reconstruction method based on wavelet fusion in electrical capacitance tomography with a rotatable electrode sensor is proposed. This method can weaken the effect of the relative position distribution between the electrodes and the phantom object, thus improving the quality of the reconstructed image. To investigate the reliability of the proposed image reconstruction method, a new mechanical design of a rotatable 12-electrode sensor is introduced. The rotation of the 12-electrode sensor is accomplished by stepping motor and gear sets, which can easily change the measurement position of the electrodes. Five independent datasets of 66 capacitance measurements can be obtained by rotating the electrode sensor 4 times with an angle of 6°from the initial position. Then solving the inverse problem for each of the datasets, 5 reconstructed images are obtained. Finally, the wavelet fusion algorithm is used to fuse the reconstructed images. To verify the validity of the method, this paper evaluates the error case of rotational accuracy, the error case of measurement data and the case of real experiment, respectively. The experimental results indicate that the method can reduce the artifacts in the reconstructed images and improve the quality of the reconstructed images. Meanwhile, this processing method can enhance the shape and edge contour of the measured phantom.

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers.

Dynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers. The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. A low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image. The conducted numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available. The proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.

Image Reconstruction of Synthetic Aperture Radiometer by Transformer

In passive microwave remote sensing of the earth, compared with real aperture radiometer, synthetic aperture radiometer is a very powerful instrument with many advantages. However, the system design is more complex than the real aperture radiometer, and the hardware ideality is often not guaranteed. The non-ideal characteristics of the system hardware will bring a variety of errors to the system, which will cause the Fourier transform relationship between the visibility function and the brightness temperature image to no longer be established, thus reducing the quality of microwave brightness temperature image reconstruction by traditional methods. In this paper, a new microwave brightness temperature image reconstruction method for synthetic aperture radiometer by Transformer is proposed. This method uses a specially designed Transformer structure to extract the spectrum features in the visibility function. This method learns the mapping relationship between the visibility function and the original scene brightness temperature image through the supervised learning method, and learns as much as possible the spectrum information contained in the original scene brightness temperature image. Moreover, when there are missing baselines, this method will supplement the missing observation frequency information, so as to obtain better reconstructed image quality. With the above advantages, this method can suppress the Gibbs oscillation, and greatly reduce the side lobe. Compared with the existing reconstruction methods, whether missing baselines or not, the proposed image reconstruction method by Transformer has advantages in image quality. We verify the performance of this brightness temperature image reconstruction method through simulation and experiment.

Detection and Separation of Close Flaws in Coarse-Grained Materials Using Ultrasonic Image Deconvolution

Ultrasonic inspection of coarse-grained steels is a common challenge in various industrial fields. This task is often difficult because of acoustic scattering that creates structural noise in the ultrasonic signals and images. Therefore, inspections usually use low-frequency probes, which achieve poor resolution with standard delay-and-sum (DAS) imaging techniques, such as the well-known total focusing method (TFM). The purpose of this paper is to evaluate the performance of a super-resolution ultrasonic imaging technique presented by Laroche et al. (IEEE Trans Comput Imaging 7:935–947, 2021) for the inspection of industrial coarse-grained materials. An image deconvolution problem (with spatially varying blur) is formulated, relying on a forward model that links the TFM image to the acoustic reflectivity map. The experiments consider an austenitic-ferritic stainless steel sample insonified using array probes at 3 MHz and 5 MHz placed in contact. The goal is to resolve two close reflectors corresponding to side-drilled holes (SDH) with diameter 0.4 mm spaced by 0.4 mm edge-to-edge and positioned at different depths (10, 20, 30, 40 mm). This configuration corresponds to a critical case where the distance between the two reflectors is much lower than the Rayleigh distance, that is the resolution limit of a DAS imaging system. These are typical cases where DAS images obtained from low-frequency inspections critically lack resolution, but where higher frequency probes cannot be used in practice, because a too week signal-to-noise ratio would affect the detection capability. As predicted by the Rayleigh criterion, TFM is not able to separate the reflectors. The proposed image reconstruction method successfully resolves the majority of the reflectors with a rather accurate distance estimation. In the context of coarse-grained structure inspection, subwavelength reflectors distant from each other by two times less than the resolution limit given by the Rayleigh criterion have been successfully detected and separated. This approach hence enables the use of low-frequency probes, in order to improve the signal-to-noise ratio, while keeping high resolution capability which can be particularly interesting for industrial applications. In particular, the proposed approach shows promising results for the sizing of a real crack in an industrial sample.

An Image Reconstruction Method of Capacitively Coupled Electrical Impedance Tomography (CCEIT) Based on DBSCAN and Image Fusion

Based on linear back projection (LBP) algorithm, density-based spatial clustering of applications with noise (DBSCAN) algorithm and image fusion technique, a new image reconstruction method of capacitively coupled electrical impedance tomography (CCEIT) is proposed in this work. LBP is used to obtain the original image of the real part and the original image of the imaginary part. The gray level thresholds of the two original images are determined by DBSCAN clustering algorithm and the initial image of the real part and the initial image of the imaginary part are obtained by gray level threshold filtering, respectively. Finally, the two initial images are fused and the final image is obtained. Image reconstruction experiments are carried out with a 12-electrode CCEIT system prototype. The experimental results verify the effectiveness of the proposed image reconstruction method. Experimental results show that the quality of the final image is satisfactory. Compared with the conventional electrical tomography (ET) image reconstruction methods, the new proposed image reconstruction method needs less prior knowledge or manual intervention and realizes more effective usage of the impedance information. The research results also indicate that the DBSCAN clustering algorithm is an effective way to determine the gray level thresholds of the original images and the mean square error (MSE) is a reasonable criterion for image fusion. Meanwhile, LBP + DBSCAN is an effective algorithm for image reconstruction and image fusion technique is an effective way to utilize the whole impedance information of the conductive gas–liquid two-phase flow.

Compressive sensing MR imaging based on adaptive tight frame and reference image

Compressive sensing magnetic resonance (MR) imaging is aimed at achieving high-quality MR image reconstruction by undersampling K-space data. It is crucial to explore prior information since compressive sensing MR imaging relies heavily on some prior assumptions, such as signal's sparse property. In this study, in order to explore the prior information fully, an improved MR image reconstruction model based on compressive sensing theory is proposed, named reference image MR imaging with adaptive tight frame. In the proposed model, an adaptive tight frame is involved to explore the sparse prior information adapt to MR images and the similarity prior information to the target image. Meanwhile, improved adaptive weighting parameters are used to trade off the sparsity between the regions with much similarity and that of little similarity. In addition, the smoothing-based fast iterative shrinkage-threshold algorithm is utilised to tackle the optimisation problem so as to speed up imaging. The experimental results demonstrate that the proposed MR image reconstruction method outperforms some state-of-the-art methods in terms of quantitative results.

A Shape-Based Statistical Inversion Method for EIT/URT Dual-Modality Imaging.

A shape-based statistical inversion method is proposed for Electrical Impedance Tomography (EIT) and Ultrasound Reflection Tomography (URT) dual-modality imaging. It is promising to improve the imaging accuracy in inclusion detection problems. The proposed image reconstruction method is based on the statistical shape inversion framework. The likelihood function is derived from EIT and URT forward models. The prior distribution is constructed using the Markov random field (MRF) prior. The measurement uncertainty is modeled by conditional error model method. The statistical shape inversion problem is solved by the Maximum a posterior (MAP) method with conventional error model. A set of numerical and experimental tests are carried out to evaluate the performance of the proposed method. The results show that the proposed EIT/URT dual-modality imaging method has obvious improvement in imaging accuracy compared to the traditional single-modality EIT and URT methods.

Study on image reconstruction of capacitively coupled electrical impedance tomography (CCEIT)

The aim of this work is to study the image reconstruction of capacitively coupled electrical impedance tomography (CCEIT) by using total impedance of a gas–liquid two-phase flow. The results of image reconstruction that utilise the real part, the imaginary part, the amplitude of the impedance, and the image reconstruction results by making use of different parts of the total impedance are investigated and discussed. By combining a linear back projection (LBP) algorithm and a K-means clustering algorithm, a new image reconstruction algorithm for CCEIT is proposed. The LBP is used to obtain the original reconstructed image. The K-means is adopted to automatically obtain the gray level threshold. The final reconstructed image is acquired with an established gray level threshold filter. With the new algorithm (LBP + K-means), the images reconstructed by using different parts of the impedance are obtained. To make full use of total impedance information, an image fusion method based on the weighted combination method is presented. With a developed 12-electrode CCEIT prototype, the image reconstruction experiments are conducted. Experimental results show that all three parts of the impedance can reflect the phase distribution characteristics of the gas–liquid two-phase flow from different aspects. Image fusion results indicate that making full use of the different parts of the total impedance of the gas–liquid two-phase flow can effectively improve the quality of the image reconstruction. The experimental results also verify the feasibility and the effectiveness of the proposed new image reconstruction algorithm. Compared with conventional algorithms, the proposed image reconstruction method can obtain comparable/better image reconstruction results with less prior knowledge. The influence of manual intervention on the image reconstruction results can be effectively avoided.

Joint activity and attenuation estimation for PET with TOF data and single events

Maximum likelihood reconstruction of activity and attenuation (MLAA) for PET data with time-of-flight (TOF) information can determine the activity distribution up to a scale, and the attenuation map Radon transform up to a related constant. Prior knowledge is widely used for the determination of the constant. However, prior knowledge could be inaccurate due to patient variation and may result in quantitation errors. Our goal is to develop a method that can determine the scale and the related constant in the TOF-MLAA algorithm to obtain quantitatively accurate activity and attenuation maps.Our idea is to utilize the single events which have depth dependent attenuation factors, contrary to coincidence events. We show that in a 2D case, with the combination of TOF information and single events, a unique solution of attenuation and activity can be achieved. A three-step iterative image reconstruction algorithm is developed. In each iteration, the activity distribution is first updated using the MLEM approach with TOF PET data; the attenuation map is then updated using MLTR with non-TOF data; finally, both activity distribution and attenuation map are updated using a scale estimated from single events. Noisy and noise-free projection data are generated for 2D XCAT phantoms through analytical simulation. Both scatter information and randoms information are assumed to be known. The proposed method and the conventional TOF-MLAA algorithm are used to reconstruct the simulated data. Conventional MLEM method with known attenuation map is implemented for comparison as well. Normalized root mean square error (NRMSE) and optimal constant are defined for the quantitative analysis of reconstructed images. Our proposed image reconstruction method achieves ~1% NRMSE for the activity map and ~5% NRMSE for the attenuation map with a correct scale after 150 iterations for noise-free data. Results of noisy simulations are consistent with noise-free data.In summary, we have proposed to use the single events for constant determination in TOF-MLAA algorithm to obtain a unique solution of attenuation map and activity distribution without prior information. Future work will be dedicated to the extension of our method to the 3D situation.

Feasibility of sinogram reconstruction based on inpainting method with decomposed sinusoid-like curve (S-curve) using total variation (TV) noise reduction algorithm in computed tomography (CT) imaging system: A simulation study

The use of computed tomography (CT) imaging system has increased significantly because it is very important role in the field of the medical diagnostic disease. However, CT has potential risk for high radiation dose and in addition to new strategy of reducing dose such as development of image reconstruction algorithm in sparse view conditions. Also, noise reduction is essential for improving image performance. In this study, with an aim to confirm feasibility of sinogram reconstruction based on inpainting method with decomposed sinusoid-like curve (S-curve) using total variation (TV) noise reduction algorithm in CT imaging system. For that purpose, we designed above-mentioned reconstruction and noise reduction algorithms and quantitatively evaluated coefficient of variation (COV), contrast to noise ratio (CNR) and root mean square error (RMSE). According to the results, our proposed image reconstruction method using TV noise reduction algorithm can acquire superb result in all evaluation parameters. The main benefit of our proposed method in sparse projection view is that it provides excellent image performance with efficient reconstruction in sinogram domain and noise reduction ratio. In conclusion, our results demonstrated that the better image performance using our proposed method can expect acquiring low scan time and low radiation dose.

Linearized image reconstruction method for ultrasound modulated electrical impedance tomography based on power density distribution

Electrical resistance tomography (ERT) is a promising measurement technique with important industrial and clinical applications. However, with limited effective measurements, it suffers from poor spatial resolution due to the ill-posedness of the inverse problem. Recently, there has been an increasing research interest in hybrid imaging techniques, utilizing couplings of physical modalities, because these techniques obtain much more effective measurement information and promise high resolution. Ultrasound modulated electrical impedance tomography (UMEIT) is one of the newly developed hybrid imaging techniques, which combines electric and acoustic modalities. A linearized image reconstruction method based on power density is proposed for UMEIT. The interior data, power density distribution, is adopted to reconstruct the conductivity distribution with the proposed image reconstruction method. At the same time, relating the power density change to the change in conductivity, the Jacobian matrix is employed to make the nonlinear problem into a linear one. The analytic formulation of this Jacobian matrix is derived and its effectiveness is also verified. In addition, different excitation patterns are tested and analyzed, and opposite excitation provides the best performance with the proposed method. Also, multiple power density distributions are combined to implement image reconstruction. Finally, image reconstruction is implemented with the linear back-projection (LBP) algorithm. Compared with ERT, with the proposed image reconstruction method, UMEIT can produce reconstructed images with higher quality and better quantitative evaluation results.

A new undersampling image reconstruction method based on total variation model

基于全变差范数最小化模型，构造了一种新的图像重构算法；利用欠采样域内的融合信息，结合构造的图像重构算法，提出了一种基于压缩感知理论的图像融合模型.数值实验表明,构造的重构算法与传统算法相比，在一定程度上减少了所需的采样数量；提出的融合模型对多类图像具有较优的融合效果.;A new image reconstruction algorithm was proposed based on the TV norm minimization model. Then using the under sampling fusion information, combined with the image reconstruction algorithm, a fusion model based on compressed sensing is proposed. Numerical results show that the proposed image reconstruction method can reduce the sampling number required to some extent compared with the traditional algorithm. The proposed fusion model has good performance for many kinds of images.

High temporal resolution and streak-free four-dimensional cone-beam computed tomography

Cone-beam computed tomography (CBCT) has been clinically used to verify patient position and to localize the target of treatment in image-guided radiation therapy (IGRT). However, when the chest and the upper abdomen are scanned, respiratory-induced motion blurring limits the utility of CBCT. In order to mitigate this blurring, respiratory-gated CBCT, i.e. 4D CBCT, was introduced. In 4D CBCT, the cone-beam projection data sets acquired during a gantry rotation are sorted into several respiratory phases. In these gated reconstructions, the number of projections for each respiratory phase is significantly reduced. Consequently, undersampling streaking artifacts are present in the reconstructed images, and the image contrast resolution is also significantly compromised. In this paper, we present a new method to simultaneously achieve both high temporal resolution (∼100 ms) and streaking artifact-free image volumes in 4D CBCT. The enabling technique is a newly proposed image reconstruction method, i.e. prior image constrained compressed sensing (PICCS), which enables accurate image reconstruction using vastly undersampled cone-beam projections and a fully sampled prior image. Using PICCS, a streak-free image can be reconstructed from 10–20 cone-beam projections while the signal-to-noise ratio is determined by a denoising feature of the selected objective function and by the prior image, which is reconstructed using all of the acquired cone-beam projections. This feature of PICCS breaks the connection between the temporal resolution and streaking artifacts' level in 4D CBCT. Numerical simulations and experimental phantom studies have been conducted to validate the method.

Nmr image reconstruction method using complex kalman filter

AbstractAs in the case of NMR‐CT, it is sometimes required that an image with a high resolution is obtained from the limited number of data because of the long data acquisition time due to the spin‐lattice relaxation time. This paper attempts to cope with such a requirement, and proposes a reconstruction method for NMR image with improved resolution from a limited number of data. The proposed image reconstruction method first models the observed signal sequence by a multidimensional complex stochastic system with a matrix state probability variable. Then the state probability variable is estimated using a complex Kalman filter. To demonstrate the usefulness of the proposed method, an image is reconstructed from 128 × 128 data acquired from the cross section of the human head using NMR‐CT of 0.1 [T]. The result is compared with the NMR image reconstructed by the traditional two‐dimensional discrete‐Fourier transform. It is shown as a result that the proposed method not only improves the image resolution but also emphasizes the edge of the image and realizes an enlargement of the image. Furthermore, it is shown that an image emphasizing the contrast due to the spin‐spin relaxation time can be obtained.