Joint Reconstruction Algorithm Research Articles

Joint reconstruction algorithm: combining synchrotron radiation with conventional X-ray computed tomography for improved imaging

Synchrotron radiation (SR) is an excellent light source for micro-CT (micro-computed tomography) applications due to its monochromaticity and high brightness, which are crucial for achieving high-resolution imaging. However, when scanning larger objects, the limited field of view (FOV) of SR will lead to data truncation, limiting its utilization efficiency. To address this limitation, this paper proposed a method to integrate conventional X-ray CT data to supplement the truncated SR data for joint reconstruction to improve imaging. We first employ a polynomial transformation to match the image gray levels from the two distinct light sources and then resample these to form joint data. Subsequently, the method derives noise images from the noise characteristics of the projection data to construct image weight constraint that accurately reflects different data quality from two sources. The flexibility of the image weight constraint also allows for its combination with various denoisers to further enhance the reconstruction quality. Experimental results demonstrate that the proposed method can leverage the strengths of both imaging modalities to facilitate larger scale and high-resolution imaging.

Lossless Decoding Method of Compressed Coded Video Based on Inter-Frame Differential Background Model

For the problems of low decoding accuracy, long decoding time, and low quality of decoded video image in the traditional lossless decoding method of compressed coded video, a lossless decoding method of compressed coded video based on inter frame difference background model is proposed. At the coding end, the inter frame difference background model is used to extract the single frame video image, and the mixed coding method is used to compress the video losslessly. At the decoding side, the CS-SOMP (compressive sensing-synchronous orthogonal matching pursuit algorithm) joint reconstruction algorithm is composed of synchronous orthogonal matching pursuit algorithm (SOMP) and K-SVD (kernel singular value decomposition) algorithm to losslessly decode the compressed encoded video. The simulation results show that the lossless decoding method based on the inter frame difference background model has higher accuracy, shorter decoding time, and ensures the quality of the decoded video image.

A novel subspace pursuit of residual correlation step algorithm for distributed compressed sensing

Multi-signal joint reconstruction is critical in distributed compressed sensing (DCS). We propose a joint reconstruction algorithm for subspace pursuit of residual correlation steps to balance reconstruction efficiency and data quality. The algorithm improves on two aspects. Firstly, the residuals are used as the step feedback condition to achieve dynamic step adjustment, reducing the iteration’s overall number. Secondly, based on the mixed-support set model (MSM), the residual non-decreasing surplus condition is set to reconstruct the common and the innovation components of the target signal in groups, which reduces the mixing error in joint reconstruction. This paper compares the performance of six algorithms of the same type under the conditions of Gaussian sparse signal and measured shock wave field sensor network data. The results show that the proposed algorithm can effectively reduce the number of measurements required for reconstruction and improve efficiency while maintaining accuracy.

Joint image reconstruction algorithm in Compton cameras

Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging.

Material classification from sparse spectral X-ray CT using vectorial total variation based on L infinity norm

The development of energy resolving photon counting detectors (PCD) has paved the way to spectral X-ray Computed Tomography (CT), with which one can simultaneously extract the energy dependence of a material's linear attenuation coefficient (LAC). Spectral CT has proved to be an advanced technique to classify materials based on their physical properties such as electron density ( ρ e ) and effective atomic number ( Z eff ). However, the application of spectral CT may be hindered by the poor image reconstruction quality when used for material identification in security screening where rapid scanning is required. Image reconstruction from few projections or low radiation exposure time would enable rapid scanning. The reconstruction quality for each energy bin may also be degraded since the division of photon counts into multiple energy bins naturally leads to higher noise levels. In this work, we explore how to perform accurate spectral CT reconstructions from such data, and propose L ∞ norm-based vectorial total variation (L ∞ -VTV) regularization that uses correlations between multiple energy bins. Using experimental data acquired with a custom laboratory instrument for spectral CT, the L ∞ -VTV is tested on “real life” phantoms consisting of materials in the range of 6 ≤ Z eff ≤ 15. For material classification from only 7 projections, the L ∞ -VTV gives the relative deviations of 3.5% for ρ e and 2.4% for Z eff , whereas the total nuclear variation (TNV) of another state-of-the-art joint reconstruction and the total variation (TV) yield those of 3.4% and 3.1%, and 3.8% and 4.2%, respectively. The L ∞ -VTV is now ready for use in security screening. • A joint reconstruction algorithm with L infinity norm improves image reconstruction. • The algorithm is robust to few projections and/or low photon counts. • Using correlations between multiple energy bins enhances material identification. • L infinity norm in the algorithm leads to suppression of outliers in gradient magnitudes. • High noise levels in specific energy bins can be alleviated using unaffected bins.

Line-wise scanning-based super-resolution imaging.

In this Letter, we present a novel, to the best of our knowledge, line-wise scanning-based super-resolution (LSSR) imaging method. To reduce point spread functions overlapping among pixels, we specifically present a super-resolution (SR) imaging architecture to capture a series of low-resolution images using a line-based optical multiplexing technique, which is able to achieve a good balance between imaging quality and speed. In addition, we propose an efficient joint reconstruction algorithm based on total variation and low-rank constraints to generate a high-resolution image from these low-resolution images that contain different spatial details. Meanwhile, existing stripe noises are efficiently suppressed. Experiments on real data show that LSSR imaging has significant advantages over other state-of-the-art methods in terms of visual quality and quantitative measurement.

The algorithm of stereo vision and shape from shading based on endoscope imaging

With medical endoscopic equipment development, minimally invasive surgery (MIS) has gradually become an essential technical means in daily medical practice. In recent years, minimally invasive surgery has been widely used because of its small incision and quick recovery. However, at the same time, minimally invasive surgery has put forward higher requirements for the operator. A 3D reconstruction framework combined with stereo vision and Shape from Shading (SFS) was proposed to improve endoscopic imaging accuracy and reduce the difficulty of minimally invasive surgery. This paper constructs a joint objective function based on the improved SFS and the classical stereo matching method. The optimization algorithm of the depth map under the joint objective function is given. Finally, the experimental verification of the joint reconstruction algorithm is carried out. The joint reconstruction framework's effectiveness is verified by qualitative and quantitative comparison and analysis based on the silica-gel-heart model and real-heart image datasets. The experimental results show that the joint reconstruction framework can restore the heart surface shape as a whole and retain the local details. Compared with the classical stereo vision and SFS methods, the proposed joint reconstruction method has better robustness and reconstruction accuracy.

2D feasibility study of joint reconstruction of attenuation and activity in limited angle TOF-PET.

Several research groups are studying organ-dedicated limited angle positron emission tomography (PET) systems to optimize performance-cost ratio, sensitivity, access to the patient and/or flexibility. Often open systems are considered, typically consisting of two detector panels of various sizes. Such systems provide incomplete sampling due to limited angular coverage and/or truncation, which leads to artefacts in the reconstructed activity images. In addition, these organ-dedicated PET systems are usually stand-alone systems, and as a result, no attenuation information can be obtained from anatomical images acquired in the same imaging session. It has been shown that the use of time-of-flight information reduces incomplete data artefacts and enables the joint estimation of the activity and the attenuation factors. In this work, we explore with simple 2D simulations the performance and stability of a joint reconstruction algorithm, for imaging with a limited angle PET system. The reconstruction is based on the so-called MLACF (Maximum Likelihood Attenuation Correction Factors) algorithm and uses linear attenuation coefficients in a known-tissue-class region to obtain absolute quantification. Different panel sizes and different time-of-flight (TOF) resolutions are considered. The noise propagation is compared to that of MLEM reconstruction with exact attenuation correction (AC) for the same PET system. The results show that with good TOF resolution, images of good visual quality can be obtained. If also a good scatter correction can be implemented, quantitative PET imaging will be possible. Further research, in particular on scatter correction, is required.

Practical joint reconstruction of activity and attenuation with autonomous scaling for time-of-flight PET

Recent research has showed that attenuation images can be determined from emission data, jointly with activity images, up to a scaling constant when utilizing the time-of-flight (TOF) information. We aim to develop practical CT-less joint reconstruction for clinical TOF PET scanners to obtain quantitatively accurate activity and attenuation images. In this work, we present a joint reconstruction of activity and attenuation based on MLAA (maximum likelihood reconstruction of attenuation and activity) with autonomous scaling determination and joint TOF scatter estimation from TOF PET data. Our idea for scaling is to use a selected volume of interest (VOI) in a reconstructed attenuation image with known attenuation, e.g. a liver in patient imaging. First, we construct a unit attenuation medium which has a similar, though not necessarily the same, support to the imaged emission object. All detectable LORs intersecting the unit medium have an attenuation factor of e −1≈ 0.3679, i.e. the line integral of linear attenuation coefficients is one. The scaling factor can then be determined from the difference between the reconstructed attenuation image and the known attenuation within the selected VOI normalized by the unit attenuation medium. A four-step iterative joint reconstruction algorithm is developed. In each iteration, (1) first the activity is updated using TOF OSEM from TOF list-mode data; (2) then the attenuation image is updated using XMLTR—a extended MLTR from non-TOF LOR sinograms; (3) a scaling factor is determined based on the selected VOI and both activity and attenuation images are updated using the estimated scaling; and (4) scatter is estimated using TOF single scatter simulation with the jointly reconstructed activity and attenuation images. The performance of joint reconstruction is studied using simulated data from a generic whole-body clinical TOF PET scanner and a long axial FOV research PET scanner as well as 3D experimental data from the PennPET Explorer scanner. We show that the proposed joint reconstruction with proper autonomous scaling provides low bias results comparable to the reference reconstruction with known attenuation.

Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning

BackgroundTo develop a low-dose cone beam CT (LD-CBCT) reconstruction method named simultaneous algebraic reconstruction technique and dual-dictionary learning (SART-DDL) joint algorithm for image guided radiation therapy (IGRT) and evaluate its imaging quality and clinical application ability.MethodsIn this retrospective study, 62 CBCT image sets from February 2018 to July 2018 at west china hospital were randomly collected from 42 head and neck patients (mean [standard deviation] age, 49.7 [11.4] years, 12 females and 30 males). All image sets were retrospectively reconstructed by SART-DDL (resultant D-CBCT image sets) with 18% less clinical raw projections. Reconstruction quality was evaluated by quantitative parameters compared with SART and Total Variation minimization (SART-TV) joint reconstruction algorithm with paired t test. Five-grade subjective grading evaluations were done by two oncologists in a blind manner compared with clinically used Feldkamp-Davis-Kress algorithm CBCT images (resultant F-CBCT image sets) and the grading results were compared by paired Wilcoxon rank test. Registration results between D-CBCT and F-CBCT were compared. D-CBCT image geometry fidelity was tested.ResultsThe mean peak signal to noise ratio of D-CBCT was 1.7 dB higher than SART-TV reconstructions (P < .001, SART-DDL vs SART-TV, 36.36 ± 0.55 dB vs 34.68 ± 0.28 dB). All D-CBCT images were recognized as clinically acceptable without significant difference with F-CBCT in subjective grading (P > .05). In clinical registration, the maximum translational and rotational difference was 1.8 mm and 1.7 degree respectively. The horizontal, vertical and sagittal geometry fidelity of D-CBCT were acceptable.ConclusionsThe image quality, geometry fidelity and clinical application ability of D-CBCT are comparable to that of the F-CBCT for head-and-neck patients with 18% less projections by SART-DDL.

Autocalibrated cardiac tissue phase mapping with multiband imaging and k-t acceleration.

To develop an autocalibrated multiband (MB) CAIPIRINHA acquisition scheme with in-plane k-taccelerationenabling multislice three-directional tissue phase mapping in one breath-hold. A k-tundersamplingscheme was integrated into a time-resolved electrocardiographic-triggered autocalibrated MB gradient-echo sequence. The sequence was used to acquire data on 4 healthy volunteers with MB factors of two (MB2) and three (MB3), which were reconstructed using a joint reconstruction algorithm that tackles both k-t and MB acceleration. Forward simulations of the imaging process were used to tune the reconstruction model hyperparameters. Direct comparisons between MB and single-band tissue phase-mapping measurements were performed. Simulations showed that the velocities could be accurately reproduced with MB2 k-t (average ± twice the SD of the RMS error of 0.08 ± 0.22cm/s and velocity peak reduction of 1.03% ± 6.47% compared with fully sampled velocities), whereas acceptable results were obtained with MB3 k-t (RMS error of 0.13 ± 0.58cm/s and peak reduction of 2.21% ± 13.45%). When applied to tissue phase-mapping data, the proposed technique allowed three-directional velocity encoding to be simultaneously acquired at two/three slices in a single breath-hold of 18 heartbeats. No statistically significant differences were detected between MB2/MB3 k-t and single-band k-t motion traces averaged over the myocardium. Regional differences were found, however, when using the American Heart Association model for segmentation. An autocalibratedMBk-tacquisition/reconstruction framework is presented that allows three-directional velocity encoding of the myocardial velocities at multiple slices in one breath-hold.

Mapping the dipole orientation distribution within a super-resolution scale via fluorescence polarization modulation.

Conventional fluorescence polarization microscopy has been largely used to monitor the orientation and the structural information of biomolecules labeled with fluorescence dipoles but suffers from the optical diffraction limit. Here, we put forward a novel algorithm to simultaneously acquire the super-resolution image and the effective orientation distribution information of dipole clusters at corresponding super-resolution. In this paper, the orientation distribution of dipole clusters is statistically modeled by its mean orientation and orientation deviation, which are, respectively, represented by the middle direction and the opening angle of a sector shape. According to this model and microscopy imaging theory, the joint reconstruction algorithm is deduced mathematically in detail based on the conjugate gradient least-squares method. By applying this algorithm to different samples, the reconstructed results prove more than twice the resolution of wide-field images and the orientation distribution information at corresponding spatial resolution. Furthermore, the high accuracy of this algorithm in reconstructing super-resolution orientation distribution information is verified by Monte Carlo simulations.

Recovery of pressure and wave speed for photoacoustic imaging under a condition of relative uncertainty

In this paper we study the photoacoustic tomography problem for which we seek to recover both the initial state of the pressure field and the wave speed of the medium from knowledge of a single boundary measurement. The goal is to propose practical assumptions to define a set of initial conditions and wave speeds over which uniqueness for this inverse problem is guaranteed. The main result of the paper is that given two sets of wave speeds and pressure profiles, they cannot produce the same acoustic measurements if the relative difference between the wave speeds is much smaller than the relative difference between the pressure profiles. Implications for iterative joint-reconstruction algorithms are discussed.

FPGA-based real-time compressed sensing of multichannel EEG signals for wireless body area networks

Abstract The purpose of this study is to solve the issues in reconstruction computation complexity of the current method in wireless body area network (WBAN) through developing compressed sensing (CS) for multichannel electroencephalogram, performing model optimization, designing a system for compressing and collecting electroencephalogram (EEG) signals, and implementing real time compression and collection of multichannel signals. Firstly, based on the distributed compressed sensing theory, we analyze the sparsity of EEG signal, screen digital sensing matrix models, design multichannel joint reconstruction algorithm, and perform optimization analysis as well as simulation verification at each step. Secondly, based on field programmable gate array (FPGA), we realize real time collection, storage, compression, and transmission of multichannel EEG by setting up a compression and collection system. Lastly, each system function module is inspected, and the performance of the compressed multichannel EEG system is evaluated from the perspective of computation complexity, reconstruction accuracy, instantaneity, etc. Evaluation results show that the improvement of real-time performance is contributed by the application of binary permutation block diagonal matrix (BPBD), which converts CS multiplications into additions with a simple circuit and reduces the computational time drastically. The average signal to noise distortion ratio for signal reconstruction reaches 21.74 dB under the compression ratio of 2, which also meets the requirement of WBAN. The proposed method has faster computation, better accuracy, and simpler coding, can be utilized in a variety of applications related to multichannel EEG, especially in situations where the system power consumption and real-time performance are critical.

Reconstruction of Compressed-Sensed Multiview Video With Disparity- and Motion-Compensated Total Variation Minimization

Compressed sensing (CS) is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Exact reconstruction may then be possible with much fewer than the Nyquist-required number of data. In this paper, we consider a multiview video system in which multiple cameras at different locations perform independent CS to simultaneously capture different views of a scene. At the decoder, we propose a disparity- and motion-compensated total variation minimization algorithm to jointly reconstruct the multiview video sequence. The experimental results show that the proposed joint reconstruction algorithm successfully exploits simultaneously intra-frame, inter-frame, and inter-view sparsity and significantly outperforms existing independent-view reconstruction, residue-view reconstruction, and motion-adaptive reconstruction algorithms.

DCS-based MBSBL joint reconstruction of multi-sensors data for energy-efficient telemonitoring of human activity

The joint reconstruction of nonsparse multi-sensors data with high quality is a challenging issue in human activity telemonitoring. In this study, we proposed a novel joint reconstruction algorithm combining distributed compressed sensing with multiple block sparse Bayesian learning. Its basic idea is that based on the joint sparsity model, the distributed compressed sensing technique is first applied to simultaneously compress the multi-sensors data for gaining the high-correlation information regarding activity as well as the energy efficiency of sensors, and then, the multiple block sparse Bayesian learning technique is employed to jointly recover nonsparse multi-sensors data with high fidelity by exploiting the joint block sparsity. The multi-sensors acceleration data from an open wearable action recognition database are selected to assess the practicality of our proposed technique. The sparse representation classification model is used to classify activity patterns using the jointly reconstructed data in order to further examine the effectiveness of our proposed method. The results showed that when compression rates are selected properly, our proposed technique can gain the best joint reconstruction performance as well as energy efficiency of sensors, which greatly contributes to the best sparse representation classification–based activity classification performance. This has a great potential for energy-efficient telemonitoring of human activity.

Fast BCS-FOCUSS and DBCS-FOCUSS with augmented Lagrangian and minimum residual methods

Block compressive sensing FOCal Underdetermined System Solver (BCS-FOCUSS) and distributed BCS-FOCUSS (DBCS-FOCUSS) are iterative algorithms for individual and joint recovery of correlated images. The performance of both these algorithms was noticed to be best within BCS framework. However, both these algorithms suffer from high computational complexity and recovery time. This is caused by the need for an explicit computation of matrix inverse in each iteration and a slow convergence from a poor starting point. In this paper, we propose a methodology to obtain fast and good initial solution using the augmented Lagrangian method to improve the convergence rate of both algorithms. We also propose to incorporate the minimum residual method to avoid matrix inversion to reduce the computational cost. Simulation studies with the proposed modified BCS-FOCUSS and DBCS-FOCUSS demonstrate a significant reduction in the computational cost and recovery time while improving reconstruction quality for both individual and joint reconstruction algorithms.

A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks

Image signals which acquired by wireless visual sensor network can be used for specific event capture. This event capture is realized by image processing at the sink node. A distributed compressive sensing scheme is used for the transmission of these image signals from the camera nodes to the sink node. A measurement and joint reconstruction algorithm for these image signals are proposed in this paper. Make advantage of spatial correlation between images within a sensing area, the cluster head node which as the image decoder can accurately co-reconstruct these image signals. The subjective visual quality and the reconstruction error rate are used for the evaluation of reconstructed image quality. Simulation results show that the joint reconstruction algorithm achieves higher image quality at the same image compressive rate than the independent reconstruction algorithm.

Block compressive sensing: Individual and joint reconstruction of correlated images

Compressive sensing provides simultaneous sensing and compression of data. Block compressive sensing (BCS) of images has gained a prominence in recent years due to low encoding complexity. In this paper, we propose the reconstruction algorithm for BCS framework based on iterative re-weighted l1 norm minimization. In the proposed algorithm, the desired signal sparsity is achieved using l1 norm minimization while Wiener filtering is incorporated as the smoothing operator. We also propose block based joint reconstruction algorithm for correlated images and video frames. The correlation is captured through the joint sparsity model by minimizing the objective function which promotes the common sparsity structure. The performance of proposed algorithms is tested on different stereo images and video data. Our analysis shows that the proposed individual reconstruction algorithm gives good compression performance as compared to existing schemes. Extensive analysis on correlated images demonstrates that the proposed joint reconstruction algorithm outperforms individual reconstruction algorithms.

High-speed compressive range imaging based on active illumination.

We report a compressive imaging method based on active illumination, which reconstructs a 3D scene at a frame rate beyond the acquisition speed limit of the camera. We have built an imaging prototype that projects temporally varying illumination pattern and demonstrated a joint reconstruction algorithm that iteratively retrieves both the range and high-temporal-frequency information from the 2D low-frame rate measurement. The reflectance and depth-map videos have been reconstructed at 1000 frames per second (fps) from the measurement captured at 200 fps. The range resolution is in agreement with the resolution calculated from the triangulation methods based on the same system geometry. We expect such an imaging method could become a simple solution to a wide range of applications, including industrial metrology, 3D printing, and vehicle navigations.