Evaluation of imaging protocol for ECT based on CS image reconstruction algorithm

Single-photon emission computerized tomography and positron emission tomography are essential med- ical imaging tools, for which the sampling angle number and scan time should be carefully chosen to give a good compromise between image quality and radiopharmaceutical dose. In this study, the image quality of different ac- quisition protocols was evaluated via varied angle number and count number per angle with Monte Carlo simulation data. It was shown that, when similar imaging counts were used, the factor of acquisition counts was more important than that of the sampling number in emission computerized tomography. To further reduce the activity requirement and the scan duration, an iterative image reconstruction algorithm for limited-view and low-dose tomography based on compressed sensing theory has been developed. The total variation regulation was added to the reconstruction process to improve the signal to noise Ratio and reduce artifacts caused by the limited angle sampling. Maximization of the maximum likelihood of the estimated image and the measured data and minimization of the total variation of the image are alternatively implemented. By using this advanced algorithm, the reconstruction process is able to achieve image quality matching or exceed that of normal scans with only half of the injection radiopharmaceutical dose.     

A Compton scattering image reconstruction algorithm based on total variation minimization

Compton scattering imaging is a novel radiation imaging method using scattered photons.Its main characteristics are detectors that do not have to be on the opposite side of the source,so avoiding the rotation process.The reconstruction problem of Compton scattering imaging is the inverse problem to solve electron densities from nonlinear equations,which is ill-posed.This means the solution exhibits instability and sensitivity to noise or erroneous measurements.Using the theory for reconstruction of sparse images,a reconstruction algorithm based on total variation minimization is proposed.The reconstruction problem is described as an optimization problem with nonlinear data-consistency constraint.The simulated results show that the proposed algorithm could reduce reconstruction error and improve image quality,especially when there are not enough measurements.     

A Combined Reconstruction Algorithm for Limited-View Multi-Element Photoacoustic Imaging

We present a photoacoustic imaging system with a linear transducer array scanning in limited-view felds and develop a combined reconstruction algorithm, which is a combination of the limited-field filtered back projection （LFBP） algorithm and the simultaneous iterative reconstruction technique （SIRT） algorithm, to reconstruct the optical absorption distribution. In this algorithm, the LFBP algorithm is exploited to reconstruct the original photoacoustic image, and then the SIRT algorithm is used to improve the quality of the final reconstructed photoacoustic image. Numerical simulations with calculated incomplete data validate the reliability of this algorithm and the reconstructed experimental results further demonstrate that the combined reconstruction algorithm effectively reduces the artifacts and blurs and yields better quality of reconstruction image than that with the LFBP algorithm.     

Holographic display based on compressive sensing (Invited Paper)

We propose a method to improve the quality of the reconstructed images based on compressive sensing principles. The pseudo-inverse matrix and the total variation minimization algorithms are combined to reduce the sampling number of the computer generated hologram. Numerical simulations are performed and the results indicate that the peak signal to noise ratio is increased and the sampling ratio is decreased at the same time for holographic display.     

Deep learning for image reconstruction in thermoacoustic tomography

Microwave-induced thermoacoustic tomography(TAT)is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging.While an image reconstruction algorithm is critical for the TAT,current reconstruction methods often creates significant artifacts and are computationally costly.In this work,we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image.We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy.For the scenarios where realistic training data are scarce or unavailable,we use the finite element method(FEM)to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method.The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness.Compared with other state-of-the-art reconstruction methods,the TAT-Net method can reduce the root mean square error to 0.0143,and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64,respectively.The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.     

High total variation-based method for sparse-view photoacoustic reconstruction

We propose a novel method by combining the total variation（TV） with the high-degree TV（HDTV） to improve the reconstruction quality of sparse-view sampling photoacoustic imaging（PAI）. A weighing function is adaptively updated in an iterative way to combine the solutions of the TV and HDTV minimizations. The fast iterative shrinkage/thresholding algorithm is implemented to solve both the TV and the HDTV minimizations with better convergence rate. Numerical results demonstrate the superiority and efficiency of the proposed method on sparse-view PAI. In vitro experiments also illustrate that the method can be used in practical sparse-view PAI.     

Sub-pixel processing algorithm of the reducing boundary recursive error in staring FPA micro-scanning imaging

<正>By analyzing the error distribution rule of the boundary recursive reconstruction algorithm in controlled micro-scanning,a sub-pixel image processing algorithm is proposed to reduce the error.The gray statistical principle is used in the algorithm to optimize the error and acquire the sub-pixel image that approximates the original image.The simulation result shows that the effect of this algorithm is better than the oversample and simple boundary recursive algorithm(BRA),and it results in a good effect both in those of visible light and infrared imaging systems.Therefore,the application of this algorithm will enhance the performance of optoelectronic imaging systems.     

An advanced fully 3D OSEM reconstruction for positron emission tomography

A fully 3D OSEM reconstruction method for positron emission tomography （PET） based on symmetries and sparse matrix technique is described. Great savings in both storage space and computation time were achieved by exploiting the symmetries of scanner and sparseness of the system matrix. More reduction of storage requirement was obtained by introducing the approximation of system matrix. Iteration-filter was performed to restrict image noise in reconstruction. Performances of simulation data and phantom data got from Micro-PET （Type： Epuls-166） demonstrated that similar image quality was achieved using the approximation of the system matrix.     

Compressive sampling photoacoustic tomography based on edge expander codes and TV regularization

A new photoacoustic （PA） signal sampling and image reconstruction method, called compressive sampling PA tomography （CSPAT）, is recently proposed to make low sampling rate and high-resolution PA tomogra- phy possible. A key problem within the CSPAT framework is the design of optic masks. We propose to use edge expander codes-based masks instead of the conventional random distribution masks, and efficient total variation （TV） regularization-based model to formulate the associated problem. The edge expander codesbased masks, corresponding to non-uniform sampling schemes, are validated by both theoretical analysis and results from computer simulations. The proposed method is expected to enhance the capability of CSPAT for reducing the number of measurements and fast data acquisition.     

Reduced aliasing artifacts using shaking projection k-space sampling trajectory

Radial imaging techniques, such as projection-reconstruction （PR）, are used in magnetic resonance imaging （MRI） for dynamic imaging, angiography, and short-T2 imaging. They are less sensitive to flow and motion artifacts, and support fast imaging with short echo times. However, aliasing and streaking artifacts are two main sources which degrade radial imaging quality. For a given fixed number of k-space projections, data distributions along radial and angular directions will influence the level of aliasing and streaking artifacts. Conventional radial k-space sampling trajectory introduces an aliasing artifact at the first principal ring of point spread function （PSF）. In this paper, a shaking projection （SP） k-space sampling trajectory was proposed to reduce aliasing artifacts in MR images. SP sampling trajectory shifts the projection alternately along the k-space center, which separates k-space data in the azimuthal direction. Simulations based on conventional and SP sampling trajectories were compared with the same number projections. A significant reduction of aliasing artifacts was observed using the SP sampling trajectory. These two trajectories were also compared with different sampling frequencies. ASP trajectory has the same aliasing character when using half sampling frequency （or half data） for reconstruction. SNR comparisons with different white noise levels show that these two trajectories have the same SNR character. In conclusion, the SP trajectory can reduce the aliasing artifact without decreasing SNR and also provide a way for undersampling recon- struction. Furthermore, this method can be applied to three-dimensional （3D） hybrid or spherical radial k-space sampling for a more efficient reduction of aliasing artifacts.     

A modified equally sloped algorithm based on the total variation algorithm in computed tomography for insufficient data

Computed tomography (CT) has become an important technique for analyzing the inner structures of material, biological and energy fields. However, there are often challenges in the practical application of CT due to insufficient data. For example, the maximum rotation angle of the sample stage is limited by sample space or image reconstruction from the limited number of views required to reduce the X‐ray dose delivered to the sample. Therefore, it is difficult to acquire CT images with complete data. In this work, an iterative reconstruction algorithm based on the minimization of the image total variation (TV) has been utilized to develop equally sloped tomography (EST), and the reconstruction was carried out from limited‐angle, few‐view and noisy data. A synchrotron CT experiment on hydroxyapatite was also carried out to demonstrate the ability of the TV‐EST algorithm. The results indicated that the new TV‐EST algorithm was capable of achieving high‐quality reconstructions from projections with insufficient data.     

双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上,仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题,显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设,重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换,提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明,本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.     

Parallel variable-density spiral imaging using nonlocal total variation reconstruction

The relatively long scan time is still a bottleneck for both clinical applications and research of magnetic resonance imaging. To reduce the data acquisition time, we propose a novel fast magnetic resonance imaging method based on parallel variable-density spiral acquisition, which combines undersampling optimization and nonlocal total variation reconstruction.The undersampling optimization promotes the incoherence of resultant aliasing artifact via the "worst-case" residual error metric, and thus accelerates the data acquisition. Moreover, nonlocal total variation reconstruction is utilized to remove such an incoherent aliasing artifact and so improve image quality. The feasibility of the proposed method is demonstrated by both numerical phantom simulation and in vivo experiment. The experimental results show that the proposed method can achieve high acceleration factor and effectively remove an aliasing artifact from data undersampling with well-preserved image details. The image quality is better than that achieved with the total variation method.     

基于压缩感知与扩展小波树的自适应压缩成像

为了在现有的采样条件下,通过新的压缩采样方式获得计算量小且质量更好的图像,提出了基于压缩感知与扩展小波树的自适应压缩成像方法。首先将图像投影到分区控制的DMD上,获得图像在低分辨率下的测量值,并通过压缩感知重构算法重构出低分辨图像,接着利用扩展小波树预测重要小波位置,通过DMD在小波域采样获取图像的细节信息,最后由小波逆变换恢复高分辨率图像。将该方法与最小化全变分算法（TVAL3）和近来提出的基于扩展小波树的自适应成像算法（EWT-ACS）效果进行对比,实验结果表明,以boat图像为例,在压缩感知采样率为0.75,整体采样率为10%的无噪声条件下,该方法相较于TVAL3、EWT-ACS算法信噪比提高了4.63 dB和2.87 dB,在附加噪声条件下成像效果也较好。该方法能极大地降低压缩感知重建算法的运行时间,同时减少采样次数,具有较好的抗噪性。     

Novel Fourier-based iterative reconstruction for sparse fan projection using alternating direction total variation minimization

Sparse-view x-ray computed tomography(CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform(NUFFT) is presented in this work along with advanced total variation(TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively.     

Fast and accurate X‐ray fluorescence computed tomography imaging with the ordered‐subsets expectation maximization algorithm

The ordered‐subsets expectation maximization algorithm (OSEM) is introduced to X‐ray fluorescence computed tomography (XFCT) and studied; here, simulations and experimental results are presented. The simulation results indicate that OSEM is more accurate than the filtered back‐projection algorithm, and it can efficiently suppress the deterioration of image quality within a large range of angular sampling intervals. Experimental results of both an artificial phantom and cirrhotic liver show that with a satisfying image quality the angular sampling interval could be improved to save on the data‐acquisition time when OSEM is employed. In addition, with an optimum number of subsets, the image reconstruction time of OSEM could be reduced to about half of the time required for one subset. Accordingly, it can be concluded that OSEM is a potential method for fast and accurate XFCT imaging.     

有限角光学计算机层析重建中的投影采样研究

针对光学计算机层析术(Optical Computerized Tomography,OCT)中有限角条件下的严重非完全数据重建问题,提出了正交投影采样,结合基于改进的代数重建术(ART)的先验知识算法,以在尽可能少的投影方向数下较好地重建含遮挡物的三维流场。通过计算机模拟,详细讨论了在有限角条件下含遮挡物的三维流场的非完全数据重建精度及误差分析。结果表明,在有限角及含遮挡物条件下,采用正交投影采样可以极大地减少严重非完全数据重建中的误差,提高重建精度,从而为将光学计算机层析术应用于非完全数据的实测中提供了参考。     

基于L1范数的微分相位衬度CT稀疏角度重建算法

微分相位衬度成像及其计算层析(CT)技术是近年出现的无损检测新方法。但是,相位衬度CT往往需要对样品进行多次扫描,这必将导致非常长的辐射时间和巨大的辐射剂量。稀疏角度重建在降低辐射剂量方面有着非常明显的优势,因此,研究针对相位衬度CT的稀疏角度重建算法就显得尤为重要。在分析了相位衬度CT的特点之后,将压缩感知理论引入相位衬度CT重建中,并在该理论框架下将L1约束融入代数迭代重建(ART)算法中,提出了一种微分相位衬度CT重建算法。数值模拟和实际实验表明,该方法可以根据少量投影数据给出较好的重建结果。     

Optimal tube potential and tube current for radiation dose reduction in routine adult head computed tomography scanning

ABSTRACT

We proposed a new, optimised scanning parameter that can reduce the patient’s radiation dose while maintaining image quality in a head computed tomography (CT) scan. We evaluated the clinical CT scan parameters (tube voltage, tube current, slice thickness pitch, scan range, rotation time, and CT dose index) for brain CT examination in a total of 52 multi-detector row spiral CTs (SOMATOM Definition AS+, Siemens Healthcare, Germany). The data were analysed, and the range of valid scan parameters was determined clinically using quartile distribution within the 95% confidence interval. The American Association of Physicists in Medicine performance evaluation phantom was used to acquire images using these scan parameters, and new, optimised CT scan parameters were proposed by analysing CT number accuracy, noise, uniformity, spatial resolution, and contrast resolution. The new CT scan parameters proposed were determined as tube voltage 100?kVp and tube current 300?mAs. Compared with conventional clinical scan parameters, tube voltage was reduced by 16.7% and tube current was decreased by 33.3%. Loss in imaging accuracy and uniformity of CT number was less than 20%, loss in noise was less than 40%, and no change in resolution was observed. Conversely, the CT dose index and effective dose was 20%–50%. A new systematic method for clinically assessing the optimised CT scan parameters were proposed, and the effective dose was decreased, with changing exposure conditions.