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 （NUFFF） 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.

Reducing the radiation dose with the adaptive statistical iterative reconstruction technique for chest CT in adults： a parameter study

Background Currently there is a trend towards reducing radiation dose while maintaining image quality during computer tomography （CT） examination.This results from the concerns about radiation exposure from CT and the potential increase in the incidence of radiation induced carcinogenesis.This study aimed to investigate the lowest radiation dose for maintaining good image quality in adult chest scanning using GE CT equipment.Methods Seventy-two adult patients were examined by Gemstone Spectral CT.They were randomly divided into six groups.We set up a different value of noise index （NI） when evaluating each group every other number from 13.0 to 23.0.The original images were acquired with a slice of 5 mm thickness.For each group,several image series were reconstructed using different levels of adaptive statistical iterative reconstruction （ASIR） （30％,50％,and 70％）.We got a total of 18 image sequences of different combinations of NI and ASIR percentage.On one hand,quantitative indicators,such as CT value and standard deviation （SD）,were assessed at the region of interest.The signal-to-noise ratio （SNR） and contrast-to-noise ratio （CNR） were calculated.The volume CT dose index （CTDI） and dose length product （DLP） were recorded.On the other hand,two radiologists with ＞5 years of experience blindly reviewed the subjective image quality using the standards we had previously set.Results The different combinations of noise index and ASIR were assessed.There was no significant difference in CT values among the 18 image sequences.The SD value was reduced with the noise index＇s reduction or ASIR＇s increase.There was a trend towards gradually lower SNR and CNR with an NI increase.The CTDI and DLP were diminishing as the NI increased.The scores from subjective image quality evaluation were reduced in all groups as the ASIR increased.Conclusions Increasing NI can reduce radiation dose.With the premise of maintaining the same image quality,using a suitable percentage of ASIR can increase the value of NI.To assure image quality,we concluded that when the NI was set at 17.0 and ASlR was 50％,the image quality could be optimal for not only satisfying the requirements of clinical diagnosis,but also achieving the purpose of low-dose scanning.

Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction

The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.

Position coordinates-based iterative reconstruction for robotic CT

Purpose Robotic CTs can achieve customized trajectory scanning with x-ray tube and detector held by flexible robotic arms rather than fixed rails or gantry.However,the motion errors of the robotic arms cannot be neglected.Hence,the reconstruction method of Robotic CTs should be suitable for arbitrary trajectory and should take motion errors into full consideration.Method In this paper,we present an iterative reconstruction method for robotic CT systems.Unlike the analytical algorithms,such as FDK,this method makes no assumption about the scan trajectory.The projection and backprojection operations are modeled by 3D distance-driven algorithm using the coordinates of x-ray source and detector center fed back from the robotic arm’s positioning system directly.Both numerical simulations and practical experiments are conducted to verify the effectiveness of this method in arbitrary trajectory reconstruction and motion errors correction for robotic CT systems.Results For our non-circular and non-planar trajectory scan,this proposed method could easily handle the reconstruction and obtain a result comparable to reference.In addition,for 0.1%motion errors,using the proposed method could improve the reconstruction quality,and the RMSE could be reduced by 30%.Conclusions This iterative reconstruction method is suitable for arbitrary trajectory scans and can decrease the degradation of image quality caused by motion errors of robotic arm.

Research Progress on Adaptive Statistical Iterative Reconstruction and its Applications

CT has been widely used for clinical diagnosis since it was introduced in China in the last century because of its superior effect on 2D anatomical observation ca-pacity and higher resolution than other techniques.With the development of CT technology in recent years,128 rows,256 rows,or higher resolution CT is available,but the negative effects of radiation dose have attracted attention.How to reduce the dose of CT and the radiation to patients and medical staff under the premise of ensuring the image quality is a hot topic for medical research.This paper reviews the effective methods of CT radiation by iterative reconstruction technology,in order to provide a reference for reducing the dose of CT and the radiation dose of patients and medical staff.

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.

Advances in deep learning for computed tomography denoising

Computed tomography(CT)has seen a rapid increase in use in recent years.Radiation from CT accounts for a significant proportion of total medical radiation.However,given the known harmful impact of radiation exposure to the human body,the excessive use of CT in medical environments raises concerns.Concerns over increasing CT use and its associated radiation burden have prompted efforts to reduce radiation dose during the procedure.Therefore,low-dose CT has attracted major attention in the radiology,since CT-associated x-ray radiation carries health risks for patients.The reduction of the CT radiation dose,however,compromises the signal-to-noise ratio,which affects image quality and diagnostic performance.Therefore,several denoising methods have been developed and applied to image processing technologies with the goal of reducing image noise.Recently,deep learning applications that improve image quality by reducing the noise and artifacts have become commercially available for diagnostic imaging.Deep learning image reconstruction shows great potential as an advanced reconstruction method to improve the quality of clinical CT images.These improvements can provide significant benefit to patients regardless of their disease,and further advances are expected in the near future.

Computed tomography colonography and radiation risk:How low can we go?

Computed tomography colonography(CTC)has become a key examination in detecting colonic polyps and colorectal carcinoma(CRC).It is particularly useful after incomplete optical colonoscopy(OC)for patients with sedation risks and patients anxious about the risks or potential discomfort associated with OC.CTC's main advantages compared with OC are its non-invasive nature,better patient compliance,and the ability to assess the extracolonic disease.Despite these advantages,ionizing radiation remains the most significant burden of CTC.This opinion review comprehensively addresses the radiation risk of CTC,incorporating imaging technology refinements such as automatic tube current modulation,filtered back projections,lowering the tube voltage,and iterative reconstructions as tools for optimizing low and ultra-low dose protocols of CTC.Future perspectives arise from integrating artificial intelligence in computed tomography machines for the screening of CRC.

Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization(ML-EM)algorithm.In this study,we extend these algorithms to Bayesian algorithms.The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme.The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor,which contains the Bayesian information.One of the extended algorithms can be applied to emission tomography and another to transmission tomography.Computer simulations are performed and compared with the corresponding un-extended algorithms.The total-variation norm is employed as the Bayesian constraint in the computer simulations.The newly developed algorithms demonstrate a stable performance.A simple Bayesian algorithm can be derived for any noise variance function.The proposed algorithms have properties such as multiplicative updating,non-negativity,faster convergence rates for bright objects,and ease of implementation.Our algorithms are inspired by Green’s one-steplate algorithm.If written in additive-update form,Green’s algorithm has a step size determined by the future image value,which is an undesirable feature that our algorithms do not have.

Iterative sparse reconstruction of spectral domain OCT signal

We propose and study an iterative sparse reconstruction for Fourier domain optical coherence tomography （FD OCT） image by solving a constrained optimization problem that minimizes L-1 norm. Our method takes the spectral shape of the OCT light source into consideration in the iterative image reconstruction procedure that allows deconvolution of the axial point spread function from the blurred image during reconstruction rather than after reconstruction. By minimizing the L-1 norm, the axial resolution and the signal to noise ratio of image can both be enhanced. The effectiveness of our method is validated using numerical simulation and experiment.

A divertor plasma configuration design method for tokamaks

The efficient and safe operation of large fusion devices strongly relies on the plasma configuration inside the vacuum chamber.It is important to construct the proper plasma equilibrium with a desired plasma configuration.In order to construct the target configuration,a shape constraint module has been developed in the tokamak simulation code（TSC）,which controls the poloidal flux and the magnetic field at several defined control points.It is used to construct the double null,lower single null,and quasi-snowflake configurations for the required target shape and calculate the required PF coils current.The flexibility and practicability of this method have been verified by the simulated results.

Image Reconstructions from Limit Views and Angle Coverage Data for a Stationary Multi-Pinhole SPECT System

The goal of this paper is to investigate different reconstruction methods for solving the limited angle problem in reconstructing the projection data from a stationary multi-pinhole system based on a three-head clinical single photon emission computed tomography （SPECT） camera. Three iterative recon- struction approaches were studied including maximum likelihood expectation maximization （MLEM）, maximum a posteriori expectation maximization （MAPEM） with a smoothing prior, and an alternating optimization schemes from MLEM and total variation （TV） optimization. A three-headed multipinhole SPECT was simulated with apertures of nine 1-mm pinholes each, and covered scanning volume of 6-mm diameter. The reconstructions were optimized for various iterations based on visual inspections, and finally 20 iterations were applied for each method. For both MLEM-TV and MAPEM-TV, various initial reconstructions before TV optimization were studied. The smoothing parameter for MAPEM and the gradient descent constant for TV were also investigated through visual comparison. The preliminary results showed the 3 reconstruction methods generated compatible images, and can restore the images from projection data suffering limited angular sampling. However, MLEM was noisy for low-count and highly limited angle data, and thus suitable smoothing in MAPEM alleviated this problem, initial reconstructions were necessary for better edge enhancement in TV. The conclusion is that TV might be potential in producing more edge-enhanced images if all parameters were optimized.

Recent Developments in Numerical Techniques for Transport-Based Medical Imaging Methods

The objective of this paper is to review recent developments in numerical reconstruction methods for inverse transport problems in imaging applications,mainly optical tomography,fluorescence tomography and bioluminescence tomography.In those inverse problems,one aims at reconstructing physical parameters,such as the absorption coefficient,the scattering coefficient and the fluorescence light source,inside heterogeneous media,from partial knowledge of transport solutions on the boundaries of the media.The physical parameters recovered can be used for diagnostic purpose.Numerical reconstruction techniques for those inverse transport problems can be roughly classified into two categories:linear reconstruction methods and nonlinear reconstruction methods.In the first type of methods,the inverse problems are linearized around some known background to obtain linear inverse problems.Classical regularization techniques are then applied to solve those inverse problems.The second type of methods are either based on regularized nonlinear least-square techniques or based on gradient-driven iterative methods for nonlinear operator equations.In either case,the unknown parameters are iteratively updated until the solutions of the transport equations with the those parameters match the measurements to a certain extent.We review linear and nonlinear reconstruction methods for inverse transport problems in medical imaging with stationary,frequency-domain and time-dependent data.The materials presented include both existing and new results.Meanwhile,we attempt to present similar algorithms for different problems in the same framework to make it more straightforward to generalize those algorithms to other inverse(transport)problems.

Temporary threshold shift in human subject exposed to noise

Using an audiometer, the effect of the noise level upon temporary threshold shift (TTS) for five trained normal subjects (left ear only) was studied. The measurements were carried out after 6 min exposure (in third octave band) for different sound pressure levels ranging between 75-105 dB at three test frequencies 2,3, and 4 kHz. The results indicated that at exposure to noise of sound pressure level (SPL) above 85 dB, TTS increases linearly with ths SPL for all the test frequencies. The work had extended to study the recovery curves for the same ears. The results indicated that the reduction in TTS on doubling the recovery times, for the two sound pressure levels 95 dB and 105 dB, occurs at a rate of nearly 3 dB. The comparison of the recovery curve at 3 kHz with that calculated using Ward's general equation for recovery was made. Finally, to study the values of TTS produced by exposure to certain noise at different test frequencies, distribution curves for two recovery times were plotted representing TTS values, for an exposure noise of 1 / 3 octave band and centre frequency 2 kHz, at different test frequencies.

Emission-Based Scatter Correction in SPECT Imaging

Scatter correction in single photon emission computed tomography （SPECT） has been focused on either using multiple-window acquisition technique or the scatter modeling technique in iterative image reconstruction. We propose a technique that uses ：only the emission data for scatter correction in SPECT. We assume that the scatter data can be approximated by convolving the primary data with a scatter kernel followed by the normalization using the scatter-to-primary ratio （SPR）, Since the emission data is the superposition of the primary data and the scatter data, the convolution normalization process approximately results in the sum of the scatter data and a convolved version of scatter data with the kernel. By applying a proper scaling factor, we can make the estimation approximately equal to or less than the scatter data anywhere in the projection domain. Phantom and patient cardiac SPECT studies show that using the proposed emission-based scatter estimation can effectively reduce the scatter-introduced background in the reconstructed images. And additionally, the computational time for scatter correction is negligible as compared to no scatter correction in iterative image reconstruction.