Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based.They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints.This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm.The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study.The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

Analytic time-of-flight positron emission tomography reconstruction: threedimensional case

In a positron emission tomography(PET)scanner,the time-of-flight(TOF)information gives us rough event position along the line-of-response(LOR).Using the TOF information for PET image reconstruction is able to reduce image noise.The state-of-the-art TOF PET image reconstruction uses iterative algorithms.This study introduces an analytic TOF PET algorithm that focuses on three-dimensional(3D)reconstruction.The proposed algorithm is in the form of backprojection filtering,in which the backprojection is performed first by using a time-resolution profile function,and then a 3D filter is applied to the backprojected image.For the list-mode data,the backprojection is carried out in the event-by-event fashion,and the timing resolution determined weighting function is used along the projection LOR.Computer simulations are carried out to verify the feasibility of the proposed algorithm.

Analytic time-of-flight positron emission tomography reconstruction: twodimensional case

In a positron emission tomography(PET)scanner,the time-of-flight(TOF)information gives us rough event position along the line-of-response(LOR).Using the TOF information for PET image reconstruction is able to reduce image noise.The state-of-the-art TOF PET image reconstruction uses iterative algorithms.Analytical image reconstruction algorithm exits for TOF PET which emulates the iterative Landweber algorithm.This paper introduces such an algorithm,focusing on two-dimensional(2D)reconstruction.The proposed algorithm is in the form of backprojection filtering,in which the backprojection is performed first,and then a 2D filter is applied to the backprojected image.For the list-mode data,the backprojection is carried out in the event-by-event fashion,and a profile function may be used along the projection LOR.The 2D filter depends on the TOF timing resolution as well as the backprojection profile function.In order to emulate the iterative algorithm effects,a Fourier-domain window function is suggested.This window function has a parameter,k,which corresponds to the iteration number in an iterative algorithm.

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Metal objects in X-ray computed tomography can cause severe artifacts.The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods.This paper proposes a projectiondomain algorithm to reduce the metal artifacts.In this algorithm,the unknowns are the metal-affected projections,while the objective function is set up in the image domain.The data fidelity term is not utilized in the objective function.The objective function of the proposed algorithm consists of two terms:the total variation of the metalremoved image and the energy of the negative-valued pixels in the image.After the metal-affected projections are modified,the final image is reconstructed via the filtered backprojection algorithm.The feasibility of the proposed algorithm has been verified by real experimental data.

Poisson-noise weighted filter for time-offlight positron emission tomography

Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter,which does not model the Poisson noise properly.This study proposes a nonstationary filter for Poisson noise control.The filter is implemented in the spatial domain in a form similar to convolution.