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.

Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging

In this study, we propose a linearized proximal alternating direction method with variable stepsize for solving total variation image reconstruction problems. Our method uses a linearized technique and the proximal function such that the closed form solutions of the subproblem can be easily derived.In the subproblem, we apply a variable stepsize, that is like Barzilai-Borwein stepsize, to accelerate the algorithm. Numerical results with parallel magnetic resonance imaging demonstrate the efficiency of the proposed algorithm.

Research of Sensitivity Encoding Reconstruction for MRI with Non-Cartesian K-Space Trajectories

The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is implemented with non-cartesian sampled k-space trajectories in this paper. SENSE has the special capability to reduce the scanning time for MRI experiments while maintaining the image resolution with under-sampling data sets. In this manner, it has become an increasingly popular technique for multiple MRI data acquisition and image reconstruction schemes. The gridding algorithm is also implemented with SENSE due to its ability in evaluating forward and adjoin operator with non-cartesian sampled data. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct unaliased images from under-sampled data. The performance of SENSE with real data set identifies the computational issues to be improved for researched.

Sensitivity Encoding Reconstruction for MRI with Gridding Algorithm

The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.

Fourier domain optical coherence tomography with ultralong depth range

The depth ranges of typical implementations of Fourier domain optical coherence tomography （FDOCT）, including spectral domain OCT （SDOCT） and swept source OCT （SSOCT）, are limited to several millimeters. To extend the depth range of current OCT systems, two novel systems with ultralong depth range were developed in this study. One is the orthogonal dispersive SDOCT （OD-SDOCT）, and the other is the recirculated swept source （R-SS） interferometer/OCT. No compromise between depth range and depth resolution is required in both systems. The developed OD-SDOCT system realized the longest depth range （over 100 mm） ever achieved by SDOCT, which is ready to be modified for depth-encoded parallel imaging on multiple sites. The developed R-SS interferometer achieved submicron precision within a depth range of 30mm, holding potential in real-time contact-free on-axis metrology of complex optical sys- tems.