Kenneth O Johnson

Barrow Neurological Institute, Phoenix, Arizona, United States

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Publications (5)9.8 Total impact

  • Nicholas R Zwart, Kenneth O Johnson, James G Pipe
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    ABSTRACT: The reconstruction of non-Cartesian k-space trajectories often requires the estimation of nonuniform sampling density. Particularly for 3D, this calculation can be computationally expensive. The method proposed in this work combines an iterative algorithm previously proposed by Pipe and Menon (Magn Reson Med 1999;41:179-186) with the optimal kernel design previously proposed by Johnson and Pipe (Magn Reson Med 2009;61:439-447). The proposed method shows substantial time reductions in estimating the densities of center-out trajectories, when compared with that of Johnson. It is demonstrated that, depending on the trajectory, the proposed method can provide reductions in execution time by factors of 12 to 85. The method is also shown to be robust in areas of high trajectory overlap, when compared with two analytical density estimation methods, producing a 10-fold increase in accuracy in one case. Initial conditions allow the proposed method to converge in fewer iterations and are shown to be flexible in terms of the accuracy of information supplied. The proposed method is not only one of the fastest and most accurate algorithms, it is also completely generic, allowing any arbitrary trajectory to be density compensated extemporaneously. The proposed method is also simple and can be implemented on parallel computing platforms in a straightforward manner.
    Magnetic Resonance in Medicine 06/2011; 67(3):701-10. · 3.27 Impact Factor
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    ABSTRACT: A novel center-out 3D trajectory for sampling magnetic resonance data is presented. The trajectory set is based on a single Fermat spiral waveform, which is substantially undersampled in the center of k-space. Multiple trajectories are combined in a "stacked cone" configuration to give very uniform sampling throughout a "hub," which is very efficient in terms of gradient performance and uniform trajectory spacing. The fermat looped, orthogonally encoded trajectories (FLORET) design produces less gradient-efficient trajectories near the poles, so multiple orthogonal hub designs are shown. These multihub designs oversample k-space twice with orthogonal trajectories, which gives unique properties but also doubles the minimum scan time for critical sampling of k-space. The trajectory is shown to be much more efficient than the conventional stack of cones trajectory, and has nearly the same signal-to-noise ratio efficiency (but twice the minimum scan time) as a stack of spirals trajectory. As a center-out trajectory, it provides a shorter minimum echo time than stack of spirals, and its spherical k-space coverage can dramatically reduce Gibbs ringing.
    Magnetic Resonance in Medicine 04/2011; 66(5):1303-11. · 3.27 Impact Factor
  • Kenneth O. Johnson, Ryan K. Robison, James G. Pipe
    IEEE Trans. Med. Imaging. 01/2011; 30:655-665.
  • Kenneth O Johnson, Ryan K Robison, James G Pipe
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    ABSTRACT: Spiral projection imaging (SPI) is a 3D, spiral based magnetic resonance imaging (MRI) acquisition scheme that allows for self-navigated motion estimation of all six degrees-of-freedom. The trajectory, a set of spiral planes, is enhanced to accommodate motion tracking by adding orthogonal planes. Rigid-body motion tracking is accomplished by comparing the overlapping data and deducing the motion that is consistent with the comparisons. The accuracy of the proposed method is quantified for simulated data and for data collected using both a phantom and a volunteer. These tests were repeated to measure the effect of off-resonance blurring, coil sensitivity, gradient warping, undersampling, and nonrigid motion (e.g., neck). The artifacts of off-resonance, coils sensitivity, and gradient warping impose an unnotable effect on the accuracy of motion estimation. The worst mean accuracy is 0.15° and 0.20 mm for the phantom while the worst mean accuracy is 0.48° and 0.34 mm when imaging a brain, indicating that the nonrigid component in human subjects slightly degrades accuracy. When applied to in vivo motion, the proposed technique considerably reduces motion artifact.
    IEEE transactions on medical imaging. 11/2010; 30(3):655-65.
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    Kenneth O Johnson, James G Pipe
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    ABSTRACT: Sampling density compensation is an important step in non-cartesian image reconstruction. One of the common techniques to determine weights that compensate for differences in sampling density involves a convolution. A new convolution kernel is designed for sampling density attempting to minimize the error in a fully reconstructed image. The resulting weights obtained using this new kernel are compared with various previous methods, showing a reduction in reconstruction error. A computationally efficient algorithm is also presented that facilitates the calculation of the convolution of finite kernels. Both the kernel and the algorithm are extended to 3D.
    Magnetic Resonance in Medicine 02/2009; 61(2):439-47. · 3.27 Impact Factor