Applications of Voxel Shifting in Magnetic Resonance Imaging

Kramer, David; Li, A; Simovsky, I; Hawryszko, Christine; Hale, John; Kaufman, Lee D.

doi:10.1097/00004424-199012000-00006

Cited by 20 publications

(11 citation statements)

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“…We also restricted ourselves to convolution-based interpolation techniques. Although recent developments have resulted in new, fundamentally different interpolation techniques, such as shape-or morphology-based methods [10,12,[17][18][19]43], or Fourierbased methods, such as voxel-shift interpolation or, equivalently, zero-filled interpolation [8,9,21,25], the vast majority of interpolation techniques used in medical image registration are convolution-based techniques. The reason for this is probably that these techniques are less complex than shape-based techniques.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

252

165

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Abstract-Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sincapproximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

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Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

252

165

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“…Let us now consider an in-plane shift along the PE direction. As given by the Fourier-shift theorem a spatial shift in image space is equivalent to a linear phase modulation in k-space along the shift direction (2,3). The sampled positions in k-space do not depend on any in-plane shift along PE direction.…”

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confidence: 99%

Superresolution in MRI?

Scheffler

2002

Magnetic Resonance in Med

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Recently, Peled and Yeshurun (1) investigated the possibility of calculating superresolution images from a set of spatially shifted, low-resolution images. Eight low-resolution images were acquired with an in-plane shift in phase encoding (PE) direction of 0, 1/4, 1/2, 3/4 pixel, and the same PE-shifts with an additional shift of 1/2 pixel in the read direction.All images were acquired with the same field-of-view (FOV) and resolution. This means that the sampled positions in k-space are identical for all images, since the sampling positions in k-space are uniquely defined by the given FOV and resolution, and vice versa. Let us now consider an in-plane shift along the PE direction. As given by the Fourier-shift theorem a spatial shift in image space is equivalent to a linear phase modulation in k-space along the shift direction (2,3). The sampled positions in k-space do not depend on any in-plane shift along PE direction. As a result, the switched gradient patterns of all three axes as well as the applied RF-pulses are completely identical for any PE-shift. From this it follows immediately that the acquired k-space data is also totally identical, except for the noise and does not depend on the in-plane shift. The actual shift of the image is always done in a postprocessing step, either by multiplying the k-space data K(k m ,k n ) with⌬y is the shift along PE direction, or by appropriate subpixel scrolling of the Fourier transformed k-space data (image). Multiplying or scrolling does not add any new information and is in no way correlated with the imaged object. In principle, the same holds for an in-plane shift along the read direction. Again, all gradient and RF pulses are identical for any shift and thus the positions of the acquired points in k-space are always the same. The shift along read is achieved by a slightly off-resonant receiver frequency, which is again equivalent to a linear phase modulation along the read direction. Thus, no new information can be acquired using different in-plane shifts since an in-plane shift is a postprocessing step and does not influence the measurement procedure (gradients and RF pulses) itself.Of course, images that are shifted by a fraction of one pixel dimension against each other look different and the subtraction of these images is not zero (or pure noise). The images are simply sampled at different positions in image space but the source data is identical.It is, therefore, not possible to increase resolution by acquiring images that are spatially shifted against each other, but it is obviously possible to increase the SNR. The images presented in Ref. 1 clearly show an increase in resolution. However, I think that this increase was probably achieved by the increased SNR and by correcting (amplifying) the T * 2 -decreased signal amplitude at the outer parts of k-space. I presume that the same results can be obtained by acquiring eight images with identical in-plane positions. REFERENCES1. Peled S, Yeshurun Y. Superresolution in MRI: application to human white matter fibe...

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“…In a previous paper, the authors attributed these improvements on MRA to a reduction of partial volume artifacts due to the small voxels generated by the VSI technique. Other authors have reported that the volume resolution and SNR on MRI are maintained after VSI (8). The results of our experimental studies indicate that ZFI improves the partial volume effect partially, that is, it does not improve spatial or volume resolution, but does reduce the artifacts caused by shape and size of image pixels.…”

Section: Discussionmentioning

confidence: 39%