Biomedical subjects

S T Nichols

Publications and source records attributed to S T Nichols.

5 recordsLinked to original sources

PubMed1991

A quantitative comparison of the TERA modeling and DFT magnetic resonance image reconstruction techniques.

The resolution of magnetic resonance images reconstructed using the discrete Fourier transform (DFT) algorithm is limited by the effective window generated by the finite data length. The transient error reconstruction approach (TERA) is an alternative reconstruction method based on autoregressive moving average (ARMA) modeling techniques. Quantitative measurements comparing the truncation artifacts present during DFT and TERA image reconstruction show that the modeling method substantially reduces these artifacts on "full" (256 X 256), "truncated" (256 X 192), and "severely truncated" (256 X 128) data sets without introducing the global amplitude distortion found in other modeling techniques. Two global measures for determining the success of modeling are suggested. Problem areas for one-dimensional modeling are examined and reasons for considering two-dimensional modeling discussed. Analysis of both medical and phantom data reconstructions are presented.

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PubMed1990

A comparison of models used as alternative magnetic resonance image reconstruction methods.

In magnetic resonance (MR) imaging, excellent reconstructions are obtained on large data sets using the inverse discrete Fourier transform (IDFT). Modeling procedures have been proposed to overcome the image artifacts from the truncation of small data sets. In this paper, a relationship between image reconstruction using modeling and the standard IDFT is presented. A comparison of the assumptions behind the Smith and Haacke models is given and an experimental evaluation of the validity of the models provided. Various methods of evaluating model coefficients are discussed. Images are reconstructed from both models using the Transient Error reconstruction approach (TERA) algorithm. TERA is an algorithm that reintroduces data information components that cannot be modeled: useful when the assumed model characteristics do not completely match all portions of the image. Although very different in their basic assumptions, both the Smith and Haacke models were found to reduce truncation artifacts and improve resolution when used with the TERA algorithm.

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PubMed1989

The use of band-selectable digital filtering in magnetic resonance image enhancement.

Manipulation of the data describing two-dimensional magnetic resonance (MR) images can be used to zoom an image, decrease image noise and artifacts by modeling, or emphasize object edges in the field of view. In this paper, a two-dimensional band-selectable digital filtering (2D-BSDF) technique is detailed. This can be used to decrease the computational burden and increase algorithm stability associated with such data manipulation. Many display devices have the ability of expanding an image by pixel or linear interpolation. Application of the efficient zooming fast Fourier transformation algorithms provides a superior quality sinc-function interpolated image. In 2D-BSDF, the ideal rectangular windows used in sinc-function interpolation are replaced by windows with a more gradual roll-off. This gradual roll-off results in a slight degradation of the image edges but substantially reduces the computation time. Modeling of MRI data has been attempted to remove noise and artifacts from the image. These algorithms are computationally expensive and frequently unstable because of the high model orders required. The 2D-BSDF can be used to prepare a reduced data set, without loss of information. A lower order model may be applied to the subset and computation times approaching that required for normal fast Fourier transform algorithms result. The absence of noise and signal from objects outside the region of interest can considerably enhance the stability of the modeling algorithms. The use of BSDF is equally applicable when used in association with the modeling of 2D NMR spectroscopy data or with edge enhancement or any other data manipulation of magnetic resonance imaging images. In this paper an explanation of 1D-BSDF is provided and an algorithm for 2D-BSDF is developed. A comparison of filter designs and computational times is given when applying the technique to zooming and modeling of MR images. Images from medical MRI data are provided.

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PubMed1988

Efficient algorithms for generating interpolated (zoomed) MR images.

This paper discusses the two-dimensional implementation of a number of modified fast Fourier transform (FFT) algorithms that efficiently interpolate (zoom) magnetic resonance (MR) images. If the original image was sampled at a rate satisfying the Nyquist criterion, these algorithms would effectively increase the sampling rate, permitting image details to be more easily discerned. The Skinner interpolating fast Fourier transform (SIFFT) avoids many of the computationally unnecessary complex multiplications that occur when interpolating using the normal fast Fourier transform algorithm. The novel interpolating fast Fourier transform (NIFFT) offers further savings when a subimage is required. Theoretical and experimental timings that compare the use of the normal FFT, SIFFT, and NIFFT algorithms for interpolation are given using magnetic resonance image reconstruction examples. Time savings of a factor of 2 to 4 are possible in typical experimental situations. Time savings of factors of 5 to 20 are possible when zooming images using two-dimensional band selectable digital filtering (2D-BSDF) in combination with decimation and the SIFFT algorithm. In 2D-BSDF, the original MRI data set is reduced in size to retain only those frequency components corresponding to a desired subimage, thereby decreasing the computational load associated with further processing. A significant reduction in computation time is achieved when modeling is combined with 2D-BSDF and SIFFT as fewer points require modeling.

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PubMed1986

Application of autoregressive modelling in magnetic resonance imaging to remove noise and truncation artifacts.

Magnetic resonance imaging data is conventionally reconstructed using two dimensional discrete Fourier transforms. However, there is growing interest in other types of spectral estimation which minimize noise and artifacts due to truncated data. This note presents preliminary results--showing the improvement obtainable using a modified autoregressive model, the Transient Error method.

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