Application of Autoregressive Moving Average Parametric Modeling in Magnetic Resonance Image Reconstruction

Smith, M. R.; Nichols, S.T.; Henkelman, R. Mark; Wood, Michael L.

doi:10.1109/tmi.1986.4307762

Cited by 76 publications

(32 citation statements)

References 14 publications

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“…A form of ARMA known as the transient error reconstruction method has been used to model MRI data successfully [15]. Unfortunately, the autoregressive portion of the ARMA model can result in poles in the transfer function, which produce high intensity spikes in the image.…”

Section: Model-based Methods For Partial -Space Fillingmentioning

confidence: 99%

Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

Paul

Pillai

2013

ISRN Biomedical Imaging

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Using the boxcar representation in the spatial domain and a signal-space representation of its frequency-weighted -space, an iterative prediction method is developed to derive an improved low-resolution phase approximation for phase correction. Compared to the homodyne filter, the proposed predictor is found to be more efficient due to its capability of exhibiting an equivalent degree of performance using a lower number of fractional lines. The phase correction performance is illustrated using partially acquired susceptibility weighted images (SWI). An extension of the predictor into higher frequency regions of phaseencodes in conjunction with a signal-space projection in the frequency-weighted partial k-space is shown to provide restoration of fine structural details of sparse magnitude images. The application of subspace projection filtering is demonstrated using magnetic resonance angiogram (MRA).

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Section: Model-based Methods For Partial -Space Fillingmentioning

confidence: 99%

Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

Paul

Pillai

2013

ISRN Biomedical Imaging

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“…We can now express Eq. [8] in the required form by setting z ϭ e i⌬ky⅐y , thereby mapping each spatial location, y, in the image domain to a corresponding location, z, along the unit circle in the complex plane, giving [9]…”

Section: Applying Padé Methods To Mr Datamentioning

confidence: 99%

Padé methods for reconstruction and feature extraction in magnetic resonance imaging

Callaghan

Larkman

Hajnal

2005

Magnetic Resonance in Med

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Methods utilizing Padé approximants are investigated for implementation with magnetic resonance imaging data and are presented both for direct image reconstruction and for feature extraction. Padé approximants are a numerical tool that can be used to accelerate the convergence of a slowly converging sequence by estimating the fully converged sequence values from early data points. Padé approximants can be calculated directly from k-space data by solving a set of linear matrix equations to produce signal values for any desired location in the image domain. This gives an estimate of the fully converged signal intensity at each pixel location in the image, raising the possibility of reconstructing a better estimate of the object from a reduced data set. These methods have been tested on phantom and human data both for image reconstruction and for feature extraction. In image reconstruction, considerable con- Image resolution is a critical parameter in many magnetic resonance imaging (MRI) studies. High-resolution data are often required in order to achieve accurate anatomic assessment and/or quantitative measurements. In conventional phase-encoded MRI, acquiring higher resolution data increases the scan time due to the serial nature of the phase encoding process. This reduces the achievable temporal resolution and also increases the risk of motion artifact corrupting the data. Lowering the resolution decreases the scan time and also increases the level of truncation artifact, which manifests as unrepresentatively high and low signal bands running parallel to any interface where there is an abrupt change in the NMR signal (1). This artifact arises when the collected k-space data do not contain all the high-frequency information present in the slice that has been imaged and is a fundamental property of the Fourier encoding scheme used in MRI. Truncation artifact can be mistaken for, or occlude, structure.For a given rate of data acquisition with a fixed field of view (FoV) (i.e., number of k-space points per unit time), a number of methods have been implemented to achieve faster image update rate, while maintaining high spatial resolution (2-6). These methods increase apparent temporal resolution by partially updating the data in each new image. In keyhole based methods (2,3) new low-resolution data sets are inserted into a high-resolution reference to give a dynamic series. In sliding window techniques a number of k-space segments are acquired and used to dynamically update a high-resolution data set by replacing the oldest segment with the newest (4,5). The k-t BLAST (6) approach allows for reduced data acquisitions by tailoring the sampling strategy to exploit correlations in both k-space and time.Another approach is to extrapolate from incomplete data. This approach has been applied to MR imaging to reduce truncation artifacts and improve resolution (7-9). In the TERA algorithm, the MR data are modeled as a subset of the transient response of an infinite impulse filter and the data are approximated by a determini...

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“…Hu and Stillman have developed a method for obtaining CSI with reduced ringing using anatomical information [18]. Autoregressive approaches have been proposed for MRI by Smith et al [19], Martin and Tirendi [20], Barone and Sebastiani [21], and for spectroscopy by Wear et al [22]. Hendrich and coworkers [23] have utilized a 2-D Fourier series window (FSW) approach and circular voxels.…”

Section: N Two-dimensional (2-d) Chemical Shift Imaging (Csi) [(2d mentioning

confidence: 99%

Constrained reconstruction applied to 2-D chemical shift imaging

Wear

Myers²,

Rajan³

et al. 1997

IEEE Trans. Med. Imaging

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Abstract-The method of constrained reconstruction, previously applied to magnetic resonance imaging (MRI), is extended to magnetic resonance spectroscopy. This method assumes a model for the MR signal. The model parameters are estimated directly from the phase encoded data. This process obviates the need for the fast Fourier transform (FFT) (which often exhibits limited resolution and ringing artifact). The technique is tested on simulated data, phantom data, and data acquired from human liver in vivo. In each case, constrained reconstruction offers spatial resolution superior to that obtained with the FFT.

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Application of Autoregressive Moving Average Parametric Modeling in Magnetic Resonance Image Reconstruction

Cited by 76 publications

References 14 publications

Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

Padé methods for reconstruction and feature extraction in magnetic resonance imaging

Constrained reconstruction applied to 2-D chemical shift imaging

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