Modified linear prediction modeling in magnetic resonance imaging

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

“…It would be much easier to model MRI signals if the corresponding image-domain data contained only sharp features. It is possible to transform raw MRI data such that Fourier transformation produces an image, which consists of predominantly sharp lines [16]. Such a transformed image can be obtained by applying a linear high pass filter.…”

Removal of truncation artefacts in magnetic resonance images by recovering missing spectral data

“…(15) indicates that the spectrum F(k) of the modulus signal f(x) can be approximately obtained from that of the modulus signal with truncation artefacts |d c (x) Ã f(x)|. Since the value of jF ½jd c ðxÞ Ã f ðxÞjj suddenly drops to zero (or a value close to zero) at the truncation frequency c, this will allow us to find the truncation frequency c in case it is not given and consequently obtain R c (k).…”

“…For reconstruction-based truncation artefact removal, a typical example is the class of classical reconstruction technique based on phase compensation [13]. Other examples are the methods based on extrapolating truncated k-space data using various techniques such as the autoregressive moving average (ARMA) model [14] or linear predication [15][16][17][18]. Directly extrapolating kspace data (or one-dimensionally transformed k-space) has to be undertaken with caution since the k-space data is rather decorrelated in comparison with the spatial data in the original image domain.…”

Constrained reconstruction applied to 2-D chemical shift imaging

“…A related algorithm was developed independently by Martin and Tirendi [20]. The object to be imaged is modeled, in one dimension, as a weighted sum of contiguous boxcar functions of varying amplitudes and widths.…”

“…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.…”