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PubMed2000

Partial-FOV reconstruction in dynamic spiral imaging.

In many applications of dynamic MR imaging, only a portion of the field-of-view (FOV) exhibits considerable variations in time. In such cases, a prior knowledge of the static part of the image allows a partial-FOV reconstruction of the dynamic section using only a fraction of the raw data. This method of reconstruction generally results in higher temporal resolution, because the scan time for partial-FOV data is shorter. The fidelity of this reconstruction technique depends, among other factors, on the accuracy of the prior information of the static section. This information is usually derived from the reconstructed images at previous time frames. This data, however, is normally corrupted by the motion artifact Because the temporal frequency contents of the motion artifact is very similar to that of the dynamic section, a temporal low-pass filter can efficiently remove this artifact from the static data. The bandwidth of the filter can be obtained from the rate of variations inside and outside the dynamic area. In general, when the temporal bandwidth is not spatially uniform, a bank of low-pass filters can provide a proper suppression of the motion artifact outside the dynamic section. This reconstruction technique is adapted for spiral acquisition and is successfully applied to cardiac fluoroscopy, doubling the temporal resolution.

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PubMed2000

On the optimality of the gridding reconstruction algorithm.

Gridding reconstruction is a method to reconstruct data onto a Cartesian grid from a set of nonuniformly sampled measurements. This method is appreciated for being robust and computationally fast. However, it lacks solid analysis and design tools to quantify or minimize the reconstruction error. Least squares reconstruction (LSR), on the other hand, is another method which is optimal in the sense that it minimizes the reconstruction error. This method is computationally intensive and, in many cases, sensitive to measurement noise. Hence, it is rarely used in practice. Despite their seemingly different approaches, the gridding and LSR methods are shown to be closely related. The similarity between these two methods is accentuated when they are properly expressed in a common matrix form. It is shown that the gridding algorithm can be considered an approximation to the least squares method. The optimal gridding parameters are defined as the ones which yield the minimum approximation error. These parameters are calculated by minimizing the norm of an approximation error matrix. This problem is studied and solved in the general form of approximation using linearly structured matrices. This method not only supports more general forms of the gridding algorithm, it can also be used to accelerate the reconstruction techniques from incomplete data. The application of this method to a case of two-dimensional (2-D) spiral magnetic resonance imaging shows a reduction of more than 4 dB in the average reconstruction error.

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