James Akao | PubMed Health

PubMed2005

Application of partial differential equation-based inpainting on sensitivity maps.

Inpainting is an image interpolation method. Partial differential equation (PDE)-based digital inpainting techniques are finding broad applications. In this paper, PDE-based inpainting techniques are applied to the field of MR parallel imaging. A novel model and its corresponding numerical method are introduced. This model is then applied to sensitivity maps. Coil sensitivity maps are important for parallel imaging, and they often require extrapolation and hole filling (holes being dark regions of low signal in MR images). These problems can be solved simultaneously by the application of inpainting techniques. Experiments for determining coil sensitivity maps for phantoms and cardiac MR images demonstrate the accuracy of the proposed model. Images generated using sensitivity encoding (SENSE) that utilizes inpainted sensitivity maps, thin-plate spline (TPS) estimated sensitivity maps, and Gaussian kernel smoothed (GKS) sensitivity maps are compared. From the experimental results, it can be seen that inpainted sensitivity maps produce better results than GKS sensitivity maps. The TPS method generates results similar to those of the inpainting technique but is much more time-consuming.

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PubMed2005

k-t GRAPPA: a k-space implementation for dynamic MRI with high reduction factor.

A novel technique called "k-t GRAPPA" is introduced for the acceleration of dynamic magnetic resonance imaging. Dynamic magnetic resonance images have significant signal correlations in k-space and time dimension. Hence, it is feasible to acquire only a reduced amount of data and recover the missing portion afterward. Generalized autocalibrating partially parallel acquisitions (GRAPPA), as an important parallel imaging technique, linearly interpolates the missing data in k-space. In this work, it is shown that the idea of GRAPPA can also be applied in k-t space to take advantage of the correlations and interpolate the missing data in k-t space. For this method, no training data, filters, additional parameters, or sensitivity maps are necessary, and it is applicable for either single or multiple receiver coils. The signal correlation is locally derived from the acquired data. In this work, the k-t GRAPPA technique is compared with our implementation of GRAPPA, TGRAPPA, and sliding window reconstructions, as described in Methods. The experimental results manifest that k-t GRAPPA generates high spatial resolution reconstruction without significant loss of temporal resolution when the reduction factor is as high as 4. When the reduction factor becomes higher, there might be a noticeable loss of temporal resolution since k-t GRAPPA uses temporal interpolation. Images reconstructed using k-t GRAPPA have less residue/folding artifacts than those reconstructed by sliding window, much less noise than those reconstructed by GRAPPA, and wider temporal bandwidth than those reconstructed by GRAPPA with residual k-space. k-t GRAPPA is applicable to a wide range of dynamic imaging applications and is not limited to imaging parts with quasi-periodic motion. Since only local information is used for reconstruction, k-t GRAPPA is also preferred for applications requiring real time reconstruction, such as monitoring interventional MRI.

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