Abstract
Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes the k-space data of the neighboring slice in the multi-slice acquisition. The missing k-space data of a highly undersampled slice are estimated by using the raw data of its neighboring slice multiplied by a weighting function generated from low resolution full k-space reference images. In-vivo MR imaging in human feet has been used to investigate the feasibility and the performance of the proposed iCS method. The results show that by using the proposed iCS reconstruction method, the average image error can be reduced and the average CNR can be improved, compared with the conventional sparse MRI reconstruction at the same undersampling rate.
Highlights
Compressed Sensing [1,2] technique has been applied to MRI to significantly reduce the raw data required for image reconstruction by undersampling the k-space using an incoherent sampling strategy [3]
The interpolated Compressed Sensing reconstruction method proposed in this work provides a way to reducing acquisition time and image data size or improving the image quality, especially contrast to noise ration (CNR) for undersampled multi-slice, twodimensional MRI
The missed k-space data of the target slice are estimated using the neighboring slice k-space data weighted by the weighting function generated from low resolution full k-space reference images of the target slice and its neighboring slice
Summary
Compressed Sensing [1,2] technique has been applied to MRI to significantly reduce the raw data required for image reconstruction by undersampling the k-space using an incoherent sampling strategy [3]. The sampling strategy and reconstruction method are key elements to achieve high quality images from significantly undersampled kspace data in compressed sensing MRI. We propose an interpolation method to further improve imaging speed or reduce raw data size while preserve the image fidelity and contrast to noise ratio (CNR) for multi-slice twodimensional sparse MR imaging in humans.
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