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Prior image constrained compressed sensing (PICCS)

2008; SPIE; Volume: 6856; Linguagem: Inglês

10.1117/12.770532

1996-756X

Guang-Hong Chen, Jie Tang, Shuai Leng,

Advanced MRI Techniques and Applications

It has been known for a long time that, in order to reconstruct a streak-free image in tomography, the sampling of view angles should satisfy the Shannon/Nyquist criterion. When the number of view angles is less than the Shannon/Nyquist limit, view aliasing artifacts appear in the reconstructed images. Most recently, it was demonstrated that it is possible to accurately reconstruct a sparse image using highly undersampled projections provided that the samples are distributed at random. The image reconstruction is carried out via an l 1 norm minimization procedure. This new method is generally referred to as compressed sensing (CS) in literature. Specifically, for an N×N image with significant image pixels, the number of samples for an accurate reconstruction of the image is . In medical imaging, some prior images may be reconstructed from a different scan or from the same acquired time-resolved data set. In this case, a new image reconstruction method, Prior Image Constrained Compressed Sensing (PICCS), has been recently developed to reconstruct images using a vastly undersampled data set. In this paper, we introduce the PICCS algorithm and demonstrate how to use this new algorithm to solve problems in medical imaging.