| Summary: | Compressed sensing is a kind of compressive sampling or sparse sampling. It is also
a new technique for acquiring and reconstructing a signal utilizing the prior
knowledge that it is sparse or compressible. According to the recently developed
mathematical theory of Compressed-Sensing (CS), images with a sparse representation can be recovered from randomly undersampled k-space data. The sparsity of MR image can be exploited to significantly reduce scan time, or alternatively, improve the resolution of MR image. However, MR image near metallic implants remains an unmet need because of severe artifacts, which mainly
stem from large metal-induced field inhomogeneities. This work addresses MRI near
metallic implants with an innovative imaging technique called "Slice Encoding for
Metal Artifact Correction" (SEMAC). The SEMAC technique does not require additional hardware, but it can be deployed to the large installed base of whole-body MRI systems. The efficacy of the SEMAC technique in eliminating metal-induced distortions with feasible scan times is validated in phantom and in vivo spine and knee studies. In this project, the study is about a new technique for accelerating SEMAC acquisition by incorporating with compressed sensing in order to greatly reduce scan times, while producing high-quality distortion correction and signal to noise ratio to SEMAC with full sampling.
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