{
  "id": "1411.6206",
  "version": "v1",
  "published": "2014-11-23T08:19:11.000Z",
  "updated": "2014-11-23T08:19:11.000Z",
  "title": "Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction",
  "authors": [
    "Dornoosh Zonoobi",
    "Shahrooz Faghih Roohi",
    "Ashraf A. Kassim"
  ],
  "comment": "conference",
  "categories": [
    "cs.CV"
  ],
  "abstract": "It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-rank and a Sparse component from compressive measurements. When applied to the problem of construction of 4D Cardiac MR image sequences in real-time from highly under-sampled $k-$space data, our proposed method achieves superior reconstruction quality compared to the other state-of-the-art methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-11-23T08:19:11.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dynamic 3d mri reconstruction",
      "sparse matrix decomposition",
      "a-priori knowledge",
      "cardiac mr image sequences",
      "superior reconstruction quality"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1411.6206Z"
    }
  }
}