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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Adluru, G. Tasdizen, T. Whitaker, R. DiBella, E. |
| Copyright Year | 2010 |
| Abstract | Obtaining high quality images in MR is desirable not only for accurate visual assessment but also for automatic processing to extract clinically relevant parameters. Filtering-based techniques are extremely useful for reducing artifacts caused due to under sampling of k-space (to reduce scan time). The recently proposed Non-Local Means (NLM) filtering method offers a promising means to denoise images. Compared to most previous approaches, NLM is based on a more realistic model of images, which results in little loss of information while removing the noise. Here we extend the NLM method for MR image reconstruction from under sampled k-space data. The method is applied on T1-weighted images of the breast and T2-weighted anatomical brain images. Results show that NLM offers a promising method that can be used for accelerating MR data acquisitions. |
| Starting Page | 4000 |
| Ending Page | 4003 |
| File Size | 1691551 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.973 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image reconstruction Pixel Breast Magnetic resonance imaging TV Noise reduction Minimization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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