Compressed sensing
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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/10356/40951 |
| _version_ | 1811686572006834176 |
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| author | Wu, Hui Juan. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wu, Hui Juan. |
| author_sort | Wu, Hui Juan. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:02:33Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/40951 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:02:33Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/409512023-07-07T15:49:31Z Compressed sensing Wu, Hui Juan. School of Electrical and Electronic Engineering Lu Wenmiao DRNTU::Engineering::Electrical and electronic engineering 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. Bachelor of Engineering 2010-06-25T02:29:16Z 2010-06-25T02:29:16Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40951 en Nanyang Technological University 61 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Wu, Hui Juan. Compressed sensing |
| title | Compressed sensing |
| title_full | Compressed sensing |
| title_fullStr | Compressed sensing |
| title_full_unstemmed | Compressed sensing |
| title_short | Compressed sensing |
| title_sort | compressed sensing |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/40951 |
| work_keys_str_mv | AT wuhuijuan compressedsensing |