Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning
Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2017-06-01
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| Series: | Measurement Science Review |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/msr-2017-0018 |
| _version_ | 1819131672868683776 |
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| author | Li Jun-Bao Liu Jing Pan Jeng-Shyang Yao Hongxun |
| author_facet | Li Jun-Bao Liu Jing Pan Jeng-Shyang Yao Hongxun |
| author_sort | Li Jun-Bao |
| collection | DOAJ |
| description | Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation. |
| first_indexed | 2024-12-22T09:19:14Z |
| format | Article |
| id | doaj.art-44b5eb5dc2e348a1801c97a25b759c5d |
| institution | Directory Open Access Journal |
| issn | 1335-8871 |
| language | English |
| last_indexed | 2024-12-22T09:19:14Z |
| publishDate | 2017-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Measurement Science Review |
| spelling | doaj.art-44b5eb5dc2e348a1801c97a25b759c5d2022-12-21T18:31:13ZengSciendoMeasurement Science Review1335-88712017-06-0117314515210.1515/msr-2017-0018msr-2017-0018Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse LearningLi Jun-Bao0Liu Jing1Pan Jeng-Shyang2Yao Hongxun3Department of Automatic Test and Control, Harbin Institute of Technology, Harbin150080, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin150001, ChinaFujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou350108, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin150001, ChinaMagnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.https://doi.org/10.1515/msr-2017-0018magnetic resonance imaging measurementsparse learningdictionary learningsuper-resolution imaging |
| spellingShingle | Li Jun-Bao Liu Jing Pan Jeng-Shyang Yao Hongxun Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning Measurement Science Review magnetic resonance imaging measurement sparse learning dictionary learning super-resolution imaging |
| title | Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning |
| title_full | Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning |
| title_fullStr | Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning |
| title_full_unstemmed | Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning |
| title_short | Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning |
| title_sort | magnetic resonance super resolution imaging measurement with dictionary optimized sparse learning |
| topic | magnetic resonance imaging measurement sparse learning dictionary learning super-resolution imaging |
| url | https://doi.org/10.1515/msr-2017-0018 |
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