Segmented Multistage Reconstruction of Magnetic Resonance Images
Compressed sensing of magnetic resonance imaging refers to the reconstruction of magnetic resonance images from partially sampled k-space data. The k-space data reduces reconstruction processing time but at the cost of increasing artifacts - especially with the higher reduction factor of the raw d...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Stefan cel Mare University of Suceava
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/aac77549b3884519b556731476a62245 |
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| Sumario: | Compressed sensing of magnetic resonance imaging refers to the reconstruction of magnetic resonance images from
partially sampled k-space data. The k-space data reduces reconstruction processing time but at the cost of
increasing artifacts - especially with the higher reduction factor of the raw data. This work proposes a
segmented region-based reconstruction technique to reduce image artifacts with enhanced quality and high
temporal resolution. The proposed method segments partially sampled k-space data in two segments according
to their frequencies. Lower frequency components at the central region are selected and predicted using
nuclear norm minimization. This part and the peripheral part of the k-space components at higher frequencies
are merged. The recovery technique iterates to reconstruct more accurate images in terms of conventional
compressed sensing techniques. The performance of the proposed method is evaluated and compared with
compressed sensing, two-stage compressed sensing, and modified total variation technique. Better results
in term of normalized mean square error NMSE, reconstruction time and structural similarity index
measure SSIM show the effectiveness of the proposed method with a high reduction factor of data. |
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