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Comparison of Super Resolution Reconstruction Acquisition Geometries for Use in Mouse Phenotyping
Author(s) -
Niranchana Manivannan,
Bradley D. Clymer,
Anna Bratasz,
Kimerly Powell
Publication year - 2013
Publication title -
international journal of biomedical imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.626
H-Index - 41
eISSN - 1687-4196
pISSN - 1687-4188
DOI - 10.1155/2013/820874
Subject(s) - image resolution , interpolation (computer graphics) , data acquisition , multislice , voxel , resolution (logic) , computer science , iterative reconstruction , artificial intelligence , computer vision , projection (relational algebra) , superresolution , 3d reconstruction , image (mathematics) , physics , nuclear magnetic resonance , algorithm , operating system
3D isotropic imaging at high spatial resolution (30–100 microns) is important for comparing mouse phenotypes. 3D imaging at high spatial resolutions is limited by long acquisition times and is not possible in many in vivo settings. Super resolution reconstruction (SRR) is a postprocessing technique that has been proposed to improve spatial resolution in the slice-select direction using multiple 2D multislice acquisitions. Any 2D multislice acquisition can be used for SRR. In this study, the effects of using three different low-resolution acquisition geometries (orthogonal, rotational, and shifted) on SRR images were evaluated and compared to a known standard. Iterative back projection was used for the reconstruction of all three acquisition geometries. The results of the study indicate that super resolution reconstructed images based on orthogonally acquired low-resolution images resulted in reconstructed images with higher SNR and CNR in less acquisition time than those based on rotational and shifted acquisition geometries. However, interpolation artifacts were observed in SRR images based on orthogonal acquisition geometry, particularly when the slice thickness was greater than six times the inplane voxel size. Reconstructions based on rotational geometry appeared smoother than those based on orthogonal geometry, but they required two times longer to acquire than the orthogonal LR images.

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