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Comparison of super resolution reconstruction acquisition geometries for use in mouse phenotyping.

Manivannan N, Clymer BD, Bratasz A, Powell KA - Int J Biomed Imaging (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.

ABSTRACT
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.

No MeSH data available.


Block diagram of Irani and Peleg's IBP algorithm.
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Related In: Results  -  Collection


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fig1: Block diagram of Irani and Peleg's IBP algorithm.

Mentions: All SRR images were reconstructed using the IBP approach proposed by Irani and Peleg [5]. IBP was chosen because it has been widely used for super resolution reconstruction in the past and because of its easy implementation. A flowchart illustrating the IBP approach is provided in Figure 1. Initially, an HR image is approximated from the average of multiple LR images {fk}k=1N that have been geometrically transformed, Tk−1, to the same orientation prior to averaging. A new set of LR images , are obtained by simulating the imaging process (blurring h, and down sampling) in the predicted HR image . For our case, a 1D Gaussian kernel with a FWHM equal to the LR slice thickness was used along the slice-select direction in the HR image for blurring because it closely matched the excitation profile used in the original image acquisition sequence. If the predicted HR image is the same as the true HR image G, then the simulated LR images should be equal to the observed LR images {fk}k=1N. Therefore, the difference between the observed and simulated LR images is upsampled and backprojected on to using linear interpolation. This results in an updated HR image that can be downsampled and the simulated LR images are compared to the observed LR images {fk}k=1N. These steps are iteratively repeated till the maximum error at the ith iteration according to is less than a preset threshold. All SRR software was developed using Matlab v.2009a (MathWorks Inc., Mass, USA).


Comparison of super resolution reconstruction acquisition geometries for use in mouse phenotyping.

Manivannan N, Clymer BD, Bratasz A, Powell KA - Int J Biomed Imaging (2013)

Block diagram of Irani and Peleg's IBP algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3794539&req=5

fig1: Block diagram of Irani and Peleg's IBP algorithm.
Mentions: All SRR images were reconstructed using the IBP approach proposed by Irani and Peleg [5]. IBP was chosen because it has been widely used for super resolution reconstruction in the past and because of its easy implementation. A flowchart illustrating the IBP approach is provided in Figure 1. Initially, an HR image is approximated from the average of multiple LR images {fk}k=1N that have been geometrically transformed, Tk−1, to the same orientation prior to averaging. A new set of LR images , are obtained by simulating the imaging process (blurring h, and down sampling) in the predicted HR image . For our case, a 1D Gaussian kernel with a FWHM equal to the LR slice thickness was used along the slice-select direction in the HR image for blurring because it closely matched the excitation profile used in the original image acquisition sequence. If the predicted HR image is the same as the true HR image G, then the simulated LR images should be equal to the observed LR images {fk}k=1N. Therefore, the difference between the observed and simulated LR images is upsampled and backprojected on to using linear interpolation. This results in an updated HR image that can be downsampled and the simulated LR images are compared to the observed LR images {fk}k=1N. These steps are iteratively repeated till the maximum error at the ith iteration according to is less than a preset threshold. All SRR software was developed using Matlab v.2009a (MathWorks Inc., Mass, USA).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.

ABSTRACT
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.

No MeSH data available.