Dictionary-based super resolution for positron emission tomography images
In this paper, a strategy to increase the resolution of positron emission tomography (PET) images, using a previously trained high resolution dictionary for the sinograms is proposed. The low resolution input sinogram is divided into patches of 5x5 samples. The sparse code of each patch is calc...
Gorde:
| Egile nagusia: | |
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| Beste egile batzuk: | |
| Formatua: | Artículo |
| Hizkuntza: | en_US |
| Argitaratua: |
2019
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| Gaiak: | |
| Sarrera elektronikoa: | https://ijcopi.org/index.php/ojs/article/view/151 |
| Etiketak: |
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| Gaia: | In this paper, a strategy to increase the resolution
of positron emission tomography (PET) images, using a
previously trained high resolution dictionary for the
sinograms is proposed. The low resolution input sinogram
is divided into patches of 5x5 samples. The sparse code of
each patch is calculated and applied to the high resolution
dictionary to obtain the best high resolution patch. The
estimated high resolution sinogram is processed by the
filtered backprojection (FBP) or by the ordered subsets
expectation maximization (OSEM) reconstruction
algorithm. Results show that, in both cases, the dictionary
method outperforms the bicubic interpolation method by
more 3% in PSNR. OSEM algorithm yields even better
results than the FBP algorithm. However, the
reconstruction time is exacerbated. |
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