Abstract
In nuclear medicine imaging coded aperture is used to improve sensitivity. Amplification of quantum noise affect the inverse filtering reconstruction. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks and so, variable coded aperture (VCA) design is used. The unique variable design enables to overcome the small terms in the Fourier transform exists in static array. However, traces of duplications are still remaining. We present combination of VCA with deep-convolutional neural network to remove noise stems from the limited abilities of inverse filtering to achieve higher SNR and resolution.
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