Real time image processing using deep-convolutional neural network for time variated pinhole array in nuclear medicine imaging system

Ariel Schwarz; Yossef Danan; Eliezer Danan; Rachel Bar-Shalom; Amir Shemer

doi:10.1117/12.2635760

13 June 2022 Real time image processing using deep-convolutional neural network for time variated pinhole array in nuclear medicine imaging system

Ariel Schwarz, Yossef Danan, Eliezer Danan, Rachel Bar-Shalom, Amir Shemer

Author Affiliations +

Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 121020M (2022) https://doi.org/10.1117/12.2635760
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

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.

Citation Download Citation

Ariel Schwarz, Yossef Danan, Eliezer Danan, Rachel Bar-Shalom, and Amir Shemer "Real time image processing using deep-convolutional neural network for time variated pinhole array in nuclear medicine imaging system", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 121020M (13 June 2022); https://doi.org/10.1117/12.2635760

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