Simultaneous dictionary learning and reconstruction from subsampled data in photoacoustic microscopy
| Autor: | Song Hu, John A. Hossack, Bo Ning, Sushanth G. Sathyanarayana |
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| Rok vydání: | 2019 |
| Předmět: |
Ground truth
Computer science business.industry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Iterative reconstruction Image plane Peak signal-to-noise ratio 030218 nuclear medicine & medical imaging Upsampling 03 medical and health sciences symbols.namesake 0302 clinical medicine Fourier transform Compressed sensing Photoacoustic microscopy Wavelet Microscopy 0202 electrical engineering electronic engineering information engineering symbols Artificial intelligence Raster scan business |
| Zdroj: | 2019 IEEE International Ultrasonics Symposium (IUS). |
| Popis: | Photoacoustic microscopy acquires volumetric RF data to obtain high resolution, high contrast, images of the microvasculature but is associated with slow acquisition of data due to mechanical raster scanning across the image plane. Recent work has shown that the acquisition speed can be increased using compressive sampling methods and subsequent reconstruction. These methods use bases (dictionaries) learned from prior fully sampled acquisitions, or classical bases such as the Fourier or wavelet bases. In this study, we present the simultaneous learning of bases, and reconstruction using only subsampled data. The algorithm was validated at two different subsampling levels 50% and 75% downsampling, and compared to the ground truth reconstruction with fully sampled data by estimating the peak signal to noise ratio (PSNR). No significant difference in performance was observed between the fully sampled (20.0±3.0 dB), 50% (19.9±2.1 dB) and 75% (19.1±2.6 dB) subsampled data. |
| Databáze: | OpenAIRE |
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