Abstract
Diagnostic ultrasound imaging is a mainstay of modern clinical medicine. While ultrasound is relatively inexpensive, portable, does not use ionizing radiation and is versatile as a diagnostic tool, its contrast is limited by artifacts, thus limiting its effectiveness. Photoacoustic microscopy leverages the specificity of endogenous optical absorption to obtain high resolution and contrast images of the microvasculature. Additionally, the utility of photoacoustic microscopy has been extended to functional imaging by imaging blood flow speed in the microvasculature. However, photoacoustic microscopy is associated with similar artifacts as in diagnostic ultrasound imaging, and dense spatial sampling is necessary to estimate blood flow, which may lead to safety considerations regarding laser fluence in tissue. Sparse modeling leverages redundancy in data to obtain parsimonious representations. Sparse modeling has been employed in other imaging modalities, such as magnetic resonance imaging (MRI), and its applications in ultrasound and photoacoustic microscopy are an emerging area of interest. In this dissertation, the utility of sparse modeling in suppressing quasi-static artifacts in echocardiography is demonstrated. Additionally, the suppression of reverberant artifacts in photoacoustic microscopy using sparse modeling is demonstrated. Finally, photoacoustic data are sparsely modeled, and blood flow speed measurements are recovered from undersampled observations using sparse modeling.
Notes
1. “Closed loop low rank echocardiographic artifact removal”, S. G. Sathyanarayana, S. T. Acton, J. A. Hossack, IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, Vol.68, Issue 3, Jul 2020, 510-525. 20xx IEEE
2. “Suppression of clutter by rank adaptive reweighted sparse coding”, S. G. Sathyanarayana, S. T. Acton, J. A. Hossack, Proceedings of IEEE International Ultrasonics Symposium (IUS 2017). 20xx IEEE
3. “Dictionary learning based reverberation removal enables depth resolved photoacoustic microscopy of the mouse brain”, S.G. Sathyanarayana, B. Ning, R. Cao, S. Hu, J. A. Hossack, Scientific reports 8 (985) (2016)
4. “Reverberation suppression using dictionary learning in optical resolution photoacoustic microscopy”, S.G. Sathyanarayana, B. Ning, S. Hu, J.A. Hossack, Proceedings of IUS 2017. 20xx IEEE
5.Recovery of blood flow from undersampled photoacoustic microscopy data using sparse modeling. S. G. Sathyanarayana, Z. Wang, N. Sun, B. Ning. S. Hu, J. A. Hossack., IEEE Transactions on Medical Imaging, 2021 20xx IEEE
