Near Infrared (NIR) and Photoacoustic (PA) Imaging are promising imaging modalities that provides functional information of the soft biological tissues in-vivo, with applica-tions in breast and brain tissue imaging. These techniques use near infrared light in the wavelength range of (600 nm - 900 nm), giving an advantage of being non-ionizing imaging modality. This makes the prolong bed-side monitoring of tissue feasible, making them highly desirable medical imaging modalities in the clinic. The computation models that are deployed in these imaging scenarios are computationally demanding and often require a high performance computing systems to deploy them in real-time. This the-sis presents three computationally e cient models for near-infrared and photoacoustic imaging, without compromising the quality of measured functional properties, to make them more appealing in clinical scenarios.
The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modi ed the BeerLambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concen-trations for NIR spectroscopic data analysis. Even though MBLL is e ective in terms of providing qualitative comparison, it su ers from its applicability across tissue types and tissue dimensions. A Lambert-W function-based modeling for light propagation in biological tissues is proposed and introduced, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coe cients o and . The model is validated against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. Numerous human and animal tissues are included to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is rst of its kind in providing accurate modeling of NIR light propagation in biological tissues.
Model based image reconstruction techniques yield better quantitative accuracy in photoacoustic (PA) image reconstruction, especially in limited data cases. An exponen-tial ltering of singular values is proposed for carrying out the image reconstruction in photoacoustic tomography. The results were compared with widely popular Tikhonov regularization, time reversal, and the state of the art least-squares QR based reconstruc-tion algorithms for three digital phantom cases with varying signal-to-noise ratios of data. The exponential ltering provided superior photoacoustic images of better quanti-tative accuracy. Moreover, the proposed ltering approach was observed to be less biased towards regularization parameter and did not come with any additional computational burden as it was implemented within the Tikhonov ltering framework. It was also shown that the standard Tikhonov ltering becomes an approximation to the proposed exponential ltering.
The model based image reconstruction techniques for photoacoustic tomography re-quire an explicit regularization. An error estimate minimization based approach was proposed and developed for the determination of regularization parameter for PA imag-ing. The regularization was used within Lanczos bidiagonalization framework, which provides the advantage of dimensionality reduction for a large system of equations. The proposed method was computationally faster than the state of the art techniques and provided similar performance in terms of quantitative accuracy in reconstructed im-ages.The estimate can also be utilized in determining suitable regularization parameter for other popular techniques such as Tikhonov,exponential ltering and `1 norm based regularization methods.
| Identifer | oai:union.ndltd.org:IISc/oai:etd.ncsi.iisc.ernet.in:2005/2831 |
| Date | January 2016 |
| Creators | Bhatt, Manish |
| Contributors | Yalavarthy, Phaneendra K |
| Source Sets | India Institute of Science |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Relation | G27896 |
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