title: Computationally efficient error estimate for evaluation of regularization in photoacoustic tomography.
abstract: The model-based image reconstruction techniques for photoacoustic (PA) tomography require an explicit regularization. An error estimate (?2) minimization-based approach was proposed and developed for the determination of a regularization parameter for PA imaging. The regularization was used within Lanczos bidiagonalization framework, which provides the advantage of dimensionality reduction for a large system of equations. It was shown that the proposed method is computationally faster than the state-of-the-art techniques and provides similar performance in terms of quantitative accuracy in reconstructed images. It was also shown that the error estimate (?2) can also be utilized in determining a suitable regularization parameter for other popular techniques such as Tikhonov, exponential, and nonsmooth (?1 and total variation norm based) regularization methods.
authors.affiliation: Indian Institute of Science, Medical Imaging Group, Department of Computational and Data Sciences, C V Raman Avenue, Bengaluru 560012, India.
authors.forename: Manish
authors.initials: M
authors.lastname: Bhatt
authors.affiliation: Indian Institute of Science, Medical Imaging Group, Department of Computational and Data Sciences, C V Raman Avenue, Bengaluru 560012, India.
authors.forename: Atithi
authors.initials: A
authors.lastname: Acharya
authors.affiliation: Indian Institute of Science, Medical Imaging Group, Department of Computational and Data Sciences, C V Raman Avenue, Bengaluru 560012, India.
authors.forename: Phaneendra K
authors.initials: PK
authors.lastname: Yalavarthy
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D019047:Phantoms, Imaging; D061088:Photoacoustic Techniques / Q000379:methods*; D014054:Tomography / Q000379:methods*