We apply reparameterization and the maximum likelihood method to a specific fluorescence-mediated tomography problem where the solution is known a priori to be extremely sparse (i.e., all image values are zero except for one). Our algorithm performs significantly better than a standard image reconstruction method, particularly for deep-seated targets, and achieves close to 150 μm accuracy in a 3 mm diameter cross-sectional area with only 12 measurements. Moreover, results do not depend on the selection of a regularization parameter or other ad hoc values, and since reconstructions can be computed very quickly, the algorithm is also suitable for real-time implementation.
© 2013 Optical Society of America
Original Manuscript: April 18, 2013
Manuscript Accepted: May 23, 2013
Published: June 28, 2013
Vol. 8, Iss. 8 Virtual Journal for Biomedical Optics
Vivian Pera, Eric Zettergren, Dana H. Brooks, and Mark Niedre, "Maximum likelihood tomographic reconstruction of extremely sparse solutions in diffuse fluorescence flow cytometry," Opt. Lett. 38, 2357-2359 (2013)