Title :
Maximum likelihood, least squares, and penalized least squares for PET
Author_Institution :
AT&T Bell Lab., Murray Hill, NJ, USA
fDate :
6/1/1993 12:00:00 AM
Abstract :
The EM algorithm is the basic approach used to maximize the log likelihood objective function for the reconstruction problem in positron emission tomography (PET). The EM algorithm is a scaled steepest ascent algorithm that elegantly handles the nonnegativity constraints of the problem. It is shown that the same scaled steepest descent algorithm can be applied to the least squares merit function, and that it can be accelerated using the conjugate gradient approach. The experiments suggest that one can cut the computation by about a factor of 3 by using this technique. The results are applied to various penalized least squares functions which might be used to produce a smoother image
Keywords :
computerised tomography; radioisotope scanning and imaging; PET; conjugate gradient approach; expectation maximization algorithm; least squares merit function; log likelihood objective function; maximum likelihood; medical diagnostic imaging; nonnegativity constraints; nuclear medicine; penalized least squares; positron emission tomography; reconstruction problem; scaled steepest ascent algorithm; scaled steepest descent algorithm; smoother image; Acceleration; Biochemistry; Blood flow; Convergence; Convolution; Detectors; Least squares methods; Maximum likelihood detection; Positron emission tomography; Sugar;
Journal_Title :
Medical Imaging, IEEE Transactions on