Scott S Hsieh1, David A Chesler2, Dominik Fleischmann1, Norbert J Pelc3. 1. Department of Radiology, Stanford University, Stanford, California 94305. 2. Department of Radiology, Massachusetts General Hospital, Charleston, Massachusetts 02114. 3. Departments of Radiology and Bioengineering, Stanford University, Stanford, California 94305.
Abstract
PURPOSE: To find an upper bound on the maximum dose reduction possible for any reconstruction algorithm, analytic or iterative, that result from the inclusion of the data statistics. The authors do not analyze noise reduction possible from prior knowledge or assumptions about the object. METHODS: The authors examined the task of estimating the density of a circular lesion in a cross section. Raw data were simulated by forward projection of existing images and numerical phantoms. To assess an upper bound on the achievable dose reduction by any algorithm, the authors assume that both the background and the shape of the lesion are completely known. Under these conditions, the best possible estimate of the density can be determined by solving a weighted least squares problem directly in the raw data domain. Any possible reconstruction algorithm that does not use prior knowledge or make assumptions about the object, including filtered backprojection (FBP) or iterative reconstruction methods with this constraint, must be no better than this least squares solution. The authors simulated 10,000 sets of noisy data and compared the variance in density from the least squares solution with those from FBP. Density was estimated from FBP images using either averaging within a ROI, or streak-adaptive averaging with better noise performance. RESULTS: The bound on the possible dose reduction depends on the degree to which the observer can read through the possibly streaky noise. For the described low contrast detection task with the signal shape and background known exactly, the average dose reduction possible compared to FBP with streak-adaptive averaging was 42% and it was 64% if only the ROI average is used with FBP. The exact amount of dose reduction also depends on the background anatomy, with statistically inhomogeneous backgrounds showing greater benefits. CONCLUSIONS: The dose reductions from new, statistical reconstruction methods can be bounded. Larger dose reductions in the density estimation task studied here are only possible with the introduction of prior knowledge, which can introduce bias.
PURPOSE: To find an upper bound on the maximum dose reduction possible for any reconstruction algorithm, analytic or iterative, that result from the inclusion of the data statistics. The authors do not analyze noise reduction possible from prior knowledge or assumptions about the object. METHODS: The authors examined the task of estimating the density of a circular lesion in a cross section. Raw data were simulated by forward projection of existing images and numerical phantoms. To assess an upper bound on the achievable dose reduction by any algorithm, the authors assume that both the background and the shape of the lesion are completely known. Under these conditions, the best possible estimate of the density can be determined by solving a weighted least squares problem directly in the raw data domain. Any possible reconstruction algorithm that does not use prior knowledge or make assumptions about the object, including filtered backprojection (FBP) or iterative reconstruction methods with this constraint, must be no better than this least squares solution. The authors simulated 10,000 sets of noisy data and compared the variance in density from the least squares solution with those from FBP. Density was estimated from FBP images using either averaging within a ROI, or streak-adaptive averaging with better noise performance. RESULTS: The bound on the possible dose reduction depends on the degree to which the observer can read through the possibly streaky noise. For the described low contrast detection task with the signal shape and background known exactly, the average dose reduction possible compared to FBP with streak-adaptive averaging was 42% and it was 64% if only the ROI average is used with FBP. The exact amount of dose reduction also depends on the background anatomy, with statistically inhomogeneous backgrounds showing greater benefits. CONCLUSIONS: The dose reductions from new, statistical reconstruction methods can be bounded. Larger dose reductions in the density estimation task studied here are only possible with the introduction of prior knowledge, which can introduce bias.
Authors: Yoshiko Sagara; Amy K Hara; William Pavlicek; Alvin C Silva; Robert G Paden; Qing Wu Journal: AJR Am J Roentgenol Date: 2010-09 Impact factor: 3.959
Authors: Mark E Baker; Frank Dong; Andrew Primak; Nancy A Obuchowski; David Einstein; Namita Gandhi; Brian R Herts; Andrei Purysko; Erick Remer; Neil Vachhani; Neil Vachani Journal: AJR Am J Roentgenol Date: 2012-07 Impact factor: 3.959
Authors: Frédéric A Miéville; François Gudinchet; Francis Brunelle; François O Bochud; Francis R Verdun Journal: Phys Med Date: 2012-01-02 Impact factor: 2.685
Authors: Adam M Alessio; Paul E Kinahan; Ken Sauer; Mannudeep K Kalra; Bruno De Man Journal: IEEE Trans Med Imaging Date: 2016-11-09 Impact factor: 10.048