Results 1  10
of
18
ConjugateGradient Preconditioning Methods for ShiftVariant PET Image Reconstruction
 IEEE Tr. Im. Proc
, 2002
"... Gradientbased iterative methods often converge slowly for tomographic image reconstruction and image restoration problems, but can be accelerated by suitable preconditioners. Diagonal preconditioners offer some improvement in convergence rate, but do not incorporate the structure of the Hessian mat ..."
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Cited by 52 (21 self)
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Gradientbased iterative methods often converge slowly for tomographic image reconstruction and image restoration problems, but can be accelerated by suitable preconditioners. Diagonal preconditioners offer some improvement in convergence rate, but do not incorporate the structure of the Hessian matrices in imaging problems. Circulant preconditioners can provide remarkable acceleration for inverse problems that are approximately shiftinvariant, i.e. for those with approximately blockToeplitz or blockcirculant Hessians. However, in applications with nonuniform noise variance, such as arises from Poisson statistics in emission tomography and in quantumlimited optical imaging, the Hessian of the weighted leastsquares objective function is quite shiftvariant, and circulant preconditioners perform poorly. Additional shiftvariance is caused by edgepreserving regularization methods based on nonquadratic penalty functions. This paper describes new preconditioners that approximate more accurately the Hessian matrices of shiftvariant imaging problems. Compared to diagonal or circulant preconditioning, the new preconditioners lead to significantly faster convergence rates for the unconstrained conjugategradient (CG) iteration. We also propose a new efficient method for the linesearch step required by CG methods. Applications to positron emission tomography (PET) illustrate the method.
GroupedCoordinate Ascent Algorithms for PenalizedLikelihood Transmission Image Reconstruction
 IEEE Tr. Med. Im
, 1996
"... This paper presents a new class of algorithms for penalizedlikelihood reconstruction of attenuation maps from lowcount transmission scans. We derive the algorithms by applying to the transmission loglikelihood a version of the convexity technique developed by De Pierro for emission tomography. The ..."
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Cited by 48 (21 self)
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This paper presents a new class of algorithms for penalizedlikelihood reconstruction of attenuation maps from lowcount transmission scans. We derive the algorithms by applying to the transmission loglikelihood a version of the convexity technique developed by De Pierro for emission tomography. The new class includes the singlecoordinate ascent (SCA) algorithmand Lange's convex algorithm for transmission tomography as special cases. The new groupedcoordinate ascent (GCA) algorithms in the class overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission MLEM algorithm or in the SCA algorithm. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradientbased methods. (3) The algorithms are easily parallelizable, unlike the SCA algorithm and perhaps linesearch algorithms. We show that the GCA algorithms converge faster than the SCA algorithm, even on conventional workstations. An ex...
Regularization for uniform spatial resolution properties in penalizedlikelihood image reconstruction
 IEEE Tr. Med. Im
, 2000
"... Traditional spaceinvariant regularization methods in tomographic image reconstruction using penalizedlikelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are spacevariant, as ..."
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Cited by 40 (20 self)
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Traditional spaceinvariant regularization methods in tomographic image reconstruction using penalizedlikelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are spacevariant, asymmetric, and objectdependent even for spaceinvariant imaging systems. We propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the leastsquares tting of a parameterized local impulse response to a desired global response. We have developed computationally e cient methods for PET systems with shiftinvariant geometric responses. We demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.
Exploring estimator biasvariance tradeoffs using the uniform CR bound
 IEEE Trans. on Sig. Proc
, 1996
"... We introduce a plane, which we call the deltasigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a un ..."
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Cited by 38 (14 self)
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We introduce a plane, which we call the deltasigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a uniform CramerRao (CR) bound on estimator variance a deltasigma tradeoff curve is specied which denes an "unachievable region" of the deltasigma plane for a specified statistical model. In order to place an estimator on this plane for comparison to the deltasigma tradeoff curve, the estimator variance, bias gradient, and bias gradient norm must be evaluated. We present a simple and accurate method for experimentally determining the bias gradient norm based on applying a bootstrap estimator to a sample mean constructed from the gradient of the loglikelihood. We demonstrate the methods developed in this paper for linear Gaussian and nonlinear Poisson inverse problems.
Aspire 3.0 User's Guide: A Sparse Iterative Reconstruction Library
 of EECS, Univ. of Michigan, Ann Arbor, MI
, 1999
"... ASPIRE 3.0 is a collection of ANSI C language programs for performing tomographic image reconstruction and image restoration using statistical methods. This user's guide describes how to compile and use the software. This work was supported in part by DOE grant DEFG0287ER60561 and NIH grants ..."
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Cited by 17 (8 self)
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ASPIRE 3.0 is a collection of ANSI C language programs for performing tomographic image reconstruction and image restoration using statistical methods. This user's guide describes how to compile and use the software. This work was supported in part by DOE grant DEFG0287ER60561 and NIH grants CA60711 and CA54362. ASPIRE 3.0 January 22, 1999 2 Notice ASPIRE 3.0 is copyright 19901998 Jeff Fessler and The University of Michigan ASPIRE 3.0 is available only to particular individuals for academic collaboration. Do not distribute this software to anyone else. ffl This code is provided as is, with absolutely no warranty. ffl Neither Jeff Fessler nor The University of Michigan assume any liability for the use or misuse of this software. There are no guarantees of its correctness, nor its efficacy for diagnostic imaging. ffl The copyright and disclaimer headers must remain in the source code. ffl We will be glad to answer a limited set of simple, precise questions. We welcome your fe...
PenalizedLikelihood Estimators and Noise Analysis for RandomsPrecorrected PET Transmission Scans
 IEEE Tr. Med. Im
, 1999
"... This paper analyzes and compares image reconstruction methods based on practical approximations to the exact loglikelihood of randomsprecorrected PET measurements. The methods apply to both emission and transmission tomography; however in this paper we focus on transmission tomography. The results ..."
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Cited by 16 (9 self)
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This paper analyzes and compares image reconstruction methods based on practical approximations to the exact loglikelihood of randomsprecorrected PET measurements. The methods apply to both emission and transmission tomography; however in this paper we focus on transmission tomography. The results of experimental PET transmission scans and variance approximations demonstrate that the "shifted Poisson" (SP) method avoids the systematic bias of the conventional dataweighted least squares (WLS) method, and leads to significantly lower variance than conventional statistical methods based on the loglikelihood of the ordinary Poisson (OP) model. We develop covariance approximations to analyze the propagation of noise from attenuation maps into emission images via the attenuation correction factors (ACFs). Empirical pixel and region variances from real transmission data agree closely with the analytical predictions. Both the approximations and the empirical results show that the performanc...
Spatiallyvariant roughness penalty design for uniform resolution in penalizedlikelihood image reconstruction
 In Proc. IEEE Intl. Conf. on Image Processing
, 1998
"... Traditional spaceinvariant regularization schemes in tomographic image reconstruction using penalizedlikelihood estimators produce images with nonuniform resolution properties. The local point spread functions that quantify the local smoothing properties of such estimators are not only spacevaria ..."
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Cited by 7 (6 self)
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Traditional spaceinvariant regularization schemes in tomographic image reconstruction using penalizedlikelihood estimators produce images with nonuniform resolution properties. The local point spread functions that quantify the local smoothing properties of such estimators are not only spacevariant and asymmetric, but are also objectdependent even for spaceinvariant systems. We propose a new regularization scheme for increased spatial uniformity and demonstrate the resolution properties of this new method versus conventional regularization schemes through an investigation of local point spread functions. 1
Statistical Image Reconstruction Algorithms Using Paraboloidal Surrogates for PET Transmission Scans
, 1999
"... Positron Emission Tomography (PET) is a diagnostic imaging tool that provides images of radioactive substances injected into the body to trace biological functions. The radioactive substance emits a positron which annihilates with an electron to produce two 511 keV photons traveling in approximately ..."
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Cited by 7 (0 self)
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Positron Emission Tomography (PET) is a diagnostic imaging tool that provides images of radioactive substances injected into the body to trace biological functions. The radioactive substance emits a positron which annihilates with an electron to produce two 511 keV photons traveling in approximately opposite directions to be coincidentally detected by two detectors. Many photons are absorbed or scattered, reducing the number of detected emission events. Attenuation correction is crucial for quantitatively accurate PET reconstructions. PET transmission scans are performed to estimate attenuation parameters which are in turn used to correct the emission scans for attenuation effects. The noise in estimating the attenuation parameters propagates to the emission images affecting their quality and quantitative correctness. Thus, attenuation image reconstruction is extremely important in PET. Conventional methods of attenuation correction are suboptimal and ignore the Poisson nature of the data. We propose to use penalized likelihood image reconstruction techniques for transmission scans. Current algorithms for transmission tomography have two important problems: 1) they are not guaranteed to converge, 2) if they converge, the convergence is slow. We develop new fast and monotonic optimization algorithms for penalized likelihood image reconstruction based on a novel paraboloidal surrogates principle. We present results showing the speed of the new optimization algorithms as compared to previous ones. We apply the algorithms to PET data obtained from an anthropomorphic thorax phantom and real patient data. A transmission scan per...
Statistical Tomographic Image Reconstruction Methods for RandomsPrecorrected PET Measurements
, 2000
"... Medical imaging systems such as positron emission tomography (PET) and electronically collimated single positron emission tomography (SPECT) record particle emission events based on timing coincidences. These systems record accidental coincidence (AC) events simultaneously with the true coincidence ..."
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Cited by 3 (0 self)
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Medical imaging systems such as positron emission tomography (PET) and electronically collimated single positron emission tomography (SPECT) record particle emission events based on timing coincidences. These systems record accidental coincidence (AC) events simultaneously with the true coincidence events. Similarly in low lightlevel imaging, thermoelectrons generated by photodetector are indistinguishable from photoelectrons generated by photoconversion, and their e#ect is similar to the AC events. During PET emission scans, accidental coincidence (AC) events occur when photons that originate from separate positronelectron annihilations are mistakenly recorded as having arisen from the same annihilation. In PET, generally a significant portion of the collected data consists of AC events that are a primary source of background noise. Also, during PET transmission scans, photons that originate from different transmission sources cause AC events. In PET, the measurements are usually precorrected for AC events by realtime subtraction of the delayed window coincidences. Randoms subtraction compensates in mean for accidental coincidences, but destroys the Poisson statistics. We develop statistical image reconstruction methods for randoms precorrected PET measurements using penalized maximum likelihood (ML) estimation. We introduce two new approximations to the complicated exact loglikelihood of the precorrected measurements: one based on a "shifted Poisson" model, and the other based on saddlepoint approximations to the measurement probability mass function (pmf). We compare estimators based on the new models to the conventional data...
Conjugategradient preconditioning methods: numerical results
 of EECS, Univ. of Michigan, Ann Arbor, MI
, 1997
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