Erratum

The influence of temperature during the development of conidia on the germination of Uncinula necatorVitis 34(1), 63-64 (1995

Regularized Emission Image Reconstruction Using Imperfect Side Information

A spatially variant penalized-likelihood method for tomographic image reconstruction based on a weighted Gibbs penalty was investigated. The penalty weights are determined from structural side information, such as the locations of anatomical boundaries in high-resolution magnetic resonance images. Such side information will be imperfect in practice, and a simple simulation demonstrated the importance of accounting for the errors in boundary locations. Methods are discussed for prescribing the penalty weights when the side information is noisy. Simulation results suggest that even imperfect side information is useful for guiding spatially variant regularization.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85869/1/Fessler110.pd

Robust Maximum- Likelihood Position Estimation in Scintillation Cameras

The classical maximum-likelihood (ML) estimator for the position of a scintillation event in a gamma camera, as derived by Gray and Macovski in 1976, requires exact knowledge of the light-spread function (LSF) of each photomultiplier tube. In practice, one must determine each LSF from noisy measurements corrupted by Poisson noise, quantization error, and electronic noise and bias. Since the ML position estimator involves derivatives of each LSF, even small measurement errors can result in degraded estimator performance. In this paper we derive a robust ML position estimator that accounts for the statistical uncertainty in LSF measurements. The form of the robust estimator diminishes contributions from the tails of the LSF, where the relative measurement errors are the largest.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85879/1/Fessler117.pd

Grouped-Coordinate Ascent Algorithms for Penalized-Likelihood Transmission Image Reconstruction

Presents a new class of algorithms for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. We derive the algorithms by applying to the transmission log-likelihood a version of the convexity technique developed by De Pierro for emission tomography. The new class includes the single-coordinate ascent (SCA) algorithm and Lange's convex algorithm for transmission tomography as special cases. The new grouped-coordinate ascent (GCA) algorithms in the class overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission maximum likelihood-expectation maximization (ML-EM) algorithm or in the SCA algorithm. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike the SCA algorithm and perhaps line-search algorithms. We show that the GCA algorithms converge faster than the SCA algorithm, even on conventional workstations. An example from a low-count positron emission tomography (PET) transmission scan illustrates the method.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86021/1/Fessler93.pd

Fast Parallelizable Algorithms for Transmission Image Reconstruction

Presents a new class of algorithm for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. The authors derive the algorithms by applying to the transmission log-likelihood a variation of the convexity technique developed by De Pierro for the emission case. The new algorithms overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission EM algorithm or in coordinate-ascent algorithms. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike coordinate-ascent algorithms and perhaps line-search algorithms. The authors show that the algorithms converge faster than several alternatives, even on conventional workstations. They give examples from low-count PET transmission scans and from truncated fan-beam SPECT transmission scans.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86006/1/Fessler136.pd