2006 - IEEE Fellow For contributions to theory and practice of image reconstruction.
Jeffrey A. Fessler mainly investigates Iterative reconstruction, Algorithm, Artificial intelligence, Computer vision and Mathematical optimization. Jeffrey A. Fessler has researched Iterative reconstruction in several fields, including Image resolution, Image quality, Image processing, Iterative method and Tomography. Jeffrey A. Fessler combines subjects such as Function, Monotonic function and Statistical model with his study of Algorithm.
The various areas that Jeffrey A. Fessler examines in his Artificial intelligence study include Magnetic resonance imaging and Computed tomography. His Computer vision research includes elements of Field, Positron emission tomography, Root mean square and Fourier transform. His Mathematical optimization research incorporates themes from Nonlinear conjugate gradient method, Expectation–maximization algorithm, Jacobian matrix and determinant, Applied mathematics and Image restoration.
Jeffrey A. Fessler spends much of his time researching Iterative reconstruction, Algorithm, Artificial intelligence, Computer vision and Mathematical optimization. His work carried out in the field of Iterative reconstruction brings together such families of science as Image quality, Image resolution, Regularization, Iterative method and Tomography. His Algorithm study incorporates themes from Image processing, Imaging phantom, Monotonic function and Rate of convergence.
The study incorporates disciplines such as Detector and Pattern recognition in addition to Artificial intelligence. His study in Computer vision is interdisciplinary in nature, drawing from both Field, Fast Fourier transform and Medical imaging. His Mathematical optimization research includes themes of Smoothing, Quadratic equation, Estimator, Applied mathematics and Image restoration.
Jeffrey A. Fessler mostly deals with Iterative reconstruction, Algorithm, Artificial intelligence, Pattern recognition and Imaging phantom. His Iterative reconstruction study integrates concerns from other disciplines, such as Image quality, Image resolution, Inverse problem, Regularization and Computed tomography. His Image quality study combines topics from a wide range of disciplines, such as Fast Fourier transform and Fourier transform.
His Algorithm research is multidisciplinary, incorporating perspectives in Image processing, Noise, Line search, Magnetic resonance imaging and Statistical model. His work deals with themes such as Mean squared error, Noise, Projection and Spect imaging, which intersect with Imaging phantom. The Noise study combines topics in areas such as Iterative method and Machine learning.
Iterative reconstruction, Artificial intelligence, Algorithm, Pattern recognition and Inverse problem are his primary areas of study. His studies deal with areas such as Image quality, Cluster analysis, Magnetic resonance imaging, Convex optimization and Compressed sensing as well as Iterative reconstruction. In his research on the topic of Image quality, Radiation dose is strongly related with Computed tomography.
His Artificial intelligence course of study focuses on Machine learning and Iterative method, Series, Filter and Training set. His work on Gradient method as part of general Algorithm research is frequently linked to Kernel, bridging the gap between disciplines. His research on Pattern recognition also deals with topics like
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Space-alternating generalized expectation-maximization algorithm
J.A. Fessler;A.O. Hero.
IEEE Transactions on Signal Processing (1994)
Nonuniform fast Fourier transforms using min-max interpolation
J.A. Fessler;B.P. Sutton.
IEEE Transactions on Signal Processing (2003)
J.M. Ollinger;J.A. Fessler.
IEEE Signal Processing Magazine (1997)
Penalized weighted least-squares image reconstruction for positron emission tomography
IEEE Transactions on Medical Imaging (1994)
Statistical image reconstruction for polyenergetic X-ray computed tomography
I.A. Elbakri;J.A. Fessler.
IEEE Transactions on Medical Imaging (2002)
Ordered subsets algorithms for transmission tomography.
H. Erdogan;Jeffrey A. Fessler.
Physics in Medicine and Biology (1999)
Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs
J.A. Fessler;W.L. Rogers.
IEEE Transactions on Image Processing (1996)
In vivo mapping of cholinergic terminals in normal aging, Alzheimer's disease, and Parkinson's disease
D. E. Kuhl;S. Minoshima;J. A. Fessler;K. A. Frey;K. A. Frey.
Annals of Neurology (1996)
Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography
IEEE Transactions on Image Processing (1996)
Globally convergent algorithms for maximum a posteriori transmission tomography
K. Lange;J.A. Fessler.
IEEE Transactions on Image Processing (1995)
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