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MRI estimation of contrast agent concentration in tissue using a neural network approach
Author(s) -
BagherEbadian Hassan,
Nagaraja Tavarekere N.,
Paudyal Ramesh,
Whitton Polly,
Panda Swayamprava,
Fenstermacher Joseph D.,
Ewing James R.
Publication year - 2007
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21332
Subject(s) - contrast (vision) , artificial neural network , estimation , computer science , biological system , artificial intelligence , nuclear magnetic resonance , pattern recognition (psychology) , biology , physics , engineering , systems engineering
Using an MRI T 1 by multiple readout pulses (TOMROP) image set, an adaptive neural network (ANN) was trained to directly estimate the concentration of a contrast agent (CA), gadolinium‐bovine serum albumin (Gd‐BSA), in tissue. In nine rats implanted with a 9L cerebral tumor, MRI acquisition of TOMROP inversion‐recovery data was followed by quantitative autoradiography (QAR) using radioiodinated serum albumin (RISA). QAR autoradiograms were used as a training set for the ANN. Precontrast and 25 min postcontrast TOMROP image sets were shown to the ANN in the form of a physical feature set related to 24 inversion‐recovery images; QAR autoradiograms at 30 min after injection of RISA were taken as the training standard for the network. After training and optimization, the ANN produced a map of Gd‐BSA concentration [g‐moles/liter]. The prediction by the ANN of CA concentration at 25 min after injection was well correlated ( r = 0.82, P < 0.0001) with the corresponding autoradiogram's measure of CA concentration. Magn Reson Med 58:290–297, 2007. © 2007 Wiley‐Liss, Inc.