MRI estimation of contrast agent concentration in tissue using a neural network approach

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.