title: Direct parametric reconstruction from undersampled (k, t)-space data in dynamic contrast enhanced MRI.
abstract: The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as "indirect". If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled (k, t)-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled (k, t)-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method.
authors: Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK; Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT London, UK. Electronic address: n.dikaios@ucl.ac.uk.
authors: Nikolaos
authors: N
authors: Dikaios
authors: Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT London, UK.
authors: Simon
authors: S
authors: Arridge
authors: Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK.
authors: Valentin
authors: V
authors: Hamy
authors: Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK.
authors: Shonit
authors: S
authors: Punwani
authors: Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK.
authors: David
authors: D
authors: Atkinson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D019047:Phantoms, Imaging; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D015656:Respiratory Mechanics; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media
keywords: Bayesian compressed sensing; Direct reconstruction; Dynamic contrast enhanced