A fast schema for parameter estimation in diffusion kurtosis imaging |
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| Affiliation: | 1. Key Laboratory of Brain Functional Genomics (MOE & STCSM), Key Laboratory of Magnetic Resonance, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai 20062, China;2. Shanghai Medical Instrumentation College, University of Shanghai Science and Technology, Shanghai 200093, China;3. Center for Developmental Neuropsychiatry, Columbia University Department of Psychiatry & New York State Psychiatric Institute, Unit 74, 1051 Riverside Drive, New York, NY 10032, USA;4. Epidemiology Division & MRI Unit, Columbia University Department of Psychiatry & New York State Psychiatric Institute, Unit 24, 1051 Riverside Drive, New York, NY 10032, USA;1. IBFM-CNR, Institute for Molecular Bioimaging and Physiology, 20090 Segrate, Italy;2. Unit of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy;3. Department of Physics, University of Milano-Bicocca, 20126 Milano, Italy;4. Department of Health Sciences – Tecnomed Foundation, University of Milano-Bicocca, 20900 Monza, Italy;1. School of Mathematical Sciences and LPMC, Nankai University, 300071, PR China;2. Department of Biomedical Engineering, Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin 300072, PR China;3. State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, PR China;1. PLA General Hospital, Beijing, China;2. Toronto General Hospital, Peter Munk Cardiac Center, University of Toronto, Toronto, Ontario, Canada;3. Piedmont Heart Institute, Atlanta, Georgia;4. Siemens Medical Solutions USA, Mountain View, California;1. Organ Transplant Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China;2. Zhongshan Medical School, Sun Yat-sen University, Guangzhou, China |
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| Abstract: | Diffusion kurtosis imaging (DKI) is a new model in magnetic resonance imaging (MRI) characterizing restricted diffusion of water molecules in living tissues. We propose a method for fast estimation of the DKI parameters. These parameters – apparent diffusion coefficient (ADC) and apparent kurtosis coefficient (AKC) – are evaluated using an alternative iteration schema (AIS). This schema first roughly estimates a pair of ADC and AKC values from a subset of the DKI data acquired at 3 b-values. It then iteratively and alternately updates the ADC and AKC until they are converged. This approach employs the technique of linear least square fitting to minimize estimation error in each iteration. In addition to the common physical and biological constrains that set the upper and lower boundaries of the ADC and AKC values, we use a smoothing procedure to ensure that estimation is robust. Quantitative comparisons between our AIS methods and the conventional methods of unconstrained nonlinear least square (UNLS) using both synthetic and real data showed that our unconstrained AIS method can significantly accelerate the estimation procedure without compromising its accuracy, with the computational time for a DKI dataset successfully reduced to only 1 or 2 min. Moreover, the incorporation of the smoothing procedure using one of our AIS methods can significantly enhance the contrast of AKC maps and greatly improve the visibility of details in fine structures. |
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| Keywords: | Diffusion kurtosis imaging Diffusion tensor imaging Parameter estimation Magnetic resonance imaging |
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