Whole lung morphometry with 3D multiple b‐value hyperpolarized gas MRI and compressed sensing
نویسندگان
چکیده
PURPOSE To demonstrate three-dimensional (3D) multiple b-value diffusion-weighted (DW) MRI of hyperpolarized 3 He gas for whole lung morphometry with compressed sensing (CS). METHODS A fully-sampled, two b-value, 3D hyperpolarized 3 He DW-MRI dataset was acquired from the lungs of a healthy volunteer and retrospectively undersampled in the ky and kz phase-encoding directions for CS simulations. Optimal k-space undersampling patterns were determined by minimizing the mean absolute error between reconstructed and fully-sampled 3 He apparent diffusion coefficient (ADC) maps. Prospective three-fold, undersampled, 3D multiple b-value 3 He DW-MRI datasets were acquired from five healthy volunteers and one chronic obstructive pulmonary disease (COPD) patient, and the mean values of maps of ADC and mean alveolar dimension (LmD ) were validated against two-dimensional (2D) and 3D fully-sampled 3 He DW-MRI experiments. RESULTS Reconstructed undersampled datasets showed no visual artifacts and good preservation of the main image features and quantitative information. A good agreement between fully-sampled and prospective undersampled datasets was found, with a mean difference of +3.4% and +5.1% observed in mean global ADC and LmD values, respectively. These differences were within the standard deviation range and consistent with values reported from healthy and COPD lungs. CONCLUSIONS Accelerated CS acquisition has facilitated 3D multiple b-value 3 He DW-MRI scans in a single breath-hold, enabling whole lung morphometry mapping. Magn Reson Med 77:1916-1925, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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