  Diffusion MRI measurements using hyperpolarized gases are generally acquired
during patient breath hold, which yields a compromise between achievable image
resolution, lung coverage and number of b-values. In this work, we propose a
novel method that accelerates the acquisition of MR diffusion data by
undersampling in both spatial and b-value dimensions, thanks to incorporating
knowledge about the signal decay into the reconstruction (SIDER). SIDER is
compared to total variation (TV) reconstruction by assessing their effect on
both the recovery of ventilation images and estimated mean alveolar dimensions
(MAD). Both methods are assessed by retrospectively undersampling diffusion
datasets of normal volunteers and COPD patients (n=8) for acceleration factors
between x2 and x10. TV led to large errors and artefacts for acceleration
factors equal or larger than x5. SIDER improved TV, presenting lower errors and
histograms of MAD closer to those obtained from fully sampled data for
accelerations factors up to x10. SIDER preserved image quality at all
acceleration factors but images were slightly smoothed and some details were
lost at x10. In conclusion, we have developed and validated a novel compressed
sensing method for lung MRI imaging and achieved high acceleration factors,
which can be used to increase the amount of data acquired during a breath-hold.
This methodology is expected to improve the accuracy of estimated lung
microstructure dimensions and widen the possibilities of studying lung diseases
with MRI.
