Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration

Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with methods in the literature in a cohort of 72 fetal datasets in the range of 20-36 weeks gestational age. Quantitative as well as radiological assessment suggest improved image quality and more accurate prediction of gestational age at scan is obtained when comparing to state of the art reconstruction methods. In addition, gestational age prediction results from our volumetric reconstructions are competitive with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of 0.618 weeks (R-2 = 0.958) is achieved when combining fetal brain and trunk information.