Fetal brain MRI

During acqusition of fetal brain magnetic resonance images (MRI), fetus is moving freely. The fetal brain MRI is therefore acquired as stacks of 2D slices, which can be acquired under one second. These slices are not aligned with respect to each other and can contain motion artefacts.

We developed a method for high-resolution reconstruction of fetal brain volumetric MRI. The method aligns the slices, excludes the motion-corrupted slices and matches the intensities in slices to remove inconsistencies.

The method is suitable for reconstruction of thick-slice clinical data as well as thin-slice data acquired with optimised sequences. Thin slice data and good approximation of point-spread function with which the data were acquired results in images with very high signal-to-noise ratio.

Top row: acquired data. Bottom row: reconstructed data.

The reconstruction software is now available for download here.

Recently, I have extended this motion correction method to reconstruct fetal diffusion MRI using spherical harmonics representation, that allows for modelling of crossing fibers.

Diffusion data also suffer distortion, we have proposed how to correct using joint motion and distortion correction it without acquiring any additional data or amending clinical sequences.

The code for reconstruction of fetal diffusion MRI is not available yet, but it will be in near future.

Relevant publications:

Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. M Kuklisova-Murgasova, G Quaghebeur, MA Rutherford, JV Hajnal, JA Schanbel. Medical Image Analysis 16(8), 2012. link (open access).

Higher order spherical harmonics reconstruction of fetal diffusion MRI with intensity correction. M Deprez, AN Price, D Christiaens, G Lockwood Estrin, L Cordero Grande, J Hutter, A Daducci, JD Tournier, MA Rutherford, S Counsell, M Bach Cuadra, JV Hajnal, IEEE Transactions on Medical Imaging, 39(4):1104-1113, 2020. pdf

Distortion correction in fetal EPI using non-rigid registration with a Laplacian constraint. M Kuklisova-Murgasova, G Lockwood-Estrin , RG Nunes, SJ Malik, MA Rutherford, D Rueckert, JV Hajnal. IEEE Transactions on Medical Imaging, 37(1):12-19, 2018. pdf