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AI-enabled Imaging, Emerging Imaging

AI enabled motion corrected quantitative MRI of the fetal brain

Project ID: 2020_006

Student: Denis Prokopenko

1st Supervisor: Jo Hajnal, King’s College London
2nd Supervisor: Daniel Rueckert, Imperial College London
Clinical Champion: Mary Rutherford, King’s College London
Industry Supervisor: Johan van den Brink and Maarten Versluis

Aim of the PhD Project:

During the second half of pregnancy the human brain undergoes exuberant growth, with both microscopic and macropic changes happening rapidly. In consequence the MRI relaxation times (T1 and T2), which are key tissue properties, change substantailly. These relaxation times can be used to characterise development, particularly in white matter. Relaxometry is a well established tool for assessing the brain in health and disease, however, there are currently no established methods for doing this in the fetus in utero. The aim of the project is to develop reliable motion tolerant methods for measuring T1 and T2 in the moving fetus, and to deploy these to conduct systematic quantitative studies over gestational age and so provide normative benchmark data.

Project Description / Background:

Our group has substantial track record in developing and deploying fully motion corrected methods for fetal 3D brain imaging in utero. A highly effective strategy is snapshot imaging of individual slices or small groups of slices, acquired fast enough to freeze fetal motion, which are then realigned to correct for changes in head position using slice to volume reconstruction (SVR)1. Past research has included developing comprehensive methods for anatomical1, diffusion2 and functional3 imaging. We have also been able to measure the relaxation time T2* by fitting a standard physics signal model directly to single shot slices acquired using a dedicated multi-echo methodology4. This approach allows the model fiting to be separated from motion correction. Methods for measuring T1 and T2 in moving tissue have been developed for the heart (ex utero) but these generally rely the constrained repetative nature of cardiac motion. Measuring these parameters in fetal brain  constitutes a special challenge because of the unpredictable nature of fetal motion. These parameters are also harder to measure than T2* as fitting the appropriate relaxometry models is likely to require combination of images with different contrasts acquired over multiple shots and controlling the spin physics is more complex. It is also critical that any methods developed have low RF power deposition, control risk of peripheral nerve stimulation in the mother and are time efficient as prolonged fetal examinations can be challenging for pregnant mothers, and increasingly the scope of these examinations is expanding as more comprehensive MRI methods are developed. The project will thus involve both research into novel sequences to achieve optimised acquisition strategies and also development of reconstruction methods that allow joint estimation of motion paramters and the desired relaxation paramters. Past reconstruction methods have been extremely computationally demanding and hence slow, so we propose to explore machine learning methods to shift the computational burden from examination time to a training phase allowing a much more clinically acceptable rapid image generation. This will build on our prior work on Deep Learning based reconstruction of cine cardiac images5, which delivered state of the art performance, and has recently started to include motion correction as part of the reconstruction6. We have also explored the application of Deep Leaning methods to SVR for anatomical imaging7, so have a strong basis from which to build the current project. If robust quantitative T1 and T2 mapping can be achived at high enough resolution, then it will become feasible to map oxygen extraction by exploiting the specific relaxation properties of haemaglobin8. Recent results on fetal angiography9 provide encouraging evidence that it is feasible to resolve fetal vessels (see illustrative figure), although achieving this with quantitative methods will be a stretch target.

Figure 1: Description below the image.

Figure 1: Motion corrected diffusion fetal brain imaging with spherical harmonic model fitting.


  1. Kuklisova-Murgasova, et al (2012). Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. MEDIA, 16(8), 1550–1564.
  2. Jiang, S., et al (2009). Diffusion Tensor Imaging (DTI) of the brain in moving subjects: Application to in-utero fetal and ex-utero studies. MRM, 62(3), 645–655.
  3. Ferrazzi, G., et al. (2014). Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction. NeuroImage, 101, 555–568.
  4. Vasylechko, S., et al V. (2015). T2* relaxometry of fetal brain at 1.5 Tesla using a motion tolerant method. MRM, 73(5), 1795–1802.
  5. Schlemper, J., et al (2018). A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE TMI, 37(2), 491–503.
  6. Current PhD project supervised by Ruekert and Hajnal
  7. Hou, B., et al. (2018). 3-D Reconstruction in Canonical Co-Ordinate Space from Arbitrarily Oriented 2-D Images. IEEE TMI, 37(8), 1737–1750.
  8. Portnoy, S., Osmond, M., Zhu, M. Y., Seed, M., Sled, J. G., & Macgowan, C. K. (2017). Relaxation properties of human umbilical cord blood at 1.5 Tesla. MRM, 77(4), 1678–1690.
  9. Jackson L et al Fetal and placental MR angiography using continuous stable state SWEEP and slice to volume registration (SVR), in submission to MRM

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