Fetal growth restriction (FGR) is a prevalent pregnancy condition
characterised by failure of the fetus to reach its genetically predetermined
growth potential. We explore the application of model fitting techniques,
linear regression machine learning models, deep learning regression, and
Haralick textured features from multi-contrast MRI for multi-fetal organ
analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets
(using a combined T2-diffusion scan) for 12 normally grown and 12 FGR
gestational age (GA) matched pregnancies. We applied the Intravoxel Incoherent
Motion Model and novel multi-compartment models for MRI fetal analysis, which
exhibit potential to provide a multi-organ FGR assessment, overcoming the
limitations of empirical indicators - such as abnormal artery Doppler findings
- to evaluate placental dysfunction. The placenta and fetal liver presented key
differentiators between FGR and normal controls (decreased perfusion, abnormal
fetal blood motion and reduced fetal blood oxygenation. This may be associated
with the preferential shunting of the fetal blood towards the fetal brain.
These features were further explored to determine their role in assessing FGR
severity, by employing simple machine learning models to predict FGR diagnosis
(100\% accuracy in test data, n=5), GA at delivery, time from MRI scan to
delivery, and baby weight. Moreover, we explored the use of deep learning to
regress the latter three variables. Image texture analysis of the fetal organs
demonstrated prominent textural variations in the placental perfusion fractions
maps between the groups (p<0.0009), and spatial differences in the incoherent
fetal capillary blood motion in the liver (p<0.009). This research serves as
a proof-of-concept, investigating the effect of FGR on fetal organs