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AI-enabled Imaging, Emerging Imaging

A new window into the uterus to visualise fetal lipid metabolism using magnetic resonance technology

Project ID: 2021_020

1st Supervisor: Enrico De Vita, King’s College London
2nd Supervisor: Po-Wah So, King’s College London
Clinical Champions: Catherine Williamson and Mary Rutherford, King’s College London
Additional Supervisors: Jo Hajnal and Jana Hutter, King’s College London

Aim of the PhD Project:

  1. Development of a novel non-invasive robust and reliable MRI and MR Spectroscopy methodology, incorporating effective motion correction, to quantify:
    • fetal liver/placental fat content
    • fetal adipose tissue
  2. Implementation of the optimised technique on a clinical population (intrahepatic cholestasis of pregnancy, ICP; gestational diabetes mellitus, GDM).

Project Description / Background:

Over the last 15 years, fetal MRI has grown into a robust modality able to provide detailed images of the fetal brain [Story 2018, Ferrazzi 2018], heart [Roy 2013, Lloyd 2018] and placenta [Hutter 2019] allowing the detection of structural and vascular abnormalities. However, in a number of situations, more invasive and potentially risky procedures are required such as amniocentesis/ cordocentesis sampling, e.g. for metabolic analysis. Magnetic Resonance spectroscopy (MRS) can be used to non-invasively quantify metabolites, including lipids [Liimatainen 2006] and neurotransmitter levels, informing on organ energetics and viability/development [Oz 2014]. While such methods are widely used in adults, fetal MRS is rarely performed prenatally as it is challenging. Conventional MRS acquisitions are easily corrupted by physiological maternal (including respiratory/bowel) and fetal movements; the higher resolutions required for small fetal organs, need longer acquisition times that augment motion artefacts; culminating in low examination success rates.

Intrahepatic cholestasis of pregnancy (ICP) and gestational diabetes mellitus (GDM) are complications of pregnancy. ICP is a metabolic disease that impairs bile acid homeostasis and is associated with impaired metabolic health (increased BMI and waist/hip girth) in the offspring. In both conditions, maternal and fetal circulating glucose and lipid concentrations are elevated, increasing the risk of pre-eclampsia. Umbilical serum lipids (from blood collected after birth) are elevated in the offspring of mothers with ICP and GDM [Papacleovoulou 2013]. Preclinical studies also showed increased lipids in the placenta and fetal organs, prenatally.
Having safe/non-invasive access to fetal/placental lipid level measurements would allow determination of the role of maternal ICP/GDM on modulating fetal lipid metabolism, and also monitoring of interventions to attenuate ICP/GDM in the mother (e.g. with insulin, metaformin or ursodeoxylic acid for GDM). However, cordocentesis is risky and cannot be used as a research tool.

This project will develop a novel motion-robust MRS/MRI method that will provide this much needed non-invasive window to assess fetal/placental lipids in vivo. Application of this method will enhance understanding of lipid metabolism in ICP/GDM and result in improved pregnancy management and offspring health.

Our proposed technique will include interleaving snapshot low-resolution 3D volume navigator MRI images with MRS data collection occurring every ~2 seconds [Henningson 2014]. Using deep- learning algorithms for registration [Ebner 2020], the navigator data will allow to: track the organ of interest and use real-time feedback to update the acquisition geometry for each MRS data readout; enable dynamic ‘re-shimming’, hence minimising spectral linewidths, enhancing spectral resolvability/quantification accuracy and SNR [Bogner 2014]. MRI methods such as Dixon and IDEAL CPMG [Sinclair 2016] will also be used for whole-body adipose tissue quantification. Dr De Vita has extensive experience in perinatal MRS and MRI in-vivo, Dr So has extensive experience of MRI and MRS of obesity, diabetes and non-alcoholic fatty liver disease models (So et al., 2007; 2016).

References:

  • Bogner W, Gagoski B, Hess AT, Bhat H, Tisdall MD, van der Kouwe AJW, Strasser B, Marjańska M, Trattnig S, Grant E, Rosen B, Andronesi OC, 3D GABA imaging with real-time motion correction, shim update and reacquisition of adiabatic spiral MRSI, Neuroimage. 2014;103:290-302.
  • Ebner M, Wang G, Li W, Aertsen M, Patel PA, Aughwane R, Melbourne A, Doel T, Dymarkowski S, De Coppi P, David AL, Deprest J, Ourselin S, Vercauteren T. An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. Neuroimage. 2020 Feb 1;206:116324.
  • Ferrazzi G, Price AN, Teixeira RPAG, Cordero-Grande L, Hutter J, Gomes A, Padormo F, Hughes E, Schneider T, Rutherford M, Kuklisova Murgasova M, Hajnal JV, An efficient sequence for fetal brain imaging at 3T with enhanced T1 contrast and motion robustness. Magn Reson Med. 2018 Jul;80(1):137-146.
  • Henningsson M, Prieto C, Chiribiri A, Vaillant G, Razavi R, Botnar RM, Whole‐heart coronary MRA with 3D affine motion correction using 3D image‐based navigation, Magnetic resonance in medicine 2014, 71 (1), 173-181.
  • Hutter J et al., Multi-modal functional MRI to explore placental function over gestation.Magn Reson Med. 2019;81(2):1191-1204.
  • Liimatainen T et al., Identification of mobile cholesterol compounds in experimental gliomas by (1)H MRS in vivo: effects of ganciclovir-induced apoptosis on lipids. FEBS Lett. 2006;580(19):4746- 50.
  • Lloyd et al., Three-dimensional visualisation of the fetal heart using prenatal MRI with motion- corrected slice-volume registration, Lancet 2018, accepted, THELANCET=D-18-04346R1
  • Morrow JM, Sinclair CD, Fischmann A, Machado PM, Reilly MM, Yousry TA, Thornton JS, Hanna MG, MRI biomarker assessment of neuromuscular disease progression: a prospective observational cohort study. Lancet Neurol. 2016;15(1):65-77.
  • Oz G et al., Clinical proton MR spectroscopy in central nervous system disorders. Radiology 2014 270(3):658-79.
  • Papacleovoulou G, Abu-Hayyeh S, Nikolopoulou E, Briz O, Owen BM, Nikolova V, Ovadia C, Huang X, Vaarasmaki M, Baumann M, Jansen E, Albrecht C, Jarvelin MR, Marin JJ, Knisely AS, Williamson C. Maternal cholestasis during pregnancy programs metabolic disease in offspring. J Clin Invest. 2013 Jul;123(7):3172-81.
  • Roy CW, Seed M, van Amerom JF, Al Nafisi B, Grosse-Wortmann L, Yoo SJ, Macgowan CK, Dynamic imaging of the fetal heart using metric optimized gating, Magn Reson Med. 2013;70(6):1598-607.
  • Rowan JA et al., Metformin in Gestational Diabetes: The Offspring Follow-Up (MiG TOFU): Body composition at 2 years of age. Diabetes Care 2011;34:2279– 84.
  • Sinclair CD, Morrow JM, Janiczek RL, Evans MR, Rawah E, Shah S, Hanna MG, Reilly MM, Yousry TA, Thornton JS. Stability and sensitivity of water T2 obtained with IDEAL-CPMG in healthy and fat- infiltrated skeletal muscle. NMR Biomed. 2016 Dec;29(12):1800-1812.
  • Story L, Hutter J, Zhang T, Shennan AH4, Rutherford M, The use of antenatal fetal magnetic resonance imaging in the assessment of patients at high risk of preterm birth, Eur J Obstet Gynecol Reprod Biol. 2018;222:134-141.
  • So PW, Ashraf A, Durieux AMS, Crum WR, Bell JD. 2018
  • So PW, Yu WS, Kuo YT, Wasserfall C, Goldstone AP, Bell JD, Frost GS. Impact of resistance starch on body fat patterning and central appetite regulation, PLoS one 2007; 2(12): e1309.

 

Figure 1: Impact of maternal ursodeoxylic acid (UCDA) treatment on fetal lipid profiles of women with Intrahepatic choleostasis of pregnancy (ICP) [from Williamson et al., unpublished]. Cholesterol, free fatty acids (FFA) and triglycerides (TG) were measured from umbilical cord serum in female and male fetuses of women with ICP. Error bars represent standard error of the mean (SEM). Data were analysed by multiple measures of ANOVA followed by Neuman Keul’s post-hoc testing. *P<0.05, n=8-10.

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