Student: Sara Neves Silva
MRI is an increasingly powerful tool used in fetal assessments to successfully manage pregnancies for the best neonatal outcomes. The main challenge in imaging the fetus is dealing with unpredictable motion. There are now more robust techniques available to deal with motion both at the time of image acquisition and post-processing. One very successful technique consists in ‘freezing’ motion by rapidly acquiring individual ‘slices’ and then sort them out/realign them during post-processing with slice-to-volume reconstruction methods [Jiang 2007]. Our fetal imaging group can now reliably use MRI to diagnose structural cardiac [Lloyd 2018], and flow abnormalities in the fetus [Roy 2013], alongside advanced fetal brain imaging [Story 2018, Ferrazzi 2018].
Magnetic Resonance spectroscopy (MRS) allows regional non-invasive quantification of metabolites and neurotransmitters levels, in the brain or elsewhere, providing direct information on brain energetics and cerebral viability/development; it is a widely used clinical and research tool in adults.
Cerebral fetal MRS has great potential to assess metabolic disorders, monitor brain development and the potentially harmful downstream effects on the developing brain of congenital heart disease or placental pathology.
Unfortunately, due to the commonly sustained fetal motion, using conventional MRS methodology, the current success rate of fetal MRS is rarely above 50% and relatively few fetal MRS studies have been published so far [Charles-Edwards 2010].
The main reason for this is that in MRS, to build sufficient signal (and signal-to-noise ratio, SNR) from a cuboidal volumes of interests (VOI) of few ml, many repeats/averages are necessary, and acquisition times of at least 5-10 minutes are common. Due to the size of the fetal brain, typical VOI volumes are even smaller.
With any fetal motion during this time, signal contamination from surrounding tissue and artefacts ensue; this often results in corrupted and unusable data. As a result, fetal MRS has not been adopted as a tool to help link the changes in physiology to abnormalities in brain development.
This project will develop a motion-robust MRS method, resilient to motion of the target anatomical VOI. The method will be based on the acquisition of relatively low-resolution 3D dual-echo volume navigator images acquired concurrently (or interleaved [Henningson 2014]) with each MRS acquisition, and within the same repetition time (TR; typically 2 seconds) [Bogner 2014].
The motion parameters extracted from extremely-fast registration of subsequently acquired navigator volumes, together with development of fast real-time feedback will allow the scanner to update the position of the VOI before each acquisition and maintain it in the same anatomical position irrespective of motion. The dual-echo data will produce field-map that will enable ‘re-shimming’, i.e. to maintain the magnetic field within the VOI as homogenous as possible, hence minimising spectral linewidths and enhancing SNR and spectral resolvability/quantification accuracy.
Monitoring the extent of motion, measuring data similarity between repeats and performing initial pre-processing online including frequency correction and summation [Rowland 2017], will enable an online quality check, i.e. assessment of the quality of the cumulative data collected. Automatic extension of data collection until data of sufficient signal to noise ratio for meaningful analysis will also be implemented.
With this methodology we aim to highly increase the success rate of fetal cerebral MRS and make it into a robust tool ready for clinical evaluation and eventually adoption into clinical routine fetal evaluation alongside other advanced MR methods.
• Bogner W, Gagoski B, Hess AT, Bhat H, Tisdall MD, van der Kouwe AJW, Strasser B, MarjaÅ„ska 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 Dec;103:290-302. doi: 10.1016/j.neuroimage.2014.09.032. Epub 2014 Sep 26.
• Charles-Edwards GD, Jan W, To M, Maxwell D, Keevil SF, Robinson R, Non-invasive detection and quantification of human foetal brain lactate in utero by magnetic resonance spectroscopy. Prenat Diagn. 2010 Mar;30(3):260-6. doi: 10.1002/pd.2463.
• 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. doi: 10.1002/mrm.27012. Epub 2017 Nov 28.
• 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.
• Hock A, Henning A, Motion correction and frequency stabilization for MRS of the human spinal cord, NMR Biomed. 2016 Apr;29(4):490-8. doi: 10.1002/nbm.3487. Epub 2016 Feb 11.
• Jiang S, Xue H, Glover A, Rutherford M, Rueckert D, Hajnal J, MRI of moving subjects using multi-slice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies. IEEE Trans Med Imaging 2007;26:967–980.
• Lloyd et al., Three-dimensional visualisation of the fetal heart using prenatal MRI with motioncorrected slice-volume registration, Lancet 2018, accepted, THELANCET=D-18-04346R1
• Rowland BC, Liao H, Adan F, Mariano L, Irvine J, Lin AP, Correcting for Frequency Drift in Clinical Brain MR Spectroscopy.J Neuroimaging. 2017 Jan;27(1):23-28. doi: 10.1111/jon.12388. Epub 2016 Sep 7.
• 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 Dec;70(6):1598-607. doi: 10.1002/mrm.24614. Epub 2013 Feb 4.
• 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 Mar;222:134-141. doi: 10.1016/j.ejogrb.2018.01.014. Epub 2018 Jan 31.