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Development of motion robust fetal MR Spectroscopy (MRS) - including machine learning and pulse sequence programming


Project Description

EPSRC Funded PhD scholarship available immediately, for start as soon as possible by October 2020.
Later starts in October 2020 can be offered to suitable candidates. However funding will in that case end by Sept 2023.
Jan 31st 2020 deadline for April 2020 start (3.5-year funding).
Apr 30th 2020 deadline for July 2020 start (3.2-year funding).
June 30th 2020 deadline for October 2020 start (3-year funding).


Location: King’s College London, Department of Bioengineering, School of Biomedical Engineering and Imaging Sciences (https://www.kcl.ac.uk/bmeis)

Short Project Description.

Measuring metabolite concentration in the fetal brain with MR Spectroscopy (MRS) has potential to assess the effects of placental dysfunction or alterations in the fetal circulation from congenital heart disease on brain development. However, due to continuous/unpredictable fetal motion, with standard MRS methodology, current success rate is low (50-70%).

We aim to develop a motion-robust MRS method resilient to fetal motion. This will be based on 3D volume navigator images collected concurrently with each MRS acquisition (every ~2 seconds), and fast real-time feedback (based on machine-learning algorithms) allowing adjustment of the VOI position to maintain it in the same anatomical position irrespective of motion. Monitoring extent of motion and data similarity between repeats will allow quality assessment of the cumulative data collected and extension of the acquisition until data of sufficient signal-to-noise ratio for meaningful analysis has been collected. The aim is for fetal MRS to become a usable, reliable and robust clinical investigation tool.

For a longer description, please see: https://www.kcl.ac.uk/health/study/studentships/div-studentships/beis/de-vita-pushparajah

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You will learn about, and use, machine learning algorithms, MR Imaging and MR spectroscopy pulse sequences, advanced data acquisition and analysis methods. You will be trained in MR-scanner software and hardware in collaboration with scanner manufacturers. You will be able to attend relevant post-graduate courses in artificial intelligence, machine learning, medical imaging and relevant healthcare technologies as well as other transferrable skills through the School’s extensive educational programme. You will also be aligned with a large cohort of PhD students enrolled in the Smart Medical Imaging CDT (https://www.imagingcdt.com/phd-programme/)

Funding Notes

3.5 year Full-time PhD studentship, for UK students and EU students fulfilling the 3-year UK residency requirement (including home/EU tuition fees, annual stipend and consumables).
Non-residential EU students receive fees-only funding.

Candidates are expected to have a BSc or MSc in Physics, Engineering or a similarly relevant subject.
Interested candidates are invited to contact the Project supervisor directly ( ) in the first instance. Then to apply through the KCL website.
A round of informal, followed by formal interviews will then be arranged.

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 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 motion-corrected 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.

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