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AI-enabled Imaging, Emerging Imaging

AI enabled ultrahigh field body MRI: application to bone marrow cancer imaging

Project ID: 2019_012

1st supervisor: Shaihan Malik, King’s College London
2nd supervisor: Jorge Cardoso, King’s College London

MRI plays an important role in the management of cancer patients. However, its sensitivity for detecting cancer is limited in some body areas e.g. lung, skeleton, by their intrinsically low MRI signal.  New ultrahigh field strength (7T) scanners can potentially achieve much higher sensitivity and spatial resolution than existing technology. This objective of this project is to bring the benefits of these new scanners to  bone-marrow imaging; we hypothesize that this improved sensitivity will detect a low burden of bone marrow cancer or metastases, changing patient management at an earlier stage.

Challenges with 7T body MRI
Body imaging at 7T is a currently serious challenge due to the highly non-uniform radiofrequency (RF) fields produced at the 300MHz resonance frequency, meaning that parallel transmission RF (PTx) systems are needed to produce useful images. Much progress has been made in using PTx in recent years1 – within our group we have developed new methods for uniform T1-weighted imaging2, T2-weighted turbo-spin-echo (TSE) 3, FLAIR4 and rapid gradient echo sequences5, for example.

However these cutting edge methods typically require bespoke measurements and calculations to be performed for each new patient, creating a workflow problem that seriously limits clinical utility at present.

This project: AI enabled imaging
The current approach of treating every new patient individually overlooks a key redundancy: while human subjects are all different, they are also similar in many ways. This project will use emerging artificial intelligence (AI) methods to replace the time-consuming calibration/calculation steps, by learning a direct relationship between imaging parameters and readily available information such as pilot scans. The result will be transformative for 7T body MRI; we aim to develop clinical quality T1, T2 and diffusion-weighted 7T-MRI sequences, focusing on cancer detection in the pelvis and thoraco-lumbar spine.

1. Padormo, F., Beqiri, A., Hajnal, J. V. & Malik, S. J. Parallel transmission for ultrahigh-field imaging. NMR Biomed. 29, 1145–1161 (2015).
2. Malik, S. J., Keihaninejad, S., Hammers, A. & Hajnal, J. V. Tailored excitation in 3D with spiral nonselective (SPINS) RF pulses. Magn. Reson. Med. 67, 1303–1315 (2012).
3. Sbrizzi, A., Beqiri, A., Hoogduin, H., Hajnal, J. & Malik, S. Local SAR Minimization of Turbo Spin-Echo Sequences by Dynamic RF Shimming. in ISMRM 1326 (2017).
4. Beqiri, A., Hoogduin, H., Sbrizzi, A., Hajnal, J. V. & Malik, S. J. Whole-brain 3D FLAIR at 7T using direct signal control. Magn. Reson. Med. 1–13 (2018). doi:10.1002/mrm.27149
5. Beqiri, A., Price, A. N., Padormo, F., Hajnal, J. V. & Malik, S. J. Extended RF shimming: Sequence-level parallel transmission optimization applied to steady-state free precession MRI of the heart. NMR Biomed. e3701 (2017). doi:10.1002/nbm.3701

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