Journal article
Challenges in glucoCEST MR body imaging at 3 Tesla
Quantitative imaging in medicine and surgery, Vol.9(10), pp.1628-1640
10/01/2019
DOI: 10.21037/qims.2019.10.05
PMCID: PMC6828585
PMID: 31728307
Abstract
Background: The aim of this study was to translate dynamic glucose enhancement (DGE) body magnetic resonance imaging (MRI) based on the glucose chemical exchange saturation transfer (glucoCEST) signal to a 3 T clinical field strength.
Methods: An infusion protocol for intravenous (i.v.) glucose was optimised using a hyperglycaemic clamp to maximise the chances of detecting exchange-sensitive MRI signal. Numerical simulations were performed to define the optimum parameters for glucoCEST measurements with consideration to physiological conditions. DGE images were acquired for patients with lymphomas and prostate cancer injected i.v. with 20% glucose.
Results: The optimised hyperglycaemic clamp infusion based on the DeFronzo method demonstrated higher efficiency and stability of glucose delivery as compared to manual determination of glucose infusion rates. DGE signal sensitivity was found to be dependent on T-2, B-1 saturation power and integration range. Our results show that motion correction and B-0 field inhomogeneity correction are crucial to avoid mistaking signal changes for a glucose response while field drift is a substantial contributor. However, after B-0 field drift correction, no significant glucoCEST signal enhancement was observed in tumour regions of all patients in vivo.
Conclusions: Based on our simulated and experimental results, we conclude that glucose-related signal remains elusive at 3 T in body regions, where physiological movements and strong effects of B-1(+) and B-0 render the originally small glucoCEST signal difficult to detect.
Details
- Title: Subtitle
- Challenges in glucoCEST MR body imaging at 3 Tesla
- Creators
- Mina Kim - UCL Institute of NeurologyFrancisco Torrealdea - University College HospitalSola Adeleke - UCL Centre for Medical Imaging, London, UK.Marilena Rega - University College HospitalVincent Evans - UCL Centre for Medical Imaging, London, UK.Teresita Beeston - UCL, Ctr Med Imaging, London, EnglandKaterina Soteriou - UCL Centre for Medical Imaging, London, UK.Stefanie Thust - National Hospital for Neurology and NeurosurgeryAaron Kujawa - UCL Institute of NeurologySachi Okuchi - UCL Institute of NeurologyElizabeth Isaac - UCL Centre for Medical Imaging, London, UK.Wivijin Piga - UCL Centre for Medical Imaging, London, UK.Jonathan R. Lambert - University College HospitalAsim Afaq - University College HospitalEleni Demetriou - UCL Institute of NeurologyPratik Choudhary - King's College Hospital NHS Foundation TrustKing Kenneth Cheung - University College London Hospitals NHS Foundation TrustSarita Naik - University College HospitalDavid Atkinson - UCL Centre for Medical Imaging, London, UK.Shonit Punwani - UCL Centre for Medical Imaging, London, UK.Xavier Golay - UCL Institute of Neurology
- Resource Type
- Journal article
- Publication Details
- Quantitative imaging in medicine and surgery, Vol.9(10), pp.1628-1640
- DOI
- 10.21037/qims.2019.10.05
- PMID
- 31728307
- PMCID
- PMC6828585
- NLM abbreviation
- Quant Imaging Med Surg
- ISSN
- 2223-4292
- eISSN
- 2223-4306
- Publisher
- Ame Publ Co
- Number of pages
- 14
- Grant note
- National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre (UCLH BRC) MR/M009106/1 / MRC; UK Research & Innovation (UKRI); Medical Research Council UK (MRC) UCL Grand Challenge scholarship 667510 / European Union; European Commission
- Language
- English
- Date published
- 10/01/2019
- Academic Unit
- Radiology
- Record Identifier
- 9984318714302771
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