2020 CME Newsletter

Dear All,

As we come to the end of this challenging year we’d like to thank all of the remarkable efforts our community has made.

From the speed with which Covid-related research projects and studies started to the change in working practices and the support and resilience shown whilst combating the pandemic. The breadth of research at the Centre has allowed us to be agile and our location within a major hospital remains our key strength.

The Centre has continued to make great progress in all our research themes and clinical challenges. We have seen success in our major infrastructure projects. Our Radiofrequency lab is in operation and our new HPC and AI platform, MONAI are online. Development of new radiotracers progresses well and we remain on course to have first in man studies.

Looking forward to next year we eagerly anticipate work on our low-field and ultra-low field initiatives getting underway, installation of our head-only MRI system and operation of our Surgical and Interventional Engineering facilities and Imaging Bioresource starting.

Here is our 2020 CME Newsletter

Best wishes,
Reza & Shalini

Silent MRI

Figure 1: (Top)Enhanced MTR eMTR demonstrating strong white to gray matter contrast. (Bottom) Myelin weighted inverse inhomogeneous MTR showing strong myelin contrast. Reproduced from Ref [2], cropped from original figure.

Magnetic Resonance Imaging (MRI) is an important step in many clinical pathways, and a crucial resource in brain imaging research. Currently, up to 37% of patients undergoing MRI report moderate to high anxiety levels, with reasons including claustrophobia, loud acoustic noise, and length of scan, while up to 30% of in-patient or emergency MR examinations are corrupted beyond use due to motion artefacts, and 14% of subjects require sedation[1]. These issues waste significant quantities of money and can make recruitment of patients into long-term studies difficult. A simple way to reduce anxiety, and hence improve the patient experience, is to reduce the acoustic noise level of an MR scan. 

The physics team in the Department of Neuroimaging at Denmark Hill have together with collaborators at GE Healthcare pioneered development of novel MRI techniques with reduced acoustic noise. In the first half of 2020, we saw several projects in silent MRI come to a conclusion, marking an important step forward in our research in silent MRI. 

The project is largely divided into development of silent structural and silent functional MRI methods, both of which have demonstrated impressive leaps forward this year. 

Emil Ljungberg successfully defended his PhD thesis which focused on how to generate useful image contrast with silent MRI. Part of his thesis was published in Magnetic Resonance in Medicine, demonstrating silent T1 mapping using the VFA method [2]. Emil is now working on implementing motion correction into silent MRI in a postdoctoral fellowship supported by the CME. 

Postdoctoral CME fellow Dr. Tobias Wood published his first silent MRI paper, focusing on magnetisation transfer (MT), and more specifically on myelin-weighted imaging using the recently proposed inhomogeneous MT (ihMT) contrast [3]. 

Nikou Damestani, an NIHR Maudsley BRC PhD student, is working on the development of silent fMRI using a sequence known as Looping Star. She presented initial results of using Looping Star with an auditory oddball paradigm, highlighting the benefits of minimising the confound of acoustic noise [4]. 

In collaboration with GE Healthcare, our team has also demonstrated the utility of a multi-contrast silent method which can capture T1, T2, and proton density images in one combined acquisition[5]. 

The team is now busy wrapping up the year with some really exciting results to share early next year. Watch this space! 


[1] Nguyen, X. V. et al. Prevalence and Financial Impact of Claustrophobia, Anxiety, Patient Motion, and Other Patient Events in Magnetic Resonance Imaging. Topics in Magnetic Resonance Imaging 29, 125–130 (2020). 
[2] E. Ljungberg et al., “Silent T1 mapping using the variable flip angle method with B1 correction,” MagnReson. Med., vol. 84, no. 2, pp. 813–824, 2020. 
[3T. C. Wood et al., “Silent myelin-weighted magnetic resonance imaging [version 2; peer review: 2 approved , 2 approved with reservations],” Wellcome Open Res., vol. 5, no. 74, pp. 1–35, 2020. 
[4] N. L. Damestani et al., “Silent functional MRI for novel sound discrimination using the auditory oddball paradigm,” in Proc. Intl. Soc. Mag. Reson. Med 28, 2020, p. 3894. 
[5] F. Wiesinger et al., “PSST… Parameter mapping Swift and SilenT,” in Proc. Intl. Soc. Mag. Reson. Med 28, 2020. 
Figure 1: (Top)Enhanced MTR eMTR demonstrating strong white to gray matter contrast. (Bottom) Myelin weighted inverse inhomogeneous MTR showing strong myelin contrast. Reproduced from Ref [2], cropped from original figure.

Figure 1: (Top)Enhanced MTR eMTR demonstrating strong white to gray matter contrast. (Bottom) Myelin weighted inverse inhomogeneous MTR showing strong myelin contrast. Reproduced from Ref [2], cropped from original figure.

Multi-Contrast Cardiac Imaging

Multi-Contrast Cardiac Imaging

Magnetic resonance imaging (MRI) is considered the gold standard for the assessment of cardiac anatomy, left ventricular (LF) function (CINE-MRI), myocardial viability (LGE-MRI), myocardial tissue characterization (T1 and T2 relaxation time mapping) and perfusion (MR-perfusion) due to its excellent soft tissue contrast, high spatial resolution and lack of ionizing radiation according to a Society for Magnetic Resonance (SCMR) expert consensus statement. However, a key limitation of the current MRI acquisition scheme is that all imaging sequences (e.g. CINE, LGE, T1 and T2 mapping, coronary MR angiography (MRA), etc.) are acquired sequentially, in different geometric orientations, at different breath-hold positions or using time inefficient navigator gating methods.

To address this limitation, we developed a novel non-invasive, radiation-free and contrast-free Magnetic Resonance Imaging (MRI) framework for comprehensive assessment of coronary and myocardial disease in a single multi-contrast and multi-parametric high-resolution 3D whole-heart scan.

This novel framework includes advanced respiratory motion correction methods which include beat-to-beat translational motion correction using image navigators (iNAV) and bin-to-bin non-rigid motion correction using the imaging data itself thereby allowing for shorter and predictable scan time resulting in improved image quality. In addition, novel imaging sequences were developed which allow the simultaneous visualization of the coronary vessels, coronary thrombus, high intensity plaque and myocardial scar (BOOST sequence) (1-3). These techniques now have been combined with advanced undersampling reconstruction techniques (ORCCA (4) and PROST (5)), which allow respiratory motion resolved reconstruction and highly undersampled reconstruction (3-4 fold) of non-rigid motion corrected whole heart coronary MR angiography (CMRA) datasets(6). To enable multi centric clinical validation we have developed works in progress packages (WIPs) together with Siemens Healthineers for our CMRA and BOOST sequence. The CMRA WIP also includes the option of performing free-breathing high-resolution motion corrected 3D myocardial viability imaging with and without black blood option, which is important for the detection of small infarctions or arrhythmic substrate. The multi-contrast BOOST sequence has also been extended to allow joint T1/T2 mapping(7) and was combined with the latest motion correction and image reconstruction developments to further increase image resolution and shorten scan time. We also have developed a motion corrected free running 3D whole heart T1 (8) and joint T1/T2 mapping technique(9) for simultaneous assessment of fibrosis and edema and which is based on a 3D radial trajectory and a low rank patched based reconstruction. All sequences are currently tested in patients with cardiovascular disease. Specifically, we have scanned 50 patients referred from the CTCA list with our high resolution CMRA protocol (0.9mm3) with the CMRA images approaching CT image quality but without the need for radiation or nephrotoxic contrast agents. The highlight of this clinical study is that all CMRA were completed successfully and that 97% of the proximal and 94% of the middle coronary segments were of diagnostic image quality (vs 99% and 98% for CTCA). Specificity and negative predicative value for identification of coronary artery disease were 93-98% and 95-100% for LM, LAD, RCA and LCx.

  1. G. Ginami et al., Simultaneous bright- and black-blood whole-heart MRI for noncontrast enhanced coronary lumen and thrombus visualization. Magn Reson Med 79, 1460-1472 (2018).
  2. G. Ginami et al., 3D whole-heart phase sensitive inversion recovery CMR for simultaneous black-blood late gadolinium enhancement and bright-blood coronary CMR angiography. J Cardiovasc Magn Reson 19, 94 (2017).
  3. G. Cruz, D. Atkinson, M. Henningsson, R. M. Botnar, C. Prieto, Highly efficient nonrigid motion-corrected 3D whole-heart coronary vessel wall imaging. Magn Reson Med, (2016).
  4. T. Correia et al., Optimized respiratory-resolved motion-compensated 3D Cartesian coronary MR angiography. Magn Reson Med 80, 2618-2629 (2018).
  5. A. Bustin et al., Five-minute whole-heart coronary MRA with sub-millimeter isotropic resolution, 100% respiratory scan efficiency, and 3D-PROST reconstruction. Magn Reson Med 81, 102-115 (2019).
  6. A. Bustin et al., 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. J Cardiovasc Magn Reson 22, 24 (2020).
  7. G. Milotta et al., 3D whole-heart isotropic-resolution motion-compensated joint T1 /T2 mapping and water/fat imaging. Magn Reson Med, (2020).
  8. H. Qi et al., Free-running 3D whole heart myocardial T1 mapping with isotropic spatial resolution. Magn Reson Med 82, 1331-1342 (2019).
  9. H. Qi et al., Free-running simultaneous myocardial T1/T2 mapping and cine imaging with 3D whole-heart coverage and isotropic spatial resolution. Magn Reson Imaging 63, 159-169 (2019).

Launch of MONAI

Earlier this year the London Medical Imaging & AI Centre for Value Based Healthcare and the School of Biomedical Engineering & Imaging Sciences at King’s College London, in partnership with industry leader NVIDIA, launched the MONAI framework. The development of this open-source toolkit for deep learning in healthcare imaging was supported by CME Software Architect Eric Kerfoot and Research Associate Richard Brown. Learn more.