Daniel J. West1, Gastao Cruz1, Olivier Jaubert1, Rui P. A. G. Teixeira1,2, Torben Schneider3, Jacques-Donald Tournier1,2, Jo Hajnal1,2, Claudia Prieto1, and Shaihan J. Malik1,2
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Centre for The Developing Brain, King's College London, London, United Kingdom, 3Philips Healthcare, Guildford, United Kingdom
Synopsis
Inhomogeneous
magnetisation transfer (ihMT) is a contrast mechanism that has shown high
specificity towards myelinated tissue. Contrast is typically generated using
sequences comprising a preparation phase with several RF saturation pulses,
followed by multiple readout periods for measurement. Here, we present a
transient acquisition scheme that alternates between periods of multi-band and
single-band RF pulses, to efficiently generate ihMT contrast during a single
data acquisition. Since signal is transiently varying throughout, we use a dictionary-based
low-rank inversion reconstruction method originally proposed for magnetic
resonance fingerprinting. Simulation, phantom and human in-vivo experiments are included.
Introduction
The recently
developed inhomogeneous magnetisation transfer (ihMT) contrast mechanism has
shown a high specificity towards myelinated tissue1,2. ihMT contrast generation
relies on using single- or dual-frequency off-resonance radiofrequency (RF)
saturation pulses that are usually applied during preparation phases prior to
readout periods for measurement3. We have previously developed
non-selective multi-band (MB) RF pulses that: (1) control magnetisation
transfer (MT) effects in variable flip angle relaxometry4 and (2) generate ihMT
contrast in a steady-state framework, through simultaneous free water excitation
and semisolid saturation5.
Building upon
these works and the fact that others have shown that ihMT contrast can be
increased by reducing duty cycle6, we now consider a transient-state
ihMT (TS-ihMT) sequence implementation. Figure 1 shows the sequence structure, with
alternating blocks of MB and single-band (1B) pulses. The on-resonance bands of
all pulses are identical, so the free water flip angle remains constant throughout;
a tissue with no MT or ihMT effect would give constant signal. However, tissues
with MT effects will respond transiently, as sketched in Figure 1.Methods
The sequence in
Figure 1 forms a cyclic steady-state that repeats many times: one cycle
(2B-1B-3B-1B) takes ~6 seconds for TR=5ms. ihMT contrast is related to the
signal difference between 2B and 3B pulse periods. We optimise properties of
this pulse cycle for efficiency of ihMT contrast generation (per square root of
acquisition time). To fully characterise the transient evolution of the
sequence, ~1000
images would be required, which is infeasible. However, temporal redundancies
in the evolution of magnetisation can be exploited with a temporal subspace
model reconstruction. To do this we employ a magnetic resonance fingerprinting
(MRF) style acquisition scheme using a radial k-space trajectory with low-rank
inversion (LRI) reconstruction technique from Assländer et al.7
Dictionary Creation: A dictionary of expected signal
evolutions was created for a range of model parameters (both free-pool and
MT/ihMT parameters) for balanced SSFP and SPGR variants of the sequence. For
bSSFP B0 variation was
additionally included. Singular value decomposition (SVD) suggests that the
dictionary can be well-characterised by few singular components.
Data Acquisition: A 3D tiny golden angle radial8, stack-of-stars k-space
trajectory with one spoke per RF pulse was implemented for both balanced and spoiled
readouts. Acquisition time was variable but used an integer number (one or two)
loops over the full cycle of all RF pulses (1200 pulses) per slice-encode
position.
Image Reconstruction: Based on a low-rank representation of
the dictionary, LRI replaces the reconstruction of the entire transient
sequence (~1000 images) by a reconstruction of six singular value images. Rather
than perform dictionary matching, the time-series data were processed to
yield MT metrics, where min and max indicate timepoints
corresponding to the minimum or maximum signals during these periods.
$$(1)ihMTR = \frac{|min(S_{2B})-min(S_{3B})|}{max(S_{1B})}$$ $$(2)MTR = \frac{min(S_{2B})+min(S_{3B})}{2max(S_{1B})}$$
Experiments were
conducted on a 1.5T Philips Ingenia MRI system. 3 phantoms were scanned: MnCl2-doped
water (0.05mM), bovine serum albumin (BSA) and prolipid 161 (PL161). One healthy male volunteer (age 32) was scanned;
sagittal view, isotropic resolution 1.5mm, total scan time of ~25
minutes for a 3D volume.Results
Optimisation
of efficiency requires modification of flip angle (FA), repetition time (TR),
RF pulse duration (τ), off-resonance frequency (∆f), nMB and n1B.
Figure 2 shows the ihMTR from TS-ihMT compared to a recently published
steady-state approach5 (referred to as
SS-ihMT for comparison), for WM-like tissue parameters3,6.
Single-slice
reconstructed phantom images from TS-ihMT are shown in Figure 3, alongside
reconstructed signal time courses for central regions-of-interest (ROIs) in
each tube, normalised by their mean values. Signals agree well with 1D encoded
measurements made without using the dictionary-based LRI.
Figure
4 shows preliminary results from TS-ihMT in-vivo experiments, using both bSSFP
and SPGR; Figure 5 shows corresponding singular images used in their
reconstruction and the log of singular values of each dictionary to highlight
the low-rank nature of the problem.Discussion
Figure
2 demonstrates that the transient method can generate an approximate 2-fold
increase in the peak ihMTR contrast compared to a steady-state approach. Phantom
data show that the LRI reconstruction produces time courses that closely match
1D measurements. The phantoms showed expected contrast with no MT or ihMT in
water, MT only in BSA and ihMTR of approximately 25% measured in PL161.
In-vivo
maps show GM-WM contrast as expected in both MTR and ihMTR, and the time
profiles are as expected. Although
acquisition of this data took ~25 minutes, the cyclic steady-state and LRI
reconstruction are flexible and acquisition time can be optimised. The time
profiles for CSF show some variability when none is expected, mainly in the
spoiled case; this is attributed to residual reconstruction errors which
generally affect the ihMTR map from the spoiled data. Potential improvements
will include updating the reconstruction to include motion estimation and
hardware imperfection, adding unmodelled effects such as diffusion/flow/partial
volume to the dictionaries, and considering alternative k-space ordering
schemes. Conclusions
Our
results show that the TS-ihMT approach can be used to flexibly measure MT and
ihMT contrast. Though we have so far used the reconstructed time course data to
create ihMTR/MTR maps, the time profiles contain much richer information and we
will explore dictionary matching and related approaches to extracting
quantitative tissue parameter estimates.Acknowledgements
This work was funded
by the King’s College London & Imperial College London EPSRC Centre for
Doctoral Training in Medical Imaging [EP/L015226/1] and supported by the
Wellcome EPSRC Centre for Medical Engineering at King's College London [WT
203148/Z/16/Z] and by the National Institute for Health Research (NIHR)
Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust
and King’s College London. The views expressed are those of the authors and not
necessarily those of the NHS, the NIHR or the Department of Health.References
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