Vruti Dattani1, Tim Bray2, Alan Bainbridge3, and Margaret A Hall-Craggs2
1Royal Free Hospital, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom, 3Department of Medical Physics, University College London Hospitals, London, United Kingdom
Synopsis
Whole
body MRI (WB-MRI) is increasingly used to image the skeleton in haematological
diseases such as multiple myeloma (MM) and inflammatory disorders such as
spondyloarthritis. WB-MRI can be used to acquire fat fraction (FF) maps, which
can assess disease severity and treatment response. However, patients with bone
pain find it difficult to lie in the scanner for long periods, necessitating
the use of parallel imaging to accelerate the acquisition. The aim of this
study was to determine the extent to which parallel imaging causes noise artifacts
and fat-water swaps in FF maps, and to assess their impact on FF measurements.
Purpose
Whole
body MRI (WB-MRI) is increasingly used to image the skeleton in haematological
diseases such as multiple myeloma (MM) and inflammatory disorders such as
spondyloarthritis. Chemical-shift encoded MRI (CSE-MRI) [1] can be used to acquire fat fraction
(FF) maps of the whole body, and can assess disease burden and treatment
response [2,3]. However, patients with MM and
spondyloarthritis often suffer from bone pain, making it difficult for them to
lie still in the scanner for extended periods and necessitating the use of
parallel imaging techniques [4]. Although generally effective,
parallel imaging can cause non-uniform noise artifacts [4,5] and may contribute to fat-water
swaps. At present, the impact of parallel imaging on FF quantitation is poorly
defined, and the choice of parallel imaging factors is often made on an
empirical basis when developing WB-MRI protocols. The aim of this study was to
assess the impact of Sensitivity Encoding (SENSE) on (i) artifact severity and (ii)
FF measurements in the bone marrow. Methods
Seven healthy subjects aged 25-35yrs underwent CSE-MRI of the pelvis on
a 3.0T system (Ingenia, Philips, Amsterdam, Netherlands) with anterior surface
and integrated posterior coils. Subjects were scanned using a six-echo spoiled
gradient echo acquisition (first echo 1.2ms, echo spacing 1.6ms, repetition
time 25ms, flip angle 3°), which was
repeated with various Sensitivity Encoding (SENSE) factors applied in both
right-left (RL) and foot-head (FH) directions, as shown in Figure 1. The SENSE
4 acquisition was also repeated using bipolar rather than monopolar readout
gradients, which enabled a shorter echo spacing and therefore an effectively
higher spectral resolution [6]. For each acquisition, complex data
were processed offline using an analytical three-point method previously
described by Berglund et al., to generate fat fraction (FF) maps for subsequent
analysis [7].
To assess the severity of fat-water swaps and non-uniform noise
artifacts resulting from the use of parallel imaging, images were randomised
and assessed by a radiology resident (TB) with expertise in musculoskeletal MRI
and in CSE-MRI, who was blinded to acquisition parameters. Both fat-water swaps
and non-uniform noise artifacts were assessed on a four point scale (0–no
artefact, 1–diagnostically irrelevant, 2–diagnostically relevant, 3–non-diagnostic)
[7]. Artifact severity was compared
across SENSE factors using a Friedman test and Dunn’s multiple comparison test.
To assess the effect of parallel imaging on FF measurements,
regions-of-interest (ROIs) were placed on pelvic bone marrow in a standardised
fashion using a dedicated in-house tool, which propagates ‘bands’ of
subchondral bone adjacent to the sacroiliac joint after the observer defines
the joint itself (Figure 2). Mean FF values for each subject were then compared
between groups using a repeated measures analysis of variance (ANOVA).
Additionally, Bland-Altman limits of agreement plots were used to assess
agreement between individual pairs of SENSE factors. Results and Discussion
Examples
of images obtained from the same subject but with varying SENSE factor are
shown in Figure 3, and artifact severity scores are shown in Figure 4. The
impact of SENSE factor on FF measurements is shown in Figure 5a, and individual
comparisons between acquisition pairs (e.g. no SENSE vs SENSE 2) are shown in
Figures 5b-e.
As
shown in Figures 3 and 4, increasing SENSE factors generally resulted in more
severe non-uniform noise artifacts and fat-water swaps. For example, noise
artifacts were significantly increased in the SENSE 4 acquisition (p=0.011)
compared to the acquisition without SENSE. However, when FF measurements were
compared across SENSE factors (Figure 5), the differences between pairs of
measurements were generally much smaller than the means (5b-e), and also
smaller than the effect of disease [3]. This suggests that the
quantitative impact of increasing SENSE factor is likely to be relatively
insignificant for most clinical purposes.
Interestingly,
at a SENSE factor of 4, fat-water swaps were less severe when using bipolar
readout gradients than when using monopolar readout gradients, suggesting that
the use of bipolar readouts may enable the use of higher SENSE factors whilst
still keeping fat-water swaps to a minimum.Conclusions
Our
data demonstrated that increasing SENSE factors resulted in more severe noise
and fat-water swaps in FF maps. However, the effect of these artifacts on FF
measurements was relatively small compared to the effect of disease [3,8]. Clearly, there is a
balance to be struck between speed and the accuracy of FF measurements; our
data will help to make rational choices regarding the use of parallel imaging
in future studies involving FF quantitation. On a practical note, the use of
bipolar readout gradients may enable the use of higher SENSE factors without
increasing the severity of fat-water swaps.Acknowledgements
This work was undertaken at UCLH/UCL, which receives funding from the Department of Health’s NIHR Biomedical Research Centre funding scheme. The views expressed in this publication are those of the authors and not necessarily those of the UK Department of Health.
TJPB is funded by Arthritis Research UK Grant 21369.
We acknowledge the use of the ISMRM Fat-Water Toolbox (http://ismrm.org/workshops/FatWater/data.htm) for some of the results shown in this article.
References
1. Dixon WT. Simple proton spectroscopic imaging. Radiology. 1984;153(1):189–94.
2. Latifoltojar, A, Hall-Craggs, M, Bainbridge, A, Yong, K, Dikaios, Nikolaos, Sokolska, M, Rabin, N, Antonelli, M, Ourselin, S, Popat, R, Rismani, A, D’Sa, S, Taylor, S, Halligan, S, Punwani S. Whole-body MRI quantitative biomarkers predict response in patients with newly diagnosed symptomatic multiple myeloma following Bortezomib induction. Eur Radiol. 2017;
3. Bray, TJP, Bainbridge, A, Punwani, S, Ioannou, Y, Hall-Craggs M. Simultaneous Quantification of Bone Edema/Adiposity and Structure in Inflamed Bone Using Chemical Shift-Encoded MRI in Spondyloarthritis. Magn Reson Med. 2017; doi:10.1002/mrm.26729.
4. Deshmane A, Gulani V, Griswold MA, Seiberlich N. Parallel MR imaging. J Magnetic Reson Imaging. 2012;36:55–72.
5. Aja-Fernández S, Vegas-Sánchez-Ferrero G, Tristán-Vega A. Noise estimation in parallel MRI: GRAPPA and SENSE. Magn Reson Imaging. 2014;32(3):281–90.
6. Hu HH, Börnert P, Hernando D, Kellman P, Ma J, Reeder S, et al. ISMRM workshop on fat-water separation: Insights, applications and progress in MRI. Magn Reson Med. 2012;68(2):378–88.
7. Berglund J, Johansson L, Ahlström H, Kullberg J. Three-point Dixon method enables whole-body water and fat imaging of obese subjects. Magn Reson Med. 2010;63(6):1659–68.
8. Latifoltojar, A, Hall-Craggs, M, Rabin, N, Popat, R, Bainbridge, A, Dikaios, N, Sokolska, M, Rismani, A, D’Sa, S, Punwani, S, Yong K. Whole body magnetic resonance imaging in newly diagnosed multiple myeloma: early changes in lesional signal fat fraction predict disease response. Br J Haematol. 2017;176(2): 222–233.