Chun Kit Wong1, Jamie X. M. Ho1, and Mary Stephenson1,2
1A*STAR-NUS Clinical Imaging Research Centre, Singapore, Singapore, 2Department of Medicine, National University of Singapore, Singapore, Singapore
Synopsis
Quantitative
magnetization transfer (qMT) parameters can potentially be used as biomarker of
diseases. In this study, qMT parameters’ nominal value are determined for
selected muscle groups in healthy human subjects’ forearm, mid-thigh, and calf.
Nominal values of qMT parameters are
determined by taking the mean value across the subjects for each muscle group. From the results, strong correlations of qMT parameters between certain muscle groups
within the same individual subjects are observed, suggesting that the qMT parameters' variation is biological in origin.Background
Background: Changes
in magnetization transfer are implicated in numerous myopathies including limb-girdle muscle
dystrophy [1,2].
However, it is important to establish normal values in a healthy population, to
enable its use as a biomarker of disease. In this study we assess quantitative
magnetization transfer (qMT) parameters in different muscle groups to provide
normative values, and establish whether there are differences between muscle
groups.
Methods
6
healthy subjects participated in the study.
All data was acquired using a Siemens Prisma 3T system using an 18ch receive
body coil for signal acquisition. qMT sets of images were acquired from
forearm, mid-thigh and calf with total acquisition as described by Sinclair et al [3] and taking 20 minutes per
region.
MR Acquisition:
Data acquisition consisted of a T1 map, generated using the DESPOT1
sequence [4], a B1map, generated using an Actual Flip angle Imaging
(AFI) sequence [5] and a set of 14 qMT images with 6 offset frequencies (1kHz,
2kHz, 5kHz, 10 kHz, 50 kHz and 100kHz) acquired with MT flip angles of 350 and
500°. Imaging parameters were as follows; 128x128 matrix with FOV 180x180mm,
slice thickness = 5mm, 16 slices (no gap), acceleration factor = 2. B1 maps
were acquired at half in slice resolution (64x64 matrix).
Data
Analysis: Data processing was performed using home-built
C++ programs. ITK [6] was used for image read/write and other imaging
operations support. Processing was performed as described by Sinclair et al. [3] using AGLLIB [7] non-linear
least squares fitting from corrected qMT images. MITK [8] was used to draw ROI
on the interested muscle groups. Seven parameters (T1a, f, T2a, T2b, MTR, RM0a, R/g) were derived from the fitted results.
Results
Mean
values of the seven parameters are tabulated against the muscle group of
interest (Table 1). Correlations between different muscle groups within the
same subject are also determined (Table 2), which notable correlations are
observed between certain muscle groups such as the T
2b between
Tibialis-anterior and
Gastrocnemius M. (corr. 0.9871), or T
1a
between
Soleus and
Gastrocnemius L. (corr. -0.8984).
Discussion
Values for
qMT parameters found in this study are in agreement with those present by
Sinclair
et al.
Correlations between individual subjects muscle
groups show significant correlation for multiple parameters (e.g.
Soleus and
Gastrocnemius L.) indicating that variation in qMTR parameters across
subjects are biological in origin and can individually provide potential
biomarkers for disease. On-going subject recruitments is expected to further minimize
data variance and hence improve the result.
Conclusions
This
study establishes normative values for qMT parameters in different muscle
groups. In addition, correlation of parameters across muscle groups show
significant correlation indicating that variation is biological in origin. This
makes them potential individual biomarkers of myopathies, each susceptible to
different biological mechanisms.
Acknowledgements
This
work has been partially funded by the NMRC NUHS Centre Grant – Medical Image
Analysis Core (NMRC/CG/013/2013).References
[1].
McDaniel J.D. J. Computer Assisted Tomography, 1999, 23(4).
[2]. Sinclair C.D. et al. J. Neurol.
Neurosurgery. 2012, 83 (29-32). [3]. Sinclair C.D. et al. Magnetic Resonance in Medicine. 2010;64:1739-48. [4]. Deoni et al. Magnetic Resonance in Medicine.
2003;49:515-26.[5]. Yarnykh. Magnetic
Resonance in Medicine. 2007;57:192-200. [6]. www.ITK.org. [7]. www.alglib.net. [8]. www.mitk.org.