Quantification of Magnetization Transfer parameters in across different muscle groups
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 T2b between Tibialis-anterior and Gastrocnemius M. (corr. 0.9871), or T1a 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.

Figures

Mean qMT parameters for different muscle groups

Correlation coefficients between different muscle groups within the same subjects (only those with strong correlations are shown).

Images for qMT analysis including a) a T1w image, b) an example MTR image, c) the measured T1



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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