Rosemary Nicholas1, Paul Greenhaff1, and Susan Francis1
1UNIVERSITY OF NOTTINGHAM, Nottingham, United Kingdom
Synopsis
Muscle volume
and fat fraction can be quantified from mDIXON scans either manually or using automated
processes. Here we compare manual volumes
of thigh and calf muscle ROIs with an automated pipeline created using FSL’s
FAST segmentation, to compare muscle volume and fat fraction across subject
groups and with their DXA values. Automatic volume segmentations correlated
highly with manually drawn measures (r=.975) as well as DXA (r=0.840). Group comparisons show COPD and post-COVID patients
had significantly lower muscle mass and higher fat fraction. Automatic
segmentation performs well compared to manually derived volumes and is more
time efficient.
Introduction
Skeletal muscle atrophy is common in ageing and various pathologies. A DXA scan is currently the most commonly used
measure to quantify lean mass. MRI provides more detailed analysis and flexibility
in the approach to the data. Here we quantify muscle volume and fat fractions in the thigh and calf of
the lower limb using automated analysis of mDIXON images. We compare results to
quantify muscle volume and fat fraction between highly active volunteers and participant
groups who experience fatigue, including COPD and COVID-19.Methods
Data were collected in
healthy volunteers (n=16), a highly physically
active older group (n=9), patients with Chronic
Obstructive Pulmonary Disease (COPD) (n=10) or Inflammatory Bowel Disease (IBD) (n=12), patients in
Post-COVID recovery (n=21,) and patients diagnosed as Pre-Frail (n=3), Table 1. Whole body mDIXON
Quant data were collected on a Philips 3T Ingenia. mDIXON scans were
collected coronally in 6 stacks using a six-echo 3D gradient echo sequence with
a flip angle of 3o, Repetition Time (TR) 10 ms, initial echo time
(TE1) 0.95 ms and echo spacing (ΔTE) 0.7 ms, reconstructed to 3 mm
slice thickness with pixel size of either 1.9231×1.9231 mm2
(or 1.7241×1.7241 mm2 in the COPD scans). An automatic pipeline was developed using FSL (FMRIB Software Library)
FAST to extract all muscles on the mDIXON scan within the calf and thigh (Fig.1). First, a crop around the limb was performed on
the images to establish a smaller area of interest. This cropped region was
then fed into FAST to segment three tissue types. The segmentation was then
cleaned of any extraneous regions with a perimeter removal and an Euclidian
distance transform, and the fat fraction map was used to remove high intensity
fat regions. From this, muscle volume and fat fraction within the muscle was extracted. Manual ROIs were drawn using Horos by a
single rater on all data sets (except the 3 Pre-Frail), and two raters segmented
the COPD subset. Raters segmented the muscle
tissue on coronal slices, every 5 slices (Fig.2). Volume measures for the whole calf/thigh was
estimated by interpolating muscle area in the missing slices. Dice coefficients were run on the manually derived regions of interest
and their corresponding automatically derived slices. Results
Interrater correlations on the manually drawn masks was good, r(42) = 0.992, p<.001 (Fig.3a), a T-Test showed
that Rater 1 yielding higher muscle mass than Rater 2 (p<.001) (Fig.2). On comparison the manually masks in the subset of volunteers that also had DXA
scans showed a good correlation, r(30) = 0.942, p<.001 (Fig.3b),
with automatic segmentations correlations being slightly lower r(40) = 0.840, p<.001 (Fig.3d). The automatic segmentation was compared with manually derived volumes for
each limb (right and left for calf and thigh) of 35 participants and highly
correlated, r(140) = 0.975, p<0.001 (Fig.3c and Fig.2). The weighted mean Dice value was 0.831±.047 for the calf and 0.821±.042 for the thigh.
Comparing results across participant groups, automatically segmented lower
limb muscle volumes (normalised for body surface area (BSA)) revealed
significant differences between groups when co-varying for age and gender in
the calf, (F(7,62) = 8.585, p<0.001), and thigh ((F(7,59) = 3.377, p<0.01)
(Fig.4a). Bonferroni corrected pairwise tests showed
the COPD group had significantly lower calf and thigh muscle mass than the
Post-COVID (p<.001 and p<.05) and older active groups (p<.005 and
p<.01), and lower calf mass than healthy volunteers (p<.005). The IBD group also had significantly lower
muscle mass than the Post-COVID group in the calf, p<.01. Fat fraction values were found to be significantly
different in the calf and the thigh (F(7,62) = 5.347, p<.001 and F(7, 59) =
4.272, p<.005) Fig.4b. The older active group had significantly lower fat fraction than the
COPD group (p<.001) in the thigh, and strong trend for differences in the
calf (p=0.52). In the Post-COVID group,
the older active group had significantly lower fat fraction in the calf
(p<.001) and strong trend level differences in the thigh (p=.06). The Post-COVD group also had higher fat
fraction values than the healthy volunteers at trend level (p=0.57). Discussion
The proposed automatic
segmentation method allows for rapid analysis and greater consistency between
raters. While correlations were high
between raters, absolute values differed due to inclusion versus exclusion of
intermuscular spaces when drawing around muscle groups. The automatic method removes manual choices to
improve consistency across raters. Methodological
differences are expected to yield somewhat different final muscle masks and
there for differences in volume and Dice coefficients , as the automatic
segmentation tends to exclude more areas that are muscle that could not be
excluded in the manual drawing without highly detailed, precise, drawing. DXA values are not separated into smaller
sections and are a single value for the lower limb including hips and glutes,
also accounting for some discrepancy. Conclusion
Automatic segmentation
provides more consistent results than manual, as well as being much less labour
intensive. Acknowledgements
No acknowledgement found.References
No reference found.