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Comparison of Muscle Image Acquisition &
Analysis Techniques
Andy Kin On Wong PhD
Assistant UHN Scientist
Scanco User Meeting, September 20, 2016 2:15 pm
Toronto General
Research Institute
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Muscle & Bone Measures in vivo
• Muscle density (MD)
• Muscle cross-sectional area (MCSA)
• Subcutaneous fat area
• vBMD, bone microstructural & mechanical properties
• Muscle area / volume
• Subcutaneous, inter & intramuscular fat volume
• Muscle proton density
• Bone microstructure, area
200 um 195 um
500 um 500 um 1.0T pMRI
pQCT
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
1. T1-Weighted Fast Spin Echo MRI
• TR/TE: 600/21 ms, NEX = 3, echoes = 2, flip angle=40o, bandwidth=25 kHz,
• 10 x 1.0 mm thick contiguous slices
• 195 µm in-plane resolution
Issue: Muscle and bone cannot be separated by using fixed thresholds
Older Adult Younger Adult Adult with diabetes
Subcutaneous fat Muscle Fascia line INTERmuscular fat
INTRAmuscular fat
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
• Participants with a range of muscle adiposity can be observed
Can be performed on the GE 1.0T pMRI within 10 minutes Or on any clinical MRI scanner
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Patterned Involvement of Muscle Dystrophy
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
MRI inter/intra-muscular fat (IMF) segmentation & quantification
• Watershed lines guide separation of different tissue regions
• Region-growing algorithm colours in tissues within signal range
Watershed Algorithm
Region-growing Algorithm
IMF Area (IMF.A) IMF Volume (IMF.V)
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Precision of Manually-Segmented IMF Measures
IMF volume accounts for up to 54% of the variance in muscle density
All Participants N=51 No Fx and No
Antiresorptives N=17
Short-term test-retest
pMRI/pQCT Variable RMSCV RMSSD LSC RMSCV RMSSD LSC
MRI IMF.A (mm2) 10.6% 72 201 9.7% 63 174
MRI IMF.V (mm3) 6.1% 261 724 5.3% 180 499
One-year test-retest N=33 N=14
MRI IMF.A (mm2) 14.9% 97 268 11.4% 58 160
MRI IMF.V (mm3) 17.1% 823 2280 22.3% 775 2149
Manual exclusion of bone and
subcutaneous fat
Seed point & thresholding
Dichotomize Island Pruning
New muscle (Mi) and IMF (Fi) seed values revised threshold
(TR)
𝑇𝑅 = 1 +𝑀𝑖−𝐹𝑖
𝐹𝑖∗ 𝑀𝑖
Revised threshold applied & and looped until TR (iteration n) = TR (iteration n-1)
Island Pruning
Semi-Automated Segmentation of IMF
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Final Segmented IMF
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Precision of Semi-Automatically Segmented IMF Measures
y = 1.14x - 47.55 R² = 0.84
0
500
1000
1500
2000
0 1000 2000
Man
ual
IMF
Are
a (m
m2)
Semi-Auto IMF Area (mm2)
y = -0.81x + 75.75 R² = 0.64
y = -0.43x + 71.92 R² = 0.47
40
45
50
55
60
65
70
75
80
0 20 40 60
pQ
CT-
De
rive
d M
usc
le D
en
sity
(m
g/c
m3
)
Percentage IMF Area (%) (Auto or Manual)
Study Comparison Variable N RMSCV RMSSD ICC
Hamilton CaMos(N=43) Age: 74.6±8.9 y BMI: 25.60±4.61 kg/m2
Test-Retest MCSA 43 0.020 329 mm2 0.991
Test-Retest IMFA 43 0.050 77 mm2 0.982
Test-Retest pIMFA 43 0.048 0.5% 0.989
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Automated Segmentation of IMF
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Other MR Imaging Techniques
• Fat/Water-suppression pulses
• Chemical Shift Fat-Water Separation (IDEAL, 3 Point Dixon)
• Diffusion tensor imaging
• MR Spectroscopy
Challenges with MR Imaging
1. Signal inhomogeneity (full body scanners) 2. Amplified fat signal (T1 and T2 bias) 3. Imperfect suppression pulses 4. Spectral complexities of fat 5. Eddy currents (phase shift)
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Partial volume artifact
0.322 mm 0.500 mm
1.000 mm
2.000 mm Figure 1. Comparison of intramuscular fat discernibility among images of varying resolutions.
2.000 mm
pQCT (Stratec XCT 2000)
Muscle Quantification on peripheral Quantitative Computed Tomography (pQCT)
-Measures muscle area, volume, density -Proxy for inter+intra-muscular fat
ISSUE: Muscle and fat must be separated from one another and from bone
Outer threshold 40 mg/cm3
Inner threshold 40 mg/cm3
Filter: F03F05F05
Contour Mode: 3
Peel Mode: 2
500 µm
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Failed muscle segmentations
• Manually re-segment around muscle fascia
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Precision & Accuracy of pQCT Muscle Outcomes Reliability Data RMSCV (% error)
Variable & Method Young Older SCI
Water-Shed MD (mg/cm3) 1.18 2.01 1.42
Threshold MD (mg/cm3) 2.36 1.77 4.06
Water-Shed MCSA (mm2) 0.49 0.93 1.38
Threshold MCSA (mm2) 2.57 1.77 2.94
Muscle density accounts for 54% of the variance in inter+intra-muscular fat
SCI = Spinal Cord Injury
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Muscle Quantification on HR-pQCT Soft Tissue Evaluation Tool
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Seed volumes Planted
Unconnected
volumes removed
Seed volumes Iteratively expanded
Areas of overlap
undefined
Areas of overlap thresholded @
-25.28 mgHA/cm3
Muscle / Fat
FINAL IMAGE
Seed volume identities: Fat threshold: 34.33-194.32 mgHA/cm3 Muscle threshold: -238.6 to -84.9 mgHA/cm3
20 Iterations
HR-pQCT Muscle Segmentation Algorithm
Comparison with pQCT
pQCT
(66% Site)
HR-pQCT
(HA calibration)
HR-pQCT
(HU calibration)
MD 70.0 (5.0) mg/cm3 66.6 (4.7) mg/cm3 31.9 (13.4) HU
MCSA 5738 (899) mm2 1506 (242) mm2 1537 (250) mm2
Muscle
Volume
---- 13.1 (2.1) cm3 12.9 (2.1) cm3
Post-menopausal women
(n=45)
Age (years) 74.6 (8.5)
Height (m) 1.62 (0.07)
Weight (kg) 68.0 (11.6)
BMI (kg/m2) 25.9 (4.3)
TUG (seconds) 11.3 (3.4)
Grip Strength (kg) 24.0 (6.0)
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Measurement R2 P-val B Beta Int
MD (mg/cm3) 0.259 <0.001 0.186 (0.089,0.283) 0.509 63.93 MCSA (mm2) 0.319 <0.001 2.229 (1.227,3.231) 0.564 2378.61
Relationship between pQCT (prox) & HR-pQCT Muscle (distal)
50
55
60
65
70
75
80
0 10 20 30 40 50 60
pQ
CT
66
% M
usc
le D
en
sity
(m
g/c
m3)
HR-pQCT 22.5 mm site Muscle Density (Houndsfield Units)
0
1250
2500
3750
5000
6250
7500
8750
10000
0 500 1000 1500 2000 2500 3000
pQ
CT
66
% M
usc
le A
rea
(mm
2)
HR-pQCT 22.5 mm site Muscle Area (mm2)
Simple linear regression analysis
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
XtremeCT2 Calf Imaging
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
~ 66% Site Muscle Analysis on XtremeCT2
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
~ 66% Site Muscle Analysis on XtremeCT2
Sample Name Site MV cm3
MV/TV
MD mg/cm3
Fat Density mg/cm3
Calf_test#003 Tibia L 84.1 0.890 68.88 -122.68
Calf_test#001 Tibia L 47.1 0.747 65.31 -134.91
Calf_test#004 Tibia L 64.1 0.785 69.90 -125.23
Calf_test#005 Tibia L 44.6 0.793 64.80 -145.10
Calf_test#006 Tibia L 44.9 0.813 64.80 -156.82
Calf_test#006 Radius R 15.0 0.809 67.35 -149.18
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Ultrasound Achilles Tendon Imaging
GE Logiq E Portable USI Systems (12 MHz, linear transducer B-
mode)
OsiriX – Manual contouring of Achilles Tendon CSA Images
obtained from transverse scan Tendon CSA obtained – values averaged
between three scans
Achilles Tendon on HR-pQCT
Standard HR-PQCT distal tibia scan 22.5 mm site
Soft Tissue Analysis Applied
A) Semi-automatic contour of the limb. B) Soft-tissue contours generated for semi-automatic threshold algorithm. C) Manually edited contours around Achilles tendon.
a) b) c)
Comparison of HR-pQCT and Ultrasound-derived Tendon CSA
Dif
fere
nce
HR
-pQ
CT
vs.
Ult
raso
un
d T
CSA
(cm
2)
Average of HR-pQCT and Ultrasound
Tendon CSA (cm2)
-1.96 SD -0.102
+1.96 SD 0.446 0.40
0.20
0.00
-0.20
Mean 0.172
• 17 Women and Men (>50 years)
• Mean Age = 63.2 years • Mean BMI = 24.9 kg/m2
0.40 0.60 0.80 1.00
Variable N Mean SD
HR-pQCT 63 0.722 0.264
Ultrasound 18 0.596 0.164
TCSA Measurements
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
Comparison of HR-pQCT and Ultrasound-derived Tendon CSA
HR-pQCT vs Ultrasound TCSA Correlation Plot
Me
an U
ltra
sou
nd
Te
nd
on
CSA
(cm
2)
Mean HR-pQCT Tendon CSA (cm2)
• Correlation: r = 0.703, p=0.002
ICC LCI UCI
Single Measure 0.662 0.280. 0.863
Average Measures0.796 0.438 0.926
ICC (2,1) Using Consistency Definition
Andy Kin On Wong, 2016. [email protected] TEL: 905-399-0329
MRI & QCT-Compatible Muscle Calibration Phantom
Phantom Properties Fatty Muscle Average Muscle Lean Muscle
[CuCl2] 4.00 mM 22.52 mM 41.04 mM
[EDTA] 0.73 mM 0.03 mM 0.01 mM
T2 relaxation time 52.65 ms 35.15 ms 29.07 ms
Density 65 mg/cm3 70.00 mg/cm3 75.00 mg/cm3
0
10
20
30
40
50
60
70
80
90
100
0.00 5.00 10.00 15.00 20.00 25.00 30.00
T2 R
elax
ati
on
(m
s)
CuCl2 Concentration (mmol/L)
50.00
55.00
60.00
65.00
70.00
75.00
80.00
0.00 5.00 10.00 15.00 20.00 25.00 30.00
Den
sity
(m
g/c
m3)
CuCl2 Concentration (mmol/L)
0
10
20
30
40
50
60
70
80
90
100
0.00 2.00 4.00 6.00 8.00 10.00 12.00
T2
Rel
ax
ati
on
(m
s)
EDTA Concentration (mmol/L)
D = 0.27[CuCl2] + 63.92, R2 = 0.84, p = 0.01
T2 = -10.13ln[CuCl2] +66.70, R2 =0.91, p<0.01
T2 = 5.72 ln[EDTA]+54.47, R2=0.86, p<0.01
Effect of MuscleFractures Conditional on Frailty
Vertical lines = 95% confidence intervals
Points =
Point estimate
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
0.00 0.10 0.20 0.30 0.40
Od
ds
for
a Fr
agili
ty F
ract
ure
CaMos Frailty Index
Johnson-Neyman technique Identified CFI values: 0.05 to 0.13 as region in which effect of Muscle Density Fractures were significant
Muscle Cross-Sectional Area & Muscle Mass:
Frailty showed no moderation effect
Canadian Musculoskeletal Imaging Network
Calgary, AB U of C, Foothills
Hospital (hr-pQCT, pMRI,
DXA)
Vancouver, BC CHHM, VCH
(hr-pQCT, pQCT, DXA)
Saskatoon, SK U of Saskatchewan
(hr-pQCT, pQCT, DXA)
Montreal, QC Shriner’s Hospital
(pQCT, DXA)
Kingston, ON Queen’s U, HMRC
(pQCT, DXA)
Toronto, ON TGH, JDMI, UHN (hr-pQCT, pQCT
pMRI, DXA)
Hamilton Coordinating Centre Adachi Medicine, McMaster
(pQCT, pMRI, DXA)
Hamilton, ON St. Peter’s, HHS (DXA, pMRI planned)
Ottawa, ON CHEO, U of Ottawa
(pQCT, DXA)
Participation from over 12 academic institutions and over 16 Canadian investigators across five provinces in Canada