Synopsis
The goal of the present work was to test the reliability of
the C2-C3 spinal cord gray matter and total cord areas measurements performed using
a 2D-PSIR sequence.
Nine healthy subjects were scanned twice with repositioning
in between the scans (test/retest) on three different 3T scanners with
different hardware.
On the phase sensitive-reconstructed images, total cord area
was measured in a semi-automated way and gray matter area was estimated by
using an automatic segmentation method.
Evaluations of contrast to noise ratio, intra-scanner
and inter-scanner reliability suggest that multicenter studies using a 2D-PSIR sequence
are feasible.Purpose
To evaluate the intra- and inter-scanner reliability of spinal
cord (SC) total cord area (TCA) and gray matter (GM) area measurements based on
2D-Phase Sensitive Inversion Recovery (2D-PSIR) imaging at 3T.
2D-PSIR has been recently shown to be very promising for reliable
measurements of SC TCA and GM area on healthy controls and multiple sclerosis patients1,2.
The goal of the present work was to test the reliability of
the SC areas measurements at the C2-C3 disc level on different scanners, each
with different hardware, at different sites.
Methods
C2-C3 images of nine healthy controls (age 33±10 [mean±SD]; 5
females/4 males) were acquired at 3 different sites, positioning the 2D-PSIR single
slice perpendicularly to the SC on a standard sagittal T2-w image1.
In each session each subject was scanned twice with
repositioning in between the scans (test/retest).
2D-PSIR sequence parameters were: 0.78x0.78x5 mm3
spatial resolution, TR/TE/TI=4000/3.22/400 ms, flip angle=10°, and 3 averages
(acquisition time: 1:50 min, magnitude and phase-sensitive images
reconstructed).
The scanners at the different sites were:
SCANNER1: Siemens Skyra 3T, 64-channel head/neck coil, pTX technology
SCANNER2: Siemens Skyra 3T, 20-channel head/neck coil
SCANNER3: Siemens Trio 3T, 12-channel head coil plus
4-element neck coil
Evaluation of contrast to noise ratio (CNR)
The CNR for cerebrospinal fluid/white matter (CSF/ WM) and
for WM/GM tissues was calculated on the magnitude-reconstructed C2-C3 images.
CNR between tissue 1 and tissue 2 was defined as CNR12=|S1-S2|/BN, where S1
and S2 were the average signals in two identical 2x2 voxels square
ROIs positioned on the tissues and BN (background noise) was the standard
deviation of the signal measured in a ROI of 100 mm2 outside the neck,
away from imaging artifacts.
The GM ROI was positioned on the anterior horn, the WM ROI
on the lateral column.
Evaluation of intra-scanner and inter-scanner reliability
TCA was measured in a semi-automated way on all the phase
sensitive-reconstructed images (9 subjects x 2 images/site x 3 sites =54) by
using the software Jim1,3 (www.xinapse.com).
GM area was estimated by using an automatic segmentation method
(described elsewhere) based on a publicly available Python implementation of
the morphological geodesic active contour method4
(github.com/pmneila/morphsnakes).
Briefly: This method uses the binary spinal cord masks (obtained
with the semi-automatic Jim segmentation) and spinal cord GM masks (manually
segmented by a neurologist expert in neuroimaging) of C2-C3 PSIR acquisitions
on 20 subjects to create a template.
This template is subsequently used in the automatic
segmentation of each PSIR acquisition: First, the cord shape template is
registered to each subject’s spinal cord shape mask with affine and non-linear
transformations. These transformations are then applied to the spinal cord GM
template. This initial result is used in an active contour algorithm to obtain
the final GM segmentation.
Intra-scanner (between the test/retest acquisitions) and inter-scanner
(pairs of test/retest acquisitions on the 3 scanners) reliabilities for TCA and
GM areas were calculated in terms of coefficients of variation (COV = standard
deviation/mean of the values).
Results
It was possible to segment the TCA in all the 54 images.
On 52/54 images (~96%) the automatic technique yielded
visually correct GM segmentations (Figure 1). One acquisition from SCANNER1 and one from SCANNER3 were
excluded from the GM analysis since based on visual inspection the segmentations
clearly failed (Figure 2).
Evaluation of CNR
The average CSF/WM CNR for the 9 subjects (mean±SD) was:
SCANNER1: 59.18±18.22
SCANNER2: 41.79±8.92
SCANNER3: 42.26±10.07
The average WM/GM CNR was:
SCANNER1: 20.74±7.99
SCANNER2: 14.96±2.30
SCANNER3: 16.60±3.76
Evaluation of intra-scanner and inter-scanner reliability
For TCA the intra-scanner COV was:
SCANNER1: 0.91%±0.58%
SCANNER2: 1.07%±0.92%
SCANNER3: 0.51%±0.49%
The inter-scanner TCA COV was:
Test: 0.97%±0.74%
Retest: 1.07%±0.50%
Mean value of the two acquisitions: 0.90%±0.38%
For GM area the intra-scanner COV was:
SCANNER1: 2.51%±1.91%
SCANNER2: 2.68%±3.20%
SCANNER3: 2.80%±2.78%
The inter-scanner GM area COV was:
Test: 4.98%±1.84%
Retest: 3.37%±2.26%
Mean value of the two acquisitions: 3.60%±1.80%
Discussion and Conclusion
The C2-C3 2D-PSIR images of 9 healthy controls had very
similar CNR on the 3 different scanners. As expected due to the advanced
hardware, mean CNR for SCANNER1 was the highest in the group.
Semi-automatic TCA and automatic GM area measurements had a
very high intra-scanner and inter-scanner reliability. The comparable values
obtained for intra- and inter-scanner measurements suggest that multicenter
studies using 2D-PSIR are feasible.
A limitation of this study is that scanners of a single
vendor were used. Future studies are necessary to verify the feasibility of
multicenter studies with different vendors’ scanners, but preliminary data (not
shown) suggests the quality of data obtainable with the 2D-PSIR protocol is not
vendor specific.
Acknowledgements
No acknowledgement found.References
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2. Schlaeger R, Papinutto N, Panara V, Bevan C, Lobach IV, Bucci M, Caverzasi E, Gelfand JM, Green AJ, Jordan KM, Stern WA, von Büdingen HC, Waubant E, Zhu AH, Goodin DS, Cree BA, Hauser SL, Henry RG. Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann Neurol. 2014 Oct;76(4):568-80.
3. Horsfield MA, Sala S, Neema M, Absinta M, Bakshi A, Sormani MP, Rocca MA, Bakshi R, Filippi M. Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage. 2010 Apr 1;50(2):446-55.
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