Daniel Güllmar1, Renat Sibgatulin1, Stefan Ropele2, and Jürgen R Reichenbach1,3
1Medical Physics Group / IDIR, Jena University Hospital, Jena, Germany, 2Department of Neurology, Medical University of Graz, Graz, Austria, 3Michael-Stifel-Center for Data driven sciences, Friedrich-Schiller-University Jena, Jena, Germany
Synopsis
If
quantitative diffusion measures are acquired at different sites but identical
hardware and protocol settings, it remains unclear if a site bias would require
a data homogenization in order to pool the data for analysis. A traveling
volunteer (including four subjects) approach was applied and different complex
diffusion measure as well as diffusion tensor metrics were computed based on
the measurements at two different sites. Our results suggest that the inter-site
differences are much smaller than the inter-subject differences in the ROI
based analysis. The voxel-wise analysis was found to be more susceptible to incomplete
artifact compensation and registration errors.
Introduction
Multi-site
studies are often an indispensable approach to conducting more extensive
studies with the available measurement time. To perform quantitative analysis
with pooled data from such a multi-site study, one has to make sure that the
used protocols and hardware preconditions are as similar (if not identical) as
possible. Especially in quantitative MRI (e.g., diffusion measurements), it is
required to assess the repeatability by measuring the same object at all sites
using the same equipment and methods. But also the means for evaluating the
correspondence of the measured parameters need to be as robust as possible.
Thus, the aim of the study was to assess the correspondence of advanced and
sophisticated diffusion measures acquired at different time points and sites on
the same subjects.Material and Methods
As part of an ongoing multi-site
multiple-sclerosis study between the Jena University Hospital, Germany, and the
University Hospital in Graz, Austria, four healthy volunteers were scanned once
at both sites, respectively. At both sites, 3T MRI (Siemens Prisma, 20 channel
head coil (16 active channels)) with slightly different software versions
(VE11B vs. VE11C) but identical MR imaging protocols and hardware settings were
utilized. The predefined MR study protocol included an advanced diffusion MR
acquisition consisting of 101 volumes, three shells, 8x b@5s/mm2,
16x b@850s/mm2, 32x b@1680s/mm2, 48x b@2500s/mm2,
1.5 mm isotropic resolution. All volumes were measured twice with reversed phase
encoding polarity to facilitate susceptibility artifact compensation. The
preprocessing included denoising [4], topup [5], and eddy current correction [6].
The ROI for white matter was defined by combining the white matter segmentations
of FSL-fast [7] and Freesurfer [8], followed by a 2D binary closing and 3D
erosion operation. Diffusion tensor metrics (FA, RD, MD) were computed using
the DTIFIT routine from FSL using weighted least squares. The spherical mean
technique (SMT, [1]) was used to map microscopic diffusion anisotropy
parameters. Neurite orientation dispersion and density imaging (NODDI, [2]) was
computed using the AMICO implementation [3]. All evaluated diffusion metrics
are listed in Table 1. The selected ROI as well as the maps of the different quantitative
diffusion measures are shown for one of the measurements for a selected axial
slice position in Figure 1. The ROIs were generated only once for the data acquired
at one site and then transferred and applied at the second site by using linear
transformation and nearest-neighbor interpolation. ROI values were evaluated by
comparing mean and median values, and by comparing empirical cumulative
distribution functions (eCDF). The time interval between the scans at the two sites
was between 2 and 4 months for each volunteer.Results
For most of the compared diffusion measures,
the inter-site differences were found to be smaller than the inter-subject
differences. This is reflected by coincident eCDF curves (see Fig. 2.,
dtifit_FA selected as an example) for the measurements conducted at the two sites
compared to the curves corresponding to measurements between volunteers. Median
as well as mean values for the white matter ROIs also show smaller differences
between the sites than between subjects. In the second analysis step, which
involved linear transformation and tri-linear data interpolation from siteB to
siteA, difference maps (siteA–siteB) for the different diffusion metrics were voxel-wise
calculated for each volunteer (see Fig. 3 for an example of the diffusion
metrics FA and ICVF(NODDI) for one volunteer). As seen, there exist spatial
inhomogeneous distributed variations, which may be due to suboptimal
registration of the data sets or discrepancies in the artifact compensation due
to slightly different head positioning and orientations. The difference maps
were subsequently analyzed by using eCDF plots of the predefined white matter
ROI (see Fig. 4). Ideally, these eCDF of the pairwise differences should cross
the point with coordinates (0, 0.5), indicating a non-biased difference. As
seen from Fig. 4, the results for MD, ICVF(NODDI),
intra-neurite-volume-fraction(SMT) are closely distributed around this specific
point, whereas the eCDFs of the FA differences are right-shifted, indicating a
general positive bias of dtifit_FA @ siteA.Discussion
Our results suggest that multi-site MR studies
investigating quantitative diffusion measures can be performed without any
further data homogenization, if identical hard- and software settings are selected.
The most serious difficulty arises most likely from an inconsistent patient
positioning in the scanner at both sites. This issue will be investigated in future
steps. One further possibility relates to physiological changes occurring in
the volunteers between the two measurements as they were performed with a time
interval of up to 4 monthsAcknowledgements
This study was financially supported by the German research foundation (RE-1123/21-1) and the Austrian Science Foundation (FWF I3001-B27).References
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