Synopsis
Quality Control (QC)
methods for clinical MR tractography using whole-brain Constrained Spherical
Deconvolution (CSD) in individual patients are used to assess the quality of
the acquired data. Clinical scoring of the resulting tractograms demonstrates
robust depiction of anatomically realistic tracts over a range of MR scanners,
acquisitions, and with varying raw image quality. Whole brain CSD shows potential
for clinical use subject to suitable QC measures.Purpose
One of the barriers to the clinical
utilization of MR diffusion tractography lies in the uncertainty regarding how
the tractography algorithm handles white matter (WM) fibres crossing within a
voxel. Recently
Constrained Spherical Deconvolution (CSD) has provided a means to
resolve the directionality issues inherent in conventional Diffusion Tensor
Imaging (DTI) and has demonstrated anatomically realistic tracts
1,2.
A further problem in clinical use is the
requirement to manually seed the starting and/or target point
for tracking, adding a degree of operator dependence. CSD
can remove this potential source of error using whole-brain tracking. Despite
these improvements, there is still uncertainty about results for
the individual patient: both from false positives (“tracks” that are
artefactual) and false negatives (non-depiction erroneously concluded to equal
non-existence).
To improve confidence in single patient CSD, we devised a simple quality control (QC) procedure using the patient’s images.
Methods
Three female and six male patients (age: mean=21.7, SD=13.3, range 3-43 yr) underwent multi-directional diffusion imaging (15, 30, 33 or 64 directions) on a 1.5T (Philips Ingenia, Siemens Aera) or 3T (Siemens Trio) MR with b-values of 800,1000 or 3000 s mm-2. Pixel size was 1.8mm2 , slice width 2.5 or 5 mm, parallel imaging reduction factor 2, and TE in the range 82-114 ms.
CSD tractography was performed in MRtrix3 using a whole-brain mask, seed and target. The single fibre (SF) response
function was computed for a harmonic order of 8 for the 64 direction images, 6 for 30 and 33 directions, and 4 for 15 directions. A fractional
anisotropy (FA) threshold of 0.7 determined the region-of-interest (ROI) for calculation
of the SF response function. 40,000 whole-brain tracks were plotted using probabilistic streaming.
Clinical evaluation (0-10) required demonstration
of:
1. Left, right corticospinal tracts as two discrete structures in
the anterior medulla and pons (CS).
2. Left, right medial lemnisci as two discrete structures in the
posterior medulla and pons (ML).
3. Transverse pontine fibres separating the corticospinal tracts from
the medial lemnisci (PF).
4. Decussation of the superior cerebellar peduncles in the midbrain
(CP).
5. Left, right optic tracts extending from the optic chiasm to the
left and right lateral geniculate bodies (OT).
6. Left, right optic radiations extending from the left and right
lateral geniculate bodies to the left/right primary visual cortex including
Meyer loops (OR).
7. Anterior commissure (AC).
8. Left, right superior longitudinal fasciculi (LF).
9. Corpus callosum (CC).
10. Subcortical U-fibres bilaterally (UF).
Quantitative QC utilised (a) whole-brain SNR from the b=0 images, (b) Contrast and CNR of the SF-ROI for the average diffusion images, (c) FA histogram (whole-brain), (d) percentile of FA threshold wrt the FA histogram, (e) median, mean, standard deviation of FA in a ROI positioned within cerebrospinal fluid (CSF).
Results
Representative slices for patients scored as 9 and 10 are shown in figure 1. Patient 2 exhibited excessive
motion and scored poorly for anatomy (score=3.5). The 15 direction CSD was also poor (score=5). 6/7 remaining patients had scores of 9
or above, with 1/7 scoring 8. Figure 2 summarizes the demonstration of
anatomical features in the study.
Depiction of the Meyers loop in the optic radiation was the least well demonstrated feature.
SNR, contrast and CNR are shown in figure 3. Contrast (but not SNR or CNR) was improved for b=3000. Patient 2's images (the worst tractogram) had very low contrast and CNR. Figure 4 shows the whole-brain FA histograms with the SF threshold indicated. The apparent
FA of an isotropic medium (CSF) is shown in figure 5, with the upper percentile FA for the SF threshold.
Discussion
The measured FA for CSF gives an
indication of the ‘directional noise’ in the image. The median CSF-FA value decreased with increased b-value, but did not necessarily result in better anatomical
scores. Low angular resolution and excessive
movement resulted in reduced anatomical scores.
Similarly image SNR, contrast and CNR did not
strongly correlate with the anatomical ranking, although the lowest contrast and CNR did result in the worst quality tractogram. Although
the only perfect score was obtained for a 3T, b=3000, n=64 acquisition, diagnostically acceptable tractograms were achievable for acquisitions made at 1.5T with lower
b-values. Previously the quality of CSD tractograms has been invested with phantoms and theoretically
4.
Conclusions
Despite a significant range of acquisition
conditions, ages, and clinical details resulting in a spread of objective QC
metrics, whole-brain CSD
demonstrated WM tract anatomy consistently. 7/9
tractograms yielded acceptable clinical information. Whilst a larger study is
needed, these results show the potential for clinical CSD-tractography to be
used on an individual patient basis.
Acknowledgements
The authors are grateful to SA Health for support, and to Angela Walls for the MR acquisitions.References
1. Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI data using spherical
deconvolution. Neuroimage (2004) 23:1176–1185.
2. Tournier JD, Mori S, Leemans A. Diffusion Tensor Imaging and Beyond.
Magnetic Resonance in Medicine (2011) 65:1532–1556.
3. Tournier JD, Calamante F, Connelly A. MRtrix: diffusion tractography in crossing fire regions. Int U IMag Syst Technol (2012) 22:53-66.
4. Tournier JD, Yeh CH, Calamante F et al. Resolving crossing fibres using constrained spherical deconvolution: Validation using
diffusion-weighted imaging phantom data.
NeuroImage (2008) 42:617–625.