Improvement of probabilistic tractography of the corticospinal tract in human brain
David Neil Manners1, Claudia Testa1, Stefania Evangelisti1, Stefano Zanigni1, Mariagrazia Popeo2,3, Caterina Tonon1, and Raffaele Lodi1

1Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy, 2Istituto Italiano di Tecnologia, Rovereto, Italy, 3Center for Neurosciences and Cognitive Systems, University of Trento, Rovereto, Italy

Synopsis

We performed an along tract analysis of the cortico-spinal tract in a group of healthy subjects using a probabilistic tractography algorithm. We were able to evaluate CST reconstruction along the tract using appropriate performance metrics, based on the congruence of fibre paths in the subject population, and the presence or absence of fibre tracks identified as originating from the precentral gyrus. The method was compatible with clinical protocols given spatial definition and tract localization obtained.

Introduction

MR tractography holds the promise of deriving neuroanatomical information from non-invasive MR studies, but its performance in practice is usually difficult to verify. Going beyond the simplest DTI tensor model is probably essential. Probabilistic tractography has been used for a number of years, but protocols for use in clinical scanners must still be optimized on a tract-by-tract basis. In the current study we aimed to develop an appropriate protocol for defining the cortical spinal tract (CST) for clinical examination.

Methods

Twenty-five healthy adult subjects (13 females), with an age range 20-83 years (mean 38), were recruited, and underwent MR scanning using a 1.5 Tesla GE Signa system. A T1-weighted axial volumetric image was acquired using the FSPGR sequence (25.6 cm2 FOV; 1 mm isotropic voxels) and axial diffusion-weighted images using a single-shot SE-EPI sequence with FOV 32x32 cm, 3 mm slice-thickness, in-plane resolution 128x128, b-value = 900 s mm-2, and 64 diffusion-weighted directions + 7 unweighted scans. The protocol was approved by the local Ethics Committee and written informed consent was obtained from all participants. Cortical parcellation/labelling of the volumetric images was performed by Freesurfer1. Volumetric images and associated labels were aligned to DWI volumes using FSL/FLIRT, followed by nonlinear deformation2. Basic DWI processing was performed using the FMRIB software library (http://www.fmrib.ox.ac.uk/fsl), applying bedpostx and probtrackx for probabilistic tractography3. The starting point of the CST (seedmask) was identified using voxels labelled by Freesurfer as lying within the precentral gyrus, separately for left and right sides. In addition fibres were constrained to pass through the posterior limb of the internal capsule (PLIC) (waypoint) and thence to the pons (termination mask). The result of the tractography was a voxelwise estimate for CST connectivity (fibre count). All subject data was linearly aligned to the FMRIB58 FA template. Voxels that contained average fibre numbers of at least 0.4% maximum were used to define a consensus CST mask. For each side of the brain, fibre count images were resampled to yield exactly 100 slices in the z-direction to cover the distance from the pons to the upper limit of the precentral gyrus. To identify protocol parameters that might improve tract definition, two operational variables were defined as quality metrics for each subject:

i) False negative rate (FNR) – false negative voxels within the seedmask without emergent fibres / all seedmask voxels;

ii) False discovery rate (FDR) – false positive voxels containing fibres but lying outside the consensus mask / all fibre-containing voxels.

Two methods of thresholding the fibre count, to reduce FDR, were tested – per subject thresholding, and slicewise thresholding, considering that the main course of the CST runs superior-inferior from the motor cortex to the pons. Several normalisation parameters were considered, and the best threshold level was side to minimize FNR and FDR.

Results

Slicewise total fibre numbers are shown in Figure 1. The pattern is qualitatively similar for all subjects: numbers increases from the highest slice reaching a peak around the level of the PLIC, before falling to a low value as the tract descends to the midbrain, with the voxel maximum fibre number showing a roughly inverse pattern, conditioned by the relative number of fibres originating at or above a given level. A performance curve for different threshold values is shown in Figure 2. The expected FNR/FDR trade-off is observed, starting from mean FNR 0.30 ± s.d. 0.13 and FDR 0.37 ± 0.13 for threshold=0. Slicewise thresholding is slightly better. False positive/negative results are demonstrated in Figure 3 for a typical subject.

Discussion

Methods for performing tractography of the CST have recently been examined4, and no single method was found to be definitely superior. Probabilistic tractography naturally generates streamlines that are unlikely to correspond to neuronal fibres, and so a method for culling false fibres is needed. Constraining fibre paths to pass through the PLIC and pons produces an adequate performance if combined with a fibre number threshold. The number of fibres in each slice (Figure 1) is determined mainly by the number of seed voxels above, and the compactness of the fibre tract (demonstrated by the maximum fibre number). Allowing for this slicewise variation marginally improves tract delineation, especially with a high threshold (Figure 3), and is thus useful for defining the main part of the tract below the corona radiata. At and above this level, additional methods will be required, especially in the lateral portions of the corona radiate and centrum semiovale, which lie on the same level as the more medial tract and are readily culled with a slicewise threshold.

Acknowledgements

No acknowledgement found.

References

1 Fischl B. FreeSurfer. NeuroImage 62(2): 774-781, 20123

2 Ardekani BA, Braun M, Hutton BF, Kanno I, Iida H. A fully automatic multimodality image registration algorithm. J Comput Assist Tomogr. 1995 Jul-Aug;19(4):615-23.

3 Behrens T, Johansen Berg H, Jbabdi S, Rushworth M, Woolrich M. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34 144-155, 2007

4 Pujol S, Wells W, Pierpaoli C, et al. The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery. J Neuroimaging. 2015 Nov;25(6):875-82.

Figures

Figure 1. Slicewise fibre statistics from 1st centile (pons) to 100th (top of seedvoxel), averaged over all subjects. Continuous line: voxels containing fibres (mean ± s.d.); dotted line: mean cumulative number of seed voxels (counting from 100th to 1st centile); dashed line: average maximum fibre number. Red: right, blue: left.

Figure 2. Mean values over all subjects of performance metrics FDR and FNR, using various threshold values (indicated on Figure), expressed as percentage either of subject’s maximum per voxel fibre number, or of per slice maximum fibre number.

Figure 3. False Positive fibres (green, right hemisphere), False Negative fibres (blue,left hemisphere), and consensus CST (red), all averaged along anterioposterior axis, and projected onto a single coronal plane of the FMRIB58 FA atlas, onto which the subject was registered. Left: per subject threshold 0.01%; right: per slice threshold 0.05%.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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