Diffusion tensor imaging-based fractional anisotropy quantification in the corticospinal tract of patients with amyotrophic lateral sclerosis using a probabilistic mixture model.

BACKGROUND AND PURPOSE: In amyotrophic lateral sclerosis (ALS), fiber degeneration within the corticospinal tract (CST) can be quantified by diffusion tensor imaging (DTI) as an indirect marker of upper motor neuron involvement. A new method of measuring quantitative DTI parameters using a probabilistic mixture model for fiber tissue and background in the corticospinal tract of patients with ALS is evaluated. MATERIALS AND METHODS: Axial echo-planar imaging (EPI) DTI datasets (6 gradient directions, 10 repetitions) were acquired for 10 patients and 20 healthy control subjects. The diffusion tensor was visualized in a multiplanar viewer using a unique color coding method. Pure fiber tissue inside a region is separated from background and mixture voxels using a probabilistic mixture model. This allows for a reduction of errors as a result of partial volume effects and measurement variability. RESULTS: Fractional anisotropy (FA) was measured within the CST at levels ranging from internal capsule to pons. Mean coefficients of variation of intrarater, scan-rescan, and inter-rater reproducibility were 2.4%, 3.0%, and 5.7%, respectively. Optimal measurement positions along the CST with respect to minimum variability and maximum difference between patients and healthy subjects were identified in the caudal half of the internal capsule. Moreover, a negative correlation between the age-corrected FA and the disease duration but not the ALS Severity scale score was found. CONCLUSION: The new software for fiber integrity quantification is suited to assess FA in the corticospinal tract with high reproducibility. Thus, this tool can be useful in future studies for monitoring disease status and potential treatment efficiency.

Uncertainty in diffusion tensor based fibre tracking.

BACKGROUND: Diffusion tensor imaging and related fibre tracking techniques have the potential to identify major white matter tracts afflicted by an individual pathology or tracts at risk for a given surgical approach. However, the reliability of these techniques is known to be limited by image distortions, image noise, low spatial resolution, and the problem of identifying crossing fibres. This paper intends to bridge the gap between the requirements of neurosurgical applications and basic research on fibre tracking uncertainty. METHOD: We acquired echo planar diffusion tensor data from both 1.5 T and 3.0 T scanners. For fibre tracking, an extended deflection-based algorithm is employed with enhanced robustness to impaired fibre integrity such as caused by diffuse or infiltrating pathological processes. Moreover, we present a method to assess and visualize the uncertainty of fibre reconstructions based on variational complex Gaussian noise, which provides an alternative to the bootstrap method. We compare fibre tracking results with and without variational noise as well as with artificially decreased image resolution and signal-to-noise. FINDINGS: Using our fibre tracking technique, we found a high robustness to decreased image resolution and signal-to-noise. Still, the effects of image quality on the tracking result will depend on the employed fibre tracking algorithm and must be handled with care, especially when being used for neurosurgical planning or resection guidance. An advantage of the variational noise approach over the bootstrap technique is that it is applicable to any given set of diffusion tensor images. CONCLUSIONS: We conclude that the presented approach allows for investigating the uncertainty of diffusion tensor imaging based fibre tracking and might offer a perspective to overcome the problem of size underestimation observed by existing techniques.

Detection of tumour infiltration in axonal fibre bundles using diffusion tensor imaging.

We present a method for the detection and quantification of white matter infiltration from human brain tumours based on Diffusion Tensor Imaging (DTI). Since white matter destruction alters the local diffusion properties, DTI has the potential to sensitively detect tumour infiltration and to quantify the degree thereof. Here, we consider three tumour patients with gliomas, two with and one without contralateral tumour progress. We use DTI to identify specific fibre systems, where infiltration has to be assessed. On this basis, the problem of arbitrary region of interest definition is solved such that tumour infiltration can be reliably quantified in particular fibre bundles. It is demonstrated at the Corpus Callosum (CC) and the Pyramidal Tract (PT) that fibre bundle infiltration can be well detected by specific visualisation techniques of diffusion tensor data. Infiltration of the CC is quantified by using a reliable method for the determination of diffusion properties inside particular fibre bundles. For an age normalised quantification of white matter infiltration we introduce the Integrity Index, which measures the diffusion anisotropy inside an infiltrated fibre bundle normalised by the diffusion anisotropy in a specific region of healthy fibre tissue. It turns out that the quantification of CC infiltration correlates with contralateral tumour progression and has the potential to serve as a surrogate marker for this process, which is crucial for surgical therapy decisions and intervention planning.