Towards user-independent DTI quantification

Klein, Jan; Stuke, Hannes; Rexilius, Jan; Stieltjes, Bram; Hahn, Horst K.; Peitgen, Heinz‐Otto

doi:10.1117/12.768763

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Additionally, we plan to investigate if clustering [14] and quantification [15] algorithms could benefit from GPU usage and accelerate probabilistic approaches [16]. There may also be interesting new possibilities for visualization of uncertainty using the geometry shader.…”

Section: Discussionmentioning

confidence: 98%

A GPU-based fiber tracking framework using geometry shaders

Köhn

Klein²,

Weiler

et al. 2009

SPIE Proceedings

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The clinical application of fiber tracking becomes more widespread. Thus it is of high importance to be able to produce high quality results in a very short time. Additionally, research in this field would benefit from fast implementation and evaluation of new algorithms. In this paper we present a GPU-based fiber tracking framework using latest features of commodity graphics hardware such as geometry shaders. The implemented streamline algorithm performs fiber reconstruction of a whole brain using 30,000 seed points in less than 120 ms on a high-end GeForce GTX 280 graphics board. Seed points are sent to the GPU which emits up to a user-defined number of fiber points per seed vertex. These are recorded to a vertex buffer that can be rendered or downloaded to main memory for further processing. If the output limit of the geometry shader is reached before the stopping criteria are fulfilled, the last vertices generated are then used in a subsequent pass where the geometry shader continues the tracking. Since all the data resides on graphics memory the intermediate steps can be visualized in real-time. The fast reconstruction not only allows for an interactive change of tracking parameters but, since the tracking code is implemented using GPU shaders, even for a runtime change of the algorithm. Thus, rapid development and evaluation of different algorithms and parameter sets becomes possible, which is of high value for e.g. research on uncertainty in fiber tracking

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Section: Discussionmentioning

confidence: 98%

A GPU-based fiber tracking framework using geometry shaders

Köhn

Klein²,

Weiler

et al. 2009

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…MS lesions have been investigated by region of interest (ROI)‐based analysis (28) and voxel‐wise FA comparisons where it has been found that FA changes occur in areas containing lesions and in areas of normal appearing white matter (27). Moreover, methods for tract‐based quantification have been developed (26, 36–39) where parameters are computed depending on the local curvature or depending on the geodesic distance from a user‐defined origin. They allow the clinicians to automatically determine DTI‐derived parameters along fibre bundles and have already been used for a reproducible quantification of fibre integrity profiles in small structures like the cingulum and the fornix (40), and for mirroring disease progression and executive functioning in MS patients (33).…”

Section: Selected Applications Of Quantitative Neuroimagingmentioning

confidence: 99%

“…(63) use an EM clustering technique to classify between tissue, background and partial volume and are able to quantify within those specific regions afterwards. This technique has also been extended to tract‐based quantification (38). However, a user‐independent quantification process cannot be guaranteed under all circumstances, as for the inherent clustering process only geometric affinity measures are used without knowledge about the underlying true anatomy.…”

Section: Learning By Example: Evaluation Of Quantitative Dtimentioning

confidence: 99%

On the Reliability of Quantitative Volumetric and Structural Neuroimaging

Klein

Laun

Erhard³

et al. 2008

Imaging Decisions

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Summary Quantitative neuroimaging techniques have become emerging technologies within clinical practice. In this paper, we survey a few clinical applications where quantification methods have received increasing attention, namely in the area of brain atrophy, lesion load computation and quantification of diffusion processes. We focus on the reliability and reproducibility of such methods and will use the example of quantitative diffusion tensor imaging to discuss methodological details. There, we show possible avenues for evaluating correctness and reliability. On the one hand, we show results from our novel hardware phantom experiments, where axonal fibres are emulated by synthetic industry fibres. On the other hand, we present a new framework for constructing software phantoms which can be used to evaluate, for example, the impact of partial volume effects in case of axonal loss as to be found in multiple sclerosis. Advantages and disadvantages as well as pitfalls of quantification and evaluation techniques are illustrated throughout the paper.

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