Name
Title | ||
|---|---|---|
Qualitative and quantitative analysis of probabilistic and deterministic fiber tracking |
Abstract | ||
|---|---|---|
Fiber tracking (FT) and quantification algorithms are approximations of reality due to limited spatial resolution, model assumptions, user-defined parameter settings, and physical imaging artifacts resulting from diffusion sequences. Until now, correctness, plausibility, and reliability of both FT and quantification techniques have mainly been verified using histologic knowledge and software or hardware phantoms. Probabilistic FT approaches aim at visualizing the uncertainty present in the data by incorporating models of the acquisition process and noise. The uncertainty is assessed by tracking many possible paths originating from a single seed point, thereby taking the tensor uncertainty into account. Based on the tracked paths, maps of connectivity probabilities can be produced, which may be used to delineate risk structures for presurgical planning. In this paper, we explore the advantages and disadvantages of probabilistic approaches compared to deterministic algorithms and give both qualitative and quantitative comparisons based on clinical data. We focus on two important clinical applications, namely, on the reconstruction of fiber bundles within the proximity of tumors and on the quantitative analysis of diffusion parameters along fiber bundles. Our results show that probabilistic FT is superior and suitable for a better reconstruction at the borders of anatomical structures and is significantly more sensitive than the deterministic approach for quantification purposes. Furthermore, we demonstrate that an alternative tracking approach, called variational noise tracking, is qualitatively comparable with a standard probabilistic method, but is computationally less expensive, thus, enhancing its appeal for clinical applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.843472 | Proceedings of SPIE |
Keywords | Field | DocType |
uncertainty,diffusion tensor imaging,probabilistic fiber tracking,variational noise fiber tracking | Computer vision,Diffusion MRI,Tensor,Computer science,Correctness,Probabilistic method,Software,Artificial intelligence,Probabilistic logic,Image resolution,Deterministic system (philosophy) | Conference |
Volume | ISSN | Citations |
7623 | 0277-786X | 3 |
PageRank | References | Authors |
0.39 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Klein | 1 | 95 | 10.94 |
| adrian grotsch | 2 | 3 | 0.39 |
| d betz | 3 | 3 | 0.39 |
| Sebastiano Barbieri | 4 | 38 | 6.13 |
| Ola Friman | 5 | 742 | 60.43 |
| Bram Stieltjes | 6 | 193 | 55.84 |
| helmut hildebrandt | 7 | 3 | 0.73 |
| Horst K. Hahn | 8 | 450 | 72.61 |