Biomedical subjects

Jennifer S W Campbell

Publications and source records attributed to Jennifer S W Campbell.

3 recordsLinked to original sources

PubMed2006

3D curve inference for diffusion MRI regularization and fibre tractography.

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibres as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [Parent, P., Zucker, S., 1989. Trace inference, curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 823-839] by using a notion of co-helicity to indicate compatibility between fibre orientations at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain. We also demonstrate the use of the technique to improve the performance of a fibre tracking algorithm.

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PubMed2005

Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques.

In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.

Algorithms↗

PubMed2005

3D curve inference for diffusion MRI regularization.

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [8] by using a notion of co-helicity to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain.

Algorithms↗