This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3-D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and a experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to a state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function.

This paper presents a method for intensity inhomogeniety removal in fMRI studies of a moving subject. In such studies, subtle changes in signal as the subject moves in the presence of a bias field can be a significant confound for BOLD signal analysis. The proposed method avoids the need for a specific tissue model or assumptions about tissue homogeneity by making use of the multiple views of the underlying bias field provided by the subject's motion. A parametric bias field model is assumed and a regression model is used to estimate the basis function weights of this model. Quantitative evaluation of the effects of motion and noise in motion estimates are performed using simulated data. Results demonstrate the strength and robustness of the new method compared to explicit segmentation based methods that estimate bias within individual timeframes, as well as the state of the art 4D nonparametric bias estimator (N4ITK). We also qualitatively demonstrate the impact of the method on resting state neuroimage analysis of a moving adult brain with simulated motion and bias fields, as well as on in-vivo moving fetal fMRI.

Motion correction of MRI sequences is a very active area of research. Several postprocessing techniques for volume correction and more recently slice correction have been proposed. Slice motion correction of fMRI data typically involves iterative registration of slices to a target volume. The target volume is usually reconstructed at each iteration using current slice motion estimates, with all possible views of the subject. However, in the presence of large movements, the quality of the reconstruction can be greatly degraded without a good initialization, and this can in turn greatly affect the accuracy and precision of the slice registration. This paper introduces a cascaded motion correction pipeline that uses subsets of data to generate coherent volumes for initial target volume based slice registration in fMRI. The pipeline incorporates distortion correction, frame motion estimation and two levels of slice to volume registration. The proposed method has been tested on moving adult and fetal fMRI data.

A crucial step in studying brain connectivity is the definition of the Regions Of Interest (ROI's) which are considered as nodes of a network graph. These ROI's identified in structural imaging reflect consistent functional regions in the anatomies being compared. However in serial studies of the developing fetal brain such functional and associated structural markers are not consistently present over time. In this study we adapt two non-atlas based parcellation schemes to study the development of connectivity networks of a fetal monkey brain using Diffusion Weighted Imaging techniques. Results demonstrate that the fetal brain network exhibits small-world characteristics and a pattern of increased cluster coefficients and decreased global efficiency. These findings may provide a route to creating a new biomarker for healthy fetal brain development.

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For deformable registration of computed tomography (CT) scans in image guided radiation therapy (IGRT) we apply Riemannian elasticity regularization. We explore the use of spatially varying elasticity parameters to encourage bone rigidity and local tissue volume change only in the gross tumor volume (GTV) and the lungs. We evaluate the method on the point-validated 4DCT breathing thorax POPI-model and demonstrate its use and properties in registration of pre-and post-chemo CT scans for contour propagation in a Hodgkin lymphoma (HL) case showing significant tumor shrinkage. For the POPI-model we achieved a total mean target registration error (TRE) of 0.92 ± 0.49 mm. Using spatially varying regularization for the HL case, deformation was limited to the GTV and lungs.

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