Optimal acquisition schemes in high angular resolution diffusion weighted imaging

The recent challenge in diffusion imaging is to find acquisition schemes and analysis approaches that call represent non-gaussian diffusion profiles in a clinically feasible measurement time. In this work we investigate the effect of b-value and the number of gradient vector directions oil Q-ball imaging and the Diffusion Orientation Transform (DOT) in a structured away using computational simulations, hardware crossing-fiber diffusion phantoms, and in-vivo brain scans. We observe that DOT is more robust to noise and independent of the b-value and number of gradients, whereas Q-ball dramatically improves the results for higher b-values and number of gradients and at recovering larger angles of crossing. We also show that Laplace-Beltrami regularization has wide applicability and generally improves the properties of DOT. Knowledge, of optimal acquisition schemes for HARDI can improve the utility, of diffusion weighted MR, imaging in the clinical setting for the diagnosis of white matter diseases and presurgical planning

Optimal acquisition schemes in high angular resolution diffusion weighted imaging

The recent challenge in diffusion imaging is to find acquisition schemes and analysis approaches that can represent non-gaussian diffusion profiles in a clinically feasible measurement time. In this work we investigate the effect of b-value and the number of gradient vector directions on Q-ball imaging and the Diffusion Orientation Transform (DOT) in a structured way using computational simulations, hardware crossing-fiber diffusion phantoms, and in-vivo brain scans. We observe that DOT is more robust to noise and independent of the b-value and number of gradients, whereas Q-ball dramatically improves the results for higher b-values and number of gradients and at recovering larger angles of crossing. We also show that Laplace-Beltrami regularization has wide applicability and generally improves the properties of DOT. Knowledge of optimal acquisition schemes for HARDI can improve the utility of diffusion weighted MR imaging in the clinical setting for the diagnosis of white matter diseases and presurgical planning

Relationships of Osteoporosis Health Beliefs to Practiced Exercise Behaviors of Women

The purpose of this study was to examine the relationship of health beliefs contained in the Health Belief Model to practiced exercise behavior of women. A descriptive correlation design was used with a convenience sample of 201 women. The revised version of the Osteoporosis Health Belief Exercise Scale developed by Kim, Horan, Gendler and Patel (1991b) was used to measure health beliefs related to osteoporosis. The ARIC/Baecke questionnaire of Habitual Physical Activity was used to measure life style physical activity. Health motivation and exercise benefits were found to be positively correlated to exercise behavior. However, susceptibility and exercise barriers were inversely correlated to exercise behavior. Perceived exercise barriers and health motivation explained the greatest variance in exercise behaviors. The Health Belief Model can be used as a guide by nurses to promote health behaviors consistent with research findings

Improved Framework for Tractography Reconstruction of the Optic Radiation - Fig 4

<p>Streamlines of the reconstructed OR in five patients with multiple sclerosis: (a) Lesion masks is shown in red. (b) Probabilistic streamlines fiber tracking by iFOD2. (c) Probabilistic streamlines fiber tracking by high order integration over fiber orientation distributions (iFOD2) adding the anatomical exclusion criteria (AEC).</p

Tractography reconstruction framework of the optic radiations.

<p>(1) Standard preprocessing of the DWIs including Echo Planar Imaging distortion correction, eddy current distortion correction and head motion correction. (2) Distortion correction of the DWI. (3) Quantitative diffusion fractional anisotropy (FA) mapping. (4-5) Subcortical segmentation and cortical parcellation from FS of the 3D-structural image. (6) Registration of the structural images to the corresponding DWI sequence. (7) Seed and target masks. (8) Probabilistic streamline fiber tracking by high order integration over fiber orientation distributions (iFOD2) derived from constrained spherical deconvolution (CSD) with a maximum harmonic order of 8 and use of ACT during tracking. (9) Conversion of the tract file into a track density image. (10) Exclusion mask comprising CSF, whole contralateral hemisphere and ipsilateral GM regions. (11) Final optic radiation reconstruction in track density image and 3D tract file.</p

Cross-vendor and cross-protocol harmonisation of diffusion MRI data: A comparative study

We present a comparison of five different methods that estimate mappings between scanners for diffusion MRI data harmonisation. The methods are evaluated on a dedicated dataset of the same subjects acquired on three distinct scanners with ‘standard’ and ‘state-of-the-art’ protocols, with the latter having higher spatial and angular resolution. Our results show that cross-vendor harmonisation and spatial/angular resolution enhancement of single-shell diffusion data sets can be performed reliably, although some challenges remain. The dataset is available upon request and can serve as a useful testbed for future method development in cross-site/cross-hardware and cross-vendor diffusion MRI harmonisation

Bland-Altman plots comparing the mean volume of OR in both HARDI datasets.

<p>Left panel corresponds to non-AEC and right panel corresponds to results with AEC method. The volume of optic radiation in each subject is the mean of both hemispheres. Most observed differences between the OR volumes in the two sequences are within mean ± 1.96 SD. Middle line indicate mean differences and dashed lines are limits of agreement, defined as mean difference plus (upper line) and minus (lower line) 1.96 SD of differences.</p

Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the 'HARDI reconstruction challenge' organized in the context of the 'ISBI 2012' conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more timeconsuming, Cartesian-grid scheme. Importantly, we show that simple pre-and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods