Browsing by Subject "TENSOR"

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  • Sairanen, V.; Leemans, A.; Tax, C. M. W. (2018)
    The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.
  • Sairanen, Viljami; Ocampo-Pineda, Mario; Granziera, Cristina; Schiavi, Simona; Daducci, Alessandro (2022)
    A B S T R A C T The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been pro-posed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Con-vex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical stud-ies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.
  • Buchert, T.; Carfora, M.; Ellis, G. F. R.; Kolb, E. W.; MacCallum, M. A. H.; Ostrowski, J. J.; Räsänen, S.; Roukema, B. F.; Andersson, L.; Coley, A. A.; Wiltshire, D. L. (2015)
    No. In a number of papers, Green and Wald argue that the standard FLRW model approximates our Universe extremely well on all scales, except close to strong-field astrophysical objects. In particular, they argue that the effect of inhomogeneities on average properties of the Universe (backreaction) is irrelevant. We show that this latter claim is not valid. Specifically, we demonstrate, referring to their recent review paper, that (i) their two-dimensional example used to illustrate the fitting problem differs from the actual problem in important respects, and it assumes what is to be proven; (ii) the proof of the trace-free property of backreaction is unphysical and the theorem about it fails to be a mathematically general statement; (iii) the scheme that underlies the trace-free theorem does not involve averaging and therefore does not capture crucial non-local effects; (iv) their arguments are to a large extent coordinate-dependent, and (v) many of their criticisms of backreaction frameworks do not apply to the published definitions of these frameworks. It is therefore incorrect to infer that Green and Wald have proven a general result that addresses the essential physical questions of backreaction in cosmology.