Discrete Optimisation for Group-Wise Cortical Surface Atlasing

Glocker, Ben; Rajchl, Martin; Rueckert, Daniel

doi:10.1109/cvprw.2016.62

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…One avenue may be to explore combining hyper-alignment (Langs et al, 2010; Haxby et al, 2011), or graph matching (Ktena et al, 2016), approaches with spatially-constrained registration, such that constraints are placed to ensure that regions cannot be matched if they are very far apart in space (Iordan et al, 2016). Alternatively, in Robinson et al (2016) we propose a group-wise registration scheme that accounts for topological variation though minimisation of rank of the feature set across the group.…”

Section: Discussionmentioning

confidence: 99%

Multimodal surface matching with higher-order smoothness constraints

et al. 2018

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In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

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Section: Discussionmentioning

confidence: 99%

Multimodal surface matching with higher-order smoothness constraints

et al. 2018

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“…Although, there is a potential for methods such as those proposed by Wang et al (2015), which make use of use intermediate templates or (Iordan et al, 2016), which combine global spatially constrained alignment with local hyper-alignment techniques. Another possible fix might be the use of groupwise methods such as (Robinson et al, 2016), where this is performed through a discrete optimisation framework that seeks to simultaneously improve pairwise correspondences between surface feature sets, while minimising a global cost relating to the rank of the features.…”

Section: Discussionmentioning

confidence: 99%

Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

et al. 2018

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We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (36-44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.

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