Building a high-resolution in vivo minimum deformation average model of the human hippocampus

Abstract

ObjectiveMinimum deformation averaging (MDA) procedures exploit the information contained in inter-individual variations to generate high-resolution, high-contrast models through iterative model building. However, MDA models built from different image contrasts reside in disparate spaces and their complementary information cannot be utilized easily. The aim of this work was to develop an algorithm for the non-linear alignment of two MDA models with different contrasts to create a high-resolution in vivo model of the human hippocampus with a spatial resolution of 300 μm.MethodsA Turbo Spin Echo MDA model covering the hippocampus was contrast matched to a whole-brain MP2RAGE MDA model and aligned using cross-correlation and non-linear transformation. The contrast matching algorithm followed a global voxel location-based approach to estimate the relationship between intensity values of the two models. The performance of the algorithm was evaluated by comparing it to a non-linear registration obtained using mutual information without contrast matching. The complimentary information from both contrasts was then utilized in an automated hippocampal subfield segmentation pipeline.ResultsThe contrast of the Turbo Spin Echo MDA model could successfully be matched to the MP2RAGE MDA model. Registration using cross correlation provided more accurate alignment of the models compared to a mutual information based approach. The segmentation using ASHS resulted in hippocampal subfield delineations that resembled the tissue boundaries observed in the Turbo Spin Echo MDA model.ConclusionThe developed contrast matching algorithm facilitated the creation of a high-resolution multi-modal in vivo MDA model of the human hippocampus. This model can be used to improve algorithms for hippocampal subfield segmentation and could potentially support the early detection of neurodegenerative diseases.

Non-Linear Realignment Improves Hippocampus Subfield Segmentation

“…Imaging the subfields of the hippocampal formation with Magnetic Resonance Imaging (MRI) has garnered great interest in recent years. Characterising subfield tissue may elucidate susceptibility to -and provide more sensitive biomarkers forneurodegenerative diseases including Alzheimer's disease (AD) (Balachandar et al, 2015;Boutet et al, 2014;Henry et al, 2011;Jacobsen et al, 2017;Kerchner et al, 2012;La Joie et al, 2013;Maruszak & Thuret, 2014;Pluta, Yushkevich, Das, & Wolk, 2012), as the subfields contain different cell types and functional properties (Duvernoy, Cattin, Risold, Vannson, & Gaudron, 2013). Increasing spatial resolution for MRI through improved acquisition and/or post-processing techniques improves segmentation of various tissue types or anatomical structures (Thomas et al, 2008).…”

“…We have previously utilised three repetitions of a four minute TSE 4 sequence (12 minutes total acquisition time) that cover the whole hippocampus with a high in-plane resolution of 0.4 mm 2 x 0.4 mm 2 and a 0.8 mm 2 slice thickness. These acquisitions were combined to yield a minimum deformation average model for investigating hippocampus subfields (Jacobsen et al, 2017). The sequence was designed to be repeated thrice to both a) boost SNR and b) to be able to potentially discard one of the repetitions in case of participant movement.…”

Non-linear realignment improves hippocampus subfield segmentation reliability

“…Imaging the subfields of the hippocampal formation with Magnetic Resonance Imaging (MRI) has garnered great interest in recent years. Characterising subfield tissue may elucidate susceptibility to -and provide more sensitive biomarkers forneurodegenerative diseases including Alzheimer's disease (AD) (Balachandar et al, 2015;Boutet et al, 2014;Henry et al, 2011;Jacobsen et al, 2017;Kerchner et al, 2012;La Joie et al, 2013;Maruszak & Thuret, 2014;Pluta, Yushkevich, Das, & Wolk, 2012), as the subfields contain different cell types and functional properties (Duvernoy, Cattin, Risold, Vannson, & Gaudron, 2013). Increasing spatial resolution for MRI through improved acquisition and/or post-processing techniques improves segmentation of various tissue types or anatomical structures (Thomas et al, 2008).…”

“…The copyright holder for this preprint (which was not this version posted April 5, 2019. ; https://doi.org/10.1101/597856 doi: bioRxiv preprint sequence (12 minutes total acquisition time) that cover the whole hippocampus with a high in-plane resolution of 0.4 mm 2 x 0.4 mm 2 and a 0.8 mm 2 slice thickness. These acquisitions were combined to yield a minimum deformation average model for investigating hippocampus subfields (Jacobsen et al, 2017). The sequence was designed to be repeated thrice to both a) boost SNR and b) to be able to potentially discard one of the repetitions in case of participant movement.…”