Anatomical variability, multi-modal coordinate systems, and precision targeting in the marmoset brain

Localising accurate brain regions needs careful evaluation in each experimental species due to their individual variability. However, the function and connectivity of brain areas is commonly studied using a single-subject cranial landmark-based stereotactic atlas in animal neuroscience. Here, we address this issue in a small primate, the common marmoset, which is increasingly widely used in systems neuroscience. We developed a non-invasive multi-modal neuroimaging-based targeting pipeline, which accounts for intersubject anatomical variability in cranial and cortical landmarks in marmosets. This methodology allowed creation of multi-modal templates (MarmosetRIKEN20) including head CT and brain MR images, embedded in coordinate systems of anterior and posterior commissures (AC-PC) and CIFTI grayordinates. We found that the horizontal plane of the stereotactic coordinate was significantly rotated in pitch relative to the AC-PC coordinate system (10 degrees, frontal downwards), and had a significant bias and uncertainty due to positioning procedures. We also found that many common cranial and brain landmarks (e.g., bregma, intraparietal sulcus) vary in location across subjects and are substantial relative to average marmoset cortical area dimensions. Combining the neuroimaging-based targeting pipeline with robot-guided surgery enabled proof-of-concept targeting of deep brain structures with an accuracy of 0.2 mm. Altogether, our findings demonstrate substantial intersubject variability in marmoset brain and cranial landmarks, implying that subject-specific neuroimaging-based localization is needed for precision targeting in marmosets. The population-based templates and atlases in grayordinates, created for the first time in marmoset monkeys, should help bridging between macroscale and microscale analyses

脳機能の解明を目指すマルチモーダルイメージングの技術開発に関する研究

Tohoku University渡部浩司課

Multi-Modal Partial Surface Matching for Intra-Operative Registration

An important task for computer-assisted surgical interventions is the alignment of pre- and intra-operative spaces allowing the transfer of pre-operative information to the current patient situation, known as intra-operative registration. Registration is usually performed by using markers or image-based techniques. Another approach is the intra-operative acquisition of organ surfaces by 3D range scanners, which are then matched to pre-operatively generated surfaces. However, this approach is not trivial, as methods for intra-operative surface matching must be able to deal with noise, distortions, deformations, and the availability of only partially overlapping, nearly flat surfaces. For these reasons, surface matching for intra-operative registration has so far only been used to account for displacements that occur in local scales, while the actual alignment is still performed manually. The main contributions of this thesis are two different approaches for automatic surface matching in intra-operative environments. The focus here is the registration of surfaces acquired by different modalities, dealing with the aforementioned issues and without relying on unique landmarks. For the first approach, surfaces are converted to graph representations and correspondences between them are identified by means of graph matching. Graphs are obtained automatically by segmenting the surfaces into regions with similar properties. As the graph matching problem is known to be NP-hard, it was solved by iteratively computing node similarity scores, and converting it to a linear assignment problem. In the second approach, correspondences are identified by the selection of two spatial configurations of landmarks that can be better fitted to each other, according to an error metric. This error metric does not only incorporate a fitting error, but also a new measure for spatial configuration reliability. The optimization problem is solved by means of a greedy algorithm. Evaluation of the two approaches was performed with several experiments, simulating intra-operative conditions. While the graph matching approach proved to be robust for the registration of small partial data, the point-based approach proved to be more reliable for noisy surfaces. Apart from being a significant contribution to the field of feature-less partial surface matching, this work represents a great effort towards the achievement of a fully automatic, marker-less, registration system for computer-assisted surgery guidance

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods