The pulvinar of the thalamus is a higher-order thalamic nucleus that is responsible for gating information flow to the cortical regions of the brain. It is involved in several cortico-thalamocortical relay circuits and is known to be affected in a number of neurological disorders. Segmenting the pulvinar in clinically acquired images is important to support studies exploring its role in brain function. In recent years, we have proposed an active shape model method to segment multiple thalamic nuclei, including the pulvinar. The model was created by manual delineation of high resolution 7T images and the process was guided by the Morel stereotactic atlas. However, this model is based on a small library of healthy subjects, and it is important to validate the reliability of the segmentation method on a larger population of clinically acquired images. The pulvinar is known to have particularly strong white matter connections to the hippocampus, which allows us to identify the pulvinar from thalamic regions of high hippocampal structural connectivity. In this study, we obtained T1-weighted and diffusion MR data from 43 healthy volunteers using a clinical 3T MRI scanner. We applied the segmentation method to the T1-weighted images to obtain the intrathalamic nuclei, and we calculated the connectivity maps between the hippocampus and thalamus using the diffusion images. Our results show that the shape model segmentation consistently localizes the pulvinar in the region with the highest hippocampal connectivity. The proposed method can be extended to other nuclei to further validate our segmentation method.
Anecdotally, surgeons sometimes observe large errors when using image guidance in endonasal surgery. We hypothesize that one contributing factor is the possibility that operating room personnel might accidentally bump the optically tracked rigid body attached to the patient after registration has been performed. In this paper we explore the registration error at the skull base that can be induced by simulated bumping of the rigid body, and find that large errors can occur when simulated bumps are applied to the rigid body. To address this, we propose a new fixation method for the rigid body based on granular jamming (i.e. using particles like ground coffee). Our results show that our granular jamming fixation prototype reduces registration error by 28%-68% (depending on bump direction) in comparison to a standard Brainlab reference headband.