Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Non-contact strain determination of cell traction effects

Irreversible tissue damage leading to organ failure is a common health problem in today's world. Regenerating these damaged tissues with the help of scaffolds is the solution offered by tissue engineering. In cases where the extra-cellular matrix (ECM) is to be replaced by an artificial substrate (scaffold) or matrix, cellular traction forces (CTF) are exerted by the cells on the scaffold surface. An ideal scaffold should exhibit mechanical characteristics similar to those of the ECM it is intended to replace. In other words, the capacity of a scaffold to withstand deformation should be comparable to that of a natural ECM. And with knowledge of those forces and deformations the properties of the scaffolds may be inferred. Digital Image Correlation (DIC), a non-contact image analysis technique enables us to measure point to point deformation of the scaffold by comparing a sequence of images captured during the process of scaffold deformation. This review discusses the methodology involved and implementation of DIC to measure displacements and strain.Irreversible tissue damage leading to organ failure is a common health problem in today's world. Regenerating these damaged tissues with the help of scaffolds is the solution offered by tissue engineering. In cases where the extra-cellular matrix (ECM) is to be replaced by an artificial substrate (scaffold) or matrix, cellular traction forces (CTF) are exerted by the cells on the scaffold surface. An ideal scaffold should exhibit mechanical characteristics similar to those of the ECM it is intended to replace. In other words, the capacity of a scaffold to withstand deformation should be comparable to that of a natural ECM. And with knowledge of those forces and deformations the properties of the scaffolds may be inferred. Digital Image Correlation (DIC), a non-contact image analysis technique enables us to measure point to point deformation of the scaffold by comparing a sequence of images captured during the process of scaffold deformation. This review discusses the methodology involved and implementation of DIC to measure displacements and strain

Image-Guided Abdominal Surgery and Therapy Delivery

ABSTRACT Image-Guided Surgery has become the standard of care in intracranial neurosurgery providing more exact resections while minimizing damage to healthy tissue. Moving that process to abdominal organs presents additional challenges in the form of image segmentation, image to physical space registration, organ motion and deformation. In this paper, we present methodologies and results for addressing these challenges in two specific organs: the liver and the kidney