Browsing by Subject "motion correction"

Diffusion tensor imaging (DTI) has emerged as a unique method to characterize brain tissue microstructure non-invasively. DTI typically provides the ability to study white matter structure with a standard voxel resolution of 8μL over imaging field-of-views of the extent of the human brain. As such, it has long been recognized as a promising tool not only in clinical research for the diagnostic and monitoring of white matter diseases, but also for investigating the fundamental biological principles underlying the organization of long and short-range cortical networks. However, the complexity of brain structure within an MRI voxel makes it difficult to dissociate the tissue origins of the measured anisotropy. The tensor characterization is a composite result of proton pools in different tissue and cell structures with diverse diffusion properties. As such, partial volume effects introduce a strong bias which can lead to spurious measurements, especially in regions with a complex tissue structure such as interdigitating crossing fibers or in convoluted cortical folds near the grey/white matter interface.

This dissertation focuses on the design and development of acquisition and image reconstruction strategies to improve the spatial resolution of diffusion imaging. After a brief review of the theory of diffusion MRI and of the basic principles of streamline tractography in the human brain, the main challenges to increasing the spatial resolution are discussed. A comprehensive characterization of artifacts due to motion and field inhomogeneities is provided and novel corrective methods are proposed to enable the acquisition of diffusion weighted data with 2D mulitslice imaging techniques with full brain coverage, increased SNR and high spatial resolutions of 1.25×1.25×1.25 mm3 within an acceptable scan time. The method is extended to a multishot k_{_z}-encoded 3D multislab spiral DTI and evaluated in normal human volunteers.

To demonstrate the increased SNR and enhanced resolution capability of the proposed methods and more generally to assess the value of high-spatial resolution in diffusion imaging, a study of cortical depth-dependence of fractional anisotropy was performed at an unprecedented in-vivo inplane resolution of 0.390×0.390μm2 and an investigation of the trade-offs between spatial resolution and cortical specificity was conducted within the connectome framework.

Ophthalmology is the only surgical specialty that routinely employs both microsurgical techniques and live intraoperative imaging, especially optical coherence tomography (OCT). Consequently, ophthalmic surgeons routinely face challenges in visualization and manipulation of small ocular tissues or in interpreting intraoperative imaging to guide their actions. This dissertation therefore seeks to advance the state-of-the-art in ophthalmic surgery with regards to manipulation, visualization, and interpretation.

First, we address the interpretation challenge in ophthalmic surgery where live volumetric imaging from OCT systems recently incorporated into surgical microscopes has freed surgeons from the otherwise universal top-down viewpoint. These new viewpoints, however, disorient surgeons when directions of their hand motions and viewed tool motions do not align. Thus, we introduce a robotic surgery paradigm to decouple surgeons' hands from their tools and ensure that viewed tool motions align in arbitrary viewpoints. We implement this concept in a physical testbed system for performing macroscopic tasks and evaluate this system through a user study with mock surgical procedures.

Next, we consider immersive virtual reality (VR) as a technique for displaying complex images and thereby overcome the visualization challenge of conveying intraoperative OCT to surgeons. Far from "blinded" to the outside world, an VR-immersed surgeon potentially has access to much more information than they could see when obligated to direct their attention through the microscope oculars alone. To provide a compelling visual experience, however, immersive VR systems require complete control over users' visual inputs and thus frequently cause motion sickness with framerates lower than 90 fps per eye. By contrast, modern volumetric OCT visualization techniques typically render at no more than 30 fps. Therefore, we introduce GPU approaches and data organization techniques for high-frame rate ray casting at 180 fps. We conduct performance analyses of these techniques, develop an interactive VR-OCT viewer, and demonstrate guidance of mock surgical procedures exclusively by live OCT and video feedthrough from within immersive VR.

Then, we focus on deep anterior lamellar keratoplasty (DALK), a promising technique for corneal transplantation, that poses such significant manipulation and visualization challenges that 59% of procedures fail. In DALK, surgeons must insert a needle 90% through the 500 μm cornea without penetrating its underlying membrane using a surgical microscope with poor depth perception. We propose a robot-assisted solution to jointly solve the manipulation and visualization challenges using a cooperatively-controlled, precise robot arm and live OCT imaging, respectively. We develop this DALK workstation with a commercial robot arm and a custom OCT scanner, evaluate its effectiveness for cooperative needle insertions in a study with corneal fellows, and assess its ability to perform fully automatic needle insertions.

Finally, we mitigate the visualization challenge surgeons face when obtaining OCT images of incompletely stabilized eyes, as happens frequently during procedures with only conscious sedation.

We introduce a robotically-aligned OCT scanner capable of automatic eye imaging without chinrests using a hybrid macro-mini approach. This same approach also enables an expanded ability to image non-surgical patients when chinrest stabilization is infeasible or when a trained ophthalmic photographer is unavailable. We validate the concept for anterior imaging in model eyes and perform both anterior and retinal fully autonomous imaging in human subjects.

Overall, these contributions have the potential to change ophthalmic and other surgeries with intraoperative 3D imaging in fundamental ways. By breaking down manipulation, visualization, and interpretation challenges, robotics and VR promise procedures that are more efficient for patients and more ergonomic for surgeons.