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

Bayesian Local Smoothing Modeling and Inference for Pre-surgical FMRI Data.

There is a growing interest in using fMRI measurements and analyses as tools for pre-surgical planning. For such applications, spatial precision and control over false negatives and false positives are vital, requiring careful design of an image smoothing method and a classification procedure. This dissertation seeks computationally efficient approaches to overcome the limitation of existing methods and address new challenges in pre-surgical fMRI analyses. In the first study, we develop a Bayesian solution for the pre-surgical analysis of a single fMRI brain image. Specifically, we propose a novel spatially adaptive conditionally autoregressive model (CWAS) that adaptively and locally smoothes the fMRI data. We introduce a Bayesian theoretical decision approach that allows control of both false positives and false negatives to identify activated and deactivated brain regions. We benchmark the proposed solution to two existing spatially adaptive smoothing models, through simulation studies and two patients' pre-surgical fMRI datasets. In the second study, we extend the idea of spatially adaptive smoothing to multiple fMRI brain images in order to leverage spatial correlations across multiple images. In particular, we propose three spatially adaptive multivariate conditional autoregressive models that can be considered as extensions of the multivariate conditional autoregressive (MCAR) model (Gelfand and Vounatsou, 2003), the CWAS model, and the model of Reich and Hodges (2008), respectively, and one mixed-effects model assuming that all observed fMRI images originate from one common image. We compare the performance of the proposed models with those from the MCAR and CWAS models using simulation studies and two sets of fMRI brain images, acquired either from the same patient, same paradigm or same patient, different paradigms. The last study is motivated by fMRI brain images acquired at two different spatial resolutions from the same patient. We develop a Bayesian hierarchical model with spatially varying coefficients to retain the spatial precision from the high resolution image while utilizing information from the low resolution image to improve estimation and inference. Comparisons between the proposed model and the CWAS model, which operates at a single spatial resolution, are performed on simulated data and a patient's multi-resolution pre-surgical fMRI data.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133339/1/zhuqingl_1.pd

2D and 3D surface image processing algorithms and their applications

This doctoral dissertation work aims to develop algorithms for 2D image segmentation application of solar filament disappearance detection, 3D mesh simplification, and 3D image warping in pre-surgery simulation. Filament area detection in solar images is an image segmentation problem. A thresholding and region growing combined method is proposed and applied in this application. Based on the filament area detection results, filament disappearances are reported in real time. The solar images in 1999 are processed with this proposed system and three statistical results of filaments are presented. 3D images can be obtained by passive and active range sensing. An image registration process finds the transformation between each pair of range views. To model an object, a common reference frame in which all views can be transformed must be defined. After the registration, the range views should be integrated into a non-redundant model. Optimization is necessary to obtain a complete 3D model. One single surface representation can better fit to the data. It may be further simplified for rendering, storing and transmitting efficiently, or the representation can be converted to some other formats. This work proposes an efficient algorithm for solving the mesh simplification problem, approximating an arbitrary mesh by a simplified mesh. The algorithm uses Root Mean Square distance error metric to decide the facet curvature. Two vertices of one edge and the surrounding vertices decide the average plane. The simplification results are excellent and the computation speed is fast. The algorithm is compared with six other major simplification algorithms. Image morphing is used for all methods that gradually and continuously deform a source image into a target image, while producing the in-between models. Image warping is a continuous deformation of a: graphical object. A morphing process is usually composed of warping and interpolation. This work develops a direct-manipulation-of-free-form-deformation-based method and application for pre-surgical planning. The developed user interface provides a friendly interactive tool in the plastic surgery. Nose augmentation surgery is presented as an example. Displacement vector and lattices resulting in different resolution are used to obtain various deformation results. During the deformation, the volume change of the model is also considered based on a simplified skin-muscle model

Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations

Rationale and Objectives: Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods: Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results: Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion: The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice

Three-dimensional morphanalysis of the face.

The aim of the work reported in this thesis was to determine the extent to which orthogonal two-dimensional morphanalytic (universally relatable) craniofacial imaging methods can be extended into the realm of computer-based three-dimensional imaging. New methods are presented for capturing universally relatable laser-video surface data, for inter-relating facial surface scans and for constructing probabilistic facial averages. Universally relatable surface scans are captured using the fixed relations principle com- bined with a new laser-video scanner calibration method. Inter- subject comparison of facial surface scans is achieved using inter- active feature labelling and warping methods. These methods have been extended to groups of subjects to allow the construction of three-dimensional probabilistic facial averages. The potential of universally relatable facial surface data for applications such as growth studies and patient assessment is demonstrated. In addition, new methods for scattered data interpolation, for controlling overlap in image warping and a fast, high-resolution method for simulating craniofacial surgery are described. The results demonstrate that it is not only possible to extend universally relatable imaging into three dimensions, but that the extension also enhances the established methods, providing a wide range of new applications

Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations

Rationale and Objectives: Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods: Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results: Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion: The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice

In Vitro Biomechanical Testing and Computational: Modeling in Spine

Two separate in vitro biomechanical studies were conducted on human cadaveric spines (Lumbar) to evaluate the stability following the implantation of two different spinal fixation devices interspinous fixation device (ISD) and Hybrid dynamic stabilizers. ISD was evaluated as a stand-alone and in combination with unilateral pedicle rod system. The results were compared against the gold standard, spinal fusion (bilateral pedicle rod system). The second study involving the hybrid dynamic system, evaluated the effect on adjacent levels using a hybrid testing protocol. A robotic spine testing system was used to conduct the biomechanical tests. This system has the ability to apply continuous unconstrained pure moments while dynamically optimizing the motion path to minimize off-axis loads during testing. Thus enabling precise control over the loading and boundary conditions of the test. This ensures test reliability and reproducibility. We found that in flexion-extension, the ISD can provide lumbar stability comparable to spinal fusion. However, it provides minimal rigidity in lateral bending and axial rotation when used as a stand-alone. The ISD with a unilateral pedicle rod system when compared to the spinal fusion construct were shown to provide similar levels of stability in all directions, though the spinal fusion construct showed a trend toward improved stiffness overall. The results for the dynamic stabilization system showed stability characteristics similar to a solid all metal construct. Its addition to the supra adjacent level (L3- L4) to the fusion (L4- L5) indeed protected the adjacent level from excessive motion. However, it essentially transformed a 1 level into a 2 level lumbar fusion with exponential transfer of motion to the fewer remaining discs (excessive adjacent level motion). The computational aspect of the study involved the development of a spine model (single segment). The kinematic data from these biomechanical studies (ISD study) was then used to validate a finite element model

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

LocoMouse: a novel system for studying the role of cerebellum in gait coordination

Smooth and efficient walking requires the coordination of movement across different parts of the body. The cerebellum plays an important role in this process, yet the specific neural circuit mechanisms of whole-body coordination are poorly understood. Although sophisticated genetic tools exist to manipulate the cerebellar circuit in mice, analyses of mouse gait have typically been limited to gross performance measures and lack detail about precision and timing of limb movements. In this project, I developed an automated, high-throughput, markerless 3D tracking system (LocoMouse) for quantifying locomotion in freely walking mice. Using LocoMouse, I showed that locomotor parameters for individual limbs vary systematically with mouse walking speed and body size. In visibly ataxic Purkinje cell degeneration (pcd) and reeler mice, I found that 3D limb trajectories and, especially, interlimb and whole-body coordination are specifically impaired. Our findings suggest a failure to predict the consequences of movement across joints, limbs, and body. These experiments were essential to establish a quantitative framework for whole-body locomotor coordination in mice (Machado, Darmohray et al. eLife 2015). The LocoMouse system was then combined with optogenetic tools to ask how different output regions of the cerebellum differentially contribute to locomotor coordination. I expressed ChR2 in Purkinje cells and stimulated their terminals in the medial, interposed, and lateral cerebellar nuclei of freely walking mice. Here, I identified locomotor parameters that were specifically related to the manipulation of each nucleus. Acute disruption of neural activity in medial and interposed nuclei immediately perturbed ongoing locomotion. In contrast, similar manipulation of Purkinje cell inputs to the lateral nucleus had no observable effect on ongoing locomotor behavior. These results are broadly consistent with previous anatomical and lesion studies suggesting a medial-to-lateral functional organization of cerebellar outputs. Taken together, these experiments isolated impairments in interlimb and whole-body coordination in mice with cerebellar manipulations. In contrast, spinal cord mutant mice revealed impairments at the intralimb level with no alteration in the interlimb coordination. I characterized distinct motor deficits associated with manipulations in different brain regions and identified and quantified core features of cerebellar ataxia in mice. These experiments establish the LocoMouse system, combined with genetic manipulations, as a powerful system to dissect cerebellar circuit mechanisms of coordinated locomotion