A Study of Efficiency, Accuracy, and Robustness in Intensity-Based Rigid Image Registration

Image registration is widely used in different areas nowadays. Usually, the efficiency, accuracy, and robustness in the registration process are concerned in applications. This thesis studies these issues by presenting an efficient intensity-based mono-modality rigid 2D-3D image registration method and constructing a novel mathematical model for intensity-based multi-modality rigid image registration. For mono-modality image registration, an algorithm is developed using RapidMind Multi-core Development Platform (RapidMind) to exploit the highly parallel multi-core architecture of graphics processing units (GPUs). A parallel ray casting algorithm is used to generate the digitally reconstructed radiographs (DRRs) to efficiently reduce the complexity of DRR construction. The optimization problem in the registration process is solved by the Gauss-Newton method. To fully exploit the multi-core parallelism, almost the entire registration process is implemented in parallel by RapidMind on GPUs. The implementation of the major computation steps is discussed. Numerical results are presented to demonstrate the efficiency of the new method. For multi-modality image registration, a new model for computing mutual information functions is devised in order to remove the artifacts in the functions and in turn smooth the functions so that optimization methods can converge to the optimal solutions accurately and efficiently. With the motivation originating from the objective to harmonize the discrepancy between the image presentation and the mutual information definition in previous models, the new model computes the mutual information function using both the continuous image function representation and the mutual information definition for continuous random variables. Its implementation and complexity are discussed and compared with other models. The mutual information computed using the new model appears quite smooth compared with the functions computed by others. Numerical experiments demonstrate the accuracy and efficiency of optimization methods in the case that the new model is used. Furthermore, the robustness of the new model is also verified

Optimal Correction of The Slice Timing Problem and Subject Motion Artifacts in fMRI

Functional magnetic resonance imaging (fMRI) is an extremely popular investigative and clinical imaging tool that allows safe and noninvasive study of the functional living brain. Fundamentally, fMRI measures a physiological signal as it changes over time. The manner in which this spatio-temporal signal is acquired can create technical challenges during image reconstruction that must be corrected for if any meaningful information is to be extracted from the data. Two particular challenges that are fundamentally intertwined with each other are temporal misalignment and spatial misalignment. Temporal misalignment is due to the nature of fMRI acquisition protocols themselves: a 3D volume is created by sampling and stacking multiple 2D slices. However, these slices are not acquired simultaneously or sequentially, and therefore will always be temporally misaligned with each other. Spatial misalignment arises when subject motion is present during the scan, resulting in individual volumes being spatially misaligned with each other. Spatial and temporal misalignment are not independent from each other, and their interaction can cause additional artifacts and reconstruction challenges if not addressed properly. The purpose of this thesis is to critically examine the problem of both spatial and temporal misalignment from a signal processing perspective, while considering the physical nature and origin of the signal itself, and develop optimal correction routines for spatial and temporal misalignment and their associated artifacts. One of the most immediate problems associated with temporal misalignment is that the order in which the slices are acquired must be known in order for correction to be possible. Surprisingly, this information is rarely provided with old or shared data, meaning that this critical preprocessing step must be skipped, significantly lowering the value of the data. We use the spatio-temporal properties of the fMRI signal to develop a robust and accurate algorithm to infer the slice acquisition order retrospectively from any fMRI scan. The ability to extract the interleave parameter from any data set allows us to perform slice timing correction even if this information had been lost, or was not provided with the scan. In the next section of this work, we develop a new optimal method of slice timing correction (Filter-Shift) based on the fundamental properties of sampling theory in digital signal processing. By examining the properties of the signal of interest (The blood oxygen level depended signal: BOLD signal), we are able to design and implement an effective FIR filter to simultaneously remove noise and reconstruct the signal of interest at any shifted offset, without the need for sub-optimal interpolation. In the final section, we investigate the effects of different motion types on the MR signal based on the Bloch equation, in order to develop a theoretical foundation from which we can create an optimal correction method. We devise a novel method to remove these artifacts: Discrete reconstruction of irregular fMRI trajectory (DRIFT). Our method calculates the exact displacement of the k-space samples due to motion at each dwell time and retrospectively corrects each slice of the fMRI volume using an inverse nonuniform Fourier transform. We conclude that a hybrid approach with both prospective and retrospective components are essentially required for optimal removal of motion artifacts from the fMRI data. The combined work of this thesis provides two theoretically sound and extremely effective correction routines, that both remove artifacts and restore the underlying sampled signal. Motion correction and slice timing correction are typically the first two preprocessing steps to be applied to any fMRI data, and thus provide the foundation for any further analysis. While many other preprocessing steps can be omitted or included depending on the analysis, motion correction and slice timing correction are unequivocally beneficial and necessary for accurate and reliable results. This work provides a theoretical and quantitative framework that describes the optimal removal of artifacts associated with motion and slice timing

Knowledge Based Measurement Of Enhancing Brain Tissue In Anisotropic Mr Imagery

Medical Image Analysis has emerged as an important field in the computer vision community. In this thesis, two important issues in medical imaging are addressed and a solution for each is derived and synergistically combined as one coherent system. Firstly, a novel approach is proposed for High Resolution Volume (HRV) construction by combining different frequency components at multiple levels, which are separated by using a multi-resolution pyramid structure. Current clinical imaging protocols make use of multiple orthogonal low resolution scans to measure the size of the tumor. The highly anisotropic data result in difficulty and even errors in tumor assessment. In previous approaches, simple interpolation has been used to construct HRVs from multiple low resolution volumes (LRVs), which fail when large inter-plane spacing is present. In our approach, Laplacian pyramids containing band-pass contents are first computed from registered LRVs. The Laplacian images are expanded in their low resolution axes separately and then fused at each level. A Gaussian pyramid is recovered from the fused Laplacian pyramid, where a volume at the bottom level of the Gaussian pyramid is the constructed HRV. The effectiveness of the proposed approach is validated by using simulated images. The method has also been applied to real clinical data and promising experimental results are demonstrated. Secondly, a new knowledge-based framework to automatically quantify the volume of enhancing tissue in brain MR images is proposed. Our approach provides an objective and consistent way to evaluate disease progression and assess the treatment plan. In our approach, enhanced regions are first located by comparing the difference between the aligned set of pre- and post-contrast T1 MR images. Since some normal tissues may also become enhanced by the administration of Gd-DTPA, using the intensity difference alone may not be able to distinguish normal tissue from the tumor. Thus, we propose a new knowledge-based method employing knowledge of anatomical structures from a probabilistic brain atlas and the prior distribution of brain tumor to identify the real enhancing tissue. Our approach has two main advantages. i) The results are invariant to the image contrast change due to the usage of the probabilistic knowledge-based framework. ii) Using the segmented regions instead of independent pixels facilitates an approach that is much less sensitive to small registration errors and image noise. The obtained results are compared to the ground truth for validation and it is shown that the proposed method can achieve accurate and consistent measurements

A Method for Predicting Dose Changes for HN Treatment Using Surface Imaging

Head and neck cancer is commonly treated with a six- to seven-week course of radiotherapy, during which a patient’s anatomy may change substantially, due to target reduction or weight loss. Anatomical changes lead to reduction in treatment quality due to decreased setup reproducibility and altered dose deposition compared to the original plan. Few clinics have developed a standard method for triggering resimulation and replan due to anatomic changes. This work investigates a new method for determining when to resimulate and replan HNC patients by utilizing their topographic anatomical changes to predict differences in planned versus delivered dose distributions. The first part of the work presents a method for deformable image registration of CT to CBCT which addresses the challenges of inaccurate Hounsfield units and truncated field of view present in CBCT. The registration method was validated on 10 HN patients using contour comparison, with average DSC of 0.82, 0.74, 0.72, and 0.69 for mandible, cord, and left and right parotid. The registration method was then used to generate dose maps and surface contours for 47 patients for the second part of this work, the development of a U-Net which takes the original dose distribution, the original surface, and the treatment day surface as input and predicts the treatment day dose distribution as output. The average RMSE and MAE between the true and predicted dose distributions for a test set of 6 patients was 4.25 and 2.15. This work proves feasibility of a dose prediction neural network using surface imaging

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Use of Multicomponent Non-Rigid Registration to Improve Alignment of Serial Oncological PET/CT Studies

Non-rigid registration of serial head and neck FDG PET/CT images from a combined scanner can be problematic. Registration techniques typically rely on similarity measures calculated from voxel intensity values; CT-CT registration is superior to PET-PET registration due to the higher quality of anatomical information present in this modality. However, when metal artefacts from dental fillings are present in a pair of CT images, a nonrigid registration will incorrectly attempt to register the two artefacts together since they are strong features compared to the features that represent the actual anatomy. This leads to localised registration errors in the deformation field in the vicinity of the artefacts. Our objective was to develop a registration technique which overcomes these limitations by using combined information from both modalities. To study the effect of artefacts on registration, metal artefacts were simulated with one CT image rotated by a small angle in the sagittal plane. Image pairs containing these simulated artifacts were then registered to evaluate the resulting errors. To improve the registration in the vicinity where there were artefacts, intensity information from the PET images was incorporated using several techniques. A well-established B-splines based non-rigid registration code was reworked to allow multicomponent registration. A similarity measure with four possible weighted components relating to the ways in which the CT and PET information can be combined to drive the registration of a pair of these dual-valued images was employed. Several registration methods based on using this multicomponent similarity measure were implemented with the goal of effectively registering the images containing the simulated artifacts. A method was also developed to swap control point displacements from the PET-derived transformation in the vicinity of the artefact. This method yielded the best result on the simulated images and was evaluated on images where actual dental artifacts were present

Left Ventricle Myocardium Segmentation from 3D Cardiac MR Images using Combined Probabilistic Atlas and Graph Cut-based Approaches

Medical imaging modalities, including Computed Tomography (CT) Magnetic Resonance Imaging (MRI) and Ultrasound (US) are critical for the diagnosis and progress monitoring of many cardiac conditions, planning, visualization and delivery of therapy via minimally invasive intervention procedures, as well as for teaching, training and simulation applications. Image segmentation is a processing technique that allows the user to extract the necessary information from an image dataset, in the form of a surface model of the region of interest from the anatomy. A wide variety of segmentation techniques have been developed and implemented for cardiac MR images. Despite their complexity and performance, many of them are intended for specific image datasets or are too specific to be employed for segmenting classical clinical quality Magnetic Resonance (MR) images. Graph Cut based segmentation algorithms have been shown to work well in regards to medical image segmentation. In addition, they are computationally efficient, which scales well to real time applications. While the basic graph cuts algorithms use lower-order statistics, combining this segmentation approach with atlas-based methods may help improve segmentation accuracy at a lower computational cost. The proposed technique will be tested at each step during the development by assessing the segmentation results against the available ground truth segmentation. Several metrics will be used to quantify the performance of the proposed technique, including computational performance, segmentation accuracy and fidelity assessed via the Sørensen-Dice Coefficient (DSC), Mean Absolute Distance (MAD) and Hausdorff Distance (HD) metrics

Registration Methods for Quantitative Imaging

At the core of most image registration problems is determining a spatial transformation that relates the physical coordinates of two or more images. Registration methods have become ubiquitous in many quantitative imaging applications. They represent an essential step for many biomedical and bioengineering applications. For example, image registration is a necessary step for removing motion and distortion related artifacts in serial images, for studying the variation of biological tissue properties, such as shape and composition, across different populations, and many other applications. Here fully automatic intensity based methods for image registration are reviewed within a global energy minimization framework. A linear, shift-invariant, stochastic model for the image formation process is used to describe several important aspects of typical implementations of image registration methods. In particular, we show that due to the stochastic nature of the image formation process, most methods for automatic image registration produce answers biased towards `blurred' images. In addition we show how image approximation and interpolation procedures necessary to compute the registered images can have undesirable effects on subsequent quantitative image analysis methods. We describe the exact sources of such artifacts and propose methods through which these can be mitigated. The newly proposed methodology is tested using both simulated and real image data. Case studies using three-dimensional diffusion weighted magnetic resonance images, diffusion tensor images, and two-dimensional optical images are presented. Though the specific examples shown relate exclusively to the fields of biomedical imaging and biomedical engineering, the methods described are general and should be applicable to a wide variety of imaging problems