Nonrigid Medical Image Registration by Finite-Element Deformable Sheet-Curve Models

Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are the indicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance (MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports a newly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specifically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines is designed to model complex local deformations. The experimental results on both digital phantoms and real MR breast images using the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significant and robust improvement in both boundary alignment and local deformation recovery

Modified mass-spring system for physically based deformation modeling

Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented

Modified mass-spring system for physically based deformation modeling

Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented

Automatic Affine and Elastic Registration Strategies for Multi-dimensional Medical Images

Medical images have been used increasingly for diagnosis, treatment planning, monitoring disease processes, and other medical applications. A large variety of medical imaging modalities exists including CT, X-ray, MRI, Ultrasound, etc. Frequently a group of images need to be compared to one another and/or combined for research or cumulative purposes. In many medical studies, multiple images are acquired from subjects at different times or with different imaging modalities. Misalignment inevitably occurs, causing anatomical and/or functional feature shifts within the images. Computerized image registration (alignment) approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. This dissertation focuses on providing automatic image registration strategies. After a through review of existing image registration techniques, we identified two registration strategies that enhance the current field: (1) an automated rigid body and affine registration using voxel similarity measurements based on a sequential hybrid genetic algorithm, and (2) an automated deformable registration approach based upon a linear elastic finite element formulation. Both methods streamlined the registration process. They are completely automatic and require no user intervention. The proposed registration strategies were evaluated with numerous 2D and 3D MR images with a variety of tissue structures, orientations and dimensions. Multiple registration pathways were provided with guidelines for their applications. The sequential genetic algorithm mimics the pathway of an expert manually doing registration. Experiments demonstrated that the sequential genetic algorithm registration provides high alignment accuracy and is reliable for brain tissues. It avoids local minima/maxima traps of conventional optimization techniques, and does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. The elastic model was shown to be highly effective to accurately align areas of interest that are automatically extracted from the images, such as brains. Using a finite element method to get the displacement of each element node by applying a boundary mapping, this method provides an accurate image registration with excellent boundary alignment of each pair of slices and consequently align the entire volume automatically. This dissertation presented numerous volume alignments. Surface geometries were created directly from the aligned segmented images using the Multiple Material Marching Cubes algorithm. Using the proposed registration strategies, multiple subjects were aligned to a standard MRI reference, which is aligned to a segmented reference atlas. Consequently, multiple subjects are aligned to the segmented atlas and a full fMRI analysis is possible

Deformable mapping technique to correlate lesions in digital breast tomosynthesis and automated breast ultrasound images

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146324/1/mp13113.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146324/2/mp13113_am.pd

Preoperative Systems for Computer Aided Diagnosis based on Image Registration: Applications to Breast Cancer and Atherosclerosis

Computer Aided Diagnosis (CAD) systems assist clinicians including radiologists and cardiologists to detect abnormalities and highlight conspicuous possible disease. Implementing a pre-operative CAD system contains a framework that accepts related technical as well as clinical parameters as input by analyzing the predefined method and demonstrates the prospective output. In this work we developed the Computer Aided Diagnostic System for biomedical imaging analysis of two applications on Breast Cancer and Atherosclerosis. The aim of the first CAD application is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. Base on the fact that automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70±0.03, 0.74±0.03 and 0.81±0.09 (out of 1) for Affine, BSpline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation. The aim of the second implemented CAD application is to assess contribution of calcification in plaque vulnerability and wall rupture and to find its maximum resistance before break in image-based models of carotid artery stenting. The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial in which cardiologists face two major problems during the placement: (i) “plaque protrusion” (i.e. elastic fibrous caps containing early calcifications that penetrate inside the stent); (ii) “plaque vulnerability” (i.e. stiff plaques with advanced calcifications that break the arterial wall or stent). Finite Element Analysis was used to simulate the balloon and stent expansion as a preoperative patient-specific virtual framework. A nonlinear static structural analysis was performed on 20 patients acquired using in vivo MDCT angiography. The Agatston Calcium score was obtained for each patient and subject-specific local Elastic Modulus (EM) was calculated. The in silico results showed that by imposing average ultimate external load of 1.1MPa and 2.3MPa on balloon and stent respectively, average ultimate stress of 55.7±41.2kPa and 171±41.2kPa are obtained on calcifications. The study reveals that a significant positive correlation (R=0.85, p<0.0001) exists on stent expansion between EM of calcification and ultimate stress as well as Plaque Wall Stress (PWS) (R=0.92, p<0.0001), comparing to Ca score that showed insignificant associations with ultimate stress (R=0.44, p=0.057) and PWS (R=0.38, p=0.103), suggesting minor impact of Ca score in plaque rupture. These average data are in good agreement with results obtained by other research groups and we believe this approach enriches the arsenal of tools available for pre-operative prediction of carotid artery stenting procedure in the presence of calcified plaques

Automated Deformable Mapping Methods to Relate Corresponding Lesions in 3D X-ray and 3D Ultrasound Breast Images

Mammography is the current standard imaging method for detecting breast cancer by using x-rays to produce 2D images of the breast. However, with mammography alone there is difficulty determining whether a lesion is benign or malignant and reduced sensitivity to detecting lesions in dense breasts. Ultrasound imaging used in conjunction with mammography has shown valuable contributions for lesion characterization by differentiating between solid and cystic lesions. Conventional breast ultrasound has high false positive rates; however, it has shown improved abilities to detect lesions in dense breasts. Breast ultrasound is typically performed freehand to produce anterior-to-posterior 2D images in a different geometry (supine) than mammography (upright). This difference in geometries is likely responsible for the finding that at least 10% of the time lesions found in the ultrasound images do not correspond with lesions found in mammograms. To solve this problem additional imaging techniques must be investigated to aid a radiologist in identifying corresponding lesions in the two modalities to ensure early detection of a potential cancer. This dissertation describes and validates automated deformable mapping methods to register and relate corresponding lesions between multi-modality images acquired using 3D mammography (Digital Breast Tomosynthesis (DBT) and dedicated breast Computed Tomography (bCT)) and 3D ultrasound (Automated Breast Ultrasound (ABUS)). The methodology involves the use of finite element modeling and analysis to simulate the differences in compression and breast orientation to better align lesions acquired from images from these modalities. Preliminary studies were performed using several multimodality compressible breast phantoms to determine breast lesion registrations between: i) cranio-caudal (CC) and mediolateral oblique (MLO) DBT views and ABUS, ii) simulated bCT and DBT (CC and MLO views), and iii) simulated bCT and ABUS. Distances between the centers of masses, dCOM, of corresponding lesions were used to assess the deformable mapping method. These phantom studies showed the potential to apply this technique for real breast lesions with mean dCOM registration values as low as 4.9 ± 2.4 mm for DBT (CC view) mapped to ABUS, 9.3 ± 2.8 mm for DBT (MLO view) mapped to ABUS, 4.8 ± 2.4 mm for bCT mapped to ABUS, 5.0 ± 2.2 mm for bCT mapped to DBT (CC view), and 4.7 ± 2.5 mm for bCT mapped to DBT (MLO view). All of the phantom studies showed that using external fiducial markers helped improve the registration capability of the deformable mapping algorithm. An IRB-approved proof-of-concept study was performed with patient volunteers to validate the deformable registration method on 5 patient datasets with a total of up to 7 lesions for DBT (CC and MLO views) to ABUS registration. Resulting dCOM’s using the deformable method showed statistically significant improvements over rigid registration techniques with a mean dCOM of 11.6 ± 5.3 mm for DBT (CC view) mapped to ABUS and a mean dCOM of 12.3 ± 4.8 mm for DBT (MLO view) mapped to ABUS. The present work demonstrates the potential for using deformable registration techniques to relate corresponding lesions in 3D x-ray and 3D ultrasound images. This methodology should improve a radiologists’ characterization of breast lesions which can reduce patient callbacks, misdiagnoses, additional patient dose and unnecessary biopsies. Additionally, this technique can save a radiologist time in navigating 3D image volumes and the one-to-one lesion correspondence between modalities can aid in the early detection of breast malignancies.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150042/1/canngree_1.pd

Doctor of Philosophy

dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload

Analysis of Dynamic Magnetic Resonance Breast Images

Dynamic Magnetic Resonance Imaging is a non-invasive technique that provides an image sequence based on dynamic information for locating lesions and investigating their structures. In this thesis we develop new methodology for analysing dynamic Magnetic Resonance image sequences of the breast. This methodology comprises an image restoration step that reduces random distortions affecting the data and an image classification step that identifies normal, benign or malignant tumoral tissues. In the first part of this thesis we present a non-parametric and a parametric approach for image restoration and classification. Both methods are developed within the Bayesian framework. A prior distribution modelling both spatial homogeneity and temporal continuity between neighbouring image pixels is employed. Statistical inference is performed by means of a Metropolis-Hastings algorithm with a specially chosen proposal distribution that out-performs other algorithms of the same family. We also provide novel procedures for estimating the hyper-parameters of the prior models and the normalizing constant so making the Bayesian methodology automatic. In the second part of this thesis we present new methodology for image classification based on deformable templates of a prototype shape. Our approach uses higher level knowledge about the tumour structure than the spatio-temporal prior distribution of our Bayesian methodology. The prototype shape is deformed to identify the structure of the malignant tumoral tissue by minimizing a novel objective function over the parameters of a set of non-affine transformations. Since these transformations can destroy the connectivity of the shape, we develop a new filter that restores connectivity without smoothing the shape. The restoration and classification results obtained from a small sample of image sequences are very encouraging. In order to validate these results on a larger sample, in the last part of the thesis we present a user friendly software package that implements our methodology

A review of bioengineering techniques applied to breast tissue: Mechanical properties, tissue engineering and finite element analysis

Female breast cancer was the most prevalent cancer worldwide in 2020, according to the Global Cancer Observatory. As a prophylactic measure or as a treatment, mastectomy and lumpectomy are often performed at women. Following these surgeries, women normally do a breast reconstruction to minimize the impact on their physical appearance and, hence, on their mental health, associated with self-image issues. Nowadays, breast reconstruction is based on autologous tissues or implants, which both have disadvantages, such as volume loss over time or capsular contracture, respectively. Tissue engineering and regenerative medicine can bring better solutions and overcome these current limitations. Even though more knowledge needs to be acquired, the combination of biomaterial scaffolds and autologous cells appears to be a promising approach for breast reconstruction. With the growth and improvement of additive manufacturing, three dimensional (3D) printing has been demonstrating a lot of potential to produce complex scaffolds with high resolution. Natural and synthetic materials have been studied in this context and seeded mainly with adipose derived stem cells (ADSCs) since they have a high capability of differentiation. The scaffold must mimic the environment of the extracellular matrix (ECM) of the native tissue, being a structural support for cells to adhere, proliferate and migrate. Hydrogels (e.g., gelatin, alginate, collagen, and fibrin) have been a biomaterial widely studied for this purpose since their matrix resembles the natural ECM of the native tissues. A powerful tool that can be used in parallel with experimental techniques is finite element (FE) modeling, which can aid the measurement of mechanical properties of either breast tissues or scaffolds. FE models may help in the simulation of the whole breast or scaffold under different conditions, predicting what might happen in real life. Therefore, this review gives an overall summary concerning the human breast, specifically its mechanical properties using experimental and FE analysis, and the tissue engineering approaches to regenerate this particular tissue, along with FE models