Automatic correspondence between 2D and 3D images of the breast

Radiologists often need to localise corresponding findings in different images of the breast, such as Magnetic Resonance Images and X-ray mammograms. However, this is a difficult task, as one is a volume and the other a projection image. In addition, the appearance of breast tissue structure can vary significantly between them. Some breast regions are often obscured in an X-ray, due to its projective nature and the superimposition of normal glandular tissue. Automatically determining correspondences between the two modalities could assist radiologists in the detection, diagnosis and surgical planning of breast cancer. This thesis addresses the problems associated with the automatic alignment of 3D and 2D breast images and presents a generic framework for registration that uses the structures within the breast for alignment, rather than surrogates based on the breast outline or nipple position. The proposed algorithm can adapt to incorporate different types of transformation models, in order to capture the breast deformation between modalities. The framework was validated on clinical MRI and X-ray mammography cases using both simple geometrical models, such as the affine, and also more complex ones that are based on biomechanical simulations. The results showed that the proposed framework with the affine transformation model can provide clinically useful accuracy (13.1mm when tested on 113 registration tasks). The biomechanical transformation models provided further improvement when applied on a smaller dataset. Our technique was also tested on determining corresponding findings in multiple X-ray images (i.e. temporal or CC to MLO) for a given subject using the 3D information provided by the MRI. Quantitative results showed that this approach outperforms 2D transformation models that are typically used for this task. The results indicate that this pipeline has the potential to provide a clinically useful tool for radiologists

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

Characterizing Mechanical Regulation of Bone Metastatic Breast Cancer Cells

Breast cancer most frequently metastasizes to the skeleton. Bone metastatic cancer is incurable and induces wide-spread bone osteolysis, resulting in significant patient morbidity and mortality. Mechanical stimuli in the skeleton are an important microenvironmental parameter that modulates tumor formation, osteolysis, and tumor cell-bone cell signaling, but which mechanical signals are the most beneficial and the corresponding molecular mechanisms are unknown. This work focused on bone matrix deformation and interstitial fluid flow based on their well-known roles in bone remodeling and in primary breast cancer. The goal of our research was to establish a platform that could define the relationship between applied dynamic mechanical forces and the resulting phenotype in bone metastatic breast cancer cells. To achieve this goal, we employed a high-throughput, multi-modal in vitro mechanical loading bioreactor to apply forces to 3D in vitro bone-mimetic scaffolds, thereby recapitulating the physiological skeletal mechanical environment. We combined this with multi-physics micro-CT-based computational simulation models to estimate the internal mechanical microenvironment during in vitro experimentation

Biomechanical properties of breast tissue, a state-of-the-art review

This paper reviews the existing literature on the tests used to determine the mechanical properties of women breast tissues (fat, glandular and tumour tissue) as well as the different values of these properties. The knowledge of the mechanical properties of breast tissue is important for cancer detection, study and planning of surgical procedures such as surgical breast reconstruction using pre-surgical methods and improving the interpretation of clinical tests. Based on the data collected from the analysed studies, some important conclusions were achieved: (1) the Young’s modulus of breast tissues is highly dependent on the tissue preload compression level, and (2) the results of these studies clearly indicate a wide variation in moduli not only among different types of tissue but also within each type of tissue. These differences were most evident in normal fat and fibroglandular tissues

A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea

The characterization of the mechanical behavior of soft living tissues is a big challenge in Biomechanics. The difficulty arises from both the access to the tissues and the manipulation in order to know their physical properties. Currently, the biomechanical characterization of the organs is mainly performed by testing ex-vivo samples or by means of indentation tests. In the first case, the obtained behavior does not represent the real behavior of the organ. In the second case, it is only a representation of the mechanical response of the indented areas. The purpose of the research reported in this thesis is the development of a methodology to in-vivo characterize the biomechanical behavior of two different organs: the breast and the cornea. The proposed methodology avoids invasive measurements to obtain the mechanical response of the organs and is able to completely characterize of the biomechanical behavior of them. The research reported in this thesis describes a methodology to in-vivo characterize the biomechanical behavior of the breast and the cornea. The estimation of the elastic constants of the constitutive equations that define the mechanical behavior of these organs is performed using an iterative search algorithm which optimizes these parameters. The search is based on the iterative variation of the elastic constants of the model in order to increase the similarity between a simulated deformation of the organ and the real one. The similarity is measured by means of a volumetric similarity function which combines overlap-based coefficients and distance-based coefficients. Due to the number of parameters to be characterized as well as the non-convergences that the solution may present in some regions, genetic heuristics were chosen to drive the search algorithm. In the case of the breast, the elastic constants of an anisotropic hyperelastic neo-Hookean model proposed to simulate the compression of the breast during an MRI-guided biopsy were estimated. Results from this analysis showed that the proposed algorithm accurately found the elastic constants of the proposed model, providing an average relative error below 10%. The methodology was validated using breast software phantoms. Nevertheless, this methodology can be easily transferred into its use with real breasts. In the case of the cornea, the elastic constants of a hyperelastic second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied in order to simulate the deformation of the human corneas due to non-contact tonometry. The iterative search was applied in order to estimate the elastic constants of the model which approximates the most the simulated deformation to the real one. Results showed that these constants can be estimated with an error of about 5%. After the results obtained for both organs, it can be concluded that the iterative search methodology presented in this thesis allows the \textit{in-vivo} estimation the patient-specific elastic constants of the constitutive biomechanical models that govern the biomechanical behavior of these two organs.Lago Ángel, MÁ. (2014). A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/44116TESI

In vivo interactions between tungsten microneedles and peripheral nerves

Tungsten microneedles are currently used to insert neural electrodes into living peripheral nerves. However, the biomechanics underlying these procedures is not yet well characterized. For this reason, the aim of this work was to model the interactions between these microneedles and living peripheral nerves. A simple mathematical framework was especially provided to model both compression of the external layer of the nerve (epineurium) and the interactions resulting from penetration of the main shaft of the microneedle inside the living nerves. The instantaneous Young's modulus, compression force, the work needed to pierce the tissue, puncturing pressure, and the dynamic friction coefficient between the tungsten microneedles and living nerves were quantified starting from acute experiments, aiming to reproduce the physical environment of real implantations. Indeed, a better knowledge of the interactions between microneedles and peripheral nerves may be useful to improve the effectiveness of these insertion techniques, and could represent a key factor for designing robot-assisted procedures tailored for peripheral nerve insertion

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

Novel 3D Ultrasound Elastography Techniques for In Vivo Breast Tumor Imaging and Nonlinear Characterization

Breast cancer comprises about 29% of all types of cancer in women worldwide. This type of cancer caused what is equivalent to 14% of all female deaths due to cancer. Nowadays, tissue biopsy is routinely performed, although about 80% of the performed biopsies yield a benign result. Biopsy is considered the most costly part of breast cancer examination and invasive in nature. To reduce unnecessary biopsy procedures and achieve early diagnosis, ultrasound elastography was proposed.;In this research, tissue displacement fields were estimated using ultrasound waves, and used to infer the elastic properties of tissues. Ultrasound radiofrequency data acquired at consecutive increments of tissue compression were used to compute local tissue strains using a cross correlation method. In vitro and in vivo experiments were conducted on different tissue types to demonstrate the ability to construct 2D and 3D elastography that helps distinguish stiff from soft tissues. Based on the constructed strain volumes, a novel nonlinear classification method for human breast tumors is introduced. Multi-compression elastography imaging is elucidated in this study to differentiate malignant from benign tumors, based on their nonlinear mechanical behavior under compression. A pilot study on ten patients was performed in vivo, and classification results were compared with biopsy diagnosis - the gold standard. Various nonlinear parameters based on different models, were evaluated and compared with two commonly used parameters; relative stiffness and relative tumor size. Moreover, different types of strain components were constructed in 3D for strain imaging, including normal axial, first principal, maximum shear and Von Mises strains. Interactive segmentation algorithms were also evaluated and applied on the constructed volumes, to delineate the stiff tissue by showing its isolated 3D shape.;Elastography 3D imaging results were in good agreement with the biopsy outcomes, where the new classification method showed a degree of discrepancy between benign and malignant tumors better than the commonly used parameters. The results show that the nonlinear parameters were found to be statistically significant with p-value \u3c0.05. Moreover, one parameter; power-law exponent, was highly statistically significant having p-value \u3c 0.001. Additionally, volumetric strain images reconstructed using the maximum shear strains provided an enhanced tumor\u27s boundary from the surrounding soft tissues. This edge enhancement improved the overall segmentation performance, and diminished the boundary leakage effect. 3D segmentation provided an additional reliable means to determine the tumor\u27s size by estimating its volume.;In summary, the proposed elastographic techniques can help predetermine the tumor\u27s type, shape and size that are considered key features helping the physician to decide the sort and extent of the treatment. The methods can also be extended to diagnose other types of tumors, such as prostate and cervical tumors. This research is aimed toward the development of a novel \u27virtual biopsy\u27 method that may reduce the number of unnecessary painful biopsies, and diminish the increasingly risk of cancer

Application of sequential cyclic compression on cancer cells in a flexible microdevice

Mechanical forces shape physiological structure and function within cell and tissue microenvironments, during which cells strive to restore their shape or develop an adaptive mechanism to maintain cell integrity depending on strength and type of the mechanical loading. While some cells are shown to experience permanent plastic deformation after a repetitive mechanical tensile loading and unloading, the impact of such repetitive compression on deformation of cells is yet to be understood. As such, the ability to apply cyclic compression is crucial for any experimental setup aimed at the study of mechanical compression taking place in cell and tissue microenvironments. Here, we demonstrate such cyclic compression using a microfluidic compression platform on live cell actin in SKOV-3 ovarian cancer cells. Live imaging of the actin cytoskeleton dynamics of the compressed cells was performed for varying pressures applied sequentially in ascending order during cell compression. Additionally, recovery of the compressed cells was investigated by capturing actin cytoskeleton and nuclei profiles of the cells at zero time and 24 h-recovery after compression in end point assays. This was performed for a range of mild pressures within the physiological range. Results showed that the phenotypical response of compressed cells during recovery after compression with 20.8 kPa differed observably from that for 15.6 kPa. This demonstrated the ability of the platform to aid in the capture of differences in cell behaviour as a result of being compressed at various pressures in physiologically relevant manner. Differences observed between compressed cells fixed at zero time or after 24 h-recovery suggest that SKOV-3 cells exhibit deformations at the time of the compression, a proposed mechanism cells use to prevent mechanical damage. Thus, biomechanical responses of SKOV-3 ovarian cancer cells to sequential cyclic compression and during recovery after compression could be revealed in a flexible microdevice. As demonstrated in this work, the observation of morphological, cytoskeletal and nuclear differences in compressed and non-compressed cells, with controlled micro-scale mechanical cell compression and recovery and using livecell imaging, fluorescent tagging and end point assays, can give insights into the mechanics of cancer cells

Integration of biomechanical models into image registration in the presence of large deformations

Prone-to-supine breast image registration has potential application in the fields of surgical and radiotherapy planning, and image guided interventions. However, breast image registration of three-dimensional images acquired in different patient positions is a challenging problem, due to large deformations induced to the soft breast tissue caused by the change in gravity loading. Biomechanical modelling is a promising tool to predict gravity induced deformations, however such simulations alone are unlikely to produce good alignment due to inter-patient variability and image acquisition related influences on the breast shape. This thesis presents a symmetric, biomechanical simulation based registration framework which aligns images in a central, stress-free configuration. Soft tissue is modelled as a neo-Hookean material and external forces are considered as the main source of deformation in the original images. The framework successively applies image derived forces directly into the unloading simulation in place of a subsequent image registration step. This results in a biomechanically constrained deformation. Using a finite difference scheme enables simulations to be performed directly in the image space. Motion constrained boundary conditions have been incorporated which can capture tangential motion of membranes and fasciae. The accuracy of the approach is assessed by measuring the target registration error (TRE) using nine prone MRI and supine CT image pairs, one prone-supine CT image pair, and four prone-supine MRI image pairs. The registration reduced the combined mean TRE for all clinical data sets from initially 69.7mm to 5.6mm. Prone-supine image pairs might not always be available in the clinical breast cancer workflow, especially prior to surgery. Hence an alternative surface driven registration methodology was also developed that incorporates biomechanical simulations, material parameter optimisation, and constrained surface matching. For three prone MR images and corresponding supine CT-derived surfaces a final mean TRE of 10.0mm was measured