Towards a more realistic biomechanical modelling of breast malignant tumours

Oncological image analysis

Cancer is one of the world's major healthcare challenges and, as such, an important application of medical image analysis. After a brief introduction to cancer, we summarise some of the major developments in oncological image analysis over the past 20 years, but concentrating those in the authors' laboratories, and then outline opportunities and challenges for the next decade.

Realistic biomechanical model of a cancerous breast for the registration of prone to supine deformations

We develop a large deformations, Finite Elements biomechanical model of a stellate breast tumour, subject to prone to supine (MRI to US) breast deformations. Based on clinical findings, we introduce a volume of increased mammographic density/stiffness around a spiculated tumour, as well as a range of reported mechanical properties, both linear elastic and hyperelastic. This novel model demonstrates that these have a non-negligible effect on stresses and displacements, which, in turn, have implications, for example, in breast registration. We also show that the choice of material properties plays a dramatic effect on the mechanical variables.

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.

Modeling and Simulation of 4D PET-CT and PET-MR Images

The driving force in the research and development of new hybrid PET-CT/MR imaging scanners is the production of images with optimum quality, accuracy, and resolution. However, the acquisition process is limited by several factors. Key issues are the respiratory and cardiac motion artifacts that occur during an imaging session. In this article the necessary tools for modeling and simulation of realistic high-resolution four-dimensional PET-CT and PET-MR imaging data are described. Beyond the need for four-dimensional simulations, accurate modeling of the acquisition process can be included within the reconstruction algorithms assisting in the improvement of image quality and accuracy of estimation of physiologic parameters from four-dimensional hybrid PET imaging.