Impact of Biomechanical Modeling of Anatomical Variations on the Uncertainties of the Delivery and Understanding of Radiation Therapy

External beam radiation therapy is an effective and widely used focal cancer therapy. However, due to anatomical changes during radiation therapy, both in the tumor and in the normal tissue, the delivered radiation dose can deviate from the planned radiation dose. These responses may compromise the delivery of the most effective treatment and lead to an increased risk of complications in normal tissues. The ability to estimate the delivered radiation to the tumor and normal tissues with high accuracy requires modeling the patient response to dose. Modern medical imaging, such as computed tomography (CT) and medical resonance imaging (MRI), provides a method to evaluate spatial and functional changes of the tumor and normal tissue over the course of radiation therapy. A comprehensive evaluation of these changes requires identification of the tumor and normal tissue, through image segmentation, and accurate alignment of images, through image registration. In the head and neck region, varying angles of neck flexion, rapid tumor response and weight loss cause early changes in healthy tissue. In the abdominal region, motion due to breathing and digestion cause changes in the tumor position and normal structures. When the deviations between delivered and planned dose are great enough, the radiation treatment plan should be reoptimized, in order to ensure that the tumor is adequately treated and that the normal tissue is maximally avoided. Estimating the delivered dose to sufficient accuracy is therefore an important requirement for effective adaptive replanning. This dissertation work develops different techniques based on biomechanical models of the anatomical changes to improve estimates of delivered dose, which can ultimately lead to improvements in treatment adaptation strategies as well as a better understanding of toxicity. A series of experiments based on finite element modeling were conducted to model the uncertainties between planned and delivered dose, as well as the potential impact of modeling on different organ sites. Abdominal normal tissue complication probability models were developed based on estimated delivered dose and their accuracy compared to traditional models based on planned dose. Following this study, a predictive model was developed for the head and neck site, in order to find how early in treatment significant deviations in planned and delivered dose could be predicted. After seeing the large potential deviations between planned and delivered dose in the head and neck site, a comprehensive study was conducted to model the changes that potentially cause these large deviations. This comprehensive head and neck model was developed in two steps; first, the positional changes due to flexion were resolved and second, the dose response to the parotid glands was modeled using finite element modeling. Each clinical site poses different challenges, and this dissertation work highlights two areas in which modeling the deviations between planned and delivered dose will improve advanced adaptive radiation therapy.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149811/1/mollyma_1.pd

Enabling technology for non-rigid registration during image-guided neurosurgery

In the context of image processing, non-rigid registration is an operation that attempts to align two or more images using spatially varying transformations. Non-rigid registration finds application in medical image processing to account for the deformations in the soft tissues of the imaged organs. During image-guided neurosurgery, non-rigid registration has the potential to assist in locating critical brain structures and improve identification of the tumor boundary. Robust non-rigid registration methods combine estimation of tissue displacement based on image intensities with the spatial regularization using biomechanical models of brain deformation. In practice, the use of such registration methods during neurosurgery is complicated by a number of issues: construction of the biomechanical model used in the registration from the image data, high computational demands of the application, and difficulties in assessing the registration results. In this dissertation we develop methods and tools that address some of these challenges, and provide components essential for the intra-operative application of a previously validated physics-based non-rigid registration method.;First, we study the problem of image-to-mesh conversion, which is required for constructing biomechanical model of the brain used during registration. We develop and analyze a number of methods suitable for solving this problem, and evaluate them using application-specific quantitative metrics. Second, we develop a high-performance implementation of the non-rigid registration algorithm and study the use of geographically distributed Grid resources for speculative registration computations. Using the high-performance implementation running on the remote computing resources we are able to deliver the results of registration within the time constraints of the neurosurgery. Finally, we present a method that estimates local alignment error between the two images of the same subject. We assess the utility of this method using multiple sources of ground truth to evaluate its potential to support speculative computations of non-rigid registration

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores