Adaptive radiation therapy ͑ART͒ is the incorporation of daily images in the radiotherapy treatment process so that the treatment plan can be evaluated and modified to maximize the amount of radiation dose to the tumor while minimizing the amount of radiation delivered to healthy tissue. Registration of planning images with daily images is thus an important component of ART. In this article, the authors report their research on multiscale registration of planning computed tomography ͑CT͒ images with daily cone beam CT ͑CBCT͒ images. The multiscale algorithm is based on the hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese ͓Multiscale Model. Simul. 2͑4͒, pp. 554-579 ͑2004͔͒. Registration is achieved by decomposing the images to be registered into a series of scales using the ͑BV, L 2 ͒ decomposition and initially registering the coarsest scales of the image using a landmark-based registration algorithm. The resulting transformation is then used as a starting point to deformably register the next coarse scales with one another. This procedure is iterated at each stage using the transformation computed by the previous scale registration as the starting point for the current registration. The authors present the results of studies of rectum, head-neck, and prostate CT-CBCT registration, and validate their registration method quantitatively using synthetic results in which the exact transformations our known, and qualitatively using clinical deformations in which the exact results are not known.

Although imatinib is an effective treatment for chronic myelogenous leukemia (CML), and nearly all patients treated with imatinib attain some form of remission, imatinib does not completely eliminate leukemia. Moreover, if the imatinib treatment is stopped, most patients eventually relapse (Cortes et al. in Clin. Cancer Res. 11:3425–3432, 2005). In Kim et al. (PLoS Comput. Biol. 4(6):e1000095, 2008), the authors presented a mathematical model for the dynamics of CML under imatinib treatment that incorporates the anti-leukemia immune response. We use the mathematical model in Kim et al. (PLoS Comput. Biol. 4(6):e1000095, 2008) to study and numerically simulate strategic treatment interruptions as a potential therapeutic strategy for CML patients. We present the results of numerous simulated treatment programs in which imatinib treatment is temporarily stopped to stimulate and leverage the anti-leukemia immune response to combat CML. The simulations presented in this paper imply that treatment programs that involve strategic treatment interruptions may prevent leukemia from relapsing and may prevent remission for significantly longer than continuous imatinib treatment. Moreover, in many cases, strategic treatment interruptions may completely eliminate leukemic cells from the body. Thus, strategic treatment interruptions may be a feasible clinical approach to enhancing the effects of imatinib treatment for CML. We study the effects of both the timing and the duration of the treatment interruption on the results of the treatment. We also present a sensitivity analysis of the results to the parameters in the mathematical model.

An image registration technique is presented for the registration of medical images using a hybrid combination of coarse-scale landmark and B-splines deformable registration techniques. The technique is particularly effective for registration problems in which the images to be registered contain large localized deformations. A brief overview of landmark and deformable registration techniques is presented. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese, A multiscale image representation using hierarchical (BV;L(2)) decompositions, Multiscale Modeling and Simulations, vol. 2, no. 4, pp. 554{579, 2004, is reviewed, and an image registration algorithm is developed based on combining the multiscale decomposition with landmark and deformable techniques. Successful registration of medical images is achieved by first obtaining a hierarchical multiscale decomposition of the images and then using landmark-based registration to register the resulting coarse scales. Corresponding bony structure landmarks are easily identified in the coarse scales, which contain only the large shapes and main features of the image. This registration is then fine tuned by using the resulting transformation as the starting point to deformably register the original images with each other using an iterated multiscale B-splines deformable registration technique. The accuracy and efficiency of the hybrid technique is demonstrated with several image registration case studies in two and three dimensions. Additionally, the hybrid technique is shown to be very robust with respect to the location of landmarks and presence of noise.

<abstract> <p>Although females in human and the great ape populations reach the end of fertility at similar ages (approximately 45 years), female humans often live well beyond their post-fertile years, while female primates typically die before or shortly after the end of fertility. The grandmother hypothesis proposes that the care-giving role provided by post-fertile females contributed to the evolution of longevity in human populations. When post-fertile females provide care for weaned infants, mothers are able to have their next baby sooner without compromising the chances of survival of their previous offspring. Thus, the post-menopausal longevity that is unique to human populations may be an evolutionary adaptation. In this work, we construct, simulate, and analyze an ordinary differential equations mathematical model to study the grandmother hypothesis. Our model describes the passage of the individuals of a population through five life stages in the cases with and without grandmothering. We demonstrate via numerical simulation of the mathematical model that grandmothering care is sufficient to significantly increase adult life expectancy. We also investigate the relationship between the number of weaned infants that a post-fertile female can care for at a given time and the steady-state age distributions of a population.</p> </abstract>

Abstract. A multiscale image registration technique is presented for the reg istration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized corre lation, and mutual information optimal linear registration are presented that determine the noise levels at which registration using these techniques fails. Further experiments in which classical denoising algorithms are applied prior to registration are presented, and it is shown that registration fails in this case for significantly high levels of noise, as well. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese [20] is presented, and accurate registration of noisy images is achieved by obtaining a hierarchical multiscale decomposition of the images and registering the resulting compo nents. This approach enables successful registration of images that contain noise levels well beyond the level at which ordinary optimal linear registration fails. Image registration experiments demonstrate the accuracy and efficiency of the multiscale registration technique, and for all noise levels, the multiscale technique is as accurate as or more accurate than ordinary registration techniques.

Purpose/Objectives(s): Most image registration algorithms ignore the underlying tissue features but simply rely on the similarity of image intensity. As thus, a spatial accuracy better than 3~5 mm is hardly achievable using any of these techniques. The aim of this work is to develop a tissue feature-based deformable algorithm to substantially improve the performance of registration for various IGRT applications. The novelties of this work include: (1) auto-detection and quantitative characterization of homologous tissue features in the input images; and (2) seamlessly incorporation of the detected tissue feature information for accurate and robust registration. Materials/Methods:The corresponding tissue features in the fixed and moving images are described by the information in the neighborhood of a point of interest. Quantitatively, the local information is characterized by using the Scale Invariance Feature Transformation (SIFT) method (the use of scalespace is to compare different images in the same scale, and is thus referred to as the SIFT), which includes scale-space extrema detection, control point localization, orientation assignment and control point descriptor. Another important feature of SIFT is the orientation histogram technique. In a 2D case, for example, the 8 X 8 neighborhood around a given point is defined as a volume. The volume is divided into four parts. In each part, the gradient magnitude of each pixel is calculated and sorted into 8 angle bins (the first bin is from 0 degree to 45 degree and so on). To further increase the precision of tissue feature association, a bi-directional mapping strategy is developed based on the intuitive fact that if a feature region in the fixed image is mapped correctly to the moving image, then it will necessarily be mapped back to the original feature region in the fixed image when we apply the inverse map to the corresponding feature region in the moving image. During the bi-directional calculation, the feature region is labeled as a matched region if the displacement vectors match. Otherwise, we consider it a mismatched region and delete it. The obtained homologous tissue features are treated as a priori knowledge in the subsequent deformable registration using thin-plate spline (TPS), or BSpline or finite element (FE) method. Results:A theoretical framework of auto-determining homologous tissue features and incorporating the information into commonly used deformable registration algorithms have been established. Several experiments using both digital phantom and clinical thoracic cases demonstrate the accuracy and efficiency of the proposed method. For each case, the algorithm convergence is confirmed by starting the registration from a large number of initial transformation parameters. It is observed that the convergence behavior is substantially improved as a result of integrating a priori anatomic knowledge when compared with the conventional approaches. Markedly improved registration accuracy is observed as compared with the results obtained without ...

Purpose/Objective: Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult and often a source of error. The purpose of this work is to automate the selection of control points for both rigid and deformable image registrations and to demonstrate the utility of the new approach by using a few examples. Materials/Methods:The registration of two images in our approach proceeds in two steps. First, a number of small control regions having distinct anatomical features are identified on the model image. Instead of attempting to find the correspondences of the control regions in the reference image through user interaction, in the proposed method, each of the control regions is mapped to the corresponding parts of the reference image by using an automated image registration algorithm. The conventional automated image registration algorithm is then used to complete the image registration process with the auto-determined control points. A normalized correlation function (intra-modality image registration) or a mutual information metric (intermodality image registration) was used as the metric in both the selection of the control volumes and the final image registration. The deformable registration was modeled by free form deformations based on spline interpolation. The limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was used to optimize the metric function. The performance of the registration-in-registration approach was examined by registering CT and FLT-PET images of a rectal cancer patient, CT and MRI images of brain tumor patient, and two sets of images of a lung case acquired at two different respiratory phases. For each case, the convergence behavior of the algorithm was studied by registering the two input images with 100 randomly initiated relative positions. The performance of the registration was evaluated by comparing with the results obtained by using direct registration without the use of auto-mapped control volumes.Results: An image registration algorithm with auto-mapped control regions has been proposed for intra-or inter-modality image registration. For each case, the convergence of the algorithm was confirmed by starting the registration calculation from 100 different initial conditions. The brain image registration suggested that the technique can match a CT and MRI images with an accuracy of 1 mm in the case of rigid body registration in less than a minute on a standard PC computer. Application of the technique to the clinical FLT-PET and CT registration showed a similar level of success. We found that the technique is especially valuable for improving the accuracy and calculation speed of deformable image registration. For the registration of CT images acquired at inhale/exhale phases of the lung patient, the BSpline calculation was speeded up by an order of magnitude with notable improvement in the registration q...

In this paper we present a generalized perturbative approximate series expansion in terms of non-orthogonal component functions. The expansion is based on a perturbative formulation where, in the non-orthogonal case, the contribution of a given component function, at each point, in the time domain or frequency in the Fourier domain, is assumed to be perturbed by contributions from the other component functions in the set. In the case of orthogonal basis functions, the formulation reduces to the non-perturbative case approximate series expansion. Application of the series expansion is demonstrated in the context of two non-orthogonal component function sets. The technique is applied to a series of non-orthogonalized Bessel functions of the first kind that are used to construct a compound function for which the coefficients are determined utilizing the proposed approach. In a second application, the technique is applied to an example associated with the inverse problem in electrophysiology and is demonstrated through decomposition of a compound evoked potential from a peripheral nerve trunk in terms of contributing evoked potentials from individual nerve fibers of varying diameter. An additional application of the perturbative approximation is illustrated in the context of a trigonometric Fourier series representation of a continuous time signal where the technique is used to compute an approximation of the Fourier series coefficients. From these examples, it will be demonstrated that in the case of non-orthogonal component functions, the technique performs significantly better than the generalized Fourier series which can yield nonsensical results.