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Computerized phantoms are finding an increasingly important role in medical imaging research. With the ability to simulate various imaging conditions, they offer a practical means with which to quantitatively evaluate and improve imaging devices and techniques. This is especially true in CT due to the high radiation levels involved with it. Despite their utility, due to the time required to develop them, only a handful of computational models currently exist of varying detail. Most phantoms available are limited to 3D and not capable of modeling patient motion. We have previously developed a technique to rapidly create highly detailed 4D extended cardiac-torso (XCAT) phantoms based on patient CT data [1]. In this study, we utilize this technique to generate 58 new adult XCAT phantoms to be added to our growing library of virtual patients available for imaging research. These computerized patients provide a valuable tool for investigating imaging devices and the effects of anatomy and motion in imaging. They also provide the essential tools to investigate patient-specific dose estimation and optimization for adults undergoing CT procedures.

The cover image is based on Advanced Review Computational anatomy and diffeomorphometry: A dynamical systems model of neuroanatomy in the soft condensed matter continuumby Michael I. Miller et al., DOI: 10.1002/wsbm.1425. The cover image is based on Advanced Review Computational anatomy and diffeomorphometry: A dynamical systems model of neuroanatomy in the soft condensed matter continuumby Michael I. Miller et al., DOI: 10.1002/wsbm.1425.

The nonlinear systems models of computational anatomy that have emerged over the past several decades are a synthesis of three significant areas of computational science and biological modeling. First is the algebraic model of biological shape as a Riemannian orbit, a set of objects under diffeomorphic action. Second is the embedding of anatomical shapes into the soft condensed matter physics continuum via the extension of the Euler equations to geodesic, smooth flows with inverses, encoding divergence for the compressibility of atrophy and expansion of growth. Third, is making human shape and form a metrizable space via geodesic connections of coordinate systems. These three themes place our formalism into the modern data science world of personalized medicine supporting inference of high‐dimensional anatomical phenotypes for studying neurodegeneration and neurodevelopment. The dynamical systems model of growth and atrophy that emerges is one which is organized in terms of forces, accelerations, velocities, and displacements, with the associated Hamiltonian momentum and the diffeomorphic flow acting as the state, and the smooth vector field the control. The forces that enter the model derive from external measurements through which the dynamical system must flow, and the internal potential energies of structures making up the soft condensed matter. We examine numerous examples on growth and atrophy. This article is categorized under: Analytical and Computational Methods > Computational MethodsLaboratory Methods and Technologies > ImagingModels of Systems Properties and Processes > Organ, Tissue, and Physiological Models Dynamics model in computational anatomy organized by force, acceleration, velocity, and displacement. The input term g represents energy derived forces from external measurements or internal energies of substructures. Images enter via the non‐linear observer.