Medical imaging involves some of the most beneficial and advanced technologies used in medicine today. However, the design and implementation of new imaging technology is incredibly complex. Doing so through clinical trials (experiments using human subjects) is often not practical or definitive due to ethical limitations, expense, time requirements, difficulty in accruing enough subjects, or a fundamental lack of ground truth (knowledge of the exact anatomy and condition of the patient). Most current approaches to assess imaging technologies outside of clinical trials rely on simplistic physical phantoms, the results from which cannot readily predict clinical efficacy. Meanwhile, the complexity of medical imaging technologies has continued to accelerate, outpacing our ability to assess them and optimize their design and clinical use. By the time we have consummated the ideal trials, the technology has moved on again. Therefore, we either work with “old validated” technology that may be less effective or put patients potentially at risk with newer unvalidated technology.

Recent work in the development of computerized phantoms has focused on the creation of ideal ldquohybridrdquo models that seek to combine the realism of a patient-based voxelized phantom with the flexibility of a mathematical or stylized phantom. We have been leading the development of such computerized phantoms for use in medical imaging research. This paper will summarize our developments dating from the original four-dimensional (4-D) mathematical cardiac-torso (MCAT) phantom, a stylized model based on geometric primitives, to the current 4-D extended cardiac-torso (XCAT) and mouse whole-body (MOBY) phantoms, hybrid models of the human and laboratory mouse based on state-of-the-art computer graphics techniques. This paper illustrates the evolution of computerized phantoms toward more accurate models of anatomy and physiology. This evolution was catalyzed through the introduction of nonuniform rational b-spline (NURBS) and subdivision (SD) surfaces, tools widely used in computer graphics, as modeling primitives to define a more ideal hybrid phantom. With NURBS and SD surfaces as a basis, we progressed from a simple geometrically based model of the male torso (MCAT) containing only a handful of structures to detailed, whole-body models of the male and female (XCAT) anatomies (at different ages from newborn to adult), each containing more than 9000 structures. The techniques we applied for modeling the human body were similarly used in the creation of the 4-D MOBY phantom, a whole-body model for the mouse designed for small animal imaging research. From our work, we have found the NURBS and SD surface modeling techniques to be an efficient and flexible way to describe the anatomy and physiology for realistic phantoms. Based on imaging data, the surfaces can accurately model the complex organs and structures in the body, providing a level of realism comparable to that of a voxelized phantom. In addition, they are very flexible. Like stylized models, they can easily be manipulated to model anatomical variations and patient motion. With the vast improvement in realism, the phantoms developed in our lab can be combined with accurate models of the imaging process (SPECT, PET, CT, magnetic resonance imaging, and ultrasound) to generate simulated imaging data close to that from actual human or animal subjects. As such, they can provide vital tools to generate predictive imaging data from many different subjects under various scanning parameters from which to quantitatively evaluate and improve imaging devices and techniques. From the MCAT to XCAT, we will demonstrate how NURBS and SD surface modeling have resulted in a major evolutionary advance in the development of computerized phantoms for imaging research.

Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semiautomated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. This paper is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT's design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques, and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

Our goal is to study the trade-off between image degradation and improved detection efficiency and resolution from allowing multiplexing in multi-pinhole (MPH) SPECT, and to determine the optimal pinhole number for MPH design. We used an analytical 3D MPH projector and two digitized phantoms: the mouse whole body (MOBY) phantom and a hot sphere phantom to generate noise-free and noisy projections, simulating pinhole collimators fitted with pre-studied pinhole patterns. We performed three schemes to achieve different degrees of multiplexing: 1. Fixed magnification and detection efficiency; 2. Fixed detection efficiency and changed magnification; 3. Fixed magnification and changed detection efficiency. We generated various noisy data sets by simulating Poisson noise using differently scaled noise-free projections and obtained 20 noise realizations for each setting. All datasets were reconstructed using 3D MPH ML-EM reconstruction method. We analyzed the quantitative accuracy by the normalized-mean-square-error. We evaluated the image contrast for the hot sphere phantom simulation, and also the image noise by the average normalized-standard-deviation of certain pixels for different degrees of multiplexing. Generally, no apparent artifacts were observed in the reconstructed images, illustrating the effectiveness of reconstructions. Bias increased for increased degree of multiplexing. Contrast was not significantly affected by multiplexing in the specific simulation scheme (1). Scheme (2) showed that excessive multiplexing to improve image resolution would not improve the overall trade-off of bias and noise compared to no multiplexing. However, scheme (3) showed that when comparing to no multiplexing, the trade-off improved initially with increased multiplexing by allowing more number of pinholes to improve detection efficiency. The trade-off reached a maximum and decreased with further multiplexing due to image degradation from increased bias. The optimal pinhole number was 7 for a compact camera with size of 12 cm times 12 cm and 9 for a standard gamma camera with size of 40 cm times 40 cm in this scheme. We conclude that the gains in improved detection efficiency and resolution by increased multiplexing are offset by increased image degradations. All the aforementioned factors must be considered in the optimum MPH collimator design for small animal SPECT imaging.