Cardiovascular disease in general and coronary artery disease (CAD) in particular, are the leading cause of death worldwide. They are principally diagnosed using either invasive percutaneous transluminal coronary angiograms or non-invasive computed tomography angiograms (CTA). Minimally invasive therapies for CAD such as angioplasty and stenting are rendered under fluoroscopic guidance. Both invasive and non-invasive imaging modalities employ ionizing radiation and there is concern for deterministic and stochastic effects of radiation. Accurate simulation to optimize image quality with minimal radiation dose requires detailed, gender-specific anthropomorphic phantoms with anatomically correct heart and associated vasculature. Such phantoms are currently unavailable. This paper describes an open source heart phantom development platform based on a graphical user interface. Using this platform, we have developed seven high-resolution cardiac/coronary artery phantoms for imaging and dosimetry from seven high-quality CTA datasets. To extract a phantom from a coronary CTA, the relationship between the intensity distribution of the myocardium, the ventricles and the coronary arteries is identified via histogram analysis of the CTA images. By further refining the segmentation using anatomy-specific criteria such as vesselness, connectivity criteria required by the coronary tree and image operations such as active contours, we are able to capture excellent detail within our phantoms. For example, in one of the female heart phantoms, as many as 100 coronary artery branches could be identified. Triangular meshes are fitted to segmented high-resolution CTA data. We have also developed a visualization tool for adding stenotic lesions to the coronaries. The male and female heart phantoms generated so far have been cross-registered and entered in the mesh-based Virtual Family of phantoms with matched age/gender information. Any phantom in this family, along with user-defined stenoses, can be used to obtain clinically realistic projection images with the Monte Carlo code penMesh for optimizing imaging and dosimetry.

Purpose:The purpose of this work is to evaluate the performance of the image acquisition chain of clinical full field digital mammography (FFDM) systems by quantifying their image quality, and how well the desired information is captured by the images. Methods: The authors present a practical methodology to evaluate FFDM using the task specific system-model-based Fourier Hotelling observer (SMFHO) signal to noise ratio (SNR), which evaluates the signal and noise transfer characteristics of FFDM systems in the presence of a uniform polymethyl methacrylate phantom that models the attenuation of a 6 cm thick 20/80 breast (20% glandular/80% adipose). The authors model the system performance using the generalized modulation transfer function, which accounts for scatter blur and focal spot unsharpness, and the generalized noise power spectrum, both estimated with the phantom placed in the field of view. Using the system model, the authors were able to estimate system detectability for a series of simulated disk signals with various diameters and thicknesses, quantified by a SMFHO SNR map. Contrast-detail (CD) curves were generated from the SNR map and adjusted using an estimate of the human observer efficiency, without performing time-consuming human reader studies. Using the SMFHO method the authors compared two FFDM systems, the GE Senographe DS and Hologic Selenia FFDM systems, which use indirect and direct detectors, respectively. Results: Even though the two FFDM systems have different resolutions, noise properties, detector technologies, and antiscatter grids, the authors found no significant difference between them in terms of detectability for a given signal detection task. The authors also compared the performance between the two image acquisition modes (fine view and standard) of the GE Senographe DS system, and concluded that there is no significant difference when evaluated by the SMFHO. The estimated human observer efficiency was 30 ± 5% when compared to the SMFHO. The results showed good agreement when compared to other model observers as well as previously published human observer data. Conclusions: This method generates CD curves from the SMFHO SNR that can be used as figures of merit for evaluating the image acquisition performance of clinical FFDM systems. It provides a way of creating an empirical model of the FFDM system that accounts for patient scatter, focal spot unsharpness, and detector blur. With the use of simulated signals, this method can predict system performance for a signal known exactly/background known exactly detection task with a limited number of images, therefore, it can be readily applied in a clinical environment.

Similarity plays a fundamental role in many areas, including data mining, machine learning, statistics and various applied domains. Inspired by the success of ensemble methods and the flexibility of trees, we propose to learn a similarity kernel called rpf-kernel through random projection forests (rpForests). Our theoretical analysis reveals a highly desirable property of rpf-kernel: far-away (dissimilar) points have a low similarity value while nearby (similar) points would have a high similarity, and the similarities have a native interpretation as the probability of points remaining in the same leaf nodes during the growth of rpForests. The learned rpf-kernel leads to an effective clustering algorithm-rpfCluster. On a wide variety of real and benchmark datasets, rpfCluster compares favorably to K-means clustering, spectral clustering and a state-of-the-art clustering ensemble algorithm-Cluster Forests. Our approach is simple to implement and readily adapt to the geometry of the underlying data. Given its desirable theoretical property and competitive empirical performance when applied to clustering, we expect rpf-kernel to be applicable to many problems of an unsupervised nature or as a regularizer in some supervised or weakly supervised settings.