In a traditional optical system the imaging performance is maximized at a single point in the operational
space. This characteristic leads to maximizing the probability of detection if the object is on axis, at the
designed conjugate, with the designed operational temperature and if the system components are manufactured
without error in form and alignment. Due to the many factors that influence the system's image
quality the probability of detection will decrease away from this peak value.
An infrared imaging system is presented that statistically creates a higher probability of detection over the
complete operational space for the Hotelling observer. The system is enabled through the use of wavefront
coding, a computational imaging technology in which optics, mechanics, detection and signal processing are
combined to enable LWIR imaging systems to be realized with detection task performance that is difficult
or impossible to obtain in the optical domain alone. The basic principles of statistical decision theory
will be presented along with a specific example of how wavefront coding technology can enable improved
performance and reduced sensitivity to some of the fundamental constraints inherent in LWIR systems.
Proc. SPIE. 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: Imaging systems, Cameras, Sensors, Image processing, Computer simulations, 3D modeling, Monte Carlo methods, Single photon emission computed tomography, Systems modeling, Information operations
The Center for Gamma-Ray Imaging is developing a number of small-animal SPECT imaging systems. These systems consist of multiple stationary detectors, each of which has its own multiple-pinhole collimator. The location of the pinhole plates (i.e., magnification), the number of pinholes within each plate, as well the pinhole locations are all adjustable. The performance of the Bayesian ideal observer sets the upper limit on task performance and can be used to optimize imaging hardware, such as pinhole configurations. Markov-chain Monte Carlo techniques have been developed to compute the ideal observer but require complete knowledge of the statistics of both the imaging system (such as the noise) and the class of random objects being imaged, in addition to an accurate forward model connecting the object to the image. Ideal observer computations using Monte Carlo techniques are burdensome because the forward model must be simulated millions of times for each imaging system. We present an efficient technique for computing the Bayesian ideal observer for multiple-pinhole, small-animal SPECT systems that accounts for both the finite-size of the pinholes and the stochastic nature of the objects being imaged. This technique relies on an efficient, radiometrically correct forward model that maps an object to an image in less than 20 milliseconds. An analysis of the error of the forward model, as well as the results of a ROC study using the ideal observer test statistic is presented.
Medical imaging is often performed for the purpose of estimating a clinically relevant parameter. For example, cardiologists are interested in the cardiac ejection fraction, the fraction of blood pumped out of the left ventricle at the end of each heart cycle. Even when the primary task of the imaging system is tumor detection, physicians frequently want to estimate parameters of the tumor, e.g. size and location. For signal-detection tasks, we advocate that the performance of an ideal observer be employed as the figure of merit for optimizing medical imaging hardware. We have examined the use
of the minimum variance of the ideal, unbiased estimator as a figure of merit for hardware optimization. The minimum variance of the ideal, unbiased estimator can be calculated using the Fisher information matrix. To account for both image noise and object variability, we used a statistical method known as Markov-chain Monte Carlo. We employed a lumpy object model and simulated imaging systems to compute our figures of merit. We have demonstrated the use of this method in comparing imaging systems for estimation tasks.
Proc. SPIE. 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: Imaging systems, Sensors, 3D modeling, Medical imaging, Monte Carlo methods, Image quality, Signal detection, Single photon emission computed tomography, Systems modeling, 3D image processing
In a pinhole imaging system, multiple pinholes are potentially beneficial since more radiation will arrive in the detector plane. However, the various images produced by each pinhole may multiplex (overlap), possibly decreasing image quality. In this work we develop the framework for comparing various pinhole configurations using ideal-observer performance as a figure of merit. We compute the ideal-observer test statistic, the likelihood ratio, using a statistical method known as Markov-Chain Monte Carlo. For different imaging systems, we estimate the likelihood ratio for many realizations of noisy image data both with and without a signal present. For each imaging system, the area under the ROC curve provides a meaningful figure of merit for hardware comparison. In this work we compare different pinhole configurations using a three-dimensional lumpy object model, a known signal (SKE), and simulated pinhole imaging systems. The results of our work will eventually serve as a basis for a design of high-resolution pinhole SPECT systems.