We use scanning model observers to predict human performance in lesion search/detection study. The observer's
task is to locate gallium-avid tumors in simulated SPECT images of a digital phantom. The goal of our model is to
predict the optimal prior strength β for human observers of smoothing priors incorporated into the reconstruction
algorithm. These priors use varying amounts of anatomical knowledge. We present results from a scanning
channelized non-prewhitening matched filter, and compare them with results from a human-observer study.
Including a step to mimic the greyscale perceptual-linearization used during the human-observer study improves
the accuracy of the model. However we find that for lesions close to an organ boundary even the improved model
does not accurately predict human performance.
We investigate the use of linear model observers to predict human performance in a localization ROC (LROC)
study. The task is to locate gallium-avid tumors in simulated SPECT images of a digital phantom. Our study is
intended to find the optimal strength of smoothing priors incorporating various degrees of anatomical knowledge.
Although humans reading the images must perform a search task, our models ignore search by assuming the lesion
location is known. We use area under the model ROC curve to predict human area under the LROC curve. We
used three models, the non-prewhitening matched filter (NPWMF), the channelized nonprewhitening (CNPW),
and the channelized Hotelling observer (CHO). All models have access to noise-free reconstructions, which are
used to compute the signal template. The NPWMF model does a poor job of predicting human performance.
The CNPW and CHO model do a somewhat better job, but still do not qualitatively capture the human results.
None of the models accurately predicts the smoothing strength which maximizes human performance.
The Bayes Inference Engine (BIE) has been used to perform a 4D reconstruction of a first-pass radiotracer bolus distribution inside a CardioWest Total Artificial Heart, imaged with the University of Arizona's FastSPECT system. The BIE estimates parameter values that define the 3D model of the radiotracer distribution at each of 41 times spanning about two seconds. The 3D models have two components: a closed surface, composed of bi-quadratic Bezier triangular surface patches, that defines the interface between the part of the blood pool that contains radiotracer and the part that contains no radiotracer, and smooth voxel-to-voxel variations in intensity within the closed surface. Ideally, the surface estimates the ventricular wall location where the bolus is infused throughout the part of the blood pool contained by the right ventricle. The voxel-to-voxel variations are needed to model an inhomogeneously-mixed bolus. Maximum a posterior (MAP) estimates of the Bezier control points and voxel values are obtained for each time frame. We show new reconstructions using the Bezier surface models, and discuss estimates of ventricular volume as a function of time, ejection fraction, and wall motion. The computation time for our reconstruction process, which directly estimates complex 3D model parameters from the raw data, is performed in a time that is competitive with more traditional voxel-based methods (ML-EM, e.g.).
In this article we build on our past attempts to reconstruct a 3D, time-varying bolus of radiotracer from first-pass data obtained by the dynamic SPECT imager, FASTSPECT, built by the University of Arizona. The object imaged is a CardioWest total artificial heart. The bolus is entirely contained in one ventricle and its associated inlet and outlet tubes. The model for the radiotracer distribution at a given time is a closed surface parameterized by 482 vertices that are connected to make 960 triangles, with nonuniform intensity variations of radiotracer allowed inside the surface on a voxel-to-voxel basis. The total curvature of the surface is minimized through the use of a weighted prior in the Bayesian framework, as is the weighted norm of the gradient of the voxellated grid. MAP estimates for the vertices, interior intensity voxels and background count level are produced. The strength of the priors, or hyperparameters, are determined by maximizing the probability of the data given the hyperparameters, called the evidence. The evidence is calculated by first assuming that the posterior is approximately normal in the values of the vertices and voxels, and then by evaluating the integral of the multi- dimensional normal distribution. This integral (which requires evaluating the determinant of a covariance matrix) is computed by applying a recent algorithm from Bai et. al. that calculates the needed determinant efficiently. We demonstrate that the radiotracer is highly inhomogeneous in early time frames, as suspected in earlier reconstruction attempts that assumed a uniform intensity of radiotracer within the closed surface, and that the optimal choice of hyperparameters is substantially different for different time frames.
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