Fibrodysplasia ossificans progressiva (FOP) is a rare autosomal dominant disorder that is characterized by episodic yet cumulative heterotopic ossification (HO) in skeletal muscles, tendons, and ligaments over a patient's lifetime. FOP is caused by missense mutations in the type I bone morphogenetic protein (BMP) receptor ACVR1. We have determined that the formation of heterotopic bone in FOP requires activation of mutant ACVR1 by Activin A, in part by showing that prophylactic inhibition of Activin A blocks HO in a mouse model of FOP. Here we piece together a natural history of developing HO lesions in mouse FOP, and determine where in the continuum of HO Activin A is required, using imaging (T2-MRI, mCT, 18F-NaF PET/CT, histology) coupled with pharmacologic inhibition of Activin A at different times during the progression of HO. First, we show that expansion of HO lesions comes about through growth and fusion of independent HO events. These events tend to arise within a neighborhood of existing lesions, indicating that already formed HO likely triggers the formation of new events. The process of heterotopic bone expansion appears to be dependent on Activin A because inhibition of this ligand suppresses the growth of nascent HO lesions and stops the emergence of new HO events. Therefore, our results reveal that Activin A is required at least up to the point when nascent HO lesions mineralize and further demonstrate the therapeutic utility of Activin A inhibition in FOP. These results provide evidence for a model where HO is triggered by inflammation but becomes "self-propagating" by a process that requires Activin A.

Researchers have shown increasing interest in block-iterative image reconstruction algorithms due to the computational and modeling advantages they provide. Although their convergence properties have been well documented, little is known about how they behave in the presence of noise. In this work, we fully characterize the ensemble statistical properties of the rescaled block-iterative expectation-maximization (RBI-EM) reconstruction algorithm and the rescaled block-iterative simultaneous multiplicative algebraic reconstruction technique (RBI-SMART). Also included in the analysis are the special cases of RBI-EM, maximum-likelihood EM (ML-EM) and ordered-subset EM (OS-EM), and the special case of RBI-SMART, SMART. A theoretical formulation strategy similar to that previously outlined for ML-EM is followed for the RBI methods. The theoretical formulations in this paper rely on one approximation, namely, that the noise in the reconstructed image is small compared to the mean image. In a second paper, the approximation will be justified through Monte Carlo simulations covering a range of noise levels, iteration points, and subset orderings. The ensemble statistical parameters could then be used to evaluate objective measures of image quality.

Attenuation is believed to be one of the major causes of false-positive cardiac single-photon emission computed tomographic perfusion images. This article provides an introduction to the approaches used to correct for nonuniform attenuation once a patient-specific attenuation map is available. Comparison is made of specific attenuation-correction algorithms from each of three major categories of compensation methods that are or will be available commercially. Examples of the use of the algorithms on simulated projections of a mathematic phantom modeling the anatomy of the upper torso are used to illustrate the ability of the methods to compensate for attenuation. The advantages and disadvantages of each approach are summarized, as well as areas that need further investigation.

Spatiotemporal reconstruction of cardiac-gated SPECT images permits us to obtain valuable information related to cardiac function. However, the task of reconstructing this four-dimensional (4-D) data set is computation intensive. Typically, these studies are reconstructed frame-by-frame: a nonoptimal approach because temporal correlations in the signal are not accounted for. In this work, we show that the compression and signal decorrelation properties of the Karhunen-Loève (KL) transform may be used to greatly simplify the spatiotemporal reconstruction problem. The gated projections are first KL transformed in the temporal direction. This results in a sequence of KL-transformed projection images for which the signal components are uncorrelated along the time axis. As a result, the 4-D reconstruction task is simplified to a series of three-dimensional (3-D) reconstructions in the KL domain. The reconstructed KL components are subsequently inverse KL transformed to obtain the entire spatiotemporal reconstruction set. Our simulation and clinical results indicate that KL processing provides image sequences that are less noisy than are conventional frame-by-frame reconstructions. Additionally, by discarding high-order KL components that are dominated by noise, we can achieve savings in computation time because fewer reconstructions are needed in comparison to conventional frame-by-frame reconstructions.