Unsupervised segmentation of the prostate using MR images based on level set with a shape prior.

Prostate cancer is the second leading cause of cancer death in American men. Current prostate MRI can benefit from automated tumor localization to help guide biopsy, radiotherapy and surgical planning. An important step of automated prostate cancer localization is the segmentation of the prostate. In this paper, we propose a fully automatic method for the segmentation of the prostate. We firstly apply a deformable ellipse model to find an ellipse that best fits the prostate shape. Then, this ellipse is used to initiate the level set and constrain the level set evolution with a shape penalty term. Finally, certain post processing methods are applied to refine the prostate boundaries. We apply the proposed method to real diffusion-weighted (DWI) MRI images data to test the performance. Our results show that accurate segmentation can be obtained with the proposed method compared to human readers.

Improved image quality and computation reduction in 4-D reconstruction of cardiac-gated SPECT images.

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.

Iterative linear minimum mean-square-error image restoration from partially known blur.

We address the problem of space-invariant image restoration when the blurring operator is not known exactly, a situation that arises regularly in practice. To account for this uncertainty, we model the point-spread function as the sum of a known deterministic component and an unknown random one. Such an approach has been studied before, but the problem of estimating the parameters of the restoration filter to our knowledge has not been addressed systematically. We propose an approach based on a Gaussian statistical assumption and derive an iterative, expectation-maximization algorithm that simultaneously restores the image and estimates the required filter parameters. We obtain two versions of the algorithm based on two different models for the statistics of the image. The computations are performed in the discrete Fourier transform domain; thus they are computationally efficient even for large images. We examine the convergence properties of the resulting estimators and evaluate their performance experimentally.

Spatial relationships in early signaling events of flow-mediated endothelial mechanotransduction.

Blood flow interactions with the vascular endothelium represent a specialized example of mechanical regulation of cell function that has important physiological and pathological cardiovascular consequences. The endothelial monolayer in vivo acts as a signal transduction interface for forces associated with flowing blood (hemodynamic forces) in the acute regulation of artery tone and chronic structural remodeling of arteries, including the pathology of atherosclerosis. Mechanisms related to spatial relationships at the cell surfaces and throughout the cell that influence flow-mediated endothelial mechanotransduction are discussed. In particular, flow-mediated ion channel activation and cytoskeletal dynamics are considered in relation to topographic analyses of the luminal and abluminal surfaces of living endothelial cells.

Image reconstruction for dynamic PET based on low-order approximation and restoration of the sinogram.

Many image-reconstruction methods have been proposed to improve the spatial resolution of positron emission tomography (PET) images and, thus, to produce better quantification. However, these techniques, which are designed for static images, may be inadequate for good reconstruction from dynamic data. We present a simple, but effective, reconstruction approach intended specifically for dynamic studies. First, the level of noise in dynamic PET data is reduced by smoothing along the time axis using a low-order approximation. Next, the denoised sinograms are restored spatially by the method of projections onto convex sets. Finally, images are reconstructed from the restored sinograms by ordinary filtered backprojection. We present experimental results that demonstrate substantial improvements in region-of-interest quantification in actual and simulated dopamine D-2 neuroreceptor-imaging studies of a monkey brain.

Effect of spatial coherence on knife-edge measurements of detector modulation transfer function.

When an image of an edge object is used in the determination of the modulation transfer function of a detector array, the partial coherence of the illumination is often ignored. Although this approximation is valid in some cases, it may not be satisfactory, particularly for the small detector elements characteristic of present-day charge-coupled devices. Here we demonstrate the effect of partial coherence on edge-based modulation transfer function determination for various pixel sizes, degrees of coherence, and f-numbers of the test optics.

Superresolved tomography by convex projections and detector motion.

If the spatial resolution of an image-acquisition system is limited by the size of its component detector elements, then scanning may be required for the signal to be fully sampled. In such cases interpolation methods are normally applied to reproduce a uniformly sampled signal from the set of observations. Alternatively, however, this step can be treated as a restoration problem, in which case the extra measurements made accessible by detector motion may contain sufficient information to superresolve the signal, i.e., to recover information beyond the limit normally associated with finite detector size. We describe the application of this concept to the problem of constructing the projection matrix from a set of noise-corrupted tomographic measurements made by a moving detector array. In particular we focus on the case encountered in many tomographic applications in which the spatial response functions are approximately stationary with object depth. The method of projections onto convex sets is used in conjunction with an underrelaxation scheme to recover the projection matrix, from which the image is reconstructed by the standard filtered backprojection algorithm. Simulation results demonstrate that this approach applied to data acquired by a wobbling positron emission tomography system can substantially enhance the quality of the reconstructed image, even in the presence of high levels of quantum noise. The projection-matrix recovery step can be performed in a matter of seconds; thus the benefits of signal recovery are gained without a significant sacrifice in computation time.

Image correlation at low light levels.

Low-light-level input images are cross correlated with a classical intensity reference image that is stored in computer memory. Experimental measurements of the correlation signal, obtained using a two-dimensional, photon-counting detector and position-computing electronics, are reported. The experimental results are in good agreement with theoretical predictions.