Penalized-likelihood reconstruction for metal artifact reduction in cone-beam CT.

CT reconstruction from metal-embedded data usually produces streak artifacts that reduce the quality of the reconstructed images. In this paper, we propose a new technique for metal artifact reduction in cone-beam CT based on statistical reconstruction. First, the metal objects are segmented in the reconstructed images and then reprojected to obtain the measurement data of the metal objects using cone-beam reconstruction.

3D left ventricular segmentation using double active contours and double active surfaces.

We propose a 3D left ventricular segmentation method for cardiac MR images. There are two steps in our method. In the first step, we improve our double active contours for segmenting the endocardial and the epicardial boundaries simultaneously without requiring any training sets.

Image recovery using partitioned-separable paraboloidal surrogate coordinate ascent algorithms.

Iterative coordinate ascent algorithms have been shown to be useful for image recovery, but are poorly suited to parallel computing due to their sequential nature. This paper presents a new fast converging parallelizable algorithm for image recovery that can be applied to a very broad class of objective functions. This method is based on paraboloidal surrogate functions and a concavity technique.

Active contours with automatic initialization for myocardial perfusion analysis.

Quantitative analysis of myocardial perfusion requires detection of myocardial boundaries in many short-axis MR images. Manual tracing of myocardial boundaries is a time-consuming and tedious task, which may limit the clinical use of quantitative analysis. In this paper, we propose an automatic detection algorithmbased on active contours.

Penalized-likelihood image reconstruction for digital holography.

Conventional numerical reconstruction for digital holography using a filter applied in the spatial-frequency domain to extract the primary image may yield suboptimal image quality because of the loss in high-frequency components and interference from other undesirable terms of a hologram. We propose a new numerical reconstruction approach using a statistical technique. This approach reconstructs the complex field of the object from the real-valued hologram intensity data.

Relaxed ordered-subset algorithm for penalized-likelihood image restoration.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surrogate (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence.