Three-dimensional statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in x-ray CT. SIR algorithms are important for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. For iterative image reconstruction algorithms to be deployed in clinical settings, the images must be quantitatively accurate and computed in clinically useful times. We describe an acceleration method that is based on adaptively varying an update factor of the additive step of the alternating minimization (AM) algorithm. Our implementation combines this method with other acceleration techniques like ordered subsets (OS) which was originally proposed for transmission tomography by Ahn, Fessler et. al [1]. Results on both an NCAT phantom and real clinical data from a Siemens Sensation 16 scanner demonstrate an improved convergence rate compared to the straightforward implementations of the alternating minimization (AM) algorithm of O'Sullivan and Benac [2] with a Huber-type edge-preserving penalty, originally proposed by Lange [3]. Our proposed acceleration method on average yields 2X acceleration of the convergence rate for both baseline and ordered subset implementations of the AM algorithm.
One-sided ultrasonic non-destructive evaluation (UNDE) uses ultrasound signals to investigate and inspect structures that are only accessible from one side. A widely used reconstruction technique in UNDE is the synthetic aperture focusing technique (SAFT). SAFT produces fast reconstruction and reasonable images for simple structures. However, for large complex structures, SAFT reconstructions suffer from noise and artifacts. To resolve some of the drawbacks of SAFT, an ultrasonic model-based iterative reconstruction (MBIR) algorithm, a method based on Bayesian estimation, was proposed that showed significant enhancement over SAFT in reducing noise and artifacts. In this paper, we build on previous investigations of the use of MBIR reconstruction on ultrasound data by proposing a spatially varying prior-model to account for artifacts from deeper regions and a 3D regularizer to account for correlations between scans from adjacent regions. We demonstrate that the use of the new prior model in MBIR can significantly improve reconstructions compared to SAFT and the previously proposed MBIR technique.