Sparse sampling in helical cone-beam CT perfect reconstruction algorithms.

In the current paper we consider the Helical Cone Beam CT. This scanning method exposes the patient to large quantities of radiation and results in very large amounts of data being collected and stored. Both these facts are prime motivators for the development of an efficient, reduced rate, sampling pattern.

Multi-line transmission combined with minimum variance beamforming in medical ultrasound imaging.

Increasing medical ultrasound imaging frame rate is important in several applications such as cardiac diagnostic imaging, where it is desirable to be able to examine the temporal behavior of fast phases in the cardiac cycle. This is particularly true in 3-D imaging, where current frame rate is still much slower than standard 2-D, B-mode imaging. Recently, a method that increases frame rate, labeled multi-line transmission (MLT), was reintroduced and analyzed.

Multi-line acquisition with minimum variance beamforming in medical ultrasound imaging.

In recent years, multiple-line acquisition (MLA) has been introduced to increase frame rate in cardiac ultrasound medical imaging. However, this method induces blocklike artifacts in the image. One approach suggested, synthetic transmit beamforming (STB), involves overlapping transmit beams which are then interpolated to remove the MLA blocking artifacts.

Statistical reconstruction algorithms for continuous wave electron spin resonance imaging.

Electron spin resonance imaging (ESRI) is an important branch of ESR that deals with heterogeneous samples ranging from semiconductor materials to small live animals and even humans. ESRI can produce either spatial images (providing information about the spatially dependent radical concentration) or spectral-spatial images, where an extra dimension is added to describe the absorption spectrum of the sample (which can also be spatially dependent). The mapping of oxygen in biological samples, often referred to as oximetry, is a prime example of an ESRI application.

Blind image deconvolution using machine learning for three-dimensional microscopy.

In this work, we propose a novel method for the regularization of blind deconvolution algorithms. The proposed method employs example-based machine learning techniques for modeling the space of point spread functions. During an iterative blind deconvolution process, a prior term attracts the point spread function estimates to the learned point spread function space.