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. We calculate bounds on the support in the frequency domain of the collected data and use these to suggest an efficient sampling pattern. A reduction of up to a factor of 2 in sampling rate is suggested. Indeed, we show that reconstruction quality is not affected by this reduction of sampling rates.

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. In MLT scanning, the transmission is simultaneously focused at several directions. This scan mode introduces artifacts that stem from the overlaps of the receive main lobe with the transmit side lobes of additional transmit directions besides the one of interest. Similar overlaps occur between the transmit main lobe with receive side lobes. These artifacts are known in the signal processing community as cross-talk. Previous studies have concentrated on proper transmit and receive apodization, as well as transmit directions arrangement in the transmit event, to reduce the cross-talk artifacts. This study examines the possibility of using adaptive beamforming, specifically, minimum variance (MV) and linearly constrained minimum variance (LCMV) beamforming, to reduce the cross-talk artifacts, and maintain or even improve image quality characteristics. Simulation results, as well as experimental phantom and in vivo cardiac data, demonstrate the feasibility of reducing cross-talk artifacts with MV beamforming. The MV and LCMV results achieve superior spatial resolution, not only over other MLT methods with data-independent apodization, but even over that of single-line transmission (SLT) without receive apodization. The MV beamformer is shown to be less sensitive to wider transmit profiles required to reduce the transmit crosstalk artifacts. MV beamforming, combined with the wider transmit profiles, can provide a good approach for MLT scanning with reduced cross-talk artifacts, without compromising spatial resolution, and even improving it. We also demonstrate that the MV and LCMV beamformers lead to almost identical results. This is because of their very similar beampatterns, except for the sharp nullifying properties that the LCMV beamformer has around interfering beams.

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. Independently, the application of minimum variance (MV) beamforming has been suggested in the context of MLA. We demonstrate here that each approach is only a partial solution and that combining them provides a better result than applying either approach separately. This is demonstrated by using both simulated and real phantom data, as well as cardiac data. We also show that the STB-compensated MV beamfomer outperforms single-line acquisition (SLA) delay- and-sum in terms of lateral resolution.

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. ESRI suffers frequently from a low signal-to-noise ratio (SNR), which results in long acquisition times and poor image quality. A broader use of ESRI is hampered by this slow acquisition, which can also be an obstacle for many biological applications where conditions may change relatively quickly over time. The objective of this work is to develop an image reconstruction scheme for continuous wave (CW) ESRI that would make it possible to reduce the data acquisition time without degrading the reconstruction quality. This is achieved by adapting the so-called "statistical reconstruction" method, recently developed for other medical imaging modalities, to the specific case of CW ESRI. Our new algorithm accounts for unique ESRI aspects such as field modulation, spectral-spatial imaging, and possible limitation on the gradient magnitude (the so-called "limited angle" problem). The reconstruction method shows improved SNR and contrast recovery vs. commonly used back-projection-based methods, for a variety of simulated synthetic samples as well as in actual CW ESRI experiments.

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. We demonstrate the usage of this regularizer within a Bayesian blind deconvolution framework and also integrate into the latter a method for noise reduction, thus creating a complete blind deconvolution method. The application of the proposed algorithm is demonstrated on synthetic and real-world three-dimensional images acquired by a wide-field fluorescence microscope, where the need for blind deconvolution algorithms is indispensable, yielding excellent results.