Biomedical subjects

Bradley Clymer

Publications and source records attributed to Bradley Clymer.

4 recordsLinked to original sources

PubMed2006

Enhanced resolution for EPR imaging by two-step deblurring.

The broad spectrum of spin probes used for electron paramagnetic resonance imaging (EPRI) result in poor spatial resolution of the reconstructed images. Conventional deconvolution procedures can enhance the resolution to some extent but obtaining high resolution EPR images is still a challenge. In this work, we have implemented and analyzed the performance of a postacquisition deblurring technique to enhance the spatial resolution of the EPR images. The technique consists of two steps; noniterative deconvolution followed by iterative deconvolution of the acquired projections which are then projected back using filtered backprojection (FBP) to reconstruct a high resolution image. Further, we have proposed an analogous technique for iterative reconstruction algorithms such as multiplicative simultaneous iterative reconstruction technique (MSIRT) which can be a method of choice for many applications. The performance of the suggested deblurring approach is evaluated using computer simulations and EPRI experiments. Results suggest that the proposed procedure is superior to the standard FBP and standard iterative reconstruction algorithms in terms of mean-square-error (MSE), spatial resolution, and visual judgment. Although the procedure is described for 2D imaging, it can be readily extended to 3D imaging.

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PubMed2006

Quasi Monte Carlo-based isotropic distribution of gradient directions for improved reconstruction quality of 3D EPR imaging.

In continuous wave (CW) electron paramagnetic resonance imaging (EPRI), high quality of reconstructed image along with fast and reliable data acquisition is highly desirable for many biological applications. An accurate representation of uniform distribution of projection data is necessary to ensure high reconstruction quality. The current techniques for data acquisition suffer from nonuniformities or local anisotropies in the distribution of projection data and present a poor approximation of a true uniform and isotropic distribution. In this work, we have implemented a technique based on Quasi-Monte Carlo method to acquire projections with more uniform and isotropic distribution of data over a 3D acquisition space. The proposed technique exhibits improvements in the reconstruction quality in terms of both mean-square-error and visual judgment. The effectiveness of the suggested technique is demonstrated using computer simulations and 3D EPRI experiments. The technique is robust and exhibits consistent performance for different object configurations and orientations.

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PubMed2006

Optimization of data acquisition for EPR imaging.

In electron paramagnetic resonance imaging (EPRI), long data acquisition time is one of the major problems limiting successful and useful biological EPRI experiments. Depending on the configuration (spatial distribution of paramagnetic species), information embedded in some objects can be characterized using a smaller number of projections, while others may require significantly larger number of projections to generate similar results. In order to optimize the acquisition process, it is therefore important to acquire a different number of projections for different objects. In this paper, a prediction scheme is demonstrated that can determine the number of projections required to achieve a preset reconstruction quality for a given object. After acquiring first few projections, corresponding partially filled k-space is analyzed. The complexity of data (to interpolate) in k-space is quantified and used to predict the number of required projections. All the projections are acquired using a mean-square difference-based adaptive acquisition technique that is also demonstrated in this work. The purpose of this non-uniform acquisition is to reduce redundancy in the acquired data which in turn decreases the number of projections required for the given object. It is also demonstrated that the performance of non-uniform acquisition is content dependant, and for certain configurations it may not be as effective as uniform acquisition in preserving signal from low intensity regions. The prediction scheme along with the non-uniform acquisition is tested using computer simulations, imaging of experimental phantoms, and in vivo imaging. Results indicate that the proposed method may save up to 50% of acquisition time. The techniques in this manuscript are described for 2D spatial imaging but can be extended to 3D imaging.

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PubMed2006

Automated on-the-fly detection and correction procedure for EPR imaging data acquisition.

Fast and reliable data acquisition is a major requirement for successful and useful biological electron paramagnetic resonance imaging (EPRI) experiments. Even a technologically advanced and professionally supervised EPRI system can exhibit instabilities initiated by perturbations such as animal motion, microphonics, and temperature changes. As a result, part of an acquired data set may become corrupted with excessive noise and distortions, which in turn may degrade the quality of the reconstructed image. In this work an automated scheme to monitor the system performance and stability over the course of an experiment is demonstrated. This method ensures that the quality of the acquired data is maintained during the experiment. For this purpose, four parameters including noise content and integration of each acquired projection are quantified and measured against those of the zero-gradient (ZG) projection, which is set as a quality benchmark. Projections with parameter values that differ substantially from the expected values are identified as damaged and consequently are reacquired. Therefore, the proposed technique not only effectively monitors the quality of acquisition, it also saves a substantial amount of acquisition time because it eliminates the necessity of repeating the entire experiment in cases in which only a small fraction of the data are corrupted.

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