Systematic approach to cutoff frequency selection in continuous-wave electron paramagnetic resonance imaging

Algebraic reconstruction of 3D spatial EPR images from high numbers of noisy projections: An improved image reconstruction technique for high resolution fast scan EPR imaging

A novel method for reconstructing 3D spatial EPR images from large numbers of noisy projections was developed that minimizes mean square error between the experimental projections and those from the reconstructed image. The method utilizes raw projection data and zero gradient spectrum to account for EPR line shape and hyperfine structure of the paramagnetic probe without the need for deconvolution techniques that are poorly suited for processing of high noise projections. A numerical phantom was reconstructed for method validation. Reconstruction time for the matrix of 128³ voxels and 16,384 noiseless projections was 4.6 min for a single iteration. The algorithm converged quickly, reaching R² ~ 0.99975 after the very first iteration. An experimental phantom sample with nitroxyl radical was measured. With 16,384 projections and a field gradient of 8 G/cm, resolutions of 0.4 mm were achieved for a cubical area of 25 × 25 × 25 mm³. Reconstruction was sufficiently fast and memory efficient making it suitable for applications with large 3D matrices and fully determined system of equations. The developed algorithm can be used with any gradient distribution and does not require adjustable filter parameters that makes for simple application. A thorough analysis of the strengths and limitations of this method for 3D spatial EPR imaging is provided.

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.

Use of multi-coil parallel-gap resonators for co-registration EPR/NMR imaging

This article reports experimental investigations on the use of RF resonators for continuous-wave electron paramagnetic resonance (cw-EPR) and proton nuclear magnetic resonance (NMR) imaging. We developed a composite resonator system with multi-coil parallel-gap resonators for co-registration EPR/NMR imaging. The resonance frequencies of each resonator were 21.8MHz for NMR and 670MHz for EPR. A smaller resonator (22mm in diameter) for use in EPR was placed coaxially in a larger resonator (40mm in diameter) for use in NMR. RF magnetic fields in the composite resonator system were visualized by measuring a homogeneous 4-hydroxy-2,2,6,6-tetramethyl-piperidinooxy (4-hydroxy-TEMPO) solution in a test tube. A phantom of five tubes containing distilled water and 4-hydroxy-TEMPO solution was also measured to demonstrate the potential usefulness of this composite resonator system in biomedical science. An image of unpaired electrons was obtained for 4-hydroxy-TEMPO in three tubes, and was successfully mapped on the proton image for five tubes. Technical problems in the implementation of a composite resonator system are discussed with regard to co-registration EPR/NMR imaging for animal experiments.

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.

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.