Evaluation of Fast Scan EPR for High-Resolution Imaging Using Nitroxide Radical Probes at 1.2 GHz

Abstract

Nitroxide probes have been commonly used for biomedical EPR imaging applications; however, image quality has been limited by the number of image projections, as well as the probe linewidth and hyperfine structure. In the current study, we evaluate the use of fast millisecond scan EPR projection acquisition along with a novel reconstruction algorithm optimized for 3D spatial EPR image reconstruction from a high number of noisy projections. This reconstruction method utilizes the raw image projection data and zero gradient spectrum to account for EPR line shape and hyperfine structure of any given paramagnetic probe without the need for deconvolution that is poorly suited for high noise data. Using fast scan EPR imaging with this reconstruction method, we image non-deuterated, deuterated and 15 N substituted nitroxide probes in experimental phantoms of complex geometries. We evaluate the image resolution that can be obtained and the imaging time required. With 16,384 projections acquired over 1 min, and a field gradient of 8 G/cm, with a 250 3 voxel 3D matrix, spatial resolutions of up to 100 µm are theoretically possible for a cubical volume of 25 × 25 × 25 mm 3 . In experiments with a variety of phantoms with mM nitroxide radical probes, resolutions of 600-250 µm were obtained with 1-10 min acquisitions, respectively. The presently obtainable signal sensitivity and noise levels of these acquisitions limited the obtainable resolution. With longer time acquisitions or further improvements in sensitivity and noise reduction, image resolutions approaching 100 µm should be possible.

Prediction of Higher Education Cost and Analysis of Sharing Ability Based on Artificial Neural Network

“…Higher education cost management and accounting is very important and even becomes an important component of financial management in colleges and universities. erefore, on the basis of cost accounting, on the basis of clarifying the dependent variables and independent variables of university cost control, cost accounting, and cost sharing ability, combined with neural network algorithm, this paper has made a key research on cost prediction [3].…”