title: Automated on-the-fly detection and correction procedure for EPR imaging data acquisition.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, Ohio State University, College of Engineering, Columbus, Ohio 43210, USA.
authors.forename: Rizwan
authors.initials: R
authors.lastname: Ahmad
authors.forename: Deepti S
authors.initials: DS
authors.lastname: Vikram
authors.forename: Sergey
authors.initials: S
authors.lastname: Petryakov
authors.forename: Yuanmu
authors.initials: Y
authors.lastname: Deng
authors.forename: Jay L
authors.initials: JL
authors.lastname: Zweier
authors.forename: Periannan
authors.initials: P
authors.lastname: Kuppusamy
authors.forename: Bradley
authors.initials: B
authors.lastname: Clymer
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001185:Artificial Intelligence; D003199:Computer Systems; D003952:Diagnostic Imaging / Q000379:methods*; D004578:Electron Spin Resonance Spectroscopy / Q000295:instrumentation / Q000379:methods*; D005354:Fibrosarcoma / Q000473:pathology*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity