Browsing by keyword "Data acquisition"
Now showing items 1-3 of 3
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4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECTCardiac single photon emission computed tomography (SPECT) images are known to suffer from both cardiac and respiratory motion blur. In this paper, we investigate a 4-D reconstruction approach to suppress the effect of respiratory motion in gated cardiac SPECT imaging. In this approach, the sequence of cardiac gated images is reconstructed with respect to a reference respiratory amplitude bin in the respiratory cycle. To combat the challenge of inherent high-imaging noise, we utilize the data counts acquired during the entire respiratory cycle by making use of a motion-compensated scheme, in which both cardiac motion and respiratory motion are taken into account. In the experiments, we first use Monte Carlo simulated imaging data, wherein the ground truth is known for quantitative comparison. We then demonstrate the proposed approach on eight sets of clinical acquisitions, in which the subjects exhibit different degrees of respiratory motion blur. The quantitative evaluation results show that the 4-D reconstruction with respiratory correction could effectively reduce the effect of motion blur and lead to a more accurate reconstruction of the myocardium. The mean-squared error of the myocardium is reduced by 22%, and the left ventricle (LV) resolution is improved by 21%. Such improvement is also demonstrated with the clinical acquisitions, where the motion blur is markedly improved in the reconstructed LV wall and blood pool. The proposed approach is also noted to be effective on correcting the spill-over effect in the myocardium from nearby bowel or liver activities.
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Improving efficiency in neuroimaging research through application of Lean principlesINTRODUCTION: "Lean" is a set of management principles which focus on increasing value and efficiency by reducing or avoiding waste (e.g., overproduction, defects, inventory, transportation, waiting, motion, over processing). It has been applied to manufacturing, education, and health care, leading to optimized process flow, increased efficiency and increased team empowerment. However, to date, it has not been applied to neuroimaging research. METHODS: Lean principles, such as Value stream mapping (e.g. a tool with which steps in the workflow can be identified on which to focus improvement efforts), 5S (e.g. an organizational method to boost workplace efficiency and efficacy) and Root-cause analysis (e.g. a problem-solving approach to identify key points of failure in a system) were applied to an ongoing, large neuroimaging study that included seven research visits per participant. All team members participated in a half-day exercise in which the entire project flow was visualized and areas of inefficiency were identified. Changes focused on removing obstacles, standardization, optimal arrangement of equipment and root-cause-analysis. A process for continuous improvement was also implemented. Total time of an experiment was recorded before implementation of Lean for two participants and after implementation of Lean for two participants. Satisfaction of team members was assessed anonymously on a 5-item Likert scale, ranging from much worsened to much improved. RESULTS: All team members (N = 6) considered the overall experience of conducting an experiment much improved after implementation of Lean. Five out of six team members indicated a much-improved reduction in time, with the final team member considering this somewhat improved. Average experiment time was reduced by 13% after implementation of Lean (from 142 and 147 minutes to 124 and 128 minutes). DISCUSSION: Lean principles can be successfully applied to neuroimaging research. Training in Lean principles for junior research scientists is recommended.
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Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME modelRigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata Specifications that extend the OME Data Model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

