Automated Functional MRI Quality Assessment

Automated Assessment of Ghost Artifacts in MRI

Introduction: Flow artifacts in MR images can appear as image ghosts within and outside the body cavity. Technical improvements aimed at suppressing these image ghosts often rely on expert scoring (1,2) or on semi-automated methods demanding tissue segmentation and estimation of statistical properties of intensity distribution (3) to evaluate the efficacy of the methods. These approaches can be...

Assessment and optimization of functional MRI analyses.

Numerous functional magnetic resonance imaging (fMRI) data analysis strategies have been used by different laboratories to enhance the detection of brain activation. A consensus has not been achieved regarding the relative statistical power of different strategies. In this report, we compared several commonly-used data analysis strategies for conventional (nonechoplanar imaging) fMRI data by ev...

Automated Data Quality Assessment of Marine Sensors

The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is necessary to ensure that the data collected ...

Multimodal Dynamic MRI for Structural and Functional Assessment of Cancer

Structural bioinformatics Gaia: Automated Quality Assessment of Protein Structure Models

Supplementary Figure S1. Parameters used for surface area and void calculations. For any two overlapping atoms i and j, various distances and angles are represented in the figure. These parameters are used in different equations shown in the main text for computation of MSA/SASA and void volume.