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Published on: December 18, 2016
Towards Automatic Quantitative Quality Control for MRI
Carolyn B Lauzon1, Brian C Caffo, Bennett A Landman
1Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235 ; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235.
Abstract:
Quality and consistency of clinical and research data collected from Magnetic Resonance Imaging (MRI) scanners may become suspect due to a wide variety of common factors including, experimental changes, hardware degradation, hardware replacement, software updates, personnel changes, and observed imaging artifacts. Standard practice limits quality analysis to visual assessment by a researcher/clinician or a quantitative quality control based upon phantoms which may not be timely, cannot account for differing experimental protocol (e.g. gradient timings and strengths), and may not be pertinent to the data or experimental question at hand. This paper presents a parallel processing pipeline developed towards experiment specific automatic quantitative quality control of MRI data using diffusion tensor imaging (DTI) as an experimental test case. The pipeline consists of automatic identification of DTI scans run on the MRI scanner, calculation of DTI contrasts from the data, implementation of modern statistical methods (wild bootstrap and SIMEX) to assess variance and bias in DTI contrasts, and quality assessment via power calculations and normative values. For this pipeline, a DTI specific power calculation analysis is developed as well as the first incorporation of bias estimates in DTI data to improve statistical analysis.
Insights
This study introduces an automated pipeline for Magnetic Resonance Imaging (MRI) data quality control, specifically for diffusion tensor imaging (DTI). It enhances data reliability by assessing experiment-specific variance and bias, improving research accuracy.
Area of Science:
- Medical Imaging
- Neuroimaging
- Biostatistics
Background:
- Magnetic Resonance Imaging (MRI) data quality is susceptible to various factors like hardware changes, software updates, and artifacts.
- Current quality control methods (visual assessment, phantom scans) are often insufficient, lacking timeliness and experiment-specific relevance.
Purpose of the Study:
- To develop and present a parallel processing pipeline for automatic, experiment-specific quantitative quality control of MRI data.
- To utilize diffusion tensor imaging (DTI) as a test case for this novel quality control pipeline.
Main Methods:
- Automatic identification of DTI scans from MRI data.
- Calculation of DTI contrasts and implementation of statistical methods (wild bootstrap, SIMEX) for variance and bias assessment.
- Development of DTI-specific power calculations and incorporation of bias estimates for improved statistical analysis.
Main Results:
- A functional parallel processing pipeline for automated DTI quality control was successfully developed.
- The pipeline enables experiment-specific quantitative assessment of MRI data quality.
- Novel methods for DTI power calculations and bias estimation were integrated.
Conclusions:
- The developed pipeline offers a robust solution for ensuring the quality and consistency of MRI data, particularly DTI.
- Automated, experiment-specific quality control improves the reliability and statistical power of neuroimaging research.
- This approach addresses limitations of traditional quality control methods, enhancing the validity of research findings.
