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Updated: Jun 22, 2026
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
Automatic quality assessment in structural brain magnetic resonance imaging
Bénédicte Mortamet1, Matt A Bernstein, Clifford R Jack
1Advanced Clinical Imaging Technology, Siemens Suisse SA, Healthcare Sector IM&WS-Centre d'Imagerie Biomédicale (CIBM), Lausanne, Switzerland. benedicte.mortamet@epfl.ch
Abstract:
MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This work proposes a fully-automatic method for measuring image quality of three-dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T(1)-weighted 1.5T and 3T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore.
Insights
This study introduces an automated method to assess 3D structural MRI image quality by analyzing air background. The technique reliably detects various artifacts, improving diagnostic accuracy and clinical workflow efficiency.
Area of Science:
- Medical Imaging
- Radiology
- Biomedical Engineering
Background:
- Magnetic Resonance Imaging (MRI) is crucial for medical diagnosis.
- Image artifacts degrade MRI reliability, challenging human and automated diagnoses.
- Assessing 3D structural MRI quality is essential for accurate interpretation.
Purpose of the Study:
- To develop a fully-automatic method for measuring 3D structural MRI image quality.
- To identify and quantify image degradation from sources like motion, blurring, and ghosting.
- To provide a tool for enhancing diagnostic reliability and clinical workflow.
Main Methods:
- Analyzing the air background of magnitude images in 3D structural MRI.
- Deriving quality measures sensitive to common MRI artifacts.
- Validating the method on a large dataset of 749 T1-weighted head scans from the ADNI study.
Main Results:
- The automated quality assessment method demonstrated high sensitivity and specificity (>85%) compared to expert ratings.
- Quality indices were independent of MRI system hardware and software variations.
- The method effectively detected image degradation from bulk motion, incomplete spoiling, blurring, and ghosting.
Conclusions:
- The proposed automated quality assessment is valuable for research and clinical MRI.
- It can improve workflow by identifying scans needing reacquisition before patient departure.
- This method enhances the reliability of MRI-based diagnoses and computer-aided detection systems.
Related Concept Videos
01:24Magnetic Resonance Imaging
01:14Brain Imaging
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).