Analysis of Factors Affecting Quality in Structural Magnetic Resonance Images

Area of Science:

• Neuroimaging
• Medical Physics
• Biostatistics

Background:

• Combining Magnetic Resonance Images (MRI) from diverse sources is crucial for research but susceptible to variations in image quality.
• Technical factors (scanner hardware, software, protocols) and participant characteristics can introduce biases in statistical outcomes.
• Ensuring MRI data quality is paramount for reliable scientific findings.

Purpose of the Study:

• To comprehensively investigate the contributions of technical and participant-related factors to anatomical MRI quality.
• To evaluate the stability of Image Quality Rating (IQR) over time and the impact of defacing.
• To identify specific technical parameters and participant characteristics influencing MRI quality.

Main Methods:

• Analyzed 2779 T1-weighted MRI volumes from two Siemens 3 Tesla scanners with varying hardware, software, and acquisition protocols.
• Utilized Computational Anatomy Toolbox (CAT12) to derive an Image Quality Rating (IQR) index for each scan.
• Assessed the influence of scanner site, coil design, software versions, acquisition protocols, spatial resolution, defacing, age, sex, and clinical conditions (including Schizophrenia, MDD, AUD, ASD).

Main Results:

• Scanner software (VB17 showed lower quality than VB15) and spatial resolution (1mm higher quality than 0.8mm) significantly affected IQR.
• Acquisition protocols also influenced IQR, with 'T1_1mm_extended' performing better.
• No significant effect of scanner site or coil design was observed; defacing did not impact IQR.
• IQR remained stable across sessions. Age interacted with sex, with IQR increasing with age in men only.
• Participants with Schizophrenia exhibited significantly higher IQR (lower quality) compared to healthy controls and those with Major Depressive Disorder.

Conclusions:

• Scanner software, acquisition protocols, and spatial resolution are critical technical determinants of MRI quality.
• Participant age and sex, along with specific clinical conditions like Schizophrenia, can influence MRI quality.
• Image Quality Rating (IQR) is a robust metric for assessing MRI quality, essential for minimizing bias in multicentric and individual studies for more accurate structural analysis.