Development of reference Image Quality Metrics for quantitative MRI research using MRIQC
Himanshu Joshi, Aarthi G, Mekha S Thomas, Gowthami Nair, Sivakumar PT, Ganesan Venkatasubramanian, Paul M. Thompson, John P John

TL;DR
This paper presents preliminary reference image quality metrics for MRI scans to help researchers assess scan quality in large datasets.
Contribution
The study proposes preliminary reference IQM ranges for MRI scans using a subset of the ABIDE dataset.
Findings
Reference IQM values for 19 scans rated 'OK' and 2 rated 'Fail' were calculated.
The proposed metrics could aid in developing automated quality control pipelines for big data MRI analyses.
Abstract
MRI Quality Control (MRIQC) has emerged as a promising tool to assess the image quality of MR acquisitions using Image Quality Metrics (IQMs). The no‐reference IQMs for structural MRIs from the ABIDE (n = 1111; from 21 sites) dataset are available on the MRIQC documentation page. These IQMs consist of measures based on noise (cnr, snr), artifact detection (art_q1), information theory (efc, fber), and other measures (fwhm) related to the spatial distribution of image intensity. No standard reference IQM ranges are available in the literature as a guide for MRI researchers. Here, we report our study's preliminary results that aim to generate reference IQM ranges for quantitative MRI analyses using a randomly chosen subset of 100 scans from the ABIDE dataset. The ratings of four raters under ‘OK’, ‘Maybe’, and ‘Fail’ were extracted without the IQMs for the randomly chosen subset of 100…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Radiomics and Machine Learning in Medical Imaging · MRI in cancer diagnosis
