Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank
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An end-to-end image analysis pipeline is presented for the abdominal MRI protocol used in the UK Biobank on the first 38,971 participants. Emphasis is on the processing steps necessary to ensure a high-level of data quality and consistency is produced in order to prepare the datasets for downstream quantitative analysis, such as segmentation and parameter estimation. Quality control procedures have been incorporated to detect and, where possible, correct issues in the raw data. Detection of fat-water swaps in the Dixon series is performed by a deep learning model and corrected automatically. Bone joints are predicted using a hybrid atlas-based registration and deep learning model for the shoulders, hips and knees. Simultaneous estimation of proton density fat fraction and transverse relaxivity (R2*) is performed using both the magnitude and phase information for the single-slice multiecho series. Approximately 98.1 processed and passed quality control, with 99.98 T1-weighted 3D volumes succeeding. Approximately 99.98 multiecho acquisitions covering the liver were successfully processed and passed quality control, with 97.6 covering the pancreas succeeding. At least one fat-water swap was detected in 1.8 participants were missing at least one knee joint and 0.8 least one shoulder joint. For the participants who received both single-slice multiecho acquisition protocols for the liver a systematic difference between the two protocols was identified and modeled using multiple linear regression. The findings presented here will be invaluable for scientists who seek to use image-derived phenotypes from the abdominal MRI protocol.
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