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An end-to-end image analysis pipeline is presented for the abdominal MRIprotocol used in the UK Biobank on the first 38,971 participants. Emphasis ison the processing steps necessary to ensure a high-level of data quality andconsistency is produced in order to prepare the datasets for downstreamquantitative analysis, such as segmentation and parameter estimation. Qualitycontrol procedures have been incorporated to detect and, where possible,correct issues in the raw data. Detection of fat-water swaps in the Dixonseries is performed by a deep learning model and corrected automatically. Bonejoints are predicted using a hybrid atlas-based registration and deep learningmodel for the shoulders, hips and knees. Simultaneous estimation of protondensity fat fraction and transverse relaxivity (R2*) is performed using boththe magnitude and phase information for the single-slice multiecho series.Approximately 98.1% of the two-point Dixon acquisitions were successfullyprocessed and passed quality control, with 99.98% of the high-resolutionT1-weighted 3D volumes succeeding. Approximately 99.98% of the single-slicemultiecho acquisitions covering the liver were successfully processed andpassed quality control, with 97.6% of the single-slice multiecho acquisitionscovering the pancreas succeeding. At least one fat-water swap was detected in1.8% of participants. With respect to the bone joints, approximately 3.3% ofparticipants were missing at least one knee joint and 0.8% were missing atleast one shoulder joint. For the participants who received both single-slicemultiecho acquisition protocols for the liver a systematic difference betweenthe two protocols was identified and modeled using multiple linear regression.The findings presented here will be invaluable for scientists who seek to useimage-derived phenotypes from the abdominal MRI protocol.

Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank