Abstract
BackgroundLiver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration.MethodsThree hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models.ResultsCompared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ≤ 0.020).ConclusionA fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration.
Highlights
Liver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion
Characterization of focal liver observations [1] requires synthesis of imaging features across multiple contrast-enhanced phases and/ or series and often demands incorporation of data across exams acquired at multiple time points
Proper spatial alignment during and between exams is challenged by the dynamic morphology of the liver and variability in patient positioning, body habitus, and physiological motion [2,3,4]
Summary
Liver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Proper spatial alignment during and between exams is challenged by the dynamic morphology of the liver and variability in patient positioning, body habitus, and physiological motion [2,3,4]. Differences in respiratory phase may shift the liver position by as much as 30 mm between acquired images [5,6,7,8] Such shifts can significantly reduce radiologists’ ability to co-localize observations across series, especially when there are multiple observations and/or exams [9,10,11]. By making images acquired at different time points, positions, or modalities geometrically similar, image registration can improve observation colocalization and reader confidence [12, 13]
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