Shahabedin Nabavi; Hossein Simchi; Mohsen Ebrahimi Moghaddam; Ahmad Ali Abin; Alejandro F. Frangi
Abstract
Population imaging studies rely on good-quality medical imagery before quantifying downstream images. This study provides an automated approach for image quality assessment (IQA) from ...
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Population imaging studies rely on good-quality medical imagery before quantifying downstream images. This study provides an automated approach for image quality assessment (IQA) from cardiovascular magnetic resonance (CMR) imaging at scale. We identify four common CMR imaging artefacts: respiratory motion, cardiac motion, Gibbs ringing, and aliasing. Four datasets, including UK Biobank, York University (YU), the Universidad Carlos III (UCIII) and CMR-Tehran, were used to perform the experiments. This study proposes two deep-learning models for CMR IQA in spatial and frequency domains. The presented spatial-domain model also has domain adaptation. The accuracies of supervised 4-fold cross-validation experiments for UK Biobank, YU, UCIII and CMR-Tehran datasets are 99.41%, 75.78%, 89.46% and 67.87% for the spatial-domain and 87.46%, 63.76%, 80.25% and 58.48% for the frequency-domain. Domain adaptation results, considering UK Biobank as the source set and YU, UCIII and CMR-Tehran as the target sets, show the domain shift gap coverage between the datasets to the extent of +11.91%, +3.93% and +16.57%, respectively. Besides, by training and testing the spatial-domain model on 30,125 images from the UK Biobank, an accuracy of 89.56% was obtained in a training time of 394.80 seconds. Meanwhile, the frequency-domain model with training and testing on 180,750 images achieves an accuracy of 87.99% in a training time of 255.04 seconds. Thus, the frequency-domain model can achieve almost the same accuracy yet 1.548 times faster than the spatial model. The proposed models can detect four common CMR imaging artefacts by receiving images or the corresponding k-spaces.
