Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks

• Published in: Simulation and Synthesis in Medical Imaging Vol. 9968; pp. 138 - 145
• Main Authors: Zhang, Le, Gooya, Ali, Dong, Bo, Hua, Rui, Petersen, Steffen E., Medrano-Gracia, Pau, Frangi, Alejandro F.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: 3D graphics & modelling; Cardiac Magnetic Resonance Image; Convolutional Neural Network; Image processing; Image Quality Assessment; Left Ventricular Outflow Tract; Pattern recognition; Short Axis Slice
• DOI: 10.1007/978-3-319-46630-9_14
• ISBN: 3319466291; 9783319466293
• ISSN: 0302-9743

Image quality assessment (IQA) is crucial in large-scale population imaging so that high-throughput image analysis can extract meaningful imaging biomarkers at scale. Specifically, in this paper, we address a seemingly basic yet unmet need: the automatic detection of missing (apical and basal) slices in Cardiac Magnetic Resonance Imaging (CMRI) scans, which is currently performed by tedious visual assessment. We cast the problem as classification tasks, where the bottom and top slices are tested for the presence of typical basal and apical patterns. Inspired by the success of deep learning methods, we train Convolutional Neural Networks (CNN) to construct a set of discriminative features. We evaluated our approach on a subset of the UK Biobank datasets. Precision and Recall figures for detecting missing apical slice (MAS) (81.61 % and 88.73 %) and missing basal slice (MBS) (74.10 % and 88.75 %) are superior to other state-of-the-art deep learning architectures. Cross-dataset experiments show the generalization ability of our approach.