Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging

Le Zhang*, Marco Pereañez, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedingChapterpeer-review

Abstract

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. MRI is arguably the most comprehensive imaging modality for noninvasive and nonionizing imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Ensuring full coverage of the left ventricle (LV) is a basic criterion of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this chapter, we propose a novel automatic method to check the coverage of LV from CMR images by using Fisher discriminative and dataset invariance (FDDI) three-dimensional (3D) convolutional neural networks (CNN) independently of image-acquisition parameters, such as imaging device, magnetic field strength, variations in protocol execution, etc. The proposed model is trained on multiple cohorts of different provenance to learn the appearance and identify missing basal and apical slices. To address this, a two-stage framework is proposed. First, the FDDI 3D CNN extracts high-level features in the common representation from different CMR datasets using adversarial approach; then these image features are used to detect missing basal and apical slices. Compared with the traditional 3D CNN strategy, the proposed FDDI 3D CNN can minimize the within-class scatter and maximize the between-class scatter, which can be adapted to other CMR image data for LV coverage assessment.

Original languageEnglish
Title of host publicationDeep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
EditorsLe Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang
Place of PublicationCham
PublisherSpringer Cham
Chapter15
Pages299-321
Number of pages23
ISBN (Electronic)9783030139698
ISBN (Print)9783030139681, 9783030139711
DOIs
Publication statusPublished - 1 Oct 2019

Publication series

NameAdvances in Computer Vision and Pattern Recognition
PublisherSpringer
ISSN (Print)2191-6586
ISSN (Electronic)2191-6594

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