TY - CHAP
T1 - Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging
AU - Zhang, Le
AU - Pereañez, Marco
AU - Piechnik, Stefan K.
AU - Neubauer, Stefan
AU - Petersen, Steffen E.
AU - Frangi, Alejandro F.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85073215808&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-13969-8_15
DO - 10.1007/978-3-030-13969-8_15
M3 - Chapter
AN - SCOPUS:85073215808
SN - 9783030139681
SN - 9783030139711
T3 - Advances in Computer Vision and Pattern Recognition
SP - 299
EP - 321
BT - Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
A2 - Lu, Le
A2 - Wang, Xiaosong
A2 - Carneiro, Gustavo
A2 - Yang, Lin
PB - Springer Cham
CY - Cham
ER -