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Contrast Media & Molecular Imaging
Volume 2018, Article ID 2391925, 11 pages
Research Article

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

1Department of Informatics, Technische Universität München, Munich, Germany
2Department of Nuclear Medicine, Klinikum Rechts der Isar, TU München, Munich, Germany
3Institute of Medical Engineering, Technische Universität München, Munich, Germany
4Department of Nuclear Medicine, Universität Würzburg, Würzburg, Germany

Correspondence should be addressed to Kuangyu Shi; ed.mut@ihs.k

Received 28 July 2017; Revised 29 November 2017; Accepted 12 December 2017; Published 8 January 2018

Academic Editor: Yun Zhou

Copyright © 2018 Lina Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), -Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.