Abstract
Dynamic Contrast Enhancement Magnetic Resonance Imaging (DCE-MRI) is gaining popularity for computer aided diagnosis (CAD) of breast cancer. However, the performance of these CAD systems is severely affected when the number of DCE-MRI series is inadequate, inconsistent or limited. This work presents a High-Quality DCE-MRI Interpolation method based on Deep Neural Network (HQI-DNN) using an end-to-end trainable Convolutional Neural Network (CNN). It gives a good solution to the problem of inconsistent and inadequate quantity of DCE-MRI series for breast cancer analysis. Starting from a nested CNN for feature learning, the dynamic contrast enhanced features of breast lesions are learned by bidirectional contrast transformations between DCE-MRI series. Each transformation contains the spatial deformation field and the intensity change, enabling a variable-length multiple series interpolation of DCE-MRI. We justified the proposed method through extensive experiments on our dataset. It produced a more efficient result of breast DCE-MRI interpolation than other methods.
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Acknowledgments
This research was supported by the Key Research and Development Program of Shaanxi Province (the General Project of Social Development) (2020SF-049); Scientific Research Project of Education Department of Shaanxi Provincial Government (19JK0808); Xi’an Science and Technology Plan Project (20YXYJ0010(5)).
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Wang, H., Feng, J., Pan, X., Yang, D., Chen, B. (2020). High-Quality Interpolation of Breast DCE-MRI Using Learned Transformations. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2020. Lecture Notes in Computer Science(), vol 12417. Springer, Cham. https://doi.org/10.1007/978-3-030-59520-3_6
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DOI: https://doi.org/10.1007/978-3-030-59520-3_6
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