By introduction of machine learning and data-driven methods, a paradigm shift has happened in the field of imaging in general and accordingly these new methods have a high translational impact in the area of medical imaging.
These impacts are not limited to only image analysis and pattern recognition techniques but also they are used widely in the field of image reconstruction. Recently, multiple groups worldwide, with encouraging results and increasing interest, are actively exploring deep learning techniques for image reconstruction and other inverse problems.
Currently, our team is working on developing new machine learning based methods for implementing MRI-only planning into the clinic. Challenges include producing robust MRI-only patient models and synthetic CT scans with accurate geometry and electron densities.
However, our novel machine learning based method allows us to simultaneously achieve electron density map for dose calculation and automatic segmentation of the target and OAR.
The successful completion of this research will provide essential tools to establish effective MRI-based RT planning in routine clinical practice, which will improve normal and target tissue delineation and localization for more accurate radiation delivery.
- Shen, C., Gonzalez, Y., Chen, L., Jiang, S., & Jia, X. (2017). Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning. arXiv preprint arXiv:1711.00414. (arXiv)
- Iqbal, Z., Nguyen, D., & Jiang, S. (2018). Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging utilizing Deep Learning. arXiv preprint arXiv:1802.07909. (arXiv)
- Iqbal, Z., Nguyen, D., Thomas, M. A., & Jiang, S. (2018). Acceleration and Quantitation of Localized Correlated Spectroscopy using Deep Learning: A Pilot Simulation Study. arXiv preprint arXiv:1806.11068. (arXiv)