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Abstract #0691

Deep Boltzmann Machines-Driven Method for In-treatment Heart Motion Tracking Using Cine MRI

Jian Wu1, Nalini Daniel1, Hilary Lashmett1, Thomas Mazur1, Michael Gach1, Laura Ochoa1, Imran Zoberi1, Su Ruan2, Mark Anastasio3, Sasa Mutic1, Maria Thomas1, and Hua Li1

1Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, United States, 2Laboratoire LITIS, University of Rouen, France, 3Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States

We developed a hierarchical deep learning shape model-driven method to automatically track the motion of the heart, a complex and highly deformable organ, on two-dimensional cine MRI images. The deep-learning shape model was trained based on a Deep Boltzmann Machine (DBM)1,2 to characterize both global and local shape properties of the heart for accurate heart segmentation on each cine frame. Preliminary experimental results demonstrate the superior shape tracking performance of our proposed method versus two other methods. The tracking method is designed for heart motion pattern analysis during MRI-guided radiotherapy and the subsequent evaluation of potential heart toxicity from radiotherapy.

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