Abstract
Magnetic resonance guided focused ultrasound (MRgFUS) is one of the most attractive emerging minimally invasive procedures for breast cancer, which induces localized hyperthermia, resulting in tumor cell death. Accurately assessing the post-ablation viability of all treated tumor tissue and surrounding margins immediately after MRgFUS thermal therapy residual tumor tissue is essential for evaluating treatment efficacy. While both thermal and vascular MRI-derived biomarkers are currently used to assess treatment efficacy, currently, no adequately accurate methods exist for the in vivo determination of tissue viability during treatment. The non-perfused volume (NPV) acquired three or more days following MRgFUS thermal ablation treatment is most correlated with the gold standard of histology. However, its delayed timing impedes real-time guidance for the treating clinician during the procedure. We present a robust deep-learning framework that leverages multiparametric MR imaging acquired during treatment to predict treatment efficacy. The network uses qualtitative T1, T2 weighted images and MR temperature image derived metrics to predict the three day post-ablation NPV. To validate the proposed approach, an ablation study was conducted on a dataset (N=6) of VX2 tumor model rabbits that had undergone MRgFUS ablation. Using a deep learning framework, we evaluated which of the acquired MRI inputs were most predictive of treatment efficacy as compared to the expert radiologist annotated 3 day post-treatment images.