(Monday, 7/13/2020) 4:30 PM - 5:30 PM [Eastern Time (GMT-4)]
Room: Track 2
Purpose: Recent evidence suggests that radiation doses to cardiac substructures are more strongly linked to late cardiac morbidities than commonly used whole-heart dose metrics. MR-guided radiation therapy presents a unique opportunity to visualize sensitive cardiac substructure location during tumor localization and for daily treatment, which allows the potential for enhanced cardiac sparing. We have established a deep learning (DL) model for rapid cardiac substructure segmentation based on low field MRI.
Methods: Twenty-three patients were retrospectively evaluated who underwent thoracic radiation therapy with a 0.35T MR-guided linac. Twenty-one patients were treated under breath-hold conditions (17-25s, 14 end-inhalation and 7 end-exhalation, 1.5x1.5x3 mm³) and two under free breathing conditions (3-minutes, 1.5 mm³ isotropic resolution). Two radiation oncologists generated ground-truth segmentations of 12 cardiac substructures (chambers, great vessels, coronary arteries, etc.) using 0.35T MRI for 114 MRIs (4-5/patient). Eighteen patients (n=90) were used to train a three-dimensional U-Net with a Dice-weighted focal loss function to manage differences in substructure size and complexity. The remaining five patients were a holdout test set to assess mean distance to agreement (MDA) and Dice similarity coefficient (DSC) to ground-truth.
Results: The model stabilized after training for 340 epochs (training error <0.001) which took ~99 hours to complete. Segmentation results varied slightly depending on scan duration (< ±0.03 change in DSC). DL provided accurate segmentations for the chambers (DSC=0.85±0.03), great vessels (DSC=0.81±0.04), and pulmonary veins (DSC=0.71±0.03). DSC for the coronary arteries was 0.42±0.07. MDA across all 12 substructures was <3.0 mm. Wilcoxon signed ranks test revealed no cardiac substructures had significant differences in volume between DL segmentations and ground-truth (p>0.05). Substructure contour generation for a new patient input took ~15 seconds.
Conclusion: This work is a critical step in providing rapid and reliable cardiac substructure segmentations that may be used to improve cardiac sparing in MR-guided radiation therapy.
Funding Support, Disclosures, and Conflict of Interest: The submitting institution holds research agreements with Philips Healthcare, ViewRay, Inc., and Modus Medical. Research partially supported by the NCI/NIH, Award R01CA204189. The PI is on the Philips Healthcare Advisory Board. This work was also partially supported by US National Science Foundation (NSF) under grant CNS-1637312.
Keywords
Heart, Image-guided Therapy, MRI
Taxonomy
IM/TH- image Segmentation: MRI
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