A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
Journal:
arXiv
Published Date:
Nov 12, 2024
Abstract
Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy
(MRIgRT) is essential for effective treatment delivery. This study aimed to
enhance motion tracking precision in MRIgRT through an automatic real-time
markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD)
framework with automatic segmentation. Materials and Methods: We developed a
novel MRIgRT motion tracking and segmentation method by integrating the ETLD
framework with an improved Chan-Vese model (ICV), named ETLD+ICV. The ETLD
framework was upgraded for real-time cine MRI, including advanced image
preprocessing, no-reference image quality assessment, an enhanced median-flow
tracker, and a refined detector with dynamic search region adjustments. ICV was
used for precise target volume coverage, refining the segmented region frame by
frame using tracking results, with key parameters optimized. The method was
tested on 3.5D MRI scans from 10 patients with liver metastases. Results:
Evaluation of 106,000 frames across 77 treatment fractions showed
sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and
98% recall for all subjects in the Beam Eye View(BEV)/Beam Path View(BPV)
orientation. The ETLD+ICV method achieved a dice global score of more than 82%
for all subjects, demonstrating the method's extensibility and precise target
volume coverage. Conclusion: This study successfully developed an automatic
real-time markerless motion tracking method for MRIgRT that significantly
outperforms current methods. The novel method not only delivers exceptional
precision in tracking and segmentation but also shows enhanced adaptability to
clinical demands, making it an indispensable asset in improving the efficacy of
radiotherapy treatments.
