Cine-MRI Simulation to Evaluate Tumor Tracking

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Conventional evaluations of tumor tracking algorithms require inter-observer segmentations from radiation oncologists on the Cine-MRI (2D sagittal MR video). Instead of performing intensive manual annotations on images, we present a 2D video simulator that uses the pre-treatment images, including a breathing model, that generates Cine-MR images in parallel with the underlined segmentation of the tumor. We include the data of seven patients within a retrospective clinical study that received stereotactic body radiation therapy for liver metastases. Each patient has a pre-treatment 4DCT scan, a pre-treatment 3D MR with tumor and liver delineations, and the treatment Cine-MRI. We augment the data with the simulator by changing breathing motion parameters and adding noise. The simulator generates a total of 84 Cine-MRI sequences, thus having 12 videos per patient. We validate the simulated versus the real Cine-MRI in terms of tumor motion. Finally, we used the simulator to evaluate the performance of real-time tumor tracking algorithms with this dataset.

OriginalsprogEngelsk
TitelSimulation and Synthesis in Medical Imaging - 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
RedaktørerDavid Svoboda, Ninon Burgos, Jelmer M. Wolterink, Can Zhao
ForlagSpringer
Publikationsdato2021
Sider131-141
ISBN (Trykt)9783030875916
DOI
StatusUdgivet - 2021
Begivenhed6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Varighed: 27 sep. 202127 sep. 2021

Konference

Konference6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
ByVirtual, Online
Periode27/09/202127/09/2021
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind12965 LNCS
ISSN0302-9743

ID: 282675760