Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report
arxiv(2024)
摘要
Radiation therapy plays a crucial role in cancer treatment, necessitating
precise delivery of radiation to tumors while sparing healthy tissues over
multiple days. Computed tomography (CT) is integral for treatment planning,
offering electron density data crucial for accurate dose calculations. However,
accurately representing patient anatomy is challenging, especially in adaptive
radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI)
provides superior soft-tissue contrast. Still, it lacks electron density
information while cone beam CT (CBCT) lacks direct electron density calibration
and is mainly used for patient positioning. Adopting MRI-only or CBCT-based
adaptive radiotherapy eliminates the need for CT planning but presents
challenges. Synthetic CT (sCT) generation techniques aim to address these
challenges by using image synthesis to bridge the gap between MRI, CBCT, and
CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation
methods using multi-center ground truth data from 1080 patients, divided into
two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image
similarity and dose-based metrics from proton and photon plans. The challenge
attracted significant participation, with 617 registrations and 22/17 valid
submissions for tasks 1/2. Top-performing teams achieved high structural
similarity indices (>0.87/0.90) and gamma pass rates for photon (>98.1
and proton (>99.0
between image similarity metrics and dose accuracy, emphasizing the need for
dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023
facilitated the investigation and benchmarking of sCT generation techniques,
providing insights for developing MRI-only and CBCT-based adaptive
radiotherapy.
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