CBCT-Based Treatment Planning In Veterinary Radiotherapy: Challenges and Practical Solutions
Abstract
Purpose
Cone-beam CT (CBCT) acquired on the linear accelerator is being increasingly used beyond image guidance to support simulation and treatment planning workflows. This work reports the clinical implementation, challenges, and solutions associated with CBCT-based treatment planning and motion management in the veterinary radiotherapy setting.
Methods
The first Elekta Evo Linac in the United States was installed at our companion animal radiotherapy clinic. The system features the AI-enhanced Iris™ CBCT that provides improved scatter correction. Image quality, Hounsfield unit (HU) accuracy, AI segmentation, and treatment-planning feasibility were first evaluated using biological phantoms, and subsequently in canine and feline patients. Workflow adaptations were developed to address CBCT-specific limitations, including restricted scan length and residual scatter artifacts in the thorax. The Symmetry 4D-CBCT preset was used to assess respiratory motion, which was managed using internal target volume (ITV) or respiratory gating.
Results
Iris™ CBCT demonstrated markedly improved image quality compared with standard CBCT, with anatomical clarity and HU values in biological tissues comparable to diagnostic CT. AI tools were deployed for tissue auto-segmentation on CBCT images. When extended coverage was required, CBCT concatenation was implemented successfully. The Symmetry 4D-CBCT workflow produced acceptable respiratory-resolved images for tumor motion evaluation and ITV delineation. Additionally, an in-house respiratory belt system was developed for gated radiotherapy. Density overrides were applied to thoracic cases to correct for lung density inaccuracies. CBCT-based treatment planning achieved dose differences within 2% of CT-based plans.
Conclusion
The Iris™ CBCT on the Elekta Evo linac offers superior image quality and HU accuracy. CBCT-based treatment planning clinic can be clinically viable in veterinary radiotherapy with the support of AI-enhanced CBCT-based segmentation, tailored workarounds for CBCT acquisition limitations, and integrated motion-management strategies.