Real-Time Infrared Motion Tracking Analysis for Patients Treated With Gated Frameless Image Guided Stereotactic Radiosurgery
| Autor: | Jannie Schasfoort, Hany Soliman, Mark Ruschin, R. Lee MacDonald, Arjun Sahgal, Young Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Cancer Research
Cone beam computed tomography Infrared Rays Frameless radiosurgery medicine.medical_treatment Nose Gamma knife Radiosurgery 030218 nuclear medicine & medical imaging Immobilization 03 medical and health sciences 0302 clinical medicine Match moving Computer Systems Fiducial Markers Humans Medicine Organ Motion Radiology Nuclear Medicine and imaging Displacement (orthopedic surgery) Radiation Brain Neoplasms business.industry Masks Cone-Beam Computed Tomography Oncology 030220 oncology & carcinogenesis Intrafraction motion Neural Networks Computer business Fiducial marker Nuclear medicine Radiotherapy Image-Guided |
| Zdroj: | International Journal of Radiation Oncology*Biology*Physics. 106:413-421 |
| ISSN: | 0360-3016 |
| DOI: | 10.1016/j.ijrobp.2019.10.030 |
| Popis: | The transition from frame-based brain stereotactic radiosurgery (SRS) to frameless delivery is supported by real-time intrafraction monitoring to ensure accurate delivery. The purpose of this study is to characterize these real-time motion traces in a large cohort of patients treated with frameless gated brain SRS and to develop patient-specific predictions of tolerance violations.SRS patients treated on the Gamma Knife Icon were immobilized using a device-specific thermoplastic head mask. A motion marker was fixed to the patient's nose, with gating and cone beam computed tomography (CBCT)-based corrections to the treatment at excursions from baseline exceeding 1.5 mm. The traces of 1446 fractions were analyzed according to magnitude (932 unique treatment plans for 462 unique individual patients), directional distribution of displacement, and stability. A neural network model was developed to predict interruptions based on a subset of trace data.The average displacement of the marker in the first fraction of all patients was 0.62 ± 0.25 mm with beam CBCT corrections, which would otherwise be modeled at 0.96 ± 0.96 mm without intrafraction motion correction (P.0001). Twenty-nine percent of fractions delivered were interrupted, of which the Z-axis (superoinferior) motion was the largest contributor to excursion. Baseline corrections significantly compensated for the magnitude of motion in all 3 dimensions (P.01). The motion relative to the first acquired CBCT was on average seen to consistently increase with treatment time, with the minimum P value occurring at 61.3 minutes. The neural network prediction model was able to predict treatment interruptions with 84% sensitivity on the first 5-minute sample of the trace.Corrections to marker position significantly decreased marker excursions in all 3 axes compared with a single CBCT alignment. Patient-specific modeling may aid in the optimization of cases selected for frameless radiosurgery to increase the accuracy of planned delivery. |
| Databáze: | OpenAIRE |
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