An open‐source software for monitoring intrafraction motion during external beam radiation therapy based on superimposition of contours of projected ROIs on cine‐MV images

Abstract

Purpose: To present an open-source software (https://github.com/CHUSRadOnc Phys/FluoMV) for monitoring intrafraction motion that is based on the visualization of superimposed contours of projected region-of-interests from DICOM RTSTRUCT files on cine-MV images acquired and displayed in real-time during radiation therapy delivery. Clinical use with prostate gold fiducial markers is presented. Methods: Projections of regions of interest (ROI) in the reference frame of the electronic portal imaging device are computed offline for different gantry angles before the first treatment fraction. During treatment delivery, the contrast of portal images is automatically adjusted using a histogram equalization algorithm. The projections associated with the current gantry angle are then superimposed on the images in real time. This allows the therapist to evaluate if the imaged structures of interest remain within their respective contours during treatment delivery and to potentially interrupt the treatment if deemed necessary. The spatial accuracy of the method was evaluated by imaging a ball bearing phantom in a setup where the position of the projected ROI is highly sensitive to gantry angle errors. The visibility of fiducial markers during one fraction of seven different volumetric modulated arc therapy (VMAT) prostate treatments is characterized. Results: The geometric validation showed a negligible systematic error μ < 0.1 mm for the position of the projections. The random errors associated with the time accuracy of the gantry angle readout were characterized by standard deviations σ ≤ 0.6 mm. The VMAT clinical treatments showed that the fiducial markers were frequently visible, allowing for a meaningful clinical use. Conclusions: The results demonstrate that the method presented is sufficiently accurate to be used for intrafraction monitoring of patients. The fact that this method could be implemented on many modern linacs at little to no cost and with

DeepImageTranslator V2: Analysis of Multimodal Medical Images using Semantic Segmentation Maps Generated through Deep Learning

“…In recent years, numerous open-source software tools have been reported in the field of medical image processing [14][15][16][17][18]. One growing area of development is the popularization of deep learning methods through the creation of userfriendly tools with a graphical interface.…”

DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning

“…In recent years, numerous open-source software tools have been reported in the field of medical image processing [12][13][14][15][16]. One growing area of development is the popularization of deep learning methods through the creation of user-friendly tools with a graphical interface.…”