The gimbaled-head radiotherapy system: Rise and downfall of a dedicated system for dynamic tumor tracking with real-time monitoring and dynamic WaveArc

Hiraoka, Masahiro; Mizowaki, Takashi; Nakamura, Mitsuhiro; Verellen, Dirk

doi:10.1016/j.radonc.2020.07.002

Cited by 15 publications

(4 citation statements)

References 47 publications

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“…The Vero4DRT system (Hitachi Ltd., Tokyo, Japan and Brainlab AG, Feldkirchen, Germany) has a gimbaled 6 MV X‐ray head and an orthogonal kV X‐ray imaging system. 16 The gimbaled‐head and imaging system were installed with a compact accelerator in an O‐ring gantry. The O‐ring gantry can rotate ± 60 ° about its vertical axis and ± 185 ° along its O‐shaped structure.…”

Section: Methodsmentioning

confidence: 99%

Feasibility study of deep learning‐based markerless real‐time lung tumor tracking with orthogonal X‐ray projection images

Zhou

Nakamura

Mukumoto

et al. 2022

J Applied Clin Med Phys

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Purpose The feasibility of a deep learning‐based markerless real‐time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X‐ray images and clinical tracking records acquired during lung cancer treatment. Methods Ten patients with lung cancer treated with marker‐implanted RTTT were included. The prescription dose was 50 Gy in four fractions, using seven‐ to nine‐port non‐coplanar static beams. This corresponds to 14–18 X‐ray tube angles for an orthogonal X‐ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four‐dimensional computed tomography. After a data augmentation approach, for each X‐ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X‐ray tube angle‐specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X‐ray projection images acquired during treatment. The predicted three‐dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker‐implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model. Results The median GTV volume and motion range were 7.42 (range, 1.18–25.74) cm 3 and 22 (range, 11–28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85 ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66–2.95) mm. The probability of the 3D deviation smaller than 5 mm was 93.6%. Conclusions The evaluation results and calculation efficiency show the proposed deep learning‐based markerless RTTT method may be feasible for patients with lung cancer.

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Section: Methodsmentioning

confidence: 99%

Feasibility study of deep learning‐based markerless real‐time lung tumor tracking with orthogonal X‐ray projection images

Zhou

Nakamura

Mukumoto

et al. 2022

J Applied Clin Med Phys

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“…The Vero4DRT system has a gimbaled 6 MV X-ray head and an orthogonal kV X-ray imaging system. 18 The gimbaled head and imaging system were installed with a compact accelerator in an O-ring gantry. The O-ring gantry can rotate ±60…”

Section: Vero4drt Systemmentioning

confidence: 99%

Development of a deep learning‐based patient‐specific target contour prediction model for markerless tumor positioning

et al. 2022

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Purpose For pancreatic cancer patients, image guided radiation therapy and real‐time tumor tracking (RTTT) techniques can deliver radiation to the target accurately. Currently, for the radiation therapy machine with kV X‐ray imaging systems, internal markers must be implemented to facilitate tumor tracking. The purpose of this study was to develop a markerless deep learning‐based pancreatic tumor positioning procedure for real‐time tumor tracking with a kV X‐ray imaging system. Methods and materials Fourteen pancreatic cancer patients treated with intensity‐modulated radiation therapy from six fixed gantry angles with a gimbal‐head radiotherapy system were included in this study. For a gimbal‐head radiotherapy system, the three‐dimensional (3D) intrafraction target position can be determined using an orthogonal kV X‐ray imaging system. All patients underwent four‐dimensional computed tomography (4DCT) simulations for treatment planning, which were divided into 10 respiratory phases. After a patient's 4DCT was acquired, for each X‐ray tube angle, 10 digitally reconstructed radiograph (DRR) images were obtained. Then, a data augmentation procedure was conducted. The data augmentation procedure first rotated the CT volume around the superior–inferior and anterior–posterior directions from −3° to 3° in 1.5° intervals. Then, the Super‐SloMo model was adapted to interpolate 10 frames between respiratory phases. In total, the data augmentation procedure expanded the data scale 250‐fold. In this study, for each patient, 12 datasets containing the DRR images from each specific X‐ray tube angle based on the radiation therapy plan were obtained. The augmented dataset was randomly divided into training and testing datasets. The training dataset contained 2000 DRR images with clinical target volume (CTV) contours labeled for fine‐tuning the pre‐trained target contour prediction model. After the fine‐tuning, the patient and X‐ray tube angle‐specific CTV contour prediction model was acquired. The testing dataset contained the remaining 500 images to evaluate the performance of the CTV contour prediction model. The dice similarity coefficient (DSC) between the area enclosed by the CTV contour and predicted contour was calculated to evaluate the model's contour prediction performance. The 3D position of the CTV was calculated based on the centroid of the contour in the orthogonal DRR images, and the 3D error of the prediction position was calculated to evaluate the CTV positioning performance. For each patient, the DSC results from 12 X‐ray tube angles and 3D error from 6 gantry angles were calculated, representing the novelty of this study. Results The mean and standard deviation (SD) of all patients’ DSCs were 0.98 and 0.015, respectively. The mean and SD of the 3D error were 0.29 mm and 0.14 mm, respectively. The global maximum 3D error was 1.66 mm, and the global minimum DSC was 0.81. The mean calculation time for CTV contour prediction was 55 ms per image. This fulfills the requirement of RTTT. Conclusions Regarding...

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“…Two systems designed for real-time target tracking are the CyberKnife (Accuray, Sunnyvale, CA) and Vero systems (Mitsubishi Heavy Industries, Tokyo, Japan and Brainlab AG, Feldkirchen, Germany). Instead of dynamically adjusting the MLC positions, the beam direction is guided by an externalinternal correlation model (Seppenwoolde et al 2007, Hoogeman et al 2009, Sothmann et al 2016, Hiraoka et al 2020. The correlation model associates the measured signal of the external marker with the position of the internal markers, either implanted fiducials or a visible lung tumor.…”

Section: Introductionmentioning

confidence: 99%

Online adaptive planning methods for intensity-modulated radiotherapy

et al. 2023

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Online adaptive radiation therapy aims at adapting a patient’s treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence’s potential application to online adaptive planning.

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The gimbaled-head radiotherapy system: Rise and downfall of a dedicated system for dynamic tumor tracking with real-time monitoring and dynamic WaveArc

Cited by 15 publications

References 47 publications

Feasibility study of deep learning‐based markerless real‐time lung tumor tracking with orthogonal X‐ray projection images

Feasibility study of deep learning‐based markerless real‐time lung tumor tracking with orthogonal X‐ray projection images

Development of a deep learning‐based patient‐specific target contour prediction model for markerless tumor positioning

Online adaptive planning methods for intensity-modulated radiotherapy

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