Tracking tumor boundary in MV‐EPID images without implanted markers: A feasibility study

Zhang, Xiaoyong; Homma, Noriyasu; Ichiji, Kei; Takai, Yoshihiro; Yoshizawa, Makoto

doi:10.1118/1.4918578

Cited by 21 publications

(21 citation statements)

References 64 publications

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“…One of the most useful applications of MV EPID is the markerless tumor tracking, in which the EPID is operated in cine mode and produces continuous portal images from the treatment beam 8, 9, 10, 11. The risk of pneumothorax from the marker implantation also prompted researchers to explore the possibility of markerless tumor tracking 12.…”

Section: Introductionmentioning

confidence: 99%

“…The risk of pneumothorax from the marker implantation also prompted researchers to explore the possibility of markerless tumor tracking 12. While most studies on tumor tracking have been focused on tracking algorithms,8, 9, 10, 11 the importance of consecutive image display on EPID cannot be neglected. The visual confirmation of the tracking accuracy during treatment greatly facilitates action against possible misalignment of beam aperture to the moving target.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of megavoltage electronic portal images for markerless tumor tracking

Cheong

Yoon

Park

et al. 2018

J Applied Clin Med Phys

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PurposeThe poor quality of megavoltage (MV) images from electronic portal imaging device (EPID) hinders visual verification of tumor targeting accuracy particularly during markerless tumor tracking. The aim of this study was to investigate the effect of a few representative image processing treatments on visual verification and detection capability of tumors under auto tracking.MethodsImages of QC‐3 quality phantom, a single patient's setup image, and cine images of two‐lung cancer patients were acquired. Three image processing methods were individually employed to the same original images. For each deblurring, contrast enhancement, and denoising, a total variation deconvolution, contrast‐limited adaptive histogram equalization (CLAHE), and median filter were adopted, respectively. To study the effect of image enhancement on tumor auto‐detection, a tumor tracking algorithm was adopted in which the tumor position was determined as the minimum point of the mean of the sum of squared pixel differences (MSSD) between two images. The detectability and accuracy were compared.ResultsDeblurring of a quality phantom image yielded sharper edges, while the contrast‐enhanced image was more readable with improved structural differentiation. Meanwhile, the denoising operation resulted in noise reduction, however, at the cost of sharpness. Based on comparison of pixel value profiles, contrast enhancement outperformed others in image perception. During the tracking experiment, only contrast enhancement resulted in tumor detection in all images using our tracking algorithm. Deblurring failed to determine the target position in two frames out of a total of 75 images. For original and denoised set, target location was not determined for the same five images. Meanwhile, deblurred image showed increased detection accuracy compared with the original set. The denoised image resulted in decreased accuracy. In the case of contrast‐improved set, the tracking accuracy was nearly maintained as that of the original image.ConclusionsConsidering the effect of each processing on tumor tracking and the visual perception in a limited time, contrast enhancement would be the first consideration to visually verify the tracking accuracy of tumors on MV EPID without sacrificing tumor detectability and detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Cheong

Yoon

Park

et al. 2018

J Applied Clin Med Phys

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“…In our recent study, we proposed a level set method (LSM)-based algorithm to track the tumor boundary in MV fluoroscopy [14], [16]. The proposed algorithm is based on region-scalable LSM [9] which is a robust image segmentation algorithm and has been applied to variety of image modalities.…”

Section: Tumor Tracking In MV Fluoroscopymentioning

confidence: 99%

“…This paper summarizes our recent studies on development of markerless tumor tracking system for adaptive radiation therapy. We will briefly introduce the kV and MV X-ray fluoroscopic imaging systems used in radiation therapy, and present a kernel-based algorithm for tracking lung tumor position in kV fluoroscopy [13], [15] and a level set method (LSM)-based algorithm for tracking tumor boundary in MV fluoroscopy [14], [16].…”

Section: Introductionmentioning

confidence: 99%

Tumor motion tracking using kV/MV X-ray fluoroscopy for adaptive radiation therapy

Zhang

Homma

Ichiji

et al. 2015

2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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In radiation therapy, inter-and intra-fractional tumor motions significantly limit the efficiency of radiation delivery, and bring potential risk to healthy organs and tissues adjacent to the tumor during treatment. To deliver a highdose radiation adapting to the intra-fractional tumor motion, real-time imaging and tracking the tumor motion during the treatment became critical tasks in radiation therapy research. In this paper, we present our recent studies on tracking the respiration-induced lung tumor motion based on kilo-voltage (kV) and mega-voltage (MV) X-ray fluoroscopy systems. Especially, our focus is on developing robust tracking algorithms to track the deformable tumor motion in real-time. For the kV fluoroscopy, a kernel-based method, which is robust against the tumor deformation and low-contrast image quality, is proposed to track the tumor position. In addition, for the MV fluoroscopy, a region-scalable level set method (LSM) is proposed to tracking the tumor boundary. Experimental results conducted on phantom and clinical data demonstrated the effectiveness of our proposed methods.

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“…However, the invasive implantation remains a daunting barrier for its wide acceptance in the clinic. A markerless approach is highly desirable and has been investigated intensively . With highly customized image pre‐processing and enhancement maneuvers, localization through registration based on cross‐correlation is successful when image contrast between tumor and its background is reasonable, but often fails at angles where tumor is obscured by mediastinum or ribs .…”

Section: Introductionmentioning

confidence: 99%

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

et al. 2020

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Purpose To design a convolutional recurrent neural network (CRNN) that calculates three‐dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D‐CBCT images. Method Under an IRB‐approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free‐breathing. Concurrently, an electromagnetic signal‐guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross‐correlation‐based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave‐one‐out strategy using data from 11 lung patients, including 5500 kV images. The root‐mean‐square error between the CRNN and motion traces was calculated to evaluate the localization accuracy. Result Three‐dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high‐performance computer cluster. Conclusions CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross‐correlation‐based registration approach, and has the potential to remove reliance on the implanted fiducials.

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Tracking tumor boundary in MV‐EPID images without implanted markers: A feasibility study

Cited by 21 publications

References 64 publications

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Tumor motion tracking using kV/MV X-ray fluoroscopy for adaptive radiation therapy

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

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