Fiducial marker and marker‐less soft‐tissue detection using fast MV fluoroscopy on a new generation EPID: Investigating the influence of pulsing artifacts and artifact suppression techniques

Verellen, Dirk; Vondel, Iwein Van de; Mazghari, Rafik El; Depuydt, Tom; Ridder, Mark De

doi:10.1118/1.4896116

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Attempts have been made to localize lung tumors on megavoltage (MV) projection images. Multiple research groups have conducted mask or template-based tumor tracking in the last decade [6][7][8]. Berbeco and his colleagues reported a series of studies on tumor tracking from a landmark-based algorithm that matches simulated images with MV projections [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

et al. 2018

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A novel method was developed to track lung tumor motion in real time during radiation therapy with the purpose to allow target radiation dose escalation while simultaneously reducing the dose to sensitive structures, thereby increasing local control without increasing toxicity. This method analyzes beam’s eye view radiation therapy treatment megavoltage (MV) images with simulated digitally reconstructed radiographs (DRRs) as references. Instead of comparing global DRRs with projection images, this method incorporates a technique that divides the global composite DRR and the corresponding MV projection into sub-images called tiles. Registration is performed independently on tile pairs in order to reduce the effects of global discrepancies due to scattering or imaging modality differences. This algorithm was evaluated by phantom studies while simulated tumors were controlled to move with various patterns in a complex humanoid torso. Approximately 15,000 phantom MV images were acquired at nine gantry angles, with different tumors moving within ranges between 10 and 20 mm. Tumors were successfully identified on every projection with a total maximum/average error of 1.84/0.98 mm. This algorithm was also applied to over 5,000 frames of MV projections acquired during radiation therapy of five lung cancer patients. This tumor-tracking methodology is capable of accurately locating lung tumors during treatment without implanting any internal fiducial markers nor delivering extra imaging radiation doses.

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Section: Introductionmentioning

confidence: 99%

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

et al. 2018

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“…During the last decade, various marker-less tumor tracking methods have been proposed. These include template matching-based techniques (Meyer et al 2006, Teske et al 2015, segmentation-based approaches (Arimura et al 2009, Zhang et al 2015, optical flow-based methods (Teo et al 2012(Teo et al , 2013, mode-seeking algorithms in image feature-space (Poels et al 2014, Zhang et al 2014, and key-point-based tracking methods (Rottmann et al 2010, Xie et al 2013.…”

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confidence: 99%

A key-point based real-time tracking of lung tumor in x-ray image sequence by using difference of Gaussians filtering and optical flow

et al. 2018

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In radiation therapy, for accurate radiation dose delivery to a target tumor and reduction of the extra exposure of normal tissues, real-time tumor tracking is typically an important technique in lung cancer treatment since lung tumors move with patients' respiration. To observe a tumor motion in real time, x-ray fluoroscopic devices can be employed, and various tracking techniques have been proposed to track tumors. However, development of a fast and accurate tracking method for clinical use is still a challenging task since the obscured image of the tumor can cause decreased tracking accuracy and can result in additional processing time for remedying the accuracy. In this study, a new key-point-based tumor tracking method, which is sufficiently fast and accurate, is presented. Given an x-ray image sequence, the proposed method employs a difference-of-Gaussians filtering technique to detect key points in the tumor region of the first frame which are robust against noise and outliers in the subsequent frames. In the subsequent frames, these key points are tracked using a fast optical flow technique, and tumor motion is estimated via their movement. To evaluate the performance, the proposed method has been tested on several clinical kV and MV x-ray image sequences. The experimental results showed that the average of the root mean square errors of tracking were [Formula: see text] and [Formula: see text] for kV and MV x-ray image sequences, respectively. This tracking performance was more accurate than previous tracking methods. In addition, the average processing times for each frame were [Formula: see text] and [Formula: see text] for kV and MV image sequences, respectively, and the proposed method was faster than previous methods as well as shorter than frame acquisition interval. Therefore, the proposed method has the potential for both highly accurate and fast tumor tracking in clinical applications.

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“…Several techniques have been investigated for improving the contrast of portal images: optimizing the acquisition process of the EPID (McGarry et al 2007), using low-Z targets (Connell and Robar 2009), using difference imaging (Kairn et al 2010), removing the flattening filter (Christensen et al 2013), stacking multiple detection layers and increasing the thickness of the scintillator (Rottmann et al 2016), and using Laplacian-of-Gaussian filters (Poels et al 2014). In addition, wavelet based methods have been implemented for contrast enhancement, like multiscale image contrast amplification (MUSICA) (Vuylsteke and Schoeters 1994).…”

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confidence: 99%

A multiresolution processing method for contrast enhancement in portal imaging

González‐López

2018

Phys. Med. Biol.

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Portal images have a unique feature among the imaging modalities used in radiotherapy: they provide direct visualization of the irradiated volumes. However, contrast and spatial resolution are strongly limited due to the high energy of the radiation sources. Because of this, imaging modalities using x-ray energy beams have gained importance in the verification of patient positioning, replacing portal imaging. The purpose of this work was to develop a method for the enhancement of local contrast in portal images. The method operates in the sub-bands of a wavelet decomposition of the image, re-scaling them in such a way that coefficients in the high and medium resolution sub-bands are amplified-an approach totally different from those operating on the image histogram, widely used nowadays. Portal images of an anthropomorphic phantom were acquired in an electronic portal imaging device. Then, different re-scaling strategies were investigated, studying the effects of the scaling parameters on the enhanced images. Also, the effect of using different types of transform was studied. Finally, the implemented methods were combined with histogram equalization methods like the contrast limited adaptive histogram equalization, and these combinations were compared. Uniform amplification of the detail sub-bands shows the best results in contrast enhancement. On the other hand, linear re-escalation of the high resolution sub-bands increases the visibility of fine detail in the images, at the expense of an increase in noise levels. Also, since processing is applied only to detail sub-bands, not to the approximation, the mean gray level of the image is minimally modified and no further display adjustments are required. It is shown that re-escalation of the detail sub-bands of portal images can be used as an efficient method for the enhancement of both the local contrast and the resolution of these images.

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Fiducial marker and marker‐less soft‐tissue detection using fast MV fluoroscopy on a new generation EPID: Investigating the influence of pulsing artifacts and artifact suppression techniques

Cited by 5 publications

References 36 publications

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

A key-point based real-time tracking of lung tumor in x-ray image sequence by using difference of Gaussians filtering and optical flow

A multiresolution processing method for contrast enhancement in portal imaging

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