Abstract:The aim of our study was to compare the image and dosimetric quality of two different imaging systems. The first one is a fluoroscopic electronic portal imaging device (first generation), while the second is based on an amorphous silicon flat-panel array (second generation). The parameters describing image quality include spatial resolution [modulation transfer function (MTF)], noise [noise power spectrum (NPS)], and signal-to-noise transfer [detective quantum efficiency (DQE)]. The dosimetric measurements wer… Show more
“…Megavoltage (MV) electronic portal imaging device (EPID) has been widely used as an on‐line verification tool for treatment field in radiation therapy 1, 2, 3, 4. While the weight of the verification tool appears to be decreased after the emergence of kilovoltage (kV) imager mounted on a linear accelerator, EPID images still have their own advantages.…”
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.
“…Megavoltage (MV) electronic portal imaging device (EPID) has been widely used as an on‐line verification tool for treatment field in radiation therapy 1, 2, 3, 4. While the weight of the verification tool appears to be decreased after the emergence of kilovoltage (kV) imager mounted on a linear accelerator, EPID images still have their own advantages.…”
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.
“…Several researchers have investigated the use of electronic portal imaging devices for quality assurance purposes such as MLC position verification (16)(17)(18) and IMRT patient specific dose verification (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). Renner et al (24) reported on four different methods that have been introduced for the purpose of using the EPID for treatment verification: 1) Compute the dose from each beam to the EPID and compare to the actual EPID image of each beam (4-15); 2) Use the EPID to verify the leaf positions for intensity modulated fields (16)(17)(18); 3) Reconstruct the dose to the patient using the exit image acquired during treatment (20)(21)(22)(23); and 4) Convert the EPID images to an incident fluence distribution and use it for dose calculation employing the patient CT anatomy (24,25).…”
A software program [MU-EPID], has been developed to perform patient specific pre-treatment quality assurance (QA) verification for intensity modulated radiation therapy (IMRT) using fluence maps measured with an electronic portal imaging device (EPID). The software converts the EPID acquired images of each IMRT beam, to fluence maps that are equivalent to those calculated by the treatment planning system (TPS). The software has the capability to process Varian, Elekta and Siemens EPID DICOM images. In the present investigation, several IMRT plans for different treatment sites were used to validate the software using the and gamma analysis comparisons were performed to evaluate the accuracy of our method. A gamma index analysis of the isocenter coronal plane was done for each plan and showed an average of 97.44% of gamma passing rate using a 3% and 3 mm gamma criterion. Isodose, DVH and dose profile comparisons were conducted between the original calculated plan and the measured reconstructed plan from the EPID images processed through the MU-EPID software. The results suggest that MU-EPID can be used clinically for patient specific IMRT QA, providing a comprehensive 3D dosimetric evaluation through DVH comparison as well as an option for a 2D gamma analysis.
“…Such alternative techniques are electronic portal imaging devices [1,3,5,7,11,20,23,35] or the digital luminescence radiography [2,4,13,16,17,32,37].…”
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