Enhancement of megavoltage electronic portal images for markerless tumor tracking

Abstract

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.

Serum Lactate Dehydrogenase Can Be Used as a Factor for Re-Evaluating First-Relapsed Multiple Myeloma

“…Consistent with that study, ISS stage at relapse in our cohort also retained prognostic significance. However, we did not find an effect of PFS1 on PFS2, which may have been due to the different treatment compared to the ASCT patients in the cohort in Cheong et al [15]. Moreover, Cheong et al [15] did not incorporate LDH, which we consider an important adverse prognostic indicator.…”

“…Cheong et al [15] reported that among 269 relapsed MM patients who received ASCT between 2000 and 2014 at UCH Hospital in London, PFS1 retained independent prognostic significance (HR 0.91 [0.87–0.96, p = 0.001]), as did ISS stage 2/3 at relapse (HR 3.70 [1.58–8.66, p = 0.003]). Consistent with that study, ISS stage at relapse in our cohort also retained prognostic significance.…”

A new registration algorithm of electronic portal imaging devices images based on the automatic detection of bone edges during radiotherapy

“…The DRrPortRegistration was used for the registration between DRR images and port films, the mean absolute average errors in the DRrPortRegistration are equal to 0.2 degrees, 0.75 mm in the X-direction, and 0.81 in the Y-direction 17 . Cheong 18 reported that the CLAHE algorithm is the best effective step for the quality enhancement on the MV EPID image in terms to tumor tracking after comparing the individual contribution of deblurring, contrast improvement (CLAHE) and denoising, the detection errors in the contrast improvement group are 2.9 mm in the Y direction and 2.7 mm in the X-direction. The DRrPortRegistration and CLAHE algorithms showed an inferior accuracy than the present registration.…”

Electronic portal image enhancement based on nonuniformity correction in wavelet domain

“…The results of our proposed and comparative algorithms implementation on AP and lateral pelvis portal images and related 3D representation of intensity have been shown in Figures 6 and 7. Here, the results show a great improvement of less noise amplification and less blurring compared with these methods specially for Cheong et al 21 and Koutsofios et al 23 The artifacts of noise amplification and blurring normally occur in over enhancement in contrast with standard AHE (see Figure 8).…”

“…Moreover, the intensity improvement of bone edges relative to the background intensity values results in enhanced contrast in the processed image. From less noise amplification and less blurriness, Figure 6 shows a great improvement of the proposed method in comparison with comparative methods, especially for Cheong et al 21 . and Koutsofios et al 23 .…”