A structure-based method for on-line matching of portal images for an optimal patient set-up in radiotherapy

Kreuder, F.; Schreiber, B.; Kausch, C.; Dössel, Olaf

doi:10.1016/s0165-5817(98)00008-4

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…Other groups have sought to create DRRs that are similar to the portal images by first mapping the attenuation coefficients in the CT from kV to MV energies and further adjusting the grayscale range in the DRRs to match the grayscale range of the portal images. 35,36,13,37 Kreuder et al 38 proposed to band-pass filter the portal images prior to registration to enhance bony ridges and reduce low-frequency artifacts such as scatter radiation and high frequency noise such as air bubbles and then setting nonridge pixels to zero before correlating the images. Findings from our previous studies indicate that the signal useful to the registration is present at the higher frequencies.…”

Section: Discussionmentioning

confidence: 99%

2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images

et al. 2009

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Section: Discussionmentioning

confidence: 99%

2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images

et al. 2009

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“…A number of 2D-2D [14][15][16][17][18][19][20][21][22][23][24][25] and 2D-3D 12,13,26-35 registration methods have been proposed for the determination of setup errors in pelvic sites. Other 2D-3D registration methods developed for sites other than the pelvis but which use the correlation coefficient or mutual information include the work by Refs.…”

Section: Discussionmentioning

confidence: 99%

“…The method proposed here is similar thematically to procedures described by Kreuder et al, 15 Munbodh et al, 12,13,26 Dekker et al, 25 and Bansal et al, [29][30][31] though with differences as indicated. Kreuder et al 15 presented a Fourier implementation of the normalized cross-correlation for the quantification of in-plane translational setup errors. Their method has been used mostly for portal-to-portal matching with additional testing on kV DRR and portal images.…”

Section: Discussionmentioning

confidence: 99%

A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy

et al. 2007

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In many radiotherapy clinics, geometric uncertainties in the delivery of 3D conformal radiation therapy and intensity modulated radiation therapy of the prostate are reduced by aligning the patient's bony anatomy in the planning 3D CT to corresponding bony anatomy in 2D portal images acquired before every treatment fraction. In this paper, we seek to determine if there is a frequency band within the portal images and the digitally reconstructed radiographs (DRRs) of the planning CT in which bony anatomy predominates over non-bony anatomy such that portal images and DRRs can be suitably filtered to achieve high registration accuracy in an automated 2D-3D single portal intensity-based registration framework. Two similarity measures, mutual information and the Pearson correlation coefficient were tested on carefully collected gold-standard data consisting of a kilovoltage cone-beam CT (CBCT) and megavoltage portal images in the anterior-posterior (AP) view of an anthropomorphic phantom acquired under clinical conditions at known poses, and on patient data. It was found that filtering the portal images and DRRs during the registration considerably improved registration performance. Without filtering, the registration did not always converge while with filtering it always converged to an accurate solution. For the pose-determination experiments conducted on the anthropomorphic phantom with the correlation coefficient, the mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters were Theta(x):0.18(0.19) degrees, Theta(y):0.04(0.04) degrees, Theta(z):0.04(0.02) degrees, t(x):0.14(0.15) mm, t(y):0.09(0.05) mm, and t(z):0.49(0.40) mm. The mutual information-based registration with filtered images also resulted in similarly small errors. For the patient data, visual inspection of the superimposed registered images showed that they were correctly aligned in all instances. The results presented in this paper suggest that robust and accurate registration can be achieved with intensity-based methods by focusing on rigid bony structures in the images while diminishing the influence of artifacts with similar frequencies as soft tissue.

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“…A number of semiautomated 1-4 and fully automated [5][6][7][8][9][10][11][12][13][14] 2D-2D registration methods have been developed for the determination of setup errors in prostate radiotherapy. A limitation of 2D-2D registration methods is that they are typically unable to accurately estimate all six transformation parameters, especially in the presence of out-of-plane displacements.…”

Section: Introductionmentioning

confidence: 99%

Automated 2D–3D registration of portal images and CT data using line‐segment enhancement

et al. 2008

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In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage ͑MV͒ portal images acquired during treatment to a reference 3D kilovoltage ͑kV͒ CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on ͑1͒ accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior ͑AP͒ view acquired at 15 different poses and ͑2͒ a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8 weeks. The mean ͑and standard deviation͒ of the absolute registration error for rotations around the right-lateral ͑RL͒, inferior-superior ͑IS͒, and posterior-anterior ͑PA͒ axes were 0.212°͑0.214°͒, 0.055°͑0.033°͒ and 0.041°͑0.039°͒, respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161͑0.131͒ mm, 0.096͑0.033͒ mm, and 0.612͑0.485͒ mm. The mean ͑and standard deviation͒ of the total registration error was 0.778͑0.543͒ mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.

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A structure-based method for on-line matching of portal images for an optimal patient set-up in radiotherapy

Cited by 10 publications

References 22 publications

2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images

2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images

A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy

Automated 2D–3D registration of portal images and CT data using line‐segment enhancement

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