Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy

He, Wenbo; Dong, Lei; Lii, M.; Lee, Andrew L.; Crevoisier, R. de; Mohan, Radhe; Cox, James D.; Kuban, Deborah A.; Cheung, R.

doi:10.1016/j.ijrobp.2004.07.677

Cited by 169 publications

(146 citation statements)

References 23 publications

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“…two CBCT image sets using our in-house deformation image registration software, CT-assisted targeting. 24,25 The images and contours were imported into our biomarker software (details below) to extract the values for the imaging features.…”

Section: A Patient Test-retest Cbct Imagesmentioning

confidence: 99%

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

et al. 2015

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Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rank correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features' interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4-13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturerprotocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6-8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from ...

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Section: A Patient Test-retest Cbct Imagesmentioning

confidence: 99%

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

et al. 2015

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“…The displacement of any other point is then determined by interpolation of the displacement for the nearest control points. This technique has a validated ability to accurately recover artificially introduced intramodality deformations (16) . More recently, a simpler technique has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of a cone‐beam computed tomography–planning computed tomography deformable image registration method for adaptive radiation therapy

Lawson

Schreibmann

Jani

et al. 2007

J Applied Clin Med Phys

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Deformable (non‐rigid) registration is an essential tool in both adaptive radiation therapy and image‐guided radiation therapy to account for soft‐tissue changes during the course of treatment. The evaluation method most commonly used to assess the accuracy of deformable image registration is qualitative human evaluation. Here, we propose a method for systematically measuring the accuracy of an algorithm in recovering artificially introduced deformations in cases of rigid geometry, and we use that method to quantify the ability of a modified basis spline (B‐Spline) registration algorithm to recover artificially introduced deformations. The evaluation method is entirely computer‐driven and eliminates biased interpretation associated with human evaluation; it can be applied to any chosen method of image registration.Our method involves using planning computed tomography (PCT) images acquired with a conventional CT simulator and cone‐beam computed tomography (CBCT) images acquired daily by a linear accelerator–mounted kilovoltage image system in the treatment delivery room. The deformation that occurs between the PCT and daily CBCT images is obtained using a modified version of the B‐Spline deformable model designed to overcome the low soft‐tissue contrast and the artifacts and distortions observed in CBCT images. Clinical CBCT images and contours of phantom and central nervous system cases were deformed (warped) with known random deformations. In registering the deformed with the non‐deformed image sets, we tracked the algorithm's ability to recover the original, non‐deformed set. Registration error was measured as the mean and maximum difference between the original and the registered surface contours from outlined structures. Using this approach, two sets of tests can be devised. To measure the residual error related to the optimizer's convergence performance, the warped CBCT image is registered to the unwarped version of itself, eliminating unknown factors such as noise and positioning errors. To study additional errors introduced by artifacts and noise in the CBCT image, the warped CBCT image is registered to the original PCT image.Using a B‐Spline deformable image registration algorithm, mean residual error introduced by the algorithm's performance on noise‐free images was less than 1 mm, with a maximum of 2 mm. The chosen deformable image registration model was capable of accommodating significant variability in structures over time, because the artificially introduced deformation magnitude did not significantly influence the residual error. On the second type of test, noise and artifacts reduced registration accuracy to a mean of 1.33 mm and a maximum of 4.86 mm.The accuracy of deformable image registration can be easily and consistently measured by evaluating the algorithm's ability to recover artificially introduced deformations in rigid cases in which the true solution is known a priori. The method is completely automated, applicable to any chosen registration algorithm, and does not require user interact...

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“…The ten patients described above were selected as the atlas cases; the atlases were applied separately to register each of the remaining 80 patients ("test" cases) by using a deformable image registration algorithm based on the accelerated "demons" algorithm [13]. The resultant displacement vector fields characterizing the individual registrations were then used to deform the atlas brachial plexus contours to obtain 10 individual segmentations for each patient.…”

Section: Deformable Image Registrationmentioning

confidence: 99%

Dose Constraints to Prevent Radiation-induced Brachial Plexopathy in Patients Treated for Lung Cancer

Amini

Welsh

Yang

et al. 2011

International Journal of Radiation Oncology*Biology*Physics

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Purpose-As the recommended radiation dose for non-small cell lung cancer (NSCLC) increases, meeting dose constraints for critical structures like the brachial plexus becomes increasingly challenging, particularly for tumors in the superior sulcus. In this retrospective analysis, we compared dose-volume histogram information with the incidence of plexopathy to establish the maximum tolerated dose to the brachial plexus.Methods and Materials-We identified 90 patients with NSCLC treated with definitive chemoradiation from March 2007 through September 2010 who had received>55 Gy to the brachial plexus. We used a multi-atlas segmentation method combined with deformable image registration to delineate the brachial plexuson the original planning CT scans and scoredplexopathy according to the Common Terminology Criteria for Adverse Events v4.03. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Results-The Meeting Presentations:This work was submitted for presentation at the 53 rd (2011) ASTRO meeting. Conflicts of Interest:The authors declare no conflicts of interest regarding the work presented here. Conclusions-For lung cancers near the apical region, brachial plexopathy is a major concern for high-dose radiation therapy. We developed a computer-assisted image segmentation method which allowed us to rapidly and consistently contour the brachial plexus and establish the dose limits to minimize the risk of brachial plexopathy. Our results could be used as a guideline in future prospective trialswithhigh dose radiation therapy for unresectable lung cancer. NIH Public Access

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Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy

Cited by 169 publications

References 23 publications

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

Quantitative evaluation of a cone‐beam computed tomography–planning computed tomography deformable image registration method for adaptive radiation therapy

Dose Constraints to Prevent Radiation-induced Brachial Plexopathy in Patients Treated for Lung Cancer

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