<scp>CALIPER</scp>: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non‐small cell lung cancer

Guy, Christopher L.; Weiss, Elisabeth; Christensen, Gary E.; Jan, Nuzhat; Hugo, Geoffrey D.

doi:10.1002/mp.12891

Cited by 20 publications

(11 citation statements)

References 35 publications

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“… 14 , 15 , 16 , 17 Such cases remain challenges for most deformable image registration algorithms and are the subject of ongoing research. 18 , 19 Because the prone and supine acquisitions of this study were performed during the same imaging session, anatomy was consistent between images used for registration. It was therefore expected that deformable image registration algorithms should register these cases with acceptable accuracy.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

Guy

Weiss

Che

et al. 2019

Advances in Radiation Oncology

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PurposeTo evaluate accuracy for 2 deformable image registration methods (in-house B-spline and MIM freeform) using image pairs exhibiting changes in patient orientation and lung volume and to assess the appropriateness of registration accuracy tolerances proposed by the American Association of Physicists in Medicine Task Group 132 under such challenging conditions via assessment by expert observers.Methods and MaterialsFour-dimensional computed tomography scans for 12 patients with lung cancer were acquired with patients in prone and supine positions. Tumor and organs at risk were delineated by a physician on all data sets: supine inhale (SI), supine exhale, prone inhale, and prone exhale. The SI image was registered to the other images using both registration methods. All SI contours were propagated using the resulting transformations and compared with physician delineations using Dice similarity coefficient, mean distance to agreement, and Hausdorff distance. Additionally, propagated contours were anonymized along with ground-truth contours and rated for quality by physician-observers.ResultsAveraged across all patients, the accuracy metrics investigated remained within tolerances recommended by Task Group 132 (Dice similarity coefficient >0.8, mean distance to agreement <3 mm). MIM performed better with both complex (vertebrae) and low-contrast (esophagus) structures, whereas the in-house method performed better with lungs (whole and individual lobes). Accuracy metrics worsened but remained within tolerances when propagating from supine to prone; however, the Jacobian determinant contained regions with negative values, indicating localized nonphysiologic deformations. For MIM and in-house registrations, 50% and 43.8%, respectively, of propagated contours were rated acceptable as is and 8.2% and 11.0% as clinically unacceptable.ConclusionsThe deformable image registration methods performed reliably and met recommended tolerances despite anatomically challenging cases exceeding typical interfraction variability. However, additional quality assurance measures are necessary for complex applications (eg, dose propagation). Human review rather than unsupervised implementation should always be part of the clinical registration workflow.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

Guy

Weiss

Che

et al. 2019

Advances in Radiation Oncology

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“…Furthermore, most patients exhibited large geometric changes between baseline and mid‐treatment images due to atelectasis resolution which dramatically altered appearance and location of lung parenchyma. Ground‐truth correspondences between image pairs were necessary for evaluation of DIRs performed with the images reported elsewhere . Creation of these landmark sets is described in the following sections.…”

Section: Methodsmentioning

confidence: 99%

“…Ground-truth correspondences between image pairs were necessary for evaluation of DIRs performed with the images reported elsewhere. 10 Creation of these landmark sets is described in the following sections.…”

Section: A Patient Datasetsmentioning

confidence: 99%

Technical Note: A method for quality assurance of landmark sets for use in evaluation of deformable image registration accuracy of lung parenchyma

et al. 2018

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Purpose To develop a quality control method to improve the accuracy of corresponding landmark sets used for deformable image registration (DIR) evaluation in the lung parenchyma. Methods An iterative workflow was developed as a method for quality assurance of landmark sets. Starting with the initial landmark set for a given image pair, a landmark‐based deformation was applied to one of the images. A difference image and a color overlay were generated using the deformed image and the other image of the pair. Inspection of these generated images at locations of landmarks allowed for the identification of misplaced landmarks. The observer responsible for creating the initial landmark set was tasked with review and revision of points flagged by the quality assurance procedure. Using the updated landmark sets, the process was repeated until all points were acceptable to the reviewer. Results Eighteen landmark sets, containing a mean (SD) of 170 (31) landmarks, were created using CT images from non‐small cell lung cancer patients exhibiting large geometric changes and atelectasis resolution, making landmark specification challenging. Following the quality assurance procedure, the final landmark sets contained a mean (SD) of 165 (25) landmarks, as points too difficult to match were removed and points were added to regions deficient in landmarks. For landmark sets in which changes were made, maximum and mean differences in landmark positions before and after quality assurance ranged between 8.7–81.5 mm and 0.3–9.6 mm, respectively. Conclusions An effective method for improving the accuracy of landmark correspondence was presented. This quality assurance approach enables more accurate evaluation of DIR for lung parenchyma in clinical image pairs in the absence of a ground truth deformation and may be applicable to other feature‐rich anatomical sites.

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“…Four-dimensional imaging solutions for in-room cone beam CT [62][63][64][65] and MRI 66 continue to evolve to improve image quality in motion-influenced sites, which is critical to move ART from clearly-defined tumors to more challenging stages of disease and sites where it may be critically needed, such as locally-advanced lung cancer. 2,67 Merging advances in realtime ART, such as motion tracking, with efficient pre-treatment online ART tools may help take advantage of the merits of both approaches for managing anatomical changes on multiple timescales. 68 Adaptation with functional imaging has traditionally been performed with offline ART.…”

Section: Open Questions and Future Directionsmentioning

confidence: 99%

Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

Green

Henke

Hugo

2019

Seminars in Radiation Oncology

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118

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Adaptive radiotherapy emerged over 20 years ago and is now an established clinical practice in a number of organ sites. No one solution for adaptive therapy exists. Rather, adaptive radiotherapy is a process which combines multiple tools for imaging, assessment of need for adaptation, treatment planning, and quality assurance of this process. Workflow is therefore a critical aspect to ensure safe, effective, and efficient implementation of adaptive radiotherapy. In this work, we discuss the tools for online and offline adaptive radiotherapy and introduce workflow concepts for these types of adaptive radiotherapy. Common themes and differences between the workflows are introduced and controversies and areas of active research are discussed.

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CALIPER: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non‐small cell lung cancer

Abstract: The CALIPER algorithm developed in this work achieves accurate image registration for challenging cases involving large geometric and topological changes in NSCLC patients, a requirement for enabling ART in this patient group.

Cited by 20 publications

References 35 publications

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

Technical Note: A method for quality assurance of landmark sets for use in evaluation of deformable image registration accuracy of lung parenchyma

Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

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