Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning

Kaus, Michael R.; Brock, Kristy K.; Pekar, Vladimir; Dawson, Laura A.; Nichol, Alan; Jaffray, David A.

doi:10.1016/j.ijrobp.2007.01.056

Cited by 136 publications

(112 citation statements)

References 27 publications

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“…As such, for the first question of “how to evaluate,” assessment of both capacities, contour propagating and dose tracking, should be included. The current techniques for DIR evaluation generally fell into three categories: contour comparison, 21 , 22 , 23 , 24 , 25 landmark tracking, 5 , 23 and voxel‐based analysis 7 , 8 , 9 , 10 , 12 , 13 . Both the contour‐ and landmark‐based analyses can yield a skewed view of the overall registration accuracy, as they only test spatial accuracy in limited regions.…”

Section: Discussionmentioning

confidence: 99%

Performance variations among clinically available deformable image registration tools in adaptive radiotherapy — how should we evaluate and interpret the result?

Nie

Pouliot

Smith

et al. 2016

J Applied Clin Med Phys

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The purpose of this study is to evaluate the performance variations in commercial deformable image registration (DIR) tools for adaptive radiation therapy and further to interpret the differences using clinically available terms. Three clinical examples (prostate, head and neck (HN), and cranial spinal irradiation (CSI) with L‐spine boost) were evaluated in this study. Firstly, computerized deformed CT images were generated using simulation QA software with virtual deformations of bladder filling (prostate), neck flexion/bite‐block repositioning/tumor shrinkage (HN), and vertebral body rotation (CSI). The corresponding transformation matrices served as a “reference” for the following comparisons. Three commercialized DIR algorithms: the free‐form deformation from MIMVista 5.5 and the RegRefine from MIMMaestro 6.0, the multipass B‐spline from VelocityAI v3.0.1, and the adaptive demons from OnQ rts 2.1.15, were applied between the initial images and the deformed CT sets. The generated adaptive contours and dose distributions were compared with the “reference” and among each other. The performance in transferring contours was comparable among all three tools with an average Dice similarity coefficient of 0.81 for all the organs. However, the dose warping accuracy appeared to rely on the evaluation end points and methodologies. Point‐dose differences could show a difference of up to 23.3 Gy inside the PTVs and to overestimate up to 13.2 Gy for OARs, which was substantial for a 72 Gy prescription dose. Dosevolume histogram‐based evaluation might not be sensitive enough to illustrate all the detailed variations, while isodose assessment on a slice‐by‐slice basis could be tedious. We further explored the possibility of using 3D gamma index analysis for warping dose variation assessment, and observed differences in dose warping using different DIR tools. Overall, our results demonstrated that evaluation based only on the performance of contour transformation could not guarantee the accuracy in dose warping, while dose‐transferring validation strongly relied on the evaluation endpoint. As dose‐transferring errors could cause misinterpretations when attempting to accumulate dose for adaptive radiation therapy and more DIR tools are available for clinical use, a standard and clinically meaningful quality assurance criterion should be established for DIR QA in the near future.PACS number(s): 87.57

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Section: Discussionmentioning

confidence: 99%

Performance variations among clinically available deformable image registration tools in adaptive radiotherapy — how should we evaluate and interpret the result?

Nie

Pouliot

Smith

et al. 2016

J Applied Clin Med Phys

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“…In this step, dose summation and evaluation were gathered using registration method described by Kaus et al [21], which is a surface-based deformable image registration strategy that enables quantitative description of geometrical change in multimodal images. This model-based method uses existing contours sets in both primary and secondary image sets and copes with image differences based on changes in the representation of the same organs.…”

Section: Methodsmentioning

confidence: 99%

“…Dosimetric evaluation of different margin selections under their alignment methods are carried out using a planning CT scan and a series of consecutive CT scans similar to planning CT at different days. The serial CT scans is contoured and aligned to the planning CT (similar to the image guidance CBCTs during treatment) while dose of original plan is generated on the aligned serial CT. All the doses on serial CTs are then transferred back to planning CT using a model-based deformable registration [21], where a final dose summation and evaluation is performed.…”

Section: Introductionmentioning

confidence: 99%

Planning Margins to CTV for Image-Guided Whole Pelvis Prostate Cancer Intensity-Modulated Radiotherapy

Wang¹,

Wang²,

Lerma³

et al. 2012

IJMPCERO

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Purpose: We investigated the margin recipes with different alignment techniques in the image-guided intensity-modulated radiotherapy (IMRT) of whole pelvis prostate cancer patients. Materials and Methods: Forty-eight computed tomography (CT) scans of eight prostate cancer patients were investigated. Each patient had an initial planning CT scan and 5 consecutive serial CT scans during the course of treatment, all of which were acquired using 3 mm slice separation and 0.94 mm resolution in the axial plane at 120 kVp, on a PQ 5000 CT scanner. Three different whole pelvis planning margin recipes, ranging from 3 to 13 mm, were investigated. A unique IMRT plan was created with each PTV on the initial CT scan, and was then registered to the 5 serial CT scans, by bony alignment or by prostate gland-based alignment. The dose computed on each serial CT scans was accumulated back to the initial CT scan using deformable image registration for final dosimetric evaluation of the interplay of the margin selection and alignment methods. Results: Elective lymph nodes coverage is shown to be independent of the choice between prostate-based and bony-anatomy-based patient repositioning. The prostate gland-based alignment greatly enhanced the coverage to the prostate and SV, especially with small margins. Meanwhile, the soft-tissue alignment also raised the incidental dose to the rectum and reduces the dose to the bladder. With small to intermediate margins, only soft-tissue alignment gave acceptable mean coverage to SV. Margin of 13 mm or more was needed for PLNs to maintain good target coverage. Conclusions: We commend prostate-based alignment along with margins less than or equal to 5 mm around prostate and SV, and margins greater than or equal to 13 mm around the vascular spaces.

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“…For this demonstration, we compute the full 62.6 Gy delivery to the exhale phase and then map the dose into the inhale phase using the point-based thin plate spline ͑TPS͒ algorithm implemented in a research version of Pinnacle planning system. 7 Volumes used as input by the TPS algorithm were lungs, heart, cord, superior mediastinal lymphatic nodes, esophagus, PTV, and GTV. The number of vertices to create the volume meshes and the number of sample points for the TPS algorithm are left at their default values.…”

Section: Iie Clinical Demonstration Casementioning

confidence: 99%

“…There are numerous algorithms that use various approaches to calculate the displacement vector field ͑DVF͒ that matches points in one image with points in another. [3][4][5][6][7] Although several different basis functions have been utilized by DIR algorithms, no known algorithm provides a DVF that maps tissue elements from between anatomic instances without error. [8][9][10] The lack of a gold standard makes it difficult to assess the DVF accuracy for arbitrary patient images; similarly, there is no universal or widely accepted method to evaluate the uncertainty of an individual patient's DVF.…”

Section: Introductionmentioning

confidence: 99%

Estimation of three‐dimensional intrinsic dosimetric uncertainties resulting from using deformable image registration for dose mapping

et al. 2010

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Purpose: This article presents a general procedural framework to assess the point-by-point precision in mapped dose associated with the intrinsic uncertainty of a deformable image registration ͑DIR͒ for any arbitrary patient. Methods: Dose uncertainty is obtained via a three-step process. In the first step, for each voxel in an imaging pair, a cluster of points is obtained by an iterative DIR procedure. In the second step, the dispersion of the points due to the imprecision of the DIR method is used to compute the spatial uncertainty. Two different ways to quantify the spatial uncertainty are presented in this work. Method A consists of a one-dimensional analysis of the modules of the position vectors, whereas method B performs a more detailed 3D analysis of the coordinates of the points. In the third step, the resulting spatial uncertainty estimates are used in combination with the mapped dose distribution to compute the point-by-point dose standard deviation. The process is demonstrated to estimate the dose uncertainty induced by mapping a 62.6 Gy dose delivered on maximum exhale to maximum inhale of a ten-phase four-dimensional lung CT. Results: For the demonstration lung image pair, the standard deviation of inconsistency vectors is found to be up to 9.2 mm with a mean of 1.3 mm. This uncertainty results in a maximum estimated dose uncertainty of 29.65 Gy if method A is used and 21.81 Gy for method B. The calculated volume with dose uncertainty above 10.00 Gy is 602 cm 3 for method A and 1422 cm 3 for method B.Conclusions: This procedure represents a useful tool to evaluate the precision of a mapped dose distribution due to the intrinsic DIR uncertainty in a patient. The procedure is flexible, allowing incorporation of alternative intrinsic error models.

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Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning

Cited by 136 publications

References 27 publications

Performance variations among clinically available deformable image registration tools in adaptive radiotherapy — how should we evaluate and interpret the result?

Performance variations among clinically available deformable image registration tools in adaptive radiotherapy — how should we evaluate and interpret the result?

Planning Margins to CTV for Image-Guided Whole Pelvis Prostate Cancer Intensity-Modulated Radiotherapy

Estimation of three‐dimensional intrinsic dosimetric uncertainties resulting from using deformable image registration for dose mapping

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