Evaluation of a deformable image registration quality assurance tool for head and neck cancer patients

Mée, M. Le; Stewart, Kate; Lathouras, Marika; Truong, Helen; Hargrave, Catriona

doi:10.1002/jmrs.428

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…A recent study revealed that, with CT images from 35 head and neck cancer patients, the “Reg Refine” registrations conducted by experienced operators were classified as good, fair and bad, corresponding to STD‐JD values of 0.05, 0.21, and 0.68, respectively. 28 In contrast, the hybrid FEM method automatically updates the MIM registrations in interior regions, reducing STD‐JDs from 0.44 to 0.17 for MIM‐II, from 0.33 to 0.15 for MIM‐CC, and from 0.24 to 0.10 for MIM‐MM. The one‐tailed Wilcoxon‐Mann‐Whitney tests show that these improvements are statistically significant, with all p ‐values less than 0.0084.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Zhong,

Kainz,

Paulson

2024

J Applied Clin Med Phys

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PurposeWe evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties.Materials and MethodsThree evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD‐JD), are used to assess the multi‐modality (MM), contour‐consistency (CC), and image‐intensity (II)‐based DIR algorithms in the MIM software package, as well as an in‐house developed, contour matching‐based finite element method (CM‐FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One‐tailed Wilcoxon‐Mann‐Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications.ResultsFor the registrations performed with the MIM‐CC, MIM‐MM, MIM‐II, and CM‐FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p‐values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD‐JDs and greater than 0.87 for MDA and DSC.ConclusionsAmong the three MIM DIR algorithms, MIM‐CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

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Section: Discussionmentioning

confidence: 99%

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Zhong,

Kainz,

Paulson

2024

J Applied Clin Med Phys

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“…JD maps were also generated within the MIM software for the ten cases, for both DIR algorithms. The MIM software creates the Jacobian determinant map using the deformation grid, which itself is obtained from the deformed CT created within MIM 16 . The plan ROIs were superimposed upon the JD maps, and the mean and standard deviations of the ROI JD values were calculated.…”

Section: Methodsmentioning

confidence: 99%

“…The MIM software creates the Jacobian determinant map using the deformation grid, which itself is obtained from the deformed CT created within MIM. 16 The plan ROIs were superimposed upon the JD maps, and the mean and standard deviations of the ROI JD values were calculated. Local expansions are characterized by JD > 1,and contractions by 0 < JD < 1; JD = 1 indicates no local volume change.…”

Section: Comparison Of Dose Accumulation Uncertainties For Two Dir Algorithmsmentioning

confidence: 99%

Use of a DVH overlay technique for quality assurance of deformable image registration‐based dose accumulation

et al. 2021

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We present a DVH overlay technique as a quality assurance (QA) metric for deformable image registration-based dose accumulation (DIR-DA). We use the technique to estimate the uncertainty in a DIR-DA for a revised treatment plan, and to compare two different DIR algorithms. Materials and methods:The required inputs to the DVH overlay workflow are deformably registered primary and secondary images, primary regions-ofinterest (ROIs),and secondary dose distribution.The primary ROIs were forward warped to the secondary image, the secondary dose was inversely warped to the primary image, and the DVHs for each image were compiled. Congruent DVHs imply minimal inverse consistency error (ICE) within an ROI. For a pancreas case re-planned after 21 fractions of a 29-fraction course, the workflow was used to quantify dose accumulation error attributable to ICE, based on a hybrid contour-and-intensity-based DIR. The usefulness of the workflow was further demonstrated by assessing the performance of two DIR algorithms (one free-form intensity-based, FFIB, the other using normalized correlation coefficients, NCC, over small neighborhood patches) as applied toward kilovoltage computed tomography (kVCT)-to-megavoltage computed tomography (MVCT) registration and five-fraction dose accumulation of ten male pelvis cases. Results: For the re-planned pancreas case, when applying the DVH-overlaybased uncertainties the resulting accumulated dose remained compliant with all but two of the original plan objectives. Among the male pelvis cases, FFIB and NCC DIR showed good invertibility within the planning target volume (PTV), according to the DVH overlay QA results. NCC DIR exhibited better invertibility for the bladder and rectum compared with FFIB. However, compared with FFIB, NCC DIR exhibited less regional deformation for the bladder and a tendency for increased local contraction of the rectum ROI. For the five-fraction summations, ICE for the PTV V 100%Rx is comparable for both algorithms (FFIB 0.8 ± 0.7%, NCC 0.7 ± 0.3%). For the bladder and rectum V 70%Rx , ICE is greater for FFIB (1.8 ± 0.7% for bladder, 1.7 ± 0.6% for rectum) than for NCC (1.0 ± 0.3% for bladder, 1.0 ± 0.4% for rectum). Conclusions:The DVH overlay technique identified instances in which a DIR exhibits favorable invertibility, implying low ICE in a DIR-based dose accumulation. Differences in the overlaid DVHs can also estimate dose accumulation errors attributable to ICE for given ROIs.

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“…To access performance of each method on registering data from different populations and acquired with different protocols and scanners, we also report evaluation metrics of the intra-population subject pairs (i.e., between HCP and HCP, ABCD and ABCD, and PPMI and PPMI) and inter-population subject pairs (i.e., between HCP and ABCD, between HCP and PPMI, and between ABCD and PPMI). In addition, to evaluate the regularity of the 8. http://dti-tk.sourceforge.net/pmwiki/pmwiki.php deformation fields, we compute the Jacobian determinant of the deformation field of each testing image pair (as used in multiple related studies [54], [83], [84]). Specifically, the Jacobian matrix at each voxel captures local properties around the voxel, where a non-positive determinant indicates that the deformation is not diffeomorphic [54], [85].…”

Section: Comparison To State-of-the-art Methodsmentioning

confidence: 99%

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

Wells

2021

Preprint

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In this paper, we present a deep learning method, DDMReg, for fast and accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. To the best of our knowledge, DDMReg is the first deep-learning-based dMRI registration method. DDMReg is a fully unsupervised method for deformable registration between pairs of dMRI datasets. We propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. We perform comparisons with four state-of-the-art registration methods. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance. In addition, DDMReg leverages deep learning techniques and provides a fast and efficient tool for dMRI registration.

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Evaluation of a deformable image registration quality assurance tool for head and neck cancer patients

Cited by 9 publications

References 15 publications

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Use of a DVH overlay technique for quality assurance of deformable image registration‐based dose accumulation

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

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