SU-GG-I-109: A Quantitative Evaluation of Velocity AI Deformable Image Registration

Lin, Horng-Ru; Ayan, A.S.; Zhai, Huifang; Zhu, Tong; Both, Stefan

doi:10.1118/1.3468142

Cited by 15 publications

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“…The generally low RMSEs for both image registration systems are consistent with average registration errors reported in the literature when evaluated using CT . However, we did find occurrences of registration errors greater than 10 mm in both image registration systems.…”

Section: Discussionsupporting

confidence: 90%

“…Singhrao et al used a head and neck phantom that included representative bony anatomy, and found maximum errors between 6.5 and 8.3 mm for Velocity's deformable image registration with CT images . This maximum error is closer to the error we found in this study than are the maximum errors found in studies of anatomical regions outside the head and neck …”

Section: Discussionsupporting

confidence: 72%

“…However, we did find occurrences of registration errors greater than 10 mm in both image registration systems. The maximum registration errors of 10.2 mm for the SI direction of the left submandibular gland on the T1‐weighted synthetic images and 12.9 mm for the SI direction of the porcine phantom T1‐weighted MR images in Velocity were higher than the maximum registration errors measured in CT‐based registration with Velocity . These large registration errors may be due to the type of surrounding tissue.…”

Section: Discussionmentioning

confidence: 78%

“…However, image registration error on commercial software using MRI has not been widely reported. A B‐spline‐based commercial software, Velocity (Velocity AI version 3.0.1, Varian Medical Systems, Palo Alto, CA, USA), has been evaluated for CT‐based image registration and shown to have an average registration error below 5 mm . In addition, our in‐house demons‐based algorithm has been validated for CT with registration error below 2 mm .…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

et al. 2017

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Purpose Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. Methods The registration error was analyzed first with synthetic MR images. These images were generated from inter-patient and intra-patient variation models trained on 28 patients. Four synthetic posttreatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pre-treatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. Results The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images. Conclusions The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

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Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

et al. 2017

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“…Thirteen Italian institutions participated in this study with the following six available commercial platforms: RayStation, MIM, VelocityAI and SmartAdapt, Mirada XD and ABAS. The DIR software with the number of the evaluated systems, the references and the corresponding institutions are summarized in Table …”

Section: Methodsmentioning

confidence: 99%

Performance of commercially available deformable image registration platforms for contour propagation using patient‐based computational phantoms: A multi‐institutional study

Loi¹,

Fusella

Lanzi

et al. 2018

Medical Physics

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Effect of DIR uncertainty on prostate passive-scattering proton therapy dose accumulation

et al. 2017

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SU-GG-I-109: A Quantitative Evaluation of Velocity AI Deformable Image Registration

Cited by 15 publications

References 0 publications

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

Performance of commercially available deformable image registration platforms for contour propagation using patient‐based computational phantoms: A multi‐institutional study

Effect of DIR uncertainty on prostate passive-scattering proton therapy dose accumulation

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