Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms
Authors: Ger Rachel B. , Yang Jinzhong , Ding Yao , Jacobsen Megan C. , Fuller Clifton D. , Howell Rebecca M. , Li Heng , Jason Stafford R. , Zhou Shouhao , Court Laurence E. 
Published: June 2017

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 with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post‐treatment 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 pretreatment 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, and 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.