Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments

Guang Li,Barbara Arora,Norman Coleman,Jacek Capala,Peter Guion,Kevin Camphausen,Holly Ning,Deborah Citrin,Robert W Miller,Huchen Xie,Roberto Maass‐Moreno

doi:10.1120/jacmp.v9i4.2781

Abstract

Registration is critical for image‐based treatment planning and image‐guided treatment delivery. Although automatic registration is available, manual, visual‐based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration result. However, the 3P fusion can be time consuming, observer dependent, as well as prone to errors, owing to the incomplete 3‐dimensional (3D) volumetric image representations. It is also limited to single‐pixel precision (the screen resolution). The 3D volumetric image registration (3DVIR) technique was developed to overcome these shortcomings. This technique introduces a 4th dimension in the registration criteria beyond the image volume, offering both visual and quantitative correlation of corresponding anatomic landmarks within the two registration images, facilitating a volumetric image alignment, and minimizing potential registration errors. The 3DVIR combines image classification in real‐time to select and visualize a reliable anatomic landmark, rather than using all voxels for alignment. To determine the detection limit of the visual and quantitative 3DVIR criteria, slightly misaligned images were simulated and presented to eight clinical personnel for interpretation. Both of the criteria produce a detection limit of 0.1 mm and 0.1°. To determine the accuracy of the 3DVIR method, three imaging modalities (CT, MR and PET/CT) were used to acquire multiple phantom images with known spatial shifts. Lateral shifts were applied to these phantoms with displacement intervals of 5.0±0.1mm. The accuracy of the 3DVIR technique was determined by comparing the image shifts determined through registration to the physical shifts made experimentally. The registration accuracy, together with precision, was found to be: 0.02±0.09mm for CT/CT images, 0.03±0.07mm for MR/MR images, and 0.03±0.35mm for PET/CT images. This accuracy is consistent with the detection limit, suggesting an absence of detectable systematic error. This 3DVIR technique provides a superior alternative to the 3P fusion method for clinical applications.PACS numbers: 87.57.nj, 87.57.nm, 87.57.‐N, 87.57.‐s

Highlights

Radiation therapy has been improved in recent years owing to technical advances, including image-based treatment planning as well as image-guided treatment delivery.[1,2,3,4,5] Multi-modality imaging techniques that have been routinely applied in radiation treatment planning (RTP)
It permits a transition to frameless intra-/extra-cranial stereotactic radiation therapy, with improved patient comfort and clinical outcome.[15,16,17,18] Image registration plays the key role in providing optimum alignment between the pre-treatment setup image and the planning image,(19-22) minimizing deviation of the radiation treatment delivery (RTD) from the RTP
Principal image registration techniques include intensity-based automatic registration, as well as visual-based manual registration.[6,7,8,9,10,23,24,25,26,27,28,29] Automated registration techniques have been used increasingly in RTP and image-guided radiation therapy (IGRT),(13, 14, 25, 27, 29) based on maximization of mutual information (MMI) of multi-modal images[30,31,32] or grayscale similarity (MGS) for single modality images.[33,34] an automatic registration may carry and propagate systematic errors,(34) reach a sub-optimal solution,(16) or even fail to achieve a reasonable alignment.[33]. Realistically, these phenomena exist because most clinical images contain a certain degree deformation, including motion induced deformation and artifacts, especially in the case of positron emission tomography (PET) imaging

Summary

Introduction

Radiation therapy has been improved in recent years owing to technical advances, including image-based treatment planning as well as image-guided treatment delivery.[1,2,3,4,5] Multi-modality imaging techniques that have been routinely applied in radiation treatment planning (RTP)include: computed tomography (CT), magnetic resonance imaging (MR), and positron emission tomography (PET). The addition of coaxial imaging equipment to megavoltage Xray accelerators, including on-site cone beam CT[11,12] and Tomotherapy Imaging,(13,14) has set a new foundation for image-guided radiation therapy (IGRT) development, by providing immediate pre-treatment verification and adjustment of a patient’s position, resulting in improved accuracy of conformal radiation treatment delivery. Simplified techniques have been reported and applied clinically, such as region-of-interest registration,(6) intensity-weighted registration,(14) and discrete rigid body approximation,(32) but manual adjustment is clinically required based on visual verification, combined with anatomical and physiological knowledge.[24,25,26,27,28]

Methods

Results

Discussion

Conclusion