Bundling 3D- and 2D-based registration of MRI to x-ray breast tomosynthesis

• Verfasst von: Cotič Smole, Patricia [VerfasserIn]
• Verfasst von: Ruiter, Nicole [VerfasserIn]
• Verfasst von: Kaiser, Clemens G. [VerfasserIn]
• Verfasst von: Krammer, Julia [VerfasserIn]
• Verfasst von: Hopp, Torsten [VerfasserIn]
• Titel: Bundling 3D- and 2D-based registration of MRI to x-ray breast tomosynthesis
• Verf.angabe: P. Cotic Smole, N.V. Ruiter, C. Kaiser, J. Krammer, T. Hopp
• E-Jahr: 2018
• Jahr: 13 March 2018
• Umfang: ?
• Fussnoten: Gesehen am 05.09.2018
• Titel Quelle: Enthalten in: Image-Guided Procedures, Robotic Interventions, and Modeling (2018 : Houston, Tex.)Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
• Ort Quelle: Bellingham, Washington, USA : SPIE, 2018
• Jahr Quelle: 2018
• Band/Heft Quelle: (2018) Artikel-Nummer 105762X
• ISBN Quelle: 978-1-5106-1642-4
• DOI: doi:10.1117/12.2295048
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1580708277

Abstract

Increasing interest in multimodal breast cancer diagnosis has led to the development of methods for MRI to X-ray mammography registration to provide direct correlation of modalities. The severe breast deformation in X-ray mammography is often tackled by biomechanical models, which however have not yet brought the registration accuracy to a clinically applicable level. We present a novel registration approach of MRI to X-ray tomosynthesis. Tomosynthesis provides three-dimensional information of the compressed breast and as such has the ability to open new possibilities in the registration of MRI and X-ray data. By bundling the 3D information from the tomosynthesis volume with the 2D projection images acquired at different measuring angles, we provide a correlation between the registration error in 3D and 2D and evaluate different 3D- and 2D-based similarity metrics to drive the optimization of the automated patient-specific registration approach. From the preliminary study of four analysed patients we found that the projected registration error is in general larger than the 3D error in case of small registration errors in the cranio-caudal direction. Although both image shape and intensitybased 2D similarity metrics showed a clear correlation with the 2D registration error at different projection angles, metrics that relied on the combined 2D and 3D information yielded in most of the cases the minimal registration error and as such had better performance than similarity metrics that rely only on the shape similarity of volumes.