Automated registration of sequential breath-hold dynamic contrast-enhanced MR images: a comparison of three techniques

作者：Rajaraman Sivaramakrishnan; Rodriguez Jeffrey J; Graff Christian; Altbach Maria I; Dragovich Tomislav; Sirlin Claude B; Korn Ronald L; Raghunand Natarajan^*

来源：Magnetic Resonance Imaging, 2011, 29(5): 668-682.

DOI：10.1016/j.mri.2011.02.012

• Dynamic
• Gadolinium
• MRI
• Mutual information
• Non-rigid registration
• Elastic registration
• Tracer kinetics

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions and also places constraints on temporal resolution vis-a-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all three methods, a 3D rigid-body registration scheme with a mutual information similarity measure was used as a preprocessing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration, which was corrected using the three schemes. All three schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI data sets is also presented.

• 出版日期2011-6