Multimodal Image Registration for Liver Radioembolization Planning and Patient Assessment

Overview

Journal Int J Comput Assist Radiol Surg

Publisher Springer

Specialties General Surgery
Radiology

Date 2018 Oct 24

PMID 30349976

Citations 9

Authors

Nadine Spahr

Smita Thoduka

Nasreddin Abolmaali

Ron Kikinis

Andrea Schenk

Affiliations

Soon will be listed here.

Abstract

Purpose: Multimodal imaging plays a key role in patient assessment and treatment planning in liver radioembolization. It will reach its full potential for convenient use in combination with deformable image registration methods. A registration framework is proposed for multimodal liver image registration of multi-phase CT, contrast-enhanced late-phase T1, T2, and DWI MRI sequences.

Methods: A chain of four pair-wise image registrations based on a variational registration framework using normalized gradient fields as distance measure and curvature regularization is introduced. A total of 103 cases of 35 patients was evaluated based on anatomical landmarks and deformation characteristics.

Results: Good anatomical correspondence and physical plausibility of the deformation fields were attained. The global mean landmark errors vary from 3.20 to 5.36 mm, strongly influenced by low resolved images in z-direction. Moderate volume changes are indicated by mean minimum and maximum Jacobian determinants of 0.44 up to 1.88. No deformation foldings were detected. The mean average divergence of the deformation fields range from 0.08 to 0.16 and the mean harmonic energies vary from 0.08 to 0.58.

Conclusion: The proposed registration solutions enable the combined use of information from multimodal imaging and provide an excellent basis for patient assessment and primary planning for liver radioembolization.

Citing Articles

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Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration.

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Lu Z, Chen G, Jiang H, Sun J, Lin K, Mok G Eur J Nucl Med Mol Imaging. 2023; 50(8):2319-2330.

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Enhancing the depth perception of DSA images with 2D-3D registration.

Zhang X, Deng Y, Tian C, Chen S, Wang Y, Zhang M Front Neurol. 2023; 14:1122021.

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