Paper
5 May 2004 Tracking alignment of sparse ultrasound with preoperative images of the liver and an interventional plan using models of respiratory motion and deformation
Jane M. Blackall, Graeme P. Penney, Andrew P. King, Andreas N. Adam, David J. Hawkes
Author Affiliations +
Abstract
We present a method for non-rigid registration of preoperative magnetic resonance (MR) images and an interventional plan to sparse intraoperative ultrasound (US) of the liver. Our clinical motivation is to enable the accurate transfer of information from preoperative imaging modalities to intraoperative ultrasound to aid needle placement for thermal ablation of liver metastases. An inital rigid registration to intraoperative coordinates is obtained using a set of ultrasound images acquired at maximum exhalation. A pre-processing step is applied to both the MR and US images. The preoperative image and plan are then aligned to a single ultrasound slice acquired at an unknown point in the breathing cycle where the liver is likely to have moved and deformed relative to the preoperative image. Alignment is constrained using a patient-specific model of breathing motion and deformation. Target registration error is estimated by carrying out simulation experiments using sparsely re-sliced MR volumes in place of real ultrasound and comparing the registration results to a gold-standard registration performed on the full MR volume. Experiments using real ultrasound are then carried out and verified using visual inspection.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jane M. Blackall, Graeme P. Penney, Andrew P. King, Andreas N. Adam, and David J. Hawkes "Tracking alignment of sparse ultrasound with preoperative images of the liver and an interventional plan using models of respiratory motion and deformation", Proc. SPIE 5367, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, (5 May 2004); https://doi.org/10.1117/12.535180
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Image registration

Liver

Ultrasonography

Motion models

Magnetic resonance imaging

Data modeling

3D image processing

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