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Mutual information based registration for ultrasound and CT datasets

 
: Firle, E.; Wesarg, S.; Dold, C.

:

Fitzpatrick, J.M. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical imaging 2004: Image processing. Vol.2 : 16 - 19 February 2004, San Diego, California, USA. Papers presented at the Image Processing Conference of the 2004 SPIE Medical Imaging Symposium
Bellingham/Wash.: SPIE, 2004 (SPIE Proceedings Series 5370)
ISBN: 0-8194-5283-1
pp.1130-1138
Image Processing Conference <2004, San Diego/Calif.>
Medical Imaging Symposium <2004, San Diego/Calif.>
English
Conference Paper
Fraunhofer IGD ()
registration; computed tomography; 3D Ultrasound; Brachytherapy

Abstract
In many applications for minimal invasive surgery the acquisition of intra-operative medical images is helpful if not absolutely necessary. Especially for Brachytherapy imaging is critically important to the safe delivery of the therapy. Modern computed tomography (CT) and magnetic resonance (MR) scanners allow minimal invasive procedures to be performed under direct imaging guidance. However, conventional scanners do not have realtime imaging capability and are expensive technologies requiring a special facility. Ultrasound (U/S) is a much cheaper and one of the most .exible imaging modalities. It can be moved to the application room as required and the physician sees what is happening as it occurs. Nevertheless it may be easier to interpret these 3D intra-operative U/S images if they are used in combination with less noisier preoperative data such as CT. The purpose of our current investigation is to develop a registration tool for automatically combining pre-operative CT volumes with intra-operatively acquired 3D U/S datasets. The applied alignment procedure is based on the information theoretic approach of maximizing the mutual information of two arbitrary datasets from di.erent modalities. Since the CT datasets include a much bigger .eld of view we introduced a bounding box to narrow down the region of interest within the CT dataset. We conducted a phantom experiment using a CIRS Model 53 U/S Prostate Training Phantom to evaluate the feasibility and accuracy of the proposed method.

: http://publica.fraunhofer.de/documents/N-22060.html