Despite its high significance, the clinical utilization of image registration remains limited because of its lengthy
execution time and a lack of easy access. The focus of this work was twofold. First, we accelerated our course-to-fine,
volume subdivision-based image registration algorithm by a novel parallel implementation that maintains the accuracy of
our uniprocessor implementation. Second, we developed a thin-client computing model with a user-friendly interface to
perform rigid and nonrigid image registration. Our novel parallel computing model uses the message passing interface
model on a 32-core cluster. The results show that, compared with the uniprocessor implementation, the parallel
implementation of our image registration algorithm is approximately 5 times faster for rigid image registration and
approximately 9 times faster for nonrigid registration for the images used. To test the viability of such systems for
clinical use, we developed a thin client in the form of a plug-in in OsiriX, a well-known open source PACS workstation
and DICOM viewer, and used it for two applications. The first application registered the baseline and follow-up MR brain images, whose subtraction was used to track progression of multiple sclerosis. The second application registered pretreatment PET and intratreatment CT of radiofrequency ablation patients to demonstrate a new capability of multimodality imaging guidance. The registration acceleration coupled with the remote implementation using a thin client should ultimately increase accuracy, speed, and access of image registration-based interpretations in a number of diagnostic and interventional applications.
Radiofrequency ablation (RFA) is emerging as the primary mode of treatment of unresectable malignant liver tumors.
With current intraoperative imaging modalities, quick, precise, and complete localization of lesions remains a challenge
for liver RFA. Fusion of intraoperative CT and preoperative PET images, which relies on PET and CT registration, can
produce a new image with complementary metabolic and anatomic data and thus greatly improve the targeting accuracy.
Unlike neurological images, alignment of abdominal images by combined PET/CT scanner is prone to errors as a result
of large nonrigid misalignment in abdominal images. Our use of a normalized mutual information-based 3D nonrigid
registration technique has proven powerful for whole-body PET and CT registration. We demonstrate here that this
technique is capable of acceptable abdominal PET and CT registration as well. In five clinical cases, both qualitative and
quantitative validation showed that the registration is robust and accurate. Quantitative accuracy was evaluated by
comparison between the result from the algorithm and clinical experts. The accuracy of registration is much less than the
allowable margin in liver RFA. Study findings show the technique's potential to enable the augmentation of
intraoperative CT with preoperative PET to reduce procedure time, avoid repeating procedures, provide clinicians with
complementary functional/anatomic maps, avoid omitting dispersed small lesions, and improve the accuracy of tumor
targeting in liver RFA.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.