Mr. Peng Lei Profile

Peng Lei

Research Assistant at Univ of Maryland Medical Ctr

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Proceedings Article | 12 March 2010 Paper

Parallel image registration with a thin client interface

Ganesh Saiprasad, Yi-Jung Lo, William Plishker, Peng Lei, Tabassum Ahmad, Raj Shekhar

Proceedings Volume 7628, 76280V (2010) https://doi.org/10.1117/12.844457

KEYWORDS: Image registration, Computed tomography, Rigid registration, Image restoration, Positron emission tomography, Magnetic resonance imaging, Image processing, Image fusion, Tumors, Neuroimaging

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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.

Proceedings Article | 21 March 2007 Paper

PET guidance for liver radiofrequency ablation: an evaluation

Peng Lei, Omkar Dandekar, Faaiza Mahmoud, David Widlus, Patrick Malloy, Raj Shekhar

Proceedings Volume 6509, 650918 (2007) https://doi.org/10.1117/12.709683

KEYWORDS: Image registration, Positron emission tomography, Computed tomography, Liver, Radiofrequency ablation, Image fusion, Tumors, Scanners, Algorithm development, Tissues

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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.

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