3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

Guang Li (National Institutes of Health, USA), Deborah Citrin (National Cancer Institute, USA), Robert W. Miller (National Cancer Institute, USA), Kevin Camphausen (National Cancer Institute, USA), Boris Mueller (Memorial Sloan-Kettering Cancer Center, USA), Borys Mychalczak (Memorial Sloan-Kettering Cancer Center, USA) and Yulin Song (Memorial Sloan-Kettering Cancer Center, USA)
DOI: 10.4018/978-1-60960-561-2.ch317
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Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.
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3D/4D Medical Imaging

A 3D medical image contains a sequence of parallel two-dimensional (2D) images representing anatomic or physiologic information in 3D space. The smallest element of a 3D image is a cubic volume called voxel. A 4D medical image contains a temporal series of 3D images. With a subsecond time resolution, it can be used for monitoring respiratory/cardiac motion (Keall, Mageras, Malter et al., 2006).

Patient motion is always expected: faster motion relative to imaging speed causes a blurring artifact; whereas slower motion may not affect image quality. A multislice CT scanner provides improved spatial and temporal resolution (Ueda et al., 2006), which can be employed for 4D imaging (Pan et al., 2004). Progresses in MRI imaging have also been reported, including parallel multichannel MRI (Bodurka, Ledden, van Gelderen et al., 2004).

Because PET resolution and speed are limited by the physics and biology behind the imaging technique, some motion suppression techniques have been developed clinically, including patient immobilization (Beyer, Tellmann, Nickel, & Pietrzyk, 2005), respiratory gating (Hehmeh, Erdi, Pan et al., 2004), and motion tracking (Montgomery, Thielemans, Mehta et al., 2006). Motion tracking data can be used to filter the imaging signals prior to PET image reconstruction for reliable motion correction. Motion blurring, if uncorrected, can reduce registration accuracy. Visual-based volumetric registration technique provides blurring correction (filtering) before registration, by defining the PET volume with reference to the CT volume, causing blurred PET surface voxels to be rendered invisible (Li, Xie, Ning et al., 2007).

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