Anatomical Distortions

Many anatomical and biological factors can affect the PET and SPECT images. Some affect the shape of the organ/structural outline, some affect the relative distributions, and some affect both. These factors need to be taken into consideration when one tries to align two sets of images to examine the similarities/ differences/changes. They impose very challenging problems for image coregistration and distinguish medical image coregistration from registrations in other imaging areas. In the following, a few major factors are discussed.

3.1 Elastic Deformations

Since the human body is not a "rigid body," an image taken at one time point with one imaging device will not be identical to another image taken at a different time. Also, different imaging devices frequently require that the patient/subject be positioned differently to achieve the optimal imaging condition. For example, imaging of the thorax with X-ray computed tomography (CT) requires the patient's arms to be up to avoid their attenuation in optimizing the signal-to-noise level of the chest images. On the other hand, for PET and SPECT, the imaging time is longer and "arms down'' is the more comfortable patient position commonly used. With the patient's arms at two extreme positions, the shape of the

FIGURE 3 The effect of axial undersampling of a pair of perfectly registered image sets, where the missing data are estimated by interpolation. The top row shows images sampled 10 mm apart. The bottom row shows the registered data set generated from interpolation of slices measured at the midpoint between the images in the top row. In general there is a good agreement in appearance of the images; however, there are discrepancies in smaller structures such as the cortex and the basal ganglia.

cavity in the thorax is different, and the relative positions of the heart, lung, and chest wall are not the same. Even the shape of the lung, for example, is changed when one raises one's arms [38,39]. Figure 4 shows the different shapes and positions of the lung when imaged with "arms up'' in CT and with "arms down'' in PET. Therefore, one cannot align the CT thorax images with PET/SPECT images using rigid body transformation.

The thorax is not the only part of the body that easily deforms. Imaging of the abdomen and the head-and-neck area of the body is similar in this aspect. Relative positions of the organs or tissue structures and the shapes of the organ/tissue structures cannot be assumed to be the same at two different times when they are imaged with different imaging devices. Even for brain images, the extracerebral tissue could be compressed differently during different imaging sessions, although the skull can generally be considered as a rigid body. With possible changes in size/shape of tumors/trauma and in some cases after surgical operations on the brain/skull, the configuration of brain structures in many cases cannot be expected to remain the same over time.

Adding to the problem of nonrigid anatomy is the problem of undersampling, since most medical imaging modalities provide three-dimensional information with a series of two-dimensional images taken at consecutive cross-sectional planes. The plane separation sometimes is not small enough to capture, without distortion, the variation along the cross-plane direction. Also, this distortion due to undersampling is heavily dependent on the sampling position (see Section 2). Therefore, in general, even with the same imaging device and trying to keep the same patient position, it is difficult to reproduce exactly the same images. One thus definitely needs to take this into consideration when coregistering two sets of images from the same patient/subject and/or assessing their alignment accuracy.

3.2 Different Image Distributions

It is obviously clear that different imaging modalities provide different anatomical or biological information and give different kinds of images (e.g., see Fig. 4 for CT and PET images of the thorax). In other chapters of this Handbook

FIGURE 4 An example of elastic anatomical deformation. Top image is an X-ray CT image of a cross-section of the chest of a patient with his arms raised above his head during the imaging. Images in the middle row are PET chest images of the same subject with his arms resting on the sides of his chest. The image on the left is a transmission PET image showing the attenuation coefficient of tissues; the image on the right is an emission PET image of FDG uptake in tissues. With different arm positions (between the CT image and the PET images), the shape of the chest and lung are seen to be quite different. After the PET images are elastically mapped (see Section 4.2 and the paper of Tai etal. [39] for more details) to the CT image, the results are shown in the bottom row to better match the configuration of the CT image. (Figure is taken from Tai et al. [39]. © 1997 IEEE.)

FIGURE 4 An example of elastic anatomical deformation. Top image is an X-ray CT image of a cross-section of the chest of a patient with his arms raised above his head during the imaging. Images in the middle row are PET chest images of the same subject with his arms resting on the sides of his chest. The image on the left is a transmission PET image showing the attenuation coefficient of tissues; the image on the right is an emission PET image of FDG uptake in tissues. With different arm positions (between the CT image and the PET images), the shape of the chest and lung are seen to be quite different. After the PET images are elastically mapped (see Section 4.2 and the paper of Tai etal. [39] for more details) to the CT image, the results are shown in the bottom row to better match the configuration of the CT image. (Figure is taken from Tai et al. [39]. © 1997 IEEE.)

(those by Pennec et al., by Van Herk, and by Hill and Hawkes), the need to register images of different distributions has been addressed extensively, and many approaches and methods have been developed to solve the problem [1,9,25-27,42]. Most approaches deal with coregistering images of different modalities (e.g., CT, MRI, PET, SPECT). However, even with the same imaging modality, the imaging conditions (e.g., the KVp and beam filtering of X-ray CT, the pulse sequence of MRI, and the tracers of PET/SPECT) could determine dominantly the relative brightness and appearance of different tissues/structures on the images. Figure 5 illustrates this phenomenon. Both images in the figure are PET images of about the same cross-section of the brain, but one was obtained using FDG (an analogue of glucose) as the tracer and the other one was

FIGURE 5 Illustration of different image distributions of the same imaging modality. Both are PET images through a mid-section of the brain. Although the images are from two different subjects, the image distributions are clearly different when two different PET tracers are used. The image at left used FDG, a glucose analogue, and reflected glucose metabolic rate in tissue (with higher rates in lighter shades). The image at right used FDOPA, an analog of l-DOPA, and indicated dopaminergic function in tissue. The regions of high uptake of FDOPA (brighter areas in the middle of the right image) clearly delineate the striatum, a brain structure known to be rich in dopaminergic neurons.

obtained using FDOPA (an analogue of L-DOPA). Since different tracers have different biochemical properties, they trace different biological processes. FDG indicates the glucose utilization of brain tissues [19,34] (higher utilization rate in gray matter than in white matter) and FDOPA reflects the functional state of the dopaminergic neuronal terminals (mostly in the striatum of the brain) [4,18]. If one wants to examine the correlation between glucose utilization rate and dopaminergic terminal function in the striatum, one needs to coregister these two sets of images of markedly different distributions. This illustrates another challenge of medical image coregistration even when there is minimal anatomical deformation.

A similar problem occurs when one needs to correct the positional shifts of the images of different time frames that are due to patient movement during the course of a dynamic study [46]. Image distributions in the early and late frames could be quite different, because usually the early frame images reflect the tracer delivery and transport and the late images represent tracer uptake that is related to the biochemical process specific to the tracer. The two processes could be quite different. For example, in the case of FDOPA, the early frame images have a relative distribution quite similar to those of FDG, except with a much higher noise level. Therefore, using image coregistra-tion to correct for possible positional shifts between the early and late frames of a FDOPA dynamic study would involve the same kind of problem as coregistering FDG to FDOPA images. An added problem in this case is the high noise level of early frame images, which usually correspond to short frames (10 to

30 sec as compared to 5 to lOmin of late frames) to catch the fast kinetic changes shortly after the tracer is administered.

3.3 Motion

Heartbeat and breathing are among the involuntary motions in our bodies that are difficult to suppress during imaging. For the cardiac motion due to the rhythmic beating of the heart, a general method to alleviate the problem is to synchronize the image data collection with the heartbeat. With this mode of image acquisition, the acquired imaging data is gated by the electrocardiograph (EKG), which is usually divided into 8 or 16 phases or gates per cycle. The acquired data corresponding to the same gate are then pooled together to reduce the noise level. The acquired image for each gate would then appear to have fixed the heart in that phase of the heartbeat. With images of multiple gates through the cardiac cycle displayed in sequence, the beating motion of the heart can be visualized.

For respiration motion, a similar strategy can be adopted in principle, except that respiration motion is not usually as regular as cardiac motion. Also, the cardiac and the respiration motions are not synchronized, so the combined motion in the thorax is not periodic and cannot be easily "gated." Although it is also possible to hold one's breath for a short time, the imaging time of most medical imaging techniques is too long for one to do it comfortably, especially considering the frail condition of some patients. These motions could thus cause image artifacts, loss of spatial resolution, and distortion of shape or image level of body structures on the resulting images.

3.4 Movement

In addition to the involuntary movements mentioned in Section 3.3, voluntary movements occur frequently during an imaging session. Especially for long imaging sessions of more than lO minutes, it is difficult for anyone to be completely motionless and to stay in the same position over such a long period of time. Most of these movements (e.g., moving arms relative to the body) cannot be accounted for by a simple rigid transformation, not to mention the image artifacts and blurring that could be caused by the movements.

3.5 Intersubject Variability

One of the unique characteristics of humans is the large difference and variability in size, height, shape, and appearance between individuals. However, in many cases (e.g., for detection of abnormality), it is desirable to compare the images of one subject with those of another to see if the size/ shape/intensity of certain substructures is affected. A common way in medical practice is to normalize a measurement (e.g., the size of liver) with respect to the weight or surface area of the subject [20,47]. Age and gender could also be taken into consideration in this normalization procedure. This method is very useful for the diagnosis of many diseases. However, variations in relative image intensities (e.g., radiotracer distributions/patterns) that might be more sensitive to abnormal or early changes are more difficult to evaluate by such simple normalization procedures. Therefore, if one wants to align images from different subjects to examine their differences in distribution/pattern, one needs to address the problem of intersubject variability.

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