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Application of PET-MRI registration techniques to cat brain imaging
Journal of Neuroscience Methods 101 (2000) 1 – 7 www.elsevier.com/locate/jneumeth
Application of PET-MRI registration techniques to cat brain imaging Yubei Shimadaa,e,*, Koji Uemurab, Babak A. Ardekanic, Tsukasa Nagaokad, Kiichi Ishiwataa, Hinako Toyamaa, Kenichirou Onoe, Michio Sendaa a
Positron Medical Center, Tokyo Metropolitan Institute of Gerontology, 1 -1 Nakachou, Itabashiku 173, Japan b School of Science and Engineering, Waseda Uni6ersity, Waseda, Japan c Research Institute for Brain and Blood Vessels, Akita, Japan d Department of Neurosurgery, Tokyo Medical and Dental Uni6ersity, Tokyo, Japan e Department of Veterinary Clinical Pathobiology, Uni6ersity of Tokyo, Tokyo, Japan Received 7 September 1999; received in revised form 1 March 2000; accepted 3 March 2000
Abstract In positron emission tomography (PET) studies of diseased animals, it is very useful to have accurate anatomical information as a reference. In human studies, anatomical information is usually obtained from magnetic resonance imaging (MRI) of the subject with retrospective registration of the subject’s PET image to the MRI. A number of PET-MRI registration techniques are used for this purpose. However, the utility of these methods has not been tested for animals image registration. This paper studies the feasibility of applying two currently used human brain PET-MRI registration techniques to cat brain images. Methods: Three 11 cats were anesthetized with isoflurane gas, and PET images were acquired with H15 C2 O, benzodiazepine receptor ligand 11 18 flumazemil (FMZ), dopamine receptor ligand C-nemonapride (NEM) and fluorodeoxy glucose ( F-FDG). The four PET scans were acquired consecutively within the same day while the cat remained fixed in the scanner. We also obtained T1-weighted and T2-weighted MRI of the cats in a 4.7 T unit. The PET images were registered to MRI using two human brain registration techniques: a semi-automatic method (SAM), which is a two-step method based on the extraction of the midsagittal plane, and an automatic method (AMIR) method that minimizes PET pixel variance within spatially connected segments determined by MRI. Results: T2-weighted MRI provided better structural information than T1 MRI. FMZ did, while FDG or H2O PET images did not, provide a structural outline of the brain. The FMZ PET image was registered to MRI satisfactorily using SAM. The striatum visualized in nemonapride PET image re-sliced with the same parameters matched the striatum identified in T2-weighted MRI. Registration by AMIR was successful by inspection for FMZ, FDG or H2O PET images in only one of the three cats. The registration error of SAM was estimated to be less than 2 mm or 2°. Conclusion: A satisfactory registration of FMZ-PET to T2-weighted MRI of the cat brain was obtained by a two-step manual registration technique. This will enhance the usefulness of PET in the field of cerebral pathophysiology. © 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Positron emission tomography; Magnetic resonance imaging; Cat brain imaging
1. Introduction Positron emission tomography (PET) is a useful imaging technique that provides information regarding brain function in laboratory animals as well as in human beings. In vivo studies on ischemia, oncology, pharmacology and physiology have been conducted in baboons (Sette et al., 1993), resus monkeys (Takechi et al., 1994), dogs (Brennan et al., 1993), cats (Moufarrij * Corresponding author.
et al., 1984; Komatsumoto et al., 1989; Heiss et al., 1994; Vexler et al., 1994; Komatsumoto et al., 1995; MacKay et al., 1996) and rats (Tamura et al., 1981; Brownell et al., 1991; Ridenour et al., 1992). Particularly, PET has the advantage of being able to follow the time course or measure a response to perturbations on the same individual. However, because of the smaller brain size, PET imaging of animal brain suffers from high image noise and poor spatial resolution, which often preclude identification of brain structures on PET images alone. The ability to image brain function is
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substantially enhanced if functional images presented by PET are contrasted with underlying anatomical structures visible on MRI or X-ray CT. Registration of brain PET to MRI with image superimposition has proven to be useful in human studies (Dann et al., 1989; Schifter et al., 1993; Senda, 1994; Ardekani et al., 1995). Nevertheless, such 3D registration techniques have not been reported for animal studies. This is partly because animal MRI is less available to researchers than human MM, and partly because stained tissue sections can provide far better resolution and contrast than in vivo morphological images such as MRI and CT. In some cases, anatomical information can be provided by a brain atlas of the animal without individual morphological images (Sakiyama et al., 1997). Recently PET has been applied to pathophysiological studies on middle cerebral artery (MCA) occlusion in cats and baboons (Pappata et al., 1993; Heiss et al., 1994). In an animal model like this, the pathologic area varies from subject to subject and should be defined individually. Tissue sections provide valuable information, but it is often difficult to match the section to the PET slices. The limitations of anatomical atlases and stained tissue sections have prompted us to study PET-MRI registration in animals. In the present study, we have applied the PET-MRI registration techniques that are used in human studies to cat brain imaging in order to evaluate their validity and feasibility. Since cats are readily available, easy to handle and not too small for imaging, they are often used as a model animal on MCA occlusion and other disturbances (Bianki and Abduakhadov, 1975; Hayman et al., 1989; Hossmann et al., 1989; Yoshida and Marmarou, 1991). We examined two human brain registration techniques: the semi-automatic method (SAM) of Senda (1994); and the automatic multimodality image registration (AMIR) method developed by Ardekani et al. (1995), and compared the registration parameters obtained by each method in addition to evaluation by visual inspection. We investi18 gated PET images acquired with H15 F-FDG and 2 O, 11 C-flumazenll (FMZ) to find out which radioactive tracer would give a better result. We also used a PET image acquired in the same position with 11C-nemonapride (NEM) that accumulates in the striatum in order to check the validity of registration methods.
2. Materials and methods
isoflurane (1.5–2.0%). The femoral artery and vein were catheterized. The anesthetized cat was positioned prone into a stereotaxic head holder, which was designed similar to a conventional holder for physiological experiments but was made of polymethyl methacrylate in order to minimize photon attenuation (Narishige, Tokyo, Japan). Ear bars were placed in the external auditory meatus and the upper teeth were mounted on a teeth bite. The head position of the cat was kept stable within this head holder throughout the series of scans. All experimental procedures were approved by the Animal Care and Use Committee of the Tokyo Metropolitan Institute of Gerontology.
2.2. PET scanning An SHR-2000 PET camera for animal scanning (Hamamatsu Photonics, Hamamatsu, Japan) was used in the Z-motion mode, which provided 14 image slices with an interslice distance of 3.25 mm, an in-plane spatial resolution of 4.0 min full width at half maximum (FWHM), and an axial resolution of 5.0 mm FWHM (Watanabe et al., 1992). The cat was positioned prone in order to obtain coronal sections, and the slice position was determined by an H15 2 O test scan so that the entire brain was covered in the axial field of view (4.2 cm). The cat underwent a measurement of blood flow with H15 2 O, benzodiazepine receptor with 11 C-flunazenil (FMZ), dopamine D2 receptor with 11Cnemonapride (NEM), and glucose metabolism with 18FFDG, all at the same slice positions. The photon attenuation was corrected using transmission data obtained with a rotating source. The tomographic images were reconstructed using a Butterworth filter with a cut off frequency of 144 cycle/cm. The blood flow measurement was performed by an intravenous injection of 200 MBq of H15 2 O and a PET scan for 3 min started at the time of injection. The glucose metabolism was imaged by injection of 100 MBq of 18F-FDG, and a PET image was acquired from 30 to 60 min post injection. The benzodiazepine receptor was imaged by injecting 200 MBq of 11C-flumazenil and a PET image was acquired from 10 to 30 min post injection. Dopamine (D2, D4) receptor distribution was imaged by injecting 200 MBq of 11C-nemonapride, and a PET image was acquired from 10 to 30 min post injection. The analyses were carried out on UNIX workstations (Silicon Graphics Inc., Mountain View, CA, USA) using the Dr. View image analysis software system (Asahi Kasei Joho System, Tokyo, Japan).
2.1. Animal preparation for PET 2.3. MRI scanning Three adult mongrel cats weighing from 3 to 4 kg were used. The cat was intubated following injection of thiopental sodium (15 mg/kg i.v.) and atropine (0.05 mg/kg s.c.). The anesthesia was maintained with
An MRI study was performed under isoflurane inhalation anesthesia within 2 weeks prior to the PET study. MRI scans were carried out on a 4.7-Tesla
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experimental imager/spectrometer system (Unity plus SIS 200/330, Varian, Palo Alto, CA, USA) equipped with a 33-cm horizontal bore magnet (Oxford Instruments, UK) with a gradient strength of 50 mT/m. For RF transmission and detection, a 16-cm diameter homogeneous quadrature coil (Varian, Palo Alto, CA, USA) was used. Automatic shimming was performed to improve magnetic field homogeneity before data collection. A total of 21 slices of T1- and T2-weighted images were obtained perpendicular to the base of the brain. The excitation and acquisition pulse sequence was TR/ TE= 600/20 ms for T1-weighted spin-echo (SE) images and TR/TE =2000/80 ms with two signal acquisitions for T2-weighted SE images. The slice thickness was 3 mm and the image matrix was 256×256, zero-filled to 512× 512 over a field of view of 10× 10 cm.
2.4. Image registration by SAM The PET-MRI semi-automatic registration method (SAM) by Senda (1994), which has been used in human studies was applied to the cat brain images. This method utilizes the fact that the midsagittal plane is a near flat plane and is easily identifiable in both PET and MRI. First, the midsagittal plane is determined from the PET and MRI independently as the regression plane of midsagittal points selected manually on the original images. Then, the original PET and MR I images are resliced at and 910 mm apart and parallel to the midsagittal plane. If the mid-sagittal plane is a flat plane and is determined accurately, the sagittally resliced PET images can be matched to the MRI of the corresponding slice through 2D shift and rotation. The
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operator manually determines the three (x-shift, y-shift and rotation) parameters by trial and error. To match the original PET to the original MRI, the 3D shift and rotation parameters are computed as a matrix product of the two transformations: mid-sagittal cut and 2D shift/rotation. This algorithm reduces the 3D matching problem to a far easier 2D matching problem. It is not fully automatic and requires manual interaction, but it does allow confirmation and adjustment by the operator. The procedure was conducted using Dr. View and AVS (Stardent Software) running on UNIX workstations. Unlike the human brain, the original cat brain PET and MRI were in the coronal orientation. Otherwise the program worked in exactly the same way as in the case of human brain. We examined the feasibility of registering each of the three types of PET images (H2O FDG and FMZ) to the MRI using SAM. Because the H2O and FDG images had high activity both outside and deep inside the brain, we found it difficult to identify on those images the structures that are necessary for the 2D matching of the sagittal images. On the other hand the FMZ image had a good contrast between the cerebral cortex and other parts within or outside the brain. Therefore we used the FMZ images in SAM. Similarly, because T2-weighted MRI presented higher contrasts between various structures as compared to T1-weighted MRI, we used the T2weighted image for registration.
2.5. Image registration by AMIR Registration between T2-weighted MRI and each of the three types of PET images (H2O, FDG and FNIZ)
Fig. 1. Coronal PET images of cat c 3 acquired with H15 2 O, FDG and FMZ registered to and superimposed on T2-weighted MRI of the same cat. The FMZ images were registered to MRI using SAM and the same transformation was applied to the H15 2 O and FDG images acquired in the same position. Note the poor indentifiability of anatomical configuration by H15 2 O or FDG.
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was performed using AMIR developed by Ardekani et al. (1995). This method receives an input of six initial parameters (three translations and three rotations) and automatically computes the final six registration parameters iteratively by minimizing the variance of PET pixel counts within spatially connected 3D segments that are determined by a K-means clustering algorithm applied to the MRI. The slices covering the brain visualized in PET were loaded for calculation. First we used all zeros as initial parameters, which usually works for human data. However, the algorithm
converged to obviously incorrect local minima in all cases. In a second run, we inputted as initial parameters estimates of X and Y translations by inspection as well as a rough estimate of the pitch angle derived from the sagittal view. The results were compared to those obtain by SAM both by visual inspection and by examining the transformation parameters. The difference in rotation was evaluated by the angle subtended by the rotated axis of the two methods, which were calculated from the scalar product of the corresponding column vectors of the transformation matrix for the two methods.
Fig. 2. Horizontal and sagittal sections of H15 2 O, FDG and FMZ PET images of cat c 3 superimposed on T2-weighted MRI obtained by reslicing the images shown in Fig. 1.
Fig. 3. The 11C-nemonapride (NEM) PET image, acquired in the same position as FMZ and resliced with the FMZ-to-MRI registration matrix, is superimposed in the MRI. Note the striatum visualized by in NEM PET matches the striatum identified in T2-weighted MRI.
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18 Fig. 4. Horizontal MRI and PET images with H15 F-FDG and 2 O, superimposition. Note the misregistration of midsagittal line in FMZ.
3. Results Figs. 1 and 2 illustrate the result of PET-MRI registration by SAM. The registration procedure was accomplished successfully using FMZ PET and T2-weighted MRI. The H15 2 O and FDG images of the same subject acquired in the same position were registered to the MRI using the parameters obtained from the FMZ registration process. In general a good registration was observed in the whole brain by inspection in the horizontal and sagittal registered images as well as in the coronal images. Although H15 2 O and FDG images lack sufficient structural information by themselves, superimposition of H15 2 O and FDG upon MRI revealed the relationship between structure and radioactivity distribution consistent with the previously reported findings. The H15 2 O image revealed higher blood flow in the cerebellum than in the cerebral cortex whereas 18F-FDG image showed equally high radioactivity in the cortex, striatum and cerebellum. The FMZ image showed high radioactivity in the cerebral cortex, but not in the cerebellum or striatum. Fig. 3 shows a validation using 11C-NEM, which accumulates in the striatum. The NEM images, which had been acquired in the same position as the FMZ image, were resliced using the transformation matrix obtained in the FMZMRI registration process. The striatum visualized in the registered NEM PET image matched the striatum identified in the MRI. The AMIR method was used independently on H15 2 O, FDG and FMZ images by inputting a rough estimate of pitch angle as well as X and Y translations as initial values of the parameters. By inspection of the FNIZ images superimposed on T2-weighted MRI, one of the three cats (c1) presented
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C-FMW of cat c3 independently registered to MRI by AMIR with
a satisfactory result. However, apparent misregistration was observed in the other two cats (c 2 and c 3). Fig. 4 shows the result of registration of FMZ, FDG and H15 2 O images to the T2-weighted MRI conducted independently using AMIR in cat c 3 that showed a poor outcome. An evident discrepancy was observed in the midsagittal line between FMZ and MRI in the horizontal section. It should be noted, however, that the FDG and H15 2 O images that were not usable for SAM were processed by AMIR. Table 1 shows the angle gap in the reslicing axis between the registration of FMZ SAM and that of FDG, H15 2 O and FMZ by AMIR. In cat c1, that gave satisfactory results by inspection, the angular disTable 1 Discrepancy in rotation in the cat PET-MRI registration expressed by the angle (°) between the reslicing axis obtained with FMZ by Senda method and that with FDG, H2O and FMZ by Ardekani methoda FDG
H2O
FMZ
Cat c 1 X Y Z
1.9 2.1 1.9
2.0 2.6 2.4
1.9 2.6 2.3
Cat c 2 X Y Z
4.2 2.7 4.4
4.2 2.5 4.2
4.4 2.7 4.5
Cat c 3 X Y Z
6.9 2.3 6.6
8.0 3.4 7.7
7.5 3.2 7.2
a X, Y, Z are defined as row, column and axis in the coronal section.
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crepancy from the results obtained by SAM was 1.9– 2.6°. The translation discrepancy was B 1 mm in x and y and 1–2 mm in z (detailed data not shown). However, in cat c2 and c3, the angular gap was larger, reflecting the misregistration observed in Fig. 4. The angular gap was relatively small among different tracer images of the same cat.
4. Discussion The present study attempted an intrasubject PETMRI registration for the cat brain by applying two methods currently used for human brain image processing. Unlike the human brain MRI acquired in a 1.5 T unit, T2-weighted MRI resolved the brain contour and intracerebral structures much better than T1-weighted MRI. This is because the T1 relaxation time is shortened due to a high magnetic field of the 4.7-T unit used in the present study, resulting in poor delineation of brain surface and intracerebral structures. On the other hand, MR signals are influenced by tissue iron in a high magnetic field, which caused clear delineation of the striatum in T2 images. Therefore the registration was performed in T2-weighted M RI instead of T1-weighted MRI. We studied two methods for PET-MRI registration: SAM and AMIR. SAM is semi-automatic and totally depends on the operator and his/her experience and interpretation of the images. As long as structures were visualized, the operator can reach a satisfactory result by visual inspection. On the other hand, AMIR is essentially an automatic process. The result may not be satisfactory by visual inspection. However, it outputs a result even from the images in which the anatomical configuration cannot be grasped and therefore the performance of the registration cannot be evaluated by visual inspection. Because the true answer is not available for the registration accuracy, we compared the reslicing axes expressed by column vectors of the transformation matrix between the two methods to evaluate the performance of registration indirectly. We also tested the result using NEM PET, which mainly accumulates in the striatum. In the PET-MRI registration by SAM, which utilized a two-step manual matching, FMZ image was successfully registered to T2-weighted MRI. Among the three PET tracers, FMZ PET was the best for manual registration because it provided a clearer brain configuration and an easier identification of the midsagittal line as 18 compared to H15 F-FDG. In CBF and FDG 2 O or images, which provide clear visualization of cerebral structures in human images, extracranial activity such as muscles and glands was strong and the cerebral cortex was not separated clearly from thalamus or striatum in the cat due to smaller white matter area. When those 18 other images (H15 F-FDG and 11C-NEM image) 2 O,
were resliced with the parameters obtained by the 11CFMZ PET, they matched the MRI (Figs. 1 and 2). Especially the striatum visualized in the 11C-NEM PET image matched the striatum identified in T2-weighted MRI (Fig. 3). This result suggested that an accurate registration in the cat can be performed with SAM using FMZ-PET and T2-weighted MRI. The registration technique AMIR gave results by inputting suitable initial parameters. The results of Fig. 4 and Table 1 suggest that it performs a good registration 18 with the images of H15 F-FDG as well as 11C-flumaz2 O, ertil in some cases but not in others. The discrepancy between the two methods in cat c 1 was 2–3° in rotations and 1–2 mm in z-shift. This suggests that the registration error in SAM, which totally depends on the ability of visual inspection, is approximately less than 2 mm or 2°. This is consistent with the report about visual inspection on human brain images (Ardekani et al., 1995; Endo et al., 1997). In human PET-MRI registration the choice between SAM and AMIR is not a question of which is better, but SAM makes up for AMIR. In human brain, we usually use AMIR first because it is easier, and it usually works. If it does not work, then we use SAM, and it always works because cerebral structures are sufficiently visualized in human FDG or CBF-PET. The present study has revealed that FMZ can, while CBF or FDG cannot, visualize cerebral structures in the cat brain. Therefore we recommend acquiring FMZ of the same position in a PET study whenever PET-MRI registration is necessary afterward. PET is able to evaluate brain functions regarding hemodynamics, pharmacology, and metabolism using various radiopharmaceuticals repeatedly on the same subject. On the other hand it has a disadvantage of obscure anatomical orientation. A number of methods have been proposed to provide the PET images of cats with anatomical information. Heiss et al. obtained the anatomical information by comparing PET images with brain tissue slices (Heiss et al., 1994). This approach is not suitable for a long follow up PET study because the tissue slices are obtained only at the end of the series of experiments and not during the follow up phase. Moreover, it is difficult to match the tissue slice accurately to the PET slice position due to distortion introduced during tissue preparation as well as lack of 3D information in the tissue slices. Sakiyama matched PET images to a cat brain atlas (Sakiyama et al., 1997). This approach worked to some extent for normal animals in spite of individual morphological variation. However, the atlas is useless when it comes to a correlational study between function and morphology on pathological regions of diseased animals. MRI is an excellent tool for such purposes and new scanners are becoming more available with better resolution. Some investigators (Anderson et al., 1994; Ogawa et al., 1994) have used devices such as specially designed head holders or masks to
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ensure identical positioning between PET and MRI, although the reproducibility has not been validated yet and the devices may impose a limitation to the experimental procedures on the animal. The accuracy of correlation between PET and morphology has not been a major topic because of limited spatial resolution of animal scanners. However, new animal PET scanners have been designed with much better resolution (Heiss et al., 1995; Cherry et al., 1997). The registration techniques will enhanced the usefulness of PET study on the cat brain. In conclusion, the semi-automatic method SAM provided satisfactory registration in all three cats. However, the draw back of this method was that it could 18 not align H15 F-FDG PET images and required 2 O or the FMZ PET images, which shows better outlines of anatomical brain structures. On the other hand, AMIR 18 registered all three H15 F-FDG and FMZ PET 2 O, images but was only successful in one of three cases. There is still a need for development of reliable and accurate PET-MRI registration for animal studies. Two lines of research must be taken in the future. (1) The feasibility of applying other currently used human brain PET-MRI registration techniques to animal images must be studied further. (2) Novel techniques must be developed specifically designed for animal image registration. The present study was a step towards the former.
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Application of PET-MRI registration techniques to cat brain imaging Yubei Shimadaa,e,*, Koji Uemurab, Babak A. Ardekanic, Tsukasa Nagaokad, Kiichi Ishiwataa, Hinako Toyamaa, Kenichirou Onoe, Michio Sendaa a
Positron Medical Center, Tokyo Metropolitan Institute of Gerontology, 1 -1 Nakachou, Itabashiku 173, Japan b School of Science and Engineering, Waseda Uni6ersity, Waseda, Japan c Research Institute for Brain and Blood Vessels, Akita, Japan d Department of Neurosurgery, Tokyo Medical and Dental Uni6ersity, Tokyo, Japan e Department of Veterinary Clinical Pathobiology, Uni6ersity of Tokyo, Tokyo, Japan Received 7 September 1999; received in revised form 1 March 2000; accepted 3 March 2000
Abstract In positron emission tomography (PET) studies of diseased animals, it is very useful to have accurate anatomical information as a reference. In human studies, anatomical information is usually obtained from magnetic resonance imaging (MRI) of the subject with retrospective registration of the subject’s PET image to the MRI. A number of PET-MRI registration techniques are used for this purpose. However, the utility of these methods has not been tested for animals image registration. This paper studies the feasibility of applying two currently used human brain PET-MRI registration techniques to cat brain images. Methods: Three 11 cats were anesthetized with isoflurane gas, and PET images were acquired with H15 C2 O, benzodiazepine receptor ligand 11 18 flumazemil (FMZ), dopamine receptor ligand C-nemonapride (NEM) and fluorodeoxy glucose ( F-FDG). The four PET scans were acquired consecutively within the same day while the cat remained fixed in the scanner. We also obtained T1-weighted and T2-weighted MRI of the cats in a 4.7 T unit. The PET images were registered to MRI using two human brain registration techniques: a semi-automatic method (SAM), which is a two-step method based on the extraction of the midsagittal plane, and an automatic method (AMIR) method that minimizes PET pixel variance within spatially connected segments determined by MRI. Results: T2-weighted MRI provided better structural information than T1 MRI. FMZ did, while FDG or H2O PET images did not, provide a structural outline of the brain. The FMZ PET image was registered to MRI satisfactorily using SAM. The striatum visualized in nemonapride PET image re-sliced with the same parameters matched the striatum identified in T2-weighted MRI. Registration by AMIR was successful by inspection for FMZ, FDG or H2O PET images in only one of the three cats. The registration error of SAM was estimated to be less than 2 mm or 2°. Conclusion: A satisfactory registration of FMZ-PET to T2-weighted MRI of the cat brain was obtained by a two-step manual registration technique. This will enhance the usefulness of PET in the field of cerebral pathophysiology. © 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Positron emission tomography; Magnetic resonance imaging; Cat brain imaging
1. Introduction Positron emission tomography (PET) is a useful imaging technique that provides information regarding brain function in laboratory animals as well as in human beings. In vivo studies on ischemia, oncology, pharmacology and physiology have been conducted in baboons (Sette et al., 1993), resus monkeys (Takechi et al., 1994), dogs (Brennan et al., 1993), cats (Moufarrij * Corresponding author.
et al., 1984; Komatsumoto et al., 1989; Heiss et al., 1994; Vexler et al., 1994; Komatsumoto et al., 1995; MacKay et al., 1996) and rats (Tamura et al., 1981; Brownell et al., 1991; Ridenour et al., 1992). Particularly, PET has the advantage of being able to follow the time course or measure a response to perturbations on the same individual. However, because of the smaller brain size, PET imaging of animal brain suffers from high image noise and poor spatial resolution, which often preclude identification of brain structures on PET images alone. The ability to image brain function is
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substantially enhanced if functional images presented by PET are contrasted with underlying anatomical structures visible on MRI or X-ray CT. Registration of brain PET to MRI with image superimposition has proven to be useful in human studies (Dann et al., 1989; Schifter et al., 1993; Senda, 1994; Ardekani et al., 1995). Nevertheless, such 3D registration techniques have not been reported for animal studies. This is partly because animal MRI is less available to researchers than human MM, and partly because stained tissue sections can provide far better resolution and contrast than in vivo morphological images such as MRI and CT. In some cases, anatomical information can be provided by a brain atlas of the animal without individual morphological images (Sakiyama et al., 1997). Recently PET has been applied to pathophysiological studies on middle cerebral artery (MCA) occlusion in cats and baboons (Pappata et al., 1993; Heiss et al., 1994). In an animal model like this, the pathologic area varies from subject to subject and should be defined individually. Tissue sections provide valuable information, but it is often difficult to match the section to the PET slices. The limitations of anatomical atlases and stained tissue sections have prompted us to study PET-MRI registration in animals. In the present study, we have applied the PET-MRI registration techniques that are used in human studies to cat brain imaging in order to evaluate their validity and feasibility. Since cats are readily available, easy to handle and not too small for imaging, they are often used as a model animal on MCA occlusion and other disturbances (Bianki and Abduakhadov, 1975; Hayman et al., 1989; Hossmann et al., 1989; Yoshida and Marmarou, 1991). We examined two human brain registration techniques: the semi-automatic method (SAM) of Senda (1994); and the automatic multimodality image registration (AMIR) method developed by Ardekani et al. (1995), and compared the registration parameters obtained by each method in addition to evaluation by visual inspection. We investi18 gated PET images acquired with H15 F-FDG and 2 O, 11 C-flumazenll (FMZ) to find out which radioactive tracer would give a better result. We also used a PET image acquired in the same position with 11C-nemonapride (NEM) that accumulates in the striatum in order to check the validity of registration methods.
2. Materials and methods
isoflurane (1.5–2.0%). The femoral artery and vein were catheterized. The anesthetized cat was positioned prone into a stereotaxic head holder, which was designed similar to a conventional holder for physiological experiments but was made of polymethyl methacrylate in order to minimize photon attenuation (Narishige, Tokyo, Japan). Ear bars were placed in the external auditory meatus and the upper teeth were mounted on a teeth bite. The head position of the cat was kept stable within this head holder throughout the series of scans. All experimental procedures were approved by the Animal Care and Use Committee of the Tokyo Metropolitan Institute of Gerontology.
2.2. PET scanning An SHR-2000 PET camera for animal scanning (Hamamatsu Photonics, Hamamatsu, Japan) was used in the Z-motion mode, which provided 14 image slices with an interslice distance of 3.25 mm, an in-plane spatial resolution of 4.0 min full width at half maximum (FWHM), and an axial resolution of 5.0 mm FWHM (Watanabe et al., 1992). The cat was positioned prone in order to obtain coronal sections, and the slice position was determined by an H15 2 O test scan so that the entire brain was covered in the axial field of view (4.2 cm). The cat underwent a measurement of blood flow with H15 2 O, benzodiazepine receptor with 11 C-flunazenil (FMZ), dopamine D2 receptor with 11Cnemonapride (NEM), and glucose metabolism with 18FFDG, all at the same slice positions. The photon attenuation was corrected using transmission data obtained with a rotating source. The tomographic images were reconstructed using a Butterworth filter with a cut off frequency of 144 cycle/cm. The blood flow measurement was performed by an intravenous injection of 200 MBq of H15 2 O and a PET scan for 3 min started at the time of injection. The glucose metabolism was imaged by injection of 100 MBq of 18F-FDG, and a PET image was acquired from 30 to 60 min post injection. The benzodiazepine receptor was imaged by injecting 200 MBq of 11C-flumazenil and a PET image was acquired from 10 to 30 min post injection. Dopamine (D2, D4) receptor distribution was imaged by injecting 200 MBq of 11C-nemonapride, and a PET image was acquired from 10 to 30 min post injection. The analyses were carried out on UNIX workstations (Silicon Graphics Inc., Mountain View, CA, USA) using the Dr. View image analysis software system (Asahi Kasei Joho System, Tokyo, Japan).
2.1. Animal preparation for PET 2.3. MRI scanning Three adult mongrel cats weighing from 3 to 4 kg were used. The cat was intubated following injection of thiopental sodium (15 mg/kg i.v.) and atropine (0.05 mg/kg s.c.). The anesthesia was maintained with
An MRI study was performed under isoflurane inhalation anesthesia within 2 weeks prior to the PET study. MRI scans were carried out on a 4.7-Tesla
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experimental imager/spectrometer system (Unity plus SIS 200/330, Varian, Palo Alto, CA, USA) equipped with a 33-cm horizontal bore magnet (Oxford Instruments, UK) with a gradient strength of 50 mT/m. For RF transmission and detection, a 16-cm diameter homogeneous quadrature coil (Varian, Palo Alto, CA, USA) was used. Automatic shimming was performed to improve magnetic field homogeneity before data collection. A total of 21 slices of T1- and T2-weighted images were obtained perpendicular to the base of the brain. The excitation and acquisition pulse sequence was TR/ TE= 600/20 ms for T1-weighted spin-echo (SE) images and TR/TE =2000/80 ms with two signal acquisitions for T2-weighted SE images. The slice thickness was 3 mm and the image matrix was 256×256, zero-filled to 512× 512 over a field of view of 10× 10 cm.
2.4. Image registration by SAM The PET-MRI semi-automatic registration method (SAM) by Senda (1994), which has been used in human studies was applied to the cat brain images. This method utilizes the fact that the midsagittal plane is a near flat plane and is easily identifiable in both PET and MRI. First, the midsagittal plane is determined from the PET and MRI independently as the regression plane of midsagittal points selected manually on the original images. Then, the original PET and MR I images are resliced at and 910 mm apart and parallel to the midsagittal plane. If the mid-sagittal plane is a flat plane and is determined accurately, the sagittally resliced PET images can be matched to the MRI of the corresponding slice through 2D shift and rotation. The
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operator manually determines the three (x-shift, y-shift and rotation) parameters by trial and error. To match the original PET to the original MRI, the 3D shift and rotation parameters are computed as a matrix product of the two transformations: mid-sagittal cut and 2D shift/rotation. This algorithm reduces the 3D matching problem to a far easier 2D matching problem. It is not fully automatic and requires manual interaction, but it does allow confirmation and adjustment by the operator. The procedure was conducted using Dr. View and AVS (Stardent Software) running on UNIX workstations. Unlike the human brain, the original cat brain PET and MRI were in the coronal orientation. Otherwise the program worked in exactly the same way as in the case of human brain. We examined the feasibility of registering each of the three types of PET images (H2O FDG and FMZ) to the MRI using SAM. Because the H2O and FDG images had high activity both outside and deep inside the brain, we found it difficult to identify on those images the structures that are necessary for the 2D matching of the sagittal images. On the other hand the FMZ image had a good contrast between the cerebral cortex and other parts within or outside the brain. Therefore we used the FMZ images in SAM. Similarly, because T2-weighted MRI presented higher contrasts between various structures as compared to T1-weighted MRI, we used the T2weighted image for registration.
2.5. Image registration by AMIR Registration between T2-weighted MRI and each of the three types of PET images (H2O, FDG and FNIZ)
Fig. 1. Coronal PET images of cat c 3 acquired with H15 2 O, FDG and FMZ registered to and superimposed on T2-weighted MRI of the same cat. The FMZ images were registered to MRI using SAM and the same transformation was applied to the H15 2 O and FDG images acquired in the same position. Note the poor indentifiability of anatomical configuration by H15 2 O or FDG.
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was performed using AMIR developed by Ardekani et al. (1995). This method receives an input of six initial parameters (three translations and three rotations) and automatically computes the final six registration parameters iteratively by minimizing the variance of PET pixel counts within spatially connected 3D segments that are determined by a K-means clustering algorithm applied to the MRI. The slices covering the brain visualized in PET were loaded for calculation. First we used all zeros as initial parameters, which usually works for human data. However, the algorithm
converged to obviously incorrect local minima in all cases. In a second run, we inputted as initial parameters estimates of X and Y translations by inspection as well as a rough estimate of the pitch angle derived from the sagittal view. The results were compared to those obtain by SAM both by visual inspection and by examining the transformation parameters. The difference in rotation was evaluated by the angle subtended by the rotated axis of the two methods, which were calculated from the scalar product of the corresponding column vectors of the transformation matrix for the two methods.
Fig. 2. Horizontal and sagittal sections of H15 2 O, FDG and FMZ PET images of cat c 3 superimposed on T2-weighted MRI obtained by reslicing the images shown in Fig. 1.
Fig. 3. The 11C-nemonapride (NEM) PET image, acquired in the same position as FMZ and resliced with the FMZ-to-MRI registration matrix, is superimposed in the MRI. Note the striatum visualized by in NEM PET matches the striatum identified in T2-weighted MRI.
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18 Fig. 4. Horizontal MRI and PET images with H15 F-FDG and 2 O, superimposition. Note the misregistration of midsagittal line in FMZ.
3. Results Figs. 1 and 2 illustrate the result of PET-MRI registration by SAM. The registration procedure was accomplished successfully using FMZ PET and T2-weighted MRI. The H15 2 O and FDG images of the same subject acquired in the same position were registered to the MRI using the parameters obtained from the FMZ registration process. In general a good registration was observed in the whole brain by inspection in the horizontal and sagittal registered images as well as in the coronal images. Although H15 2 O and FDG images lack sufficient structural information by themselves, superimposition of H15 2 O and FDG upon MRI revealed the relationship between structure and radioactivity distribution consistent with the previously reported findings. The H15 2 O image revealed higher blood flow in the cerebellum than in the cerebral cortex whereas 18F-FDG image showed equally high radioactivity in the cortex, striatum and cerebellum. The FMZ image showed high radioactivity in the cerebral cortex, but not in the cerebellum or striatum. Fig. 3 shows a validation using 11C-NEM, which accumulates in the striatum. The NEM images, which had been acquired in the same position as the FMZ image, were resliced using the transformation matrix obtained in the FMZMRI registration process. The striatum visualized in the registered NEM PET image matched the striatum identified in the MRI. The AMIR method was used independently on H15 2 O, FDG and FMZ images by inputting a rough estimate of pitch angle as well as X and Y translations as initial values of the parameters. By inspection of the FNIZ images superimposed on T2-weighted MRI, one of the three cats (c1) presented
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C-FMW of cat c3 independently registered to MRI by AMIR with
a satisfactory result. However, apparent misregistration was observed in the other two cats (c 2 and c 3). Fig. 4 shows the result of registration of FMZ, FDG and H15 2 O images to the T2-weighted MRI conducted independently using AMIR in cat c 3 that showed a poor outcome. An evident discrepancy was observed in the midsagittal line between FMZ and MRI in the horizontal section. It should be noted, however, that the FDG and H15 2 O images that were not usable for SAM were processed by AMIR. Table 1 shows the angle gap in the reslicing axis between the registration of FMZ SAM and that of FDG, H15 2 O and FMZ by AMIR. In cat c1, that gave satisfactory results by inspection, the angular disTable 1 Discrepancy in rotation in the cat PET-MRI registration expressed by the angle (°) between the reslicing axis obtained with FMZ by Senda method and that with FDG, H2O and FMZ by Ardekani methoda FDG
H2O
FMZ
Cat c 1 X Y Z
1.9 2.1 1.9
2.0 2.6 2.4
1.9 2.6 2.3
Cat c 2 X Y Z
4.2 2.7 4.4
4.2 2.5 4.2
4.4 2.7 4.5
Cat c 3 X Y Z
6.9 2.3 6.6
8.0 3.4 7.7
7.5 3.2 7.2
a X, Y, Z are defined as row, column and axis in the coronal section.
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crepancy from the results obtained by SAM was 1.9– 2.6°. The translation discrepancy was B 1 mm in x and y and 1–2 mm in z (detailed data not shown). However, in cat c2 and c3, the angular gap was larger, reflecting the misregistration observed in Fig. 4. The angular gap was relatively small among different tracer images of the same cat.
4. Discussion The present study attempted an intrasubject PETMRI registration for the cat brain by applying two methods currently used for human brain image processing. Unlike the human brain MRI acquired in a 1.5 T unit, T2-weighted MRI resolved the brain contour and intracerebral structures much better than T1-weighted MRI. This is because the T1 relaxation time is shortened due to a high magnetic field of the 4.7-T unit used in the present study, resulting in poor delineation of brain surface and intracerebral structures. On the other hand, MR signals are influenced by tissue iron in a high magnetic field, which caused clear delineation of the striatum in T2 images. Therefore the registration was performed in T2-weighted M RI instead of T1-weighted MRI. We studied two methods for PET-MRI registration: SAM and AMIR. SAM is semi-automatic and totally depends on the operator and his/her experience and interpretation of the images. As long as structures were visualized, the operator can reach a satisfactory result by visual inspection. On the other hand, AMIR is essentially an automatic process. The result may not be satisfactory by visual inspection. However, it outputs a result even from the images in which the anatomical configuration cannot be grasped and therefore the performance of the registration cannot be evaluated by visual inspection. Because the true answer is not available for the registration accuracy, we compared the reslicing axes expressed by column vectors of the transformation matrix between the two methods to evaluate the performance of registration indirectly. We also tested the result using NEM PET, which mainly accumulates in the striatum. In the PET-MRI registration by SAM, which utilized a two-step manual matching, FMZ image was successfully registered to T2-weighted MRI. Among the three PET tracers, FMZ PET was the best for manual registration because it provided a clearer brain configuration and an easier identification of the midsagittal line as 18 compared to H15 F-FDG. In CBF and FDG 2 O or images, which provide clear visualization of cerebral structures in human images, extracranial activity such as muscles and glands was strong and the cerebral cortex was not separated clearly from thalamus or striatum in the cat due to smaller white matter area. When those 18 other images (H15 F-FDG and 11C-NEM image) 2 O,
were resliced with the parameters obtained by the 11CFMZ PET, they matched the MRI (Figs. 1 and 2). Especially the striatum visualized in the 11C-NEM PET image matched the striatum identified in T2-weighted MRI (Fig. 3). This result suggested that an accurate registration in the cat can be performed with SAM using FMZ-PET and T2-weighted MRI. The registration technique AMIR gave results by inputting suitable initial parameters. The results of Fig. 4 and Table 1 suggest that it performs a good registration 18 with the images of H15 F-FDG as well as 11C-flumaz2 O, ertil in some cases but not in others. The discrepancy between the two methods in cat c 1 was 2–3° in rotations and 1–2 mm in z-shift. This suggests that the registration error in SAM, which totally depends on the ability of visual inspection, is approximately less than 2 mm or 2°. This is consistent with the report about visual inspection on human brain images (Ardekani et al., 1995; Endo et al., 1997). In human PET-MRI registration the choice between SAM and AMIR is not a question of which is better, but SAM makes up for AMIR. In human brain, we usually use AMIR first because it is easier, and it usually works. If it does not work, then we use SAM, and it always works because cerebral structures are sufficiently visualized in human FDG or CBF-PET. The present study has revealed that FMZ can, while CBF or FDG cannot, visualize cerebral structures in the cat brain. Therefore we recommend acquiring FMZ of the same position in a PET study whenever PET-MRI registration is necessary afterward. PET is able to evaluate brain functions regarding hemodynamics, pharmacology, and metabolism using various radiopharmaceuticals repeatedly on the same subject. On the other hand it has a disadvantage of obscure anatomical orientation. A number of methods have been proposed to provide the PET images of cats with anatomical information. Heiss et al. obtained the anatomical information by comparing PET images with brain tissue slices (Heiss et al., 1994). This approach is not suitable for a long follow up PET study because the tissue slices are obtained only at the end of the series of experiments and not during the follow up phase. Moreover, it is difficult to match the tissue slice accurately to the PET slice position due to distortion introduced during tissue preparation as well as lack of 3D information in the tissue slices. Sakiyama matched PET images to a cat brain atlas (Sakiyama et al., 1997). This approach worked to some extent for normal animals in spite of individual morphological variation. However, the atlas is useless when it comes to a correlational study between function and morphology on pathological regions of diseased animals. MRI is an excellent tool for such purposes and new scanners are becoming more available with better resolution. Some investigators (Anderson et al., 1994; Ogawa et al., 1994) have used devices such as specially designed head holders or masks to
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ensure identical positioning between PET and MRI, although the reproducibility has not been validated yet and the devices may impose a limitation to the experimental procedures on the animal. The accuracy of correlation between PET and morphology has not been a major topic because of limited spatial resolution of animal scanners. However, new animal PET scanners have been designed with much better resolution (Heiss et al., 1995; Cherry et al., 1997). The registration techniques will enhanced the usefulness of PET study on the cat brain. In conclusion, the semi-automatic method SAM provided satisfactory registration in all three cats. However, the draw back of this method was that it could 18 not align H15 F-FDG PET images and required 2 O or the FMZ PET images, which shows better outlines of anatomical brain structures. On the other hand, AMIR 18 registered all three H15 F-FDG and FMZ PET 2 O, images but was only successful in one of three cases. There is still a need for development of reliable and accurate PET-MRI registration for animal studies. Two lines of research must be taken in the future. (1) The feasibility of applying other currently used human brain PET-MRI registration techniques to animal images must be studied further. (2) Novel techniques must be developed specifically designed for animal image registration. The present study was a step towards the former.
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