Co-registration of glucose metabolism with positron emission tomography and vascularity with fluorescent diffuse optical tomography in mouse tumors
© Tong et al.; licensee Springer. 2012
Received: 17 November 2011
Accepted: 24 February 2012
Published: 7 May 2012
Bimodal molecular imaging with fluorescence diffuse optical tomography (fDOT) and positron emission tomography (PET) has the capacity to provide multiple molecular information of mouse tumors. The objective of the present study is to co-register fDOT and PET molecular images of tumors in mice automatically.
The coordinates of bimodal fiducial markers (FM) in regions of detection were automatically detected in planar optical images (x, y positions) in laser pattern optical surface images (z position) and in 3-D PET images. A transformation matrix was calculated from the coordinates of the FM in fDOT and in PET and applied in order to co-register images of mice bearing neuroendocrine tumors.
The method yielded accurate non-supervised co-registration of fDOT and PET images. The mean fiducial registration error was smaller than the respective voxel sizes for both modalities, allowing comparison of the distribution of contrast agents from both modalities in mice. Combined imaging depicting tumor metabolism with PET-[18 F]2-deoxy-2-fluoro-d-glucose and blood pool with fDOT demonstrated partial overlap of the two signals.
This automatic method for co-registration of fDOT with PET and other modalities is efficient, simple and rapid, opening up multiplexing capacities for experimental in vivo molecular imaging.
KeywordsCo-registration fDOT PET Fiducial marker detection Optical surface image Neuroendocrine tumors MEN2A
The complexity of tumors and their sophisticated interactions with their environment call for imaging methods capable of detecting a diversity of tumor hallmarks [1, 2]. Positron emission tomography (PET) with 18 F]2-deoxy-2-fluoro-d-glucose (FDG), the most efficient imaging method to detect cancer, is an indicator of tumor energy metabolism dominated by aerobic glycolysis in both cancer and tumor-associated inflammatory cells . However, FDG-PET carries no information about other cancer hallmarks such as angiogenesis, replicative immortality, evasion of growth suppressors, capacity to metastasize, and yields at best indirect information on resistance to apoptosis and proliferation . PET imaging with other radiotracers can complement FDG but cannot be performed in the same imaging session. It also increases radiation exposure. As far as experimental molecular imaging is concerned, multiple PET sessions are difficult to envision on a large scale because of high cost and low practicability.
Recent progress in fluorescence diffuse optical tomography (fDOT) has made 3-D optical molecular imaging of live animals a reality [4–9]. In contrast to imaging modalities demanding heavy instrumentation such as PET, fDOT is based on relatively simple hardware that does not require a sophisticated technological environment or tedious safety precautions. fDOT offers nano-molar sensitivity , long follow-up times (days) and is relatively low cost, and opens in vivo molecular imaging to a huge portfolio of fluorescent probes and beacons [5, 6, 8, 10]. We  and others  have proposed the combination of PET-FDG and fDOT as a method of choice to provide multiple molecular data on experimental tumors in mice.
Given the small size (50–500 mm3) of tumors in mice and the resolution of small-animal PET and fDOT scanners (1–2 mm), accurate and reliable co-registration between both modalities is essential. Among different co-registration methods that have been developed, such as geometrical co-calibration  and dynamic contrast methods , the use of fiducial markers (FM)  in close position to the body of the animal is the most straightforward and universal approach today. The coordinates of the FM in images acquired independently is used for the geometrical transformations leading to the fusion of images. Co-registration of large data sets from different imaging modalities results in time-consuming, tedious and operator-dependant image analyses when performed manually. Therefore, methods for the automatic identification of the FM's coordinates have been developed for co-registration of computed tomography (CT), PET and magnetic resonance imaging (MRI) modalities [13–18]. However, so far, these methods have not been adapted to co-registration with fDOT because fDOT reconstructions are spatially restricted and do not cover the FM positioning.
Here, we introduce the use of surface images obtained during the fDOT acquisition session for the automatic identification of the FM's positions in space. Surface reconstruction by laser patterning  can retrieve the 3-D surface of the subject and of the FM in close vicinity. Surface reconstruction is directly implemented into the 3-D fDOT-PET combined image. We show that this method efficiently performs co-registration of fDOT and PET images of the same mouse with a co-registration error (fiducial registration error, FRE) smaller than the intrinsic resolution of PET and fDOT. This new automatic method facilitates the accurate co-registration of fDOT with PET and other imaging modalities. As a proof of concept, we imaged mice bearing neuroendocrine tumors for glycolytic metabolism with FDG-PET and for tumoral blood pool with a fluorescent blood pool contrast agent. We show that these two tumoral hallmarks occupy partially overlapping volumes, suggesting that the tumor-induced induction of blood supply could be spatially restricted to a portion of the tumor mass.
Materials and methods
Plexiglas cubes containing FM
Animal experiments were performed under an animal use and care protocol approved by the animal ethics committee and conducted in accordance with Directive 2010/63/EU of the European Parliament. Six female nude mice (body weight of approximately 25 g) were obtained from Elevage Janvier (Le Genest Saint Isle, France) and received a subcutaneous injection in the flank of 106 PC12-multiple endocrine neoplasis syndrome type 2A (MEN2A) cells . The mice were anesthetized by continuous gaseous anesthesia (1–2% isoflurane in O2) and imaged sequentially by fDOT and PET. The near-infrared (NIR) fluorescent dye Sentidye® (20 nmol; Fluoptics, Grenoble, France) was injected 3 h before starting the fDOT acquisition at a volume of 100 μL. FDG (7,400 kBq in 100 μL; Flucis, IBA, France) was administered 1 h before the PET scan. Each mouse underwent a 20-min fDOT acquisition followed by a 30-min PET acquisition. The anesthetized mice were transferred from the fDOT to PET scanner by means of the mouse supporting plate, while great care was taken to avoid movement of the animal in regard to its support. The contact of the ventral side of a nude mouse with the Plexiglas surface of the mouse holder is sticky enough to ensure that the mouse is not moving when transferred between scanners located at the same room.
Acquisition of the optical images
Images were acquired in the 3-D optical imager TomoFluo3-D (Cyberstar, Grenoble, France) . To obtain the position of the FM, two optical images covering both the subject and the FM were acquired with the mouse placed in prone position: (1) a planar white light image recorded from a camera snapshot yielding the x and y coordinates (Figure 1B), and (2) an image of the 3-D surface of the animal, acquired by rapid consecutive camera shootings during axial scanning with an inclined green planar laser of the TomoFluo3-D, yielding the z coordinates (Figure 1C). One image was recorded for each laser position during laser scanning of the animal in the axial direction, and all images were then combined into a single image . A surface image of the animal was then reconstructed from the intersection curve between the surface of the animal and the surface of the supporting plate by triangulation .
The fDOT image was obtained by a 20-min scan of a defined volume of interest covering the tumor using excitation by a 680-nm laser on the anterior side of the animal  and recording with a CCD camera fixed above its dorsal side. The scanning grid consisted of 7 × 6 sources in steps of 2 mm, and the detection area was 15 × 13 mm2. A 2 × 2 binning was applied, and the mesh volume corresponding to the detection area was mathematically discretized in voxels of 0.67 × 0.67 × 1 mm3 size to build the reconstruction mesh volume. Finally, the inverse problem of the tomographic reconstruction was solved with the algebraic reconstruction technique .
PET image acquisition
A 30-min scan was acquired in a Focus 220 MicroPET scanner (Siemens, Knoxville, TN, USA). Image acquisition and reconstruction used the MicroPET Manager Software (Siemens-Concorde Microsystems) based on a filtered back-projection algorithm. The attenuation correction was based on the segmentation of the emission map ; the dimensions of reconstruction volumes were 256 × 256 × 95 with a voxel size of 0.475 × 0.475 × 0.796 mm3. The counts were decay-corrected and expressed in Bq/cm3.
Detection of the optical planar (x, y) FM coordinates.
Detection of the optical altitude (z) FM coordinates.
Detection of PET FM coordinates.
Transformation from the mouse photograph to the PET volume.
where , and S z = 1 (i.e., no scaling in the z direction).
where RS are the rotation matrix elements R multiplied by the scaling matrix elements S of Equation 8.
The barycenter-based method
For the mouse photograph, as the barycenter was calculated in two dimensions, the left upper corner of each ROD was taken as origin, and the vector r i corresponded to the relative position from this origin to each pixel within the ROD. The signal intensity of each pixel inside the ROD gave the value of m i . For the PET image, the barycenter was calculated in 3-D, and the position of the first voxel (left upper in coronal view and first section in the z direction) of the 3-D ROD was taken as the origin. The position of each voxel relative to this point was taken as r i and the value of each voxel as m i . The barycenter calculated for each ROD represented the FM's position.
The general outline of the co-registration method is depicted schematically in Figure 1. Five steps are implemented successively: (1) registration of (x opt, y opt, z 0opt), i.e., the x, y and z coordinates of the optical FM using both the white light (Figure 1 B) and optical surface (Figure 1C) images; (2) calculation of T fDOT-photo , the transformation matrix from the fDOT reconstruction image to the whole body image; (3) registration of (x PET, y PET, z PET), i.e., the x, y and z coordinates of the FM in the PET images (Figure 1D,E); (4) calculation of T photo-PET , the transformation matrix from optical to PET volumes (Figure 1H); and (5) calculation of T fDOT-PET , the transformation matrix for the co-registration of the fDOT volumes with the PET volumes.
The complete image processing method was written in C++ and integrated in Python with a user-friendly interface within the Brainvisa image processing software (http://brainvisa.info/index_f.html) . Input files include (1) the mouse photograph, (2) the optical surface scan, (3) a header file containing the position of the fDOT image relative to the camera's field of view, and (4) the PET volume image. With this user-friendly toolbox, three transformation matrices (T fDOT-photo , T photo-PET , and T fDOT-PET ) are calculated and sent as outputs. The program is completed in 20–30 s on a Dell PrecisionTM T7500 (Dell SA, Montpellier, France) workstation with a 64-bit Intel® Xeon® quad-core processor (Intel Corporation, Montpellier SAS, France).
Quantitative evaluation of the co-registration
Fiducial registration error-planar justification
Fiducial registration errors
In all cases, statistically significant differences were found between the FRE values of the MI and BC approaches (student's t test, p = 0.0003 and p = 0.0007 for optical and PET images, respectively), while no statistically significant differences were found between the MI and the MC approach (p = 0.51 and p = 0.55 for optical and PET images, respectively). Taken together, these results indicate that the MI approach has the same co-registration performance with the MC approach and that both are more precise than the BC approach.
Evaluation of co-registration quality in the z direction
In order to evaluate the co-registration error in the z direction of our MI approach, the mouse surface image was fused with the PET image using the transformation matrix T photo-PET . The average error in the z direction was 0.37 ± 0.06 mm, a value smaller than the voxel size in the z direction (1 mm in optical images and 0.475 mm in PET images). Taking into account the error in the z direction, the 3-D FRE value of our MI approach was 0.452 mm for the optical image and 0.446 mm for the PET image.
Fiducial localization error and target registration error
Using the average value of the FRE calculated previously for MI approach (here, N = 4), the FLE was 0.35 mm for the PET images and 0.37 mm for the optical images. The FLE values were smaller than the 3-D FRE values that are independent from the fiducial configuration .
The mean TRE was 0.26 mm for optical images and 0.25 mm for PET image. The calculated TRE was similar as the FRE for both modalities, indicating that the co-registration error derived from the FM position and other positions on the image remain coherent.
Co-registration of tumor vascularity and metabolism
The fDOT-to-PET co-registration method was applied to six female nude mice bearing xenografts tumor of PC12-MEN2A cancer cells that mimic a human medullar thyroid carcinoma . The diameter of tumors ranged from 4.5–8 mm, corresponding to tumor weights of 50–270 mg. Each mouse received two injections; the first injection was the fluorescent probe Sentidye®, a probe that passively accumulates in tumors by virtue of the enhanced permeability and retention effect, and the second one is FDG. Each mouse was imaged sequentially with fDOT and PET.
There was a strong correlation between the volume of the tumor and the total uptake of FDG, but no correlation between the tumor volume and the concentration of FDG in the tumor expressed in percent of injected dose per cubic centimeter. In other words, the radioactivity concentration remained independent of the tumor volume. The fDOT signal also increased with tumor size but plateaued for tumors larger than 150 mg, corresponding to a tumor with a diameter larger than 6 mm. Again, no correlation was found between tumor volume and the concentration of the fDOT signal in the tumor area.
There is a clear trend towards multi-modal imaging of tumor as the best way to provide relevant information on a significant number of cancer hallmarks [1, 27]. Recent studies have demonstrated the possibility to combine fDOT and PET imaging for co-registered localization of two or more biological processes at the molecular level [5, 8]. These studies required the use of CT for the anatomical structure, therefore, leading to irradiation of the animal that has an effect on tumor cell development. This prompted us to develop direct co-registration of PET/fDOT without use of CT.
The method for co-registration of PET and fDOT presented here is based on the detection of the position coordinates of FM visible with both modalities. While detection of radiolabeled FM with PET is relatively straightforward, it is not trivial for fDOT since planar optical images do not provide any information on the position of the FM in the z axis. Moreover, the acquisition of 3-D fDOT data is slow and would require extremely long acquisition times to scan a complete volume of the mouse body. The FM is not contiguous to the mouse body, requiring simultaneous 3-D acquisition for unconnected objects (i.e., mouse body plus FM), a difficult task to implement. Finally, mice need complete depilation since hair and fur skew the optical transmission of fluorescent signals. With these limitations in mind, we developed a method based on the combination of a planar optical image with a surface scan of the animal that automatically determine the full 3-D coordinates of four FM placed around the body of the animal. Interestingly, the planar image and the surface scan are acquired in the same referential as the fDOT volume and, thus, intrinsically co-registered. After an initialization step to define the regions in which the FM should be detected (the RODs), this method allows for automatic detection of the coordinates of the FM and immediately co-registers the fDOT image with the PET image. Since the FM are permanently attached to the animal plate holder, the definition of the RODs needs to be performed only once for a given animal supporting plate and remains valid for all following imaging sessions with that plate.
The performance of co-registration with the present automatic method was comparable to that of manual methods and found to be better than that of the BC approach, supporting the view that our automatic co-registration method is at least as accurate as an experienced human observer. The method is based on a simple principle which is the same as the manual co-registration, i.e., detection of the points with maximum intensity, while barycenter approaches use intensity weighting. However, Wang et al. have reported that co-registration based on barycenter intensity weighting performed better than an unweighted intensity approach [13, 14]. This apparent discrepancy is likely due to the shape of the signal from the FM in the PET images. The FM signal in PET is generally not well-rounded because of the noise generated by the reconstruction from punctiform sources known as the ‘star’ artifact. This adds irrelevant weight to the RODs and renders the detection of the barycenter largely dependent on the size and localization of the ROD leading to unpredictable and often poorly reproducible calculation of the FM's position. In contrast, detection by maximal intensity is well adapted to automatic co-registration and can be generalized to any ROD to give reliable co-registered image results. As long as the signals from the FM remain above the surrounding background in the defined regions, the size and shape of the RODs have no influence on the results and can be larger or smaller than three times the diameter of the FM signal size. The aforementioned evaluation results show the advantages of our method in terms of reliability and robustness. Moreover, the method allows the precise and fast co-registration of individually taken images without any operator-dependent bias which can be advantageous when a study involves a large amount of experiments.
Two general solutions are available to compute the point-based rigid body transformation R, T between the optical and PET coordinates: the iterative method  and the closed form solution method [24, 29]. The closed form solution method is generally superior to the iterative method in terms of efficiency and robustness . Eggert et al. have compared four popular algorithms , each of which compute the translational and rotational components of the transform in closed form, as the solution to a least squares error criterion given by Equation 3. They found the SVD to give the best results in terms of 3-D accuracy and stability . This algorithm is, therefore, chosen in the present study.
The proposed co-registration method can easily be customized for specific applications. The method can also be applied to co-registration between fDOT and modalities other than PET, such as CT or MRI, as long as the images contain the relevant FM. In addition, the method is adaptable to most fDOT or planar optical imaging instruments with surface reconstruction. Moreover, once the RODs have been initialized for a mouse support with FM, multiple experiments can be co-registered automatically with the same or different mice. It is noteworthy that co-registration of fDOT and PET offers the interesting possibility to co-register and, therefore, visualize two purely molecular contrast-based imaging methods without the need for referring to another anatomical imaging method. Where FDG is routinely used for the staging of tumors, it can be complemented with fDOT optical images of a second tumor-related activity fused to the FDG readouts.
As a proof of concept, we co-registered images of two well-established hallmarks of cancer: deregulation of glucose consumption through aerobic glycolysis and increased vascularization through angiogenesis and neo-vessel formation. Automatic co-registration of glycolysis with FDG-PET and vascularity with Sentidye® fDOT clearly demonstrated that the two signals were independently distributed in multiple endocrine neoplasis syndrome type 2A (MEN2A-induced medullary thyroid carcinoma (MTC) xenografts). MEN2A syndromes are linked to a mutation of the rearranged during transfection (RET) proto-oncogene coding for a membrane tyrosine kinase receptor. The mutation induces constitutive activation of the RET signaling pathway through dimerization of RET and is found in families with hereditary MTC and other disorders . There is evidence that RET constitutively activates angiogenesis, likely through increased VEGF secretion, in MTC , and RET inhibitors induce inhibition of angiogenesis . The new blood vessels produced within tumors by chronically activated angiogenesis (‘angiogenic switch’) are abnormal in their structure, distorted, enlarged, leaky and cause erratic blood flow and hemorrhagic lakes within the tumor . Sentidye® is a NIR fluorescence lipidic nano-particle that accumulates passively in the leaky, abnormal vascular network of MEN2A-MTC tumors. The observation that it distributes in a pattern distinct from that of FDG is indicative of the fact that the tumors are organized into regions with distinct underlying physiological and molecular characteristics. Assuming that the distribution of the FDG signal indicates the localization of cancer cells and serves as a reference to the optical signal , it can be concluded that the hypervascular part of the tumor covers approximately 40 % of the tumor area. The spatial accuracy of both PET and fDOT reconstructions has been validated with the use of CT in a previous study . In addition, in a different set of combined PET/fDOT scans, the tumor area was sampled after sacrifice of the mice and sectioned for histology (data not shown). The ex vivo distribution of the probes matched the in vivo reconstructions perfectly. We, therefore, conclude that the mismatch in the observed contrast distribution between fDOT and PET is due to the nature of the process underlying probe distribution and not to artifacts in the co-registration method. Interestingly, high FDG uptake which reflects aerobic glycolysis is not superimposed with high vascularity in MEN2A-MTC tumors. It would be interesting to explore further the fine architecture of tumors with other molecular tracers in order to segment tumoral regions based on, e.g., oxygen pressure, pH, apoptosis, proliferation or other important hallmarks of cancer . Alternatively, it could be envisioned to co-register the distribution of a labeled drug or refine the anatomical information or superimpose maps of diffusion or viscoelastic properties using other imaging techniques such as MRI or ultrasound.
In summary, co-registered fDOT-PET is a translational in vivo molecular imaging modality of simple implementation that brings relevant information to experimental studies of tumors. Accurate co-registration combining these two molecular imaging modalities likely to (1) facilitate in vitro to in vivo correlations through ex vivo fluorescent imaging of pathological samples , (2) document the mechanism of uptake of clinically used radiotracers and contrast agents by adding complementary molecular information , (3) decipher the changes induced by administration of therapy , and (4) validate the in vivo targeting capacity of new molecular probes prior to radioactive labeling for PET or SPECT or tagging with paramagnetic atoms for MRI . Future applications could include the use of other types of fluorescent probes, in particular, those that are activated only after interaction with the target for the monitoring of a variety of tumor-related molecules . Additionally, since optical imaging allows the imaging of several probes with distinct emission spectra at the same time, the concept of complementing the FDG signal with a growing number of information could be further extended. The benefits from the fusion of fDOT and PET in combination with CT are expected to give rise to scanners where the two modalities are integrated within the same apparatus, and there are ongoing efforts for the development of this type of methods [34–36]. The combination of these two modalities offers new opportunities for describing tissue physiopathology non-invasively at refined molecular levels and opens experimental molecular imaging to simultaneous detection of multiple molecular targets and activities (‘multiplexing’).
We are grateful to Frédéric Dollé and the radiochemistry group of Orsay for supplying [18 F]FDG, to Vincent Brulon and Yohann Fontyn for superb animal PET expertise, to Alexandra Winkeler and Renaud Maroy for support and comments on the manuscript, and to Nicolas Bernards for his help to the linguistic revision. Sentidye was a kind gift of Fluoptics, Grenoble, France. This study was supported by grants from Agence Nationale de la Recherche (ANR TomoFluo3-D) and European Union Program FMT-XCT (grant agreement no. 201792) to AG and BT. XT was supported by a PhD grant from CEA and Beijing Uniquanta Technology Co. Ltd. The authors declare no conflict of interest.
- Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell 2011, 144: 646–674. 10.1016/j.cell.2011.02.013PubMedView ArticleGoogle Scholar
- Egeblad M, Nakasone ES, Werb Z: Tumors as organs: complex tissues that interface with the entire organism. Dev Cell 2010, 18: 884–901. 10.1016/j.devcel.2010.05.012PubMed CentralPubMedView ArticleGoogle Scholar
- Gillies RJ, Robey I, Gatenby RA: Causes and consequences of increased glucose metabolism of cancers. J Nucl Med 2008, 49: 24S-42S. 10.2967/jnumed.107.047258PubMedView ArticleGoogle Scholar
- Ntziachristos V, Ripoll J, Wang LV, Weissleder R: Looking and listening to light: the evolution of whole body photonic imaging. Nat Biotechnol 2005, 23: 313–320. 10.1038/nbt1074PubMedView ArticleGoogle Scholar
- Garofalakis A, Dubois A, Kuhnast B, Dupont DM, Janssens I, Mackiewicz N, Dollé F, Tavitian B, Ducongé F: In vivo validation of free-space fluorescence tomography using nuclear imaging. Opt Lett 2010, 35: 3024–3026. 10.1364/OL.35.003024PubMedView ArticleGoogle Scholar
- Ntziachristos V, Tung C, Bremer C, Weissleder R: Fluorescence molecular tomography resolves protease activity in vivo. Nat Med 2002, 8: 757–760. 10.1038/nm729PubMedView ArticleGoogle Scholar
- Koenig A, Hervé L, Gonon G, Josserand V, Berger M, Dinten JM, Boutet J, Peltié P, Coll JL, Rizo P: Fluorescence diffuse optical tomography for free-space and multifluorophore studies. J Biomed Opt 2010, 15: 016016. 10.1117/1.3309738PubMedView ArticleGoogle Scholar
- Nahrendorf M, Keliher E, Marinelli B, Waterman P, Feruglio PF, Fexon L, Pivovarov M, Swirski FK, Pittet MJ, Vinegoni C, Weissleder R: Hybrid PET-optical imaging using targeted probes. PNAS 2010, 107: 7910–7915. 10.1073/pnas.0915163107PubMed CentralPubMedView ArticleGoogle Scholar
- Kossodo S, Pickarski M, Lin SA, Gleason A, Gaspar R, Buono C, Ho G, Blusztajn A, Cuneo G, Zhang J, Jensen J, Hargreaves R, Coleman P, Hartman G, Rajopadhye M, le Duong T, Sur C, Yared W, Peterson J, Bednar B: Dual in vivo quantification of integrin-targeted and protease-activated agents in cancer using fluorescence molecular tomography (FMT). Mol Imag Biol 2009, 12: 488–499.View ArticleGoogle Scholar
- Martin A, Aguirre J, Sarasa A, Garofalakis A, Meyer H, Mamalaki C, Ripoll J, Planas AM: In vivo non-invasive assessment of stroke-induces immune depression by three-dimensional fluorescence molecular tomography in transgenic mice expressing GFP in T-lymphocytes. Mol Imag 2008, 7: 157–167.Google Scholar
- Cao L, Breithaupt M, Peter J: Geometrical co-calibration of a tomographic optical system with CT for intrinsically co-registered imaging. Phys Med Biol 2010, 55: 1591–1606. 10.1088/0031-9155/55/6/004PubMedView ArticleGoogle Scholar
- Hillman EMC, Moore A: All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast. Nat Photonics 2007, 1: 526–530. 10.1038/nphoton.2007.146PubMed CentralPubMedView ArticleGoogle Scholar
- Wang MY, Maurer CR Jr, Fitzpatrick JM, Maciunas RJ: An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head. IEEE Trans Biomed Eng 1996, 43: 627–637. 10.1109/10.495282PubMedView ArticleGoogle Scholar
- Wang M, Song Z: Automatic localization of the center of fiducial markers in 3D CT/MRI images for image-guided neurosurgery. Pattern Recogn Lett 2009, 30: 414–420. 10.1016/j.patrec.2008.11.001View ArticleGoogle Scholar
- Fitzpatrick JM, West JB, Maurer CR Jr: Predicting error in rigid-body, point-based registration. IEEE Trans Med Imag 1998, 17: 694–702. 10.1109/42.736021View ArticleGoogle Scholar
- West J, Fitzpatrick JM, Wang MY, Dawant BM, Maurer CR Jr, Kessler RM, Maciunas RJ, Barillot C, Lemoine D, Collignon A, Maes F, Suetens P, Vandermeulen D, van den Elsen PA, Napel S, Sumanaweera TS, Harkness B, Hemler PF, Hill DL, Hawkes DJ, Studholme C, Maintz JB, Viergever MA, Malandain G, Pennec X, Noz ME, Maguire GQ, Pollack M Jr, Pelizzari CA, Robb RA, et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. J Comput Assist Tomogr 1997, 21: 554–566. 10.1097/00004728-199707000-00007PubMedView ArticleGoogle Scholar
- Yang C, Wu T, Lin M, Huang Y, Guo W, Chen C, Wang T, Yin W, Lee J: Multimodality imaging combination in small animal via point-based registration. Nucl Instrum Meth A 2006, 569: 240–244. 10.1016/j.nima.2006.08.045View ArticleGoogle Scholar
- Liu X, Cevikalp H, Fitzpatrick JM: Marker orientation in fiducial registration. In Medical Imaging 2003: Image Processing, 2003; Proceeding of SPIE. 5032: 1176–1185.
- Bernardini F, Rushmeier H: The 3D model acquisition pipeline. Comput Graph 2002, 21: 149–172.Google Scholar
- Michiels FM, Chappuis S, Caillou B, Pasini A, Talbot M, Monier R, Lenoir GM, Feunteun J, Billaud M: Development of medullary thyroid carcinoma in transgenic mice expressing the RET protooncogene altered by a multiple endocrine neoplasia type 2A mutation. PNAS 1997, 947: 3330–3335.View ArticleGoogle Scholar
- Siegel S, Dahlbom M: Implementation and evaluation of a calculated attenuation correction for PET. IEEE Trans Nucl Sci 1992, 39: 1117–1121. 10.1109/23.159770View ArticleGoogle Scholar
- Marks RJ II: Introduction to Shannon Sampling and Interpolation Theory. New York: Springer; 1991.View ArticleGoogle Scholar
- Deriche R Proceedings of the 2nd International Conference on Image Processing. In Recursively implementing the Gaussian and its derivatives. Singapore; 1992:263–267.Google Scholar
- Eggert DW, Lorusso A, Fisher RB: Estimating 3-D rigid body transformations: a comparison of four major algorithms. Mach Vis Appl 1997, 9: 272–290. 10.1007/s001380050048View ArticleGoogle Scholar
- Gower JC, Dijksterhuis GB: Procrustes Problems. Oxford, UK: Oxford University Press; 2004.View ArticleGoogle Scholar
- Cointepas Y, Mangin J-F, Garnero L, Poline J-B, Benali H: BrainVISA: software platform for visualization and analysis of multi-modality brain data. NeuroImage 2001, 13: S98. 10.1016/S1053-8119(01)91441-7View ArticleGoogle Scholar
- Culver J, Akers W, Achilefu S: Multimodality molecular imaging with combined optical and SPECT/PET modalities. J Nucl Med 2008, 49: 169–172. 10.2967/jnumed.107.043331PubMedView ArticleGoogle Scholar
- Zhang Z: Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vis 1994, 13: 119–148. 10.1007/BF01427149View ArticleGoogle Scholar
- Horn BKP: Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am 1987, 4: 629–642.View ArticleGoogle Scholar
- Jhiang SM: The RET proto-oncogene in human cancers. Oncogene 2000, 19: 5590–5597. 10.1038/sj.onc.1203857PubMedView ArticleGoogle Scholar
- Soh EY, Sobhi SA, Wong MG, Meng YG, Siperstein AE, Clark OH, Duh QY: Thyroid-stimulating hormone promotes the secretion of vascular endothelial growth factor in thyroid cancer cell lines. Surgery 1996, 120: 944–947. 10.1016/S0039-6060(96)80038-9PubMedView ArticleGoogle Scholar
- Petrangolini G, Cuccuru G, Lanzi C, Tortoreto M, Belluco S, Pratesi G, Cassinelli G, Zunino F: Apoptotic cell death induction and angiogenesis inhibition in large established medullary thyroid carcinoma xenografts by ret inhibitor rpi-1. Biochem Pharmacol 2006, 72: 405–41431. 10.1016/j.bcp.2006.05.002PubMedView ArticleGoogle Scholar
- Nagy JA, Chang SH, Shih SC, Dvorak AM, Dvorak HF: Heterogeneity of the tumor vasculature. Semin Thromb Hemost 2010, 36: 321–331. 10.1055/s-0030-1253454PubMed CentralPubMedView ArticleGoogle Scholar
- Solomon M, Nothdurft R, Akers WJ, Edwards WB, Liang K, Xu B, Achilefu S, Culver JP: Photonics West 2012, Multimodal Biomedical Imaging VII, Proceedings of SPIE.8216–2.
- Li C, Wang G, Qi J, Cherry SR: Three-dimensional fluorescence optical tomography in small-animal imaging using simultaneous positron-emission-tomography priors. Opt Lett 2009, 34: 2933–2935. 10.1364/OL.34.002933PubMed CentralPubMedView ArticleGoogle Scholar
- Li C, Yang Y, Mitchell GS, Cherry SR: Simultaneous PET and multispectral 3-dimensional fluorescence optical tomography imaging system. J Nucl Med 2011, 52: 1268–1275. 10.2967/jnumed.110.082859PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.