在I-123 ADAM SPECT 影像上，影像分析經常被用來進行腦部功能性的評估。然而在長時間ADAM影像的影像特性上，長時間造影以及ADAM影像與MRI的差異性過大，導致無法有效直接將MRI影像與ADAM影像對位。而不正確的對位進一步導致功能性影像評估的問題。為了解決這個問題，我們提出兩個新的對位策略。
我們首先提出了一連續對位方法 [13]， 這個方法利用連續對位的方法將長時間I-123 ADAM影像透過每個時間點的I-123 ADAM對位的策略對應至MRI。雖然這個方法在結果上相當的優異，但是整體的成本過高而且耗時。我們於是提出了一兩階段對位的方法[13]，這個方法僅需要個人10分鐘I-123 ADAM以及MRI兩個時間點的取像成本，而且結果與連續對位的方法相當的接近。雖然兩階段的對位方式比起需要每個時間點的連續對位更有效率，但是在這個方法中卻需要個人的MRI和10分鐘ADAM造影。
接著我們提出採用標準腦的方式來取代個人的MRI和10分鐘ADAM影像。驗證實驗證明了本文所提的10分鐘標準腦對位與原始的兩階段對位相比，能夠達到可接受的效能同時又大幅降低病人的取像成本。此外，由於AOI僅需要在MRI標準腦上面圈選一次，而自動在所有的六小時ADAM成像上進行該區域的評估，因此不同觀察者之間的誤差可以被克服。
總結而言，本文所提出的10min ADAM標準腦方法可以使得病人僅在長時間的ADAM血清運轉體成像上，僅需進行一次的第六小時的取像。因此，本方法能夠有效降低病人的取像負擔，使得長時間ADAM影像評估在臨床應用上變得更便利，低成本以及可靠。

The image analysis for I-123 ADAM SPECT is commonly performed for evaluating the functional activities in apparatus or brain regions. However, the image characteristics from long time point ADAM imaging contain too many variations for reliable registration to the corresponding MRI.
The conventional registration strategy fails to directly register the last time point ADAM image to the MRI correctly due to the large intensity variation generated during long time ADAM imaging and the difference between the two imaging modalities. Inaccurate registration may lead to an erroneous interpretation when functional images are incorrectly mapped to the corresponding anatomical structure. To reduce the registration bias, we propose two new registration methods.
We first propose the sequential registration method [13], which yields good accuracy by registering the brain images sequentially. Although the resulting registration is good, the required sequential imaging is still expensive, tedious, and time-consuming. We next propose a two-step registration method [13] that requires imaging at only two time-points, accuracy close to the sequential method while greatly reducing the tedium and time required for the procedure. Although the two-step registration is more efficient than the sequential method which requires the individual imaging at every time point, it still needs to acquire the individual MRI, 10-min ADAM and 6-hour ADAM images.
We next propose to use the standard images instead of the individual MR and 10-min ADAM imaging such that the evaluation process can be computed directly on the 6-hour ADAM image. The validation experiments proved that the proposed 10-min ADAMstd two-step registration method achieves acceptable performance in comparison with the original two-step method. Moreover, after the AOI on the MRIstd is carefully drawn in an off-line process once, the resulting AOI can be used to automatically evaluate the brain function on all the individual 6-hour ADAM images. The inter-observer variations can also be resolved.
In summary, the proposed 10-min ADAMstd method makes the patient only needs to take the 6-hour ADAM image in a long time serotonin transporter (SPECT/ADAM) evaluation and thus significantly reduces patient loading and becomes more convenient, inexpensive and reliable for clinical applications.

[1] Amen, D.G. and Carmichael, B.D., High-resolution brain SPECT imaging in ADHD, Ann Clin Psychiatry, 9, 81-6, June, 1997.
[2] Ter-Pogossian, M.M.; M.E. Phelps, E.J. Hoffman, N.A. Mullani. "A positron-emission transaxial tomograph for nuclear imaging (PET)". Radiology 114 89–98. 1975.
[3] Herman, G. T., “Fundamentals of computerized tomography: Image reconstruction from projection”, 2nd edition, Springer, 2009.
[4] William R. Hendee, PhD and Christopher J. Morgan, MD "Magnetic Resonance Imaging Part I—Physical Principles", West J Med. 141, 491–500, 1984.
[5] Lin KJ, Liu CY, Wey SP, Hsiao IT, et al. Brain SPECT imaging and whole-body biodistribution with [123I]ADAM-a serotonin transporter radiotracer in healthy human subjects. J. Nucl Med Biol.; 33:193-202, 2006.
[6] Koch W, Schaaff N, Pöpperl G. [I-123] ADAM and SPECT in patients with borderline personality disorder and healthy control subjects. J Psychiatry Neurosci. 32: 234-240, 2007.
[7] Sacher J, Asenbaum S. Binding kinetics of 123 IADAM in healthy controls: a selective SERT radioligand. J Neurol, 10, 1-8, 2006.
[8] Booij J, Win Md. Brain kinetics of the new selective serotonin transporter tracer (123)I ADAM in healthy young adults. Nucl Med Biol, 33, 185-191, 2006.

[9] Yokoi T, Soma T, Shinohara H, et al. Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images. Ann Nucl Med, 18, 659-667, 2004.
[10] Zhu YM, Cochoff SM. Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information. J Nucl Med, 43:160-166, 2002.
[11] Julin P, Lindqvist J, Svensson L, et al. „”MRI-Guided SPECT Measurements of Medial Temporal Lobe Blood Flow in Alzheimer's Diseased”. J Nucl Med, 38, 914-919, 1997.
[12] Hogan RE, Cook MJ, Binns DW, et al. “Perfusion patterns in postictal 99mTc-HMPAO SPECT after coregistration with MRI in patients with mesial temporal lobe epilepsy ”. J Neurol Neurosurg Psychiatry. 63, 235-239, 1997.
[13] Chung-I Huang, Wei-Jen Yao, Yung-Nien Sun*, "New Methods for Registering Long-Time I-123 ADAM SPECT Image Sequences to Magnetic Resonance Images", J Nucl Med Commun, 31, 734-40, 2010.
[14] Chung-I Huang,Wei-Jen Yao; Yung-Nien Sun, “FUNCTIONAL EVALUATION BY USING MULTI-STEP REGISTRATION WITH STANDARD MR BRAIN IMAGE” , The 3nd Biomedical Engineering International Conference 2010.
[15] Chang L.T. “A method for attenuation correction in radionuclide computed tomography”. IEEE Trans. Nucl, 25:638-643, 1978.
[16] Wilcoxon, F, "Individual comparisons by ranking methods". Biometrics Bulletin 1, 80–83, 1945.
[17] Hollander, M. and Wolfe, D. A, Nonparametric Statistical Methods (2nd Ed.), 1999.
[18] Lehmann, E. L, NONPARAMETRICS: Statistical Methods Based On Ranks, 1975.
[19] Maintz J.B.A., Viergever M.A., “A survey of medical image registration”. Med Image Anal. 2, 1-36. 1998.
[20] Pallotta S., Gilard M.C., Bettinardil V., Rizzo G., Landoni C., Striano G., Masi R., Fazio F., “Application of a surface matching image registration technique to the correlation of cardiac studies in positron emission tomography (PET) by transmission images”. Phys Med Biol. 40, 1695-1708, 1995.
[21] P.A. Viola, “Alignment by Maximization of Mutual Information”, PhD thesis, Massachusetts Institute of Technology, 1995.
[22] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information”, IEEE Trans. Med. Imag., 16, 187-198, 1997.
[23] W.M. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, “Multi-modal volume registration by maximization of mutual information”, Med. Image. Anal., 1, 35-51, 1996.
[24] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal, “Automated multi-modality image registration based on information theory”, in Information Process. Med. Imaging, 1, 263-274, 1995.

[25] I. Vajda, Theory of Statistical Inference and Information, Kluwer Academic, Dordrecht, The Netherlands, 1989.
[26] D.L.G. Hill, D.J. Hawkes, N. A. Harrison, and C.F. Ruff, “A strategy for automated multimodality image registration incorporating anatomical knowledge and imager characteristics,” in Information Processing in Medical Imaging, H.H. Barrett and A.F. Gmitro, Eds., vol. 687 of Lecture Notes in Computer Science, pp. 182–196, 1993.
[27] J. Tsao and P. Lauterbur, “Generalized clustering-based registration for multi-modality images”, in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, no. 2, pp. 667-670, 1998.
[28] C.E. Shannon, The mathematical theory of communication (parts 1 and 2), Bell
Syst. Tech. J., vol. 27, pp. 379–423 and 623–656, 1948.
[29] C. Studholme, D.L.G. Hill, and D.J. Hawkes, An overlap invariant entropy measure of 3D medical image alignment, Pattern Recognition, vol. 32, pp. 71–86, 1999.
[30] R.P. Woods, J.C. Mazziotta, and S.R. Cherry, “Rapid automated algorithm for aligning and reslicing PET images”, J. Comput. Assist. Tomogr., vol. 16, iss. 4, pp. 620-633, 1992.
[31] R.P. Woods, J.C. Mazziotta, and S.R. Cherry, “MRI-PET registration with automated algorithm”, J. Comput. Assist. Tomogr., vol. 17, iss. 4, pp. 536-546, 1993.

[32] W. H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical Recipes in C, 2nd ed., Cambridge University Press, Cambridge, U.K., 1992.
[33] F. Maes, D. Vandermeulen, and P. Suetens, “Comparative evaluation of multi- resolution optimization strategies for multimodality image registration by maximization of mutual information”, Med. Image. Anal., 3, 373-386, 1999.
[34] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, “Mutual information matching in multiresolution contexts”, Image and Vision Computing, 19,1-2, 45-52, 2001.
[35] Mary L. Comer, Edward J. Delp, “Segmentation of Textured Images Using a
Multiresolution Gaussian Autoregressive Model”, IEEE Trans. Image Process, 8, 3, 1999.
[36] P. The´venaz, M. Unser, An efficient mutual information optimizer for multiresolution image registration. International Conference on Image Processing, Proc. IEEE, 833–837, 1998.
[37] C. Studholme, D.L.G. Hill, D.J. Hawkes, “Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures”, Medical Physics 24, 25–35, 1997.
[38] P. Thévenaz and M. Unser, “Optimization of mutual information for multiresolution image registration”, IEEE Trans. Image Processing, 12, 2083-2099, 2000.
[39] H.M. Chen and P.K, Varshney, “A pyramid approach for multimodality image registration based on mutual information” in Proceedings of 3rd international conference on information fusion, 9-15, 2000.
[40] Yokoi T, Soma T, Shinohara H, et al. Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images. Ann Nucl Med. 18, 659-667, 2004.
[41] W R CRUM, DPhil, T HARTKENS, PhD and D L G HILL, PhD, "Non-rigid image registration: theory and practice”, The British Journal of Radiology, 77, 140–153, 2004.
[42] Senthil Periaswamy and Hany Farid “Elastic Registration in the Presence of
Intensity Variations” IEEE Trans. Med. Imag, 22, 865-875, 2003.
[43] C. V. Stewart, C. L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE Trans. Med. Imag, 22, 1379–1394, 2002.
[44] Sharp,G.C; Lee,S.W.;Wehe, D.K. “ICP registration using invariant features” IEEE Trans. Pattern Anal. Mach. Intell, VOL. 24, NO. 1, 2002.
[45] S. F. Kristi Boesen, J. Huang, J. Germann, J. Stern, D. Louis Collins, A. C. Evans, and D. A. Rottenberg, “Inter-rater reproducibility of 3D cortical and subcortical landmark points,” 11th Annu.Meeting Organization Human Brain Mapping, Toronto, Canada, 2005.
[46] Dinggang S, Christos D. “HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration”. IEEE trans. Med.Imag, 21:1421-1439, 2002.
[47] Dong Xu, Hua Li "Geometric moment invariants", Pattern Recognition, 240–249, 41,2008.
[48] C. H. Lo and H. S. Don, “3-D moment forms: Their construction and application to object identification and positioning,” IEEE Trans. Pattern Anal. Mach. Intell., 11, 1053–1064, 1989.
[49] Wenzhan Dai Kangtai Wang "An Image Edge Detection Algorithm Based on Local Entropy " IEEE International Conference on Digital Object Identifier, 2007.
[50] Guorong Wu, Feihu Qi, Dinggang Shen "Learning-Based Deformable Registration of MR Brain Images" IEEE trans. Med.Imag, 25, 1145-1157, 2006.
[51] T. Kadir and M. Brady, “Saliency, scale and image description,” Int. J.
Comput. Vision, 45, 83–105, 2001.
[52] T. Kadir, A. Zisserman, and M. Brady, “An affine invariant salient region
detector,” in Proc. Eur. Conf. Comput. Vision, 228–241, 2004.
[53] D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: Application to breast MR images,” IEEE Trans. Med. Imag, 18, 712–721, 1999.
[54] G. Wahba, “Spline models for observational data,” Soc. Industr. Applied
Math., 1990.
[55] Russakoff DB, Rohlfing T, Adler JR Jr, Maurer CR Jr. “Intensity-based 2D-3D spine image registration incorporating a single fiducial marker.” Acad Radiol. Jan, 12, 37-50, 2005.

[56] Shojaii, Rushin; Martel, Anne L. “Multi-Modality fiducial marker for validation of registration of medical images with histology” Medical Imaging, 7623, 762331-762338, 2010.
[57] C. R. Maurer Jr., J. M. Fitzpatrick, M. Y. Wang, R. L. Galloway, R. J. Maciunas, and G. S. Allen, “Registration of head volume images using implantable fiducial markers,” IEEE Trans. Med. Imag, 16, 447-462, 1997.