Lima J.J.Pedroso, de (ed.). Nuclear Medicine Physics
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328 Nuclear Medicine Physics
136. D. L. G. Hill, C. Studholme, D. J. Hawkes. Multi-resolution voxel similarity
measures for MR-PET registration. Proc. IPMI 95, 187–198, 1995.
137. H. Luan, F. Qi, Z. Xue, L. Chen, D. Shen. Multimodality image registration
by maximization of quantitative–qualitative measure of mutual information.
Pattern Recogn., 41(1):285–298, 2008.
138. W. Wells, P. Viola, H. Atsumi, S. Nakajima, R. Kikinis. Multi-modal volume reg-
istration by maximization of mutual information. Med. Image Anal., 1(1):35–51,
1996.
139. J. B. A. Viergever, M. A. Pluim, J. P. W. Maintz. Mutual-information-based reg-
istration of medical images: A survey. Med. Imaging, IEEE Trans., 22(8):986–1004,
Aug. 2003.
140. L. Freire, A. Roche, J. Mangin. What is the best similarity measure for motion
correction of fMRI time series. IEEE Trans. Med. Imaging, 21(5):470–484, 2002.
141. A.D. Fright, W.R., Linney. Registration of 3-d head surfaces using multiple
landmarks. IEEE Trans. Med. Imag., 12(3):515–520, Sep 1993.
142. R. P. Woods, J. C. Mazziotta, S. R. Cherry. MRI-PET registration with automated
algorithm. J. Comput. Assist. Tomogr., 17(4):536–546, 1993.
143. J. Stewart. Calculus. Brooks/Cole Publishing Co., Pacific Grove, CA, USA, 2007.
144. J. R. Shewchuk. An introduction to the conjugate gradient method without the
agonizing pain. Technical report, Pittsburgh, PA, USA, 1994.
145. S. Henn. A Levenberg–Marquardt scheme for nonlinear image registration. BIT
Numer. Math., 43(4):743–759, 2003.
146. G. Taubin. An improved algorithm for algebraic curve and surface fitting. Com-
puter Vision, 1993. Proceedings Fourth International Conference on, pp. 658–665,
11–14 May 1993.
147. T. van den Elsen, P. A. Napel, S. Adler, J. Hemler, P. F. Sumanaweera. A sys-
tem for multimodality image fusion. Computer-Based Medical Systems, 1994.
Proceedings 1994 IEEE Seventh Symposium on, pp. 335–340, 10–12 June 1994.
148. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Suetens. Multimodality
image registration by maximization of mutual information. IEEE Trans. Med.
Imag., 16(2):187–198, Apr 1997.
149. W.-H. Tsui, H. Rusinek, P. Van Gelder, S. Lebedev. Fast surface-fitting algorithm
for 3d image registration. Med. Imag. 1993: Image Process., 1898(1):14–23, 1993.
150. M. van Herk, H. M. Kooy. Automatic three-dimensional correlation of CT-CT,
CT-MRI, and CT-SPECT using chamfer matching. Med. Phys., 21(7):1163–1178,
1994.
151. J. L. Andersson, A. Sundin, S. Valind. A method for coregistration of pet and mr
brain images. J. Nucl. Med., 36(7):1307–1315, Jul 1995.
152. W. Press, S. Teukolsky, W. Vetterling, B. Flannery. Numerical Recipes in C (2nd
Edition) Cambridge University Press, Cambridge, UK, 1992.
153. D. Whitley. A genetic algorithm tutorial. Statist. Comput., 4:65–85, 1994.
154. D. Whitley. Genetic algorithms and evolutionary computing. Van Nostrand’s
Scientific Encyclopedia, 2002.
155. M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA,
USA, 1996.
156. S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi. Optimization by simulated annealing.
Science, Number 4598, 13 May 1983, 220, 4598:671–680, 1983.
157. V. Cerny. Thermodynamical approach to the traveling salesman problem: An
efficient simulation algorithm. J. Optim. Theory Appl., 45(1):41–51, January 1985.
Imaging Methodologies 329
158. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller.
Equation of state calculations by fast computing machines. J.Chem. Phys.,
21(6):1087–1092, 1953.
159. P. Thèvenaz, T. Blu, M. Unser. Image interpolation and resampling. In N.
Bankman, ed., Handbook of Medical Imaging, Processing and Analysis, Academic
Press, pp. 393–420, 2000.
160. C. Spitzer, K. Lehmann, T. M. Gonner. Survey: Interpolation methods in medical
image processing. Med. Imag., IEEE Trans., 18(11):1049–1075, 1999.
161. J. Duchon. Splines minimizing rotation-invariant semi-norms in sobolev
spaces. Springer Lecture Notes in Math. 571, Springer-Verlag, Berlin, pp. 85–100.
162. J. Meinguet. Multivariate interpolation at arbitrary points made simple. Zeit.
Angew. Math. Phys. (ZAMP), 30(2):292–304, 1979.
163. A. Goshtasby. Registration of images with geometric distortions. Geosci. Remote
Sensing, IEEE Trans., 26(1):60–64, 1988.
164. K. Eriksson, A. P. Astrom. Bijective image registration using thin-plate splines.
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, 3:798–801,
2006.
165. R. Bajcsy, S. Kovacic. Multiresolution elastic matching. Comput. Vision Graph.
Image Process, 46(1):1–21, 1989.
166. G. E. Christensen, G. E. Christensen, R. D. Rabbitt, M. I. Miller. Deformable tem-
plates using large deformation kinematics. IEEE Trans. Image Process, 5(10):1435–
1447, 1996.
167. M. Bro-Nielsen, C. Gramkow. Fast fluid registration of medical images. In Proc.
Visualization in Biomedical Computing (VBC’96), Hamburg, Germany, September,
Springer Lecture Notes in Computer Science, 1131:267–276, 1996.
168. B. G. Siim, W. T. Laux, M. D. Rutland, B. N. Palmer, W. R. Wilson.
Scintigraphic imaging of the hypoxia marker 99mTechnetium-labeled 2,2*-
(1,4-Diaminobutane)bis(2-methyl-3-butanone) Dioxime (99mTc-labeled HL-
91; Prognox): noninvasive detection of tumor response to the antivascu-
lar agent 5,6-Dimethylxanthenone-4-acetic Acid. Cancer Res., 60:4582–4588,
2000.
169. J. C. W. Crawley. Quantitation of uptake of bromine-77 in the human brain. Clin.
Phys. Physiol. Meas., 5:121–124, 1984.
170. D. J. Yang, E. E. Kim, T. Inoue. Targeted molecular imaging in oncology. Ann.
Nucl. Med., 20:1–11, 2006.
171. N. Pandit-Taskar. Oncologic imaging in gynecologic malignancies. J. Nucl. Med.,
46:1842–1850, 2005.
172. A. Del Sole, A. Falini, L. Ravasi, L. Ottobrini, D. De Marchis, E. Bombardieri, G.
Lucignani.Anatomical and biochemical investigation of primary brain tumours.
Eur. J. Nucl. Med., 28:1851–1872, 2001.
173. C. van de Wiele, C. Lahorte, W. Oyen, O. Boerman, I. Goethals, G. Slegers, ARA.
Dierckx. Nuclear medicine imaging to predict response to radiotherapy. Int. J.
Radiat. Oncol. Biol. Phys., 55:5–15, 2003.
174. J. R. Buscombe, E. Bombardieri. Imaging cancer using single photon techniques.
Q. J. Nucl. Med., 49:121–31, 2005.
175. H. Kawai, J. Toyohar, H. Kado, T. Nakagawa, S. Takamatsu, T. Furukaw,
Y. Yonekur, T. Kubota, Y. Fujibayashi. Acquisition of resistance to antitumor
alkylating agent ACNU: A possible target of positron emission tomography
monitoring. Nucl. Med. Biol., 33:29–35, 2006.
330 Nuclear Medicine Physics
176. B. Gagel, P. Reinartz, C. Demirel, H. J. Kaiser, M. Zimny, M. Piroth, M. Pinkawa,
S. Stanzel, B. Asadpour, K. Hamacher, H. Coenen, U. Buell, M. J. Eble. 18F fluo-
romisonidazole and [18F] fluorodeoxyglucose positron emission tomography in
response evaluation after chemo-/radiotherapy of non-small-cell lung cancer: a
feasibility study. BMC Cancer, 6:51–59, 2006.
177. M. Yoshimoto, S. Kinuya, A. Kawashima, R. Nishii, K. Yokoyam, K. Kawai.
Radioiodinated VEGF to image tumor angiogenesis in a LS180 tumor xenograft
model. Nucl. Med. Biol., 33:963–969, 2006.
178. J. Toretsky, A. Levenson, I. N. Weinberg, J. F. Tait, A. Uren, R. C. Mease. Prepara-
tion of F-18 labeled annexin V: Apotential PET radiopharmaceutical for imaging
cell death. Nucl. Med. Biol., 31:747–752, 2004.
179. L. Aloj, A. Zannetti, C. Caracó, S. Del Vecchio, M. Salvatore. Bcl-2 overexpression
prevents 99mTc-MIBI uptake in breast cancer cell lines. Eur. J. Nucl. Med. Mol.
Imag., 31:521–527, 2004.
180. S. Del Vecchio, M. Salvatore. 99mTc-MIBI in the evaluation of breast cancer
biology. Eur. J. Nucl. Med. Mol. Imag., 31(Suppl. 1):S88–S96, 2004.
181. S. DelVecchio, A. Ciarmiello, M. I. Potena, M. V. Carriero, C. Mainolfil, G. Botti,
R. Thomas, M. Cerra, G. D’Aiuto, T. Tsuruo, M. Salvatorel. In vivo detection of
multidrug-resistant (MDR1) phenotype by technetium-99m sestamibi scan in
untreated breast cancer patients. Eur. J. Nucl. Med., 24:150–159, 1997.
182. S. Kinuya, J. Bai, K. Shiba, K. Yokoyama, H. Mori, M. Fukuoka, N. Watanabe,
N. Shuke, T. Michigishi, N. Tonami. 99mTc-sestamibi to monitor treatment with
antisense oligodeoxynucleotide complementary to MRP mRNAin human breast
cancer cells. Ann. Nucl. Med., 20:29–34, 2006.
183. N. H. Hendrikse, E. J. F. Franssen, WTA. van der Graaf, W. Vaalburg, E. G. E. de
Vries. Visualization of multidrug resistance in vivo. Eur. J. Nucl. Med., 26:283–293,
1999.
184. J. R. Murren. Modulating multidrug resistance: Can we target this therapy? Clin.
Cancer Res., 8:633–635, 2002.
185. W. Guo, G. H. Hinkle, R. J. Lee. 99mTc-HYNIC-Folate: A novel receptor-based
targeted radiopharmaceutical for tumor imaging. J. Nucl. Med., 40:1563–1569,
1999.
186. I. Madar, B. Bencherif, J. Lever, R. F. Heitmiller, S. C. Yang, M. Brock, J. Brahmer,
H. Ravert, R. Dannals, J. J. Frost. Imaging δ- and μ-opioid receptors by PET in
lung carcinoma patients. J. Nucl. Med., 48:207–213, 2007.
187. M. Mirowski, R. Wiercioch, A. Janecka, E. Balcerczak, E. Byszewska, D. Birn-
baum, S. Byzia, P. Garnuszek, R. Wierzbickia. Uptake of radiolabeled morphi-
ceptin and its analogs by experimental mammary adenocarcinoma: In vitro and
in vivo studies. Nucl. Med. Biol., 31:451–457, 2004.
188. C. Zeng, S. Vangveravong, J. Xu, K. C. Chang, R. S. Hotchkiss, K. T. Wheeler, D.
Shen, Z. P. Zhuang, H. F. Kung, R. H. Mach. Subcellular localization of Sigma-
2 receptors in breast cancer cells using two-photon and confocal microscopy.
Cancer Res., 67:6708–6716, 2007.
189. E. Bombardieri, F. Crippa, L. Maffioli, M. Greco. Nuclear medicine techniques
for the study of breast cancer. Eur. J. Nucl. Med., 24:809–824, 1997.
190. F. G. Blankenberg, H. W. Strauss. Nuclear medicine applications in molecular
imaging. J. Mag. Res. Imaging, 16:352–361, 2002.
191. D. J. Hnatowich. Observations on the role of nuclear medicine in molecular
imaging. J. Cell. Biochem., Suppl. 39:18–24, 2002.
Imaging Methodologies 331
192. S. H. Britz-Cunningham, S. J. Adelstein. Molecular targeting with radionuclides:
State of the science. J. Nucl. Med., 44:1945–1961, 2003.
193. C. E. Deppe, P. J. Heering, S. Viengchareun, B. Grabensee, N. Farman, M. Lombés.
Cyclosporine a and FK506 inhibit transcriptional activity of the human min-
eralocorticoid receptor: A cell-based model to investigate partial aldosterone
resistance in kidney transplantation. Endocrinology, 143:1932–1941, 2002.
194. P. T. Fox, A. R. Laird, L. Lancaster. Coordinate-based voxel-wise meta-analysis:
Dividends of spatial normalization. Report of a Virtual Workshop. Human Brain
Mapping, 25:1–5, 2005.
195. P. Broca, Bull. Soc. Anthropol. (Paris) 1861, 2:235; Bull. Soc. Anat. (Paris), 6:330,398,
1861.
196. S. S. Ketty. The nitrous oxide method for the quantitative determination of cere-
bral blood flow in man—Theory, procedure and normal values. J. Clin. Invest.,
27:476, 1948.
197. W. D. Obrist. Determination of regional cerebral blood flow by inhalation of
133-Xenon. Circ. Res., 20:124, 1967.
198. N. A. Lassen. Regional cerebral blood-flow in stroke by Xe-133 inhalation and
emission tomography. Stroke, 12:284, 1981.
199. G. Neves, S. F. Cooke, T. V. Bliss. Synaptic plasticity, memory and the hippocam-
pus: A neural network approach to causality. Nat. Rev. Neurosci., 9(1):65–75,
2008.
200. M. S. A. Graziano, C. S. R. Taylor, T. Moore. Complex movements evoked by
microstimulation of precentral cortex. Neuron, 34:841–851, 2002.
201. B. M. Ramsden, C. P. Hung, A. W. Roe. Real and illusory contour processing in
area V1 of the primate: A cortical balancing act. Cerebral Cortex, 11:648–665, 2001.
202. A. A. Vein, M. L. C. Maat-Schieman. Famous Russian brains: Historical attempts
to understand intelligence. Brain, 131(Pt 2):583–590, 2008.
203. N. Bernasconi, S. Duchesne, A. Janke, J. Lerch, D. L. Collins, A. Bernasconi.
Whole-brain voxel-based statistical analysis of gray matter and white matter
in temporal lobe epilepsy. Neuroimage, 23(2):17–723, 2004.
204. E. Courchesne, H. J. Chisum, A. Cowles, J. Covington, B. Egaas, M. Harwood, S.
Hinds, G. A. Press. Normal brain development and aging: Quantitative analysis
at in vivo MR imaging in healthy volunteers. Radiology, 216:672–682, 2000.
205. J. G. Frontera. Evaluation of the immediate effects of some fixatives upon
measurements of the brains of macaques. J. Comp. Neurol., 109:417–438, 1958.
206. H. Stephan. Methodische studien uber den quantitativen vergleicharchitektonis-
cher struktureinheite des gehirns. Z. Wiss. Sool., 164:1–2, 1960.
207. S. M. Blinkov, I. I. Glezer. The Human Brain in Figures and Tables: A Quantitative
Hand-book. Plenum Press and Basic Books, New York, NY, pp. 334–338, 1968.
208. K. Brodmann, Vergleichende localizationlehr der Grosshirnrinde in ihren Prinzipien
dargestellt auf Grund des Zellenbaues. J.A. Barth, Leipzig, 1909.
209. J. Talairach, P. Tournoux. Co-planar stereotaxic atlas of the human brain. 3-
Dimensional proportional system: An approach to cerebral imaging. Translated
by Mark Rayport. Thieme Medical Publishers, Inc., Stuttgart - New York, NY,
1988.
210. J. L. Lancaster, D. Tordesillas-Gutiérrez, M. Martinez, F. Salinas, A. Evans, K.
Zilles, J. C. Mazziotta, P. T. Fox. Bias between MNI and Talairach coordinates
analysed using the ICBM-152 brain template. Hum. Brain Map., 28:1194–1205,
2007.
332 Nuclear Medicine Physics
211. D. C. Costa, C. Tannock, J. Brostoff. Brainstem perfusion is impaired in patients
with myalgic encephalomyelitis/chronic fatigue syndrome. Quart. J. Med.,
88:767–773, 1995.
212. A. Greco,C. Tannock, J. Brostoff,D. C. Costa. Brain MR imaging in chronic fatigue
syndrome. Am. J. Neuroradiol. (AJNR), 18:1265–1269, 1997.
213. P. Ehrlich. Das Sauerstoff-Bederfuis des Organismus: eine farbenalytische Studie.
Hirschward, Berlin, 1885.
214. P. Ehrlich. Uber die Beziehungen von chemische Constitution, Vertheilung, und
Pharmakologisher Wirkung. Collected Studies in Immunology. (Reproduced and
translated). Wiley, New York, 567–595, 1906.
215. L. Stern, R. Gautier. Rapports entre le liquide céphalo-rachidien et la circulation
sanguine. Arch. Int. Physiol., 17:138–192, 1921.
216. L. Stern, R. Gautier. 1992 Les rapports entre le liquide céphalo-raquidien et les
éléments nerveux de l’axe cérébrospinal. Arch. Int. Physiol., 17:391–448, 1921.
217. H. Davson. History of the blood–brain barrier concept. In E. A. Neuwelt, ed.,
Implications of the Blood–Brain Barrier and its Manipulation. Plenum, New York,
27–52, 1989.
218. H. Becker, G. Quadbeck. Tierexperimentelle tersuchungen uber die Funktsions-
wise der Blut-Hirnschranke. Z. Naturforsch., 7B:493, 1952.
219. B. B. Brodie, H. Kurtz, L. S. Schanker. The importance of dissociation constant
and lipid-solubility in influencing the passage of drugs into the cerebrospinal
fluid. J. Pharmacol., 130:20–25, 1960.
220. K. Welch. A model for the distribution of materials in the fluids of the central
nervous system. Brain Res., 16:453–468, 1969.
221. H. Spatz. Die Bedeutung der vitalen Fäbund für die Lehre vom Stoffaustausch
zwischen dem Zentralnervensystem und dem übrigen Körper. Arch. Psychiat.
Nervenheilk., 101:267–358, 1934.
222. Riser. Le liquide céphalo-rachidien. Masson, Paris, 1920.
223. J. Wolff. Beiträge zur Ultrastruktur der Kapillaren im Zentralnervensystem. Z.
Zellforsch., 60:409–431, 1963.
224. C. D. Clemente, E. A. Holst. Pathological changes in neurons, neuroglia and
blood–brain barrier induced by x-irradiation of the heads of monkeys. Acta
Neurol. Psychiatr. Scand., 71:66–79, 1954.
225. M. W. Brightman, T. S. Reese. Junctions between intimately apposed cell
membranes in the vertebrate brain. J. Cell Biol., 40:648–677, 1969.
226. D. C. Costa. The blood–brain barrier, in Nuclear Medicine in Clinical Diagnosis
and Treatment, 3rd ed., Eds. P. J. Ell and S. S. Gambhir, Churchill Livingstone,
Edinburgh, London, New York, chap. 89:1235–1246, 2004.
227. H. Davson. The blood–brain barrier. J. Physiol., 255:1–28, 1976.
228. W. Oldendorf, H. Davson. Transport in the central nervous system. Proc. Roy.
Soc., 60:326–328, 1967.
229. P.A. Stewart, M. J. Wiley. Developing nervous tissue induces formation of blood–
brain barrier characteristics in invading endothelial cells: a study using quail–
chick transplantation chimeras. Dev. Biol., 84:183–192, 1981.
230. R. C. Janzer, M. C. Raff. Astrocytes induce blood–brain barrier properties in
endothelial cells. Nature, 325:253–257, 1987.
231. J.-H. Tao-Cheng, Z. Nagy, M. W.Brightman. Tight junctions of brain endothelium
in vitro are enhanced by astroglia. J. Neurosci., 7:3293–3299, 1987.
Imaging Methodologies 333
232. M. W. Brightman. The anatomic basis of the blood–brain barrier. In E.A. Neuwelt,
ed., Implications of the Blood–Brain Barrier and its Manipulation. Plenum, New York,
pp. 53–83, 1989.
233. C. Crone. Tight and leaky endothelia. In H. Ussing, N. Bindslev, N. A. Lassen, O.
Sten-Knudsen, ed., Water Transport Across Epithelia. Munksgaard, Copenhagen,
pp. 256–267, 1981.
234. C. Crone, S. P. Olesen. Electrical resistance of brain microvascular endothelium.
Brain Res., 241:49–55, 1982.
235. R. A. Hawkins, D. R. Peterson, J. R. Viña. The complementary membranes
forming the blood–brain barrier. IUBMB Life, 54:101–107, 2002.
236. R. L. O’Kane, R. A. Hawkins. Na-dependent transport of large neutral amino
acids occurs at the abluminal membrane of the blood–brain barrier. Am. J. Physiol.
Endocrinol. Metab., 285:1167–1173, 2003.
237. C. Crone. The permeability of capillaries in various organs as determined by use
of the indicator diffusion method. Acta Physiol. Scand., 58:292–305, 1963.
238. C. Crone. Lack of selectivity to small ions in paracellular pathways in cerebral
and muscle capillaries of the frog. J. Physiol., 353:317–337, 1984.
239. E. M. Renkin. Transport of potassium-42 from blood to tissue in isolated mam-
malian skeletal muscle. Am. J. Physiol., 197:1205–1210,
1959.
240. M. W. B. Bradbury. The Concept of a Blood–Brain Barrier. Wiley, New York, 1979.
241. J. O. M. Eichling, M. E. Raichle, R. L. Grubb et al. Evidence of the limitations
of water as a freely diffusible tracer in brain of the rhesus monkey. Circ. Res.,
35:358–364, 1974.
242. O. B. Paulson, M. M. Hertz, T. G. Bolwig et al. Filtration and diffusion of water
across the blood–brain barrier in man. Microvasc. Res., 13:113–124, 1977.
243. W. M. Partridge, L. J. Mietus. Transport of steroid hormones through the rat
blood–brain barrier. Primary role of albumin bound hormone. J. Clin. Invest.,
64:145–154, 1979.
244. A. Yuwiler, W. H. Oldendorf, E. Geller et al. Effect of albumin binding and amino
acid competition on tryptophan uptake into brain. J. Neurochem., 28:1015–1023,
1977.
245. W. M. Pardridge, E. M. Landaw. Tracer kinetic model of blood–brain barrier
transport of plasma protein bound ligands. Empiric testing of the free hormone
hypothesis. J. Clin. Invest., 74:745–752, 1984.
246. W. M. Partridge. Carrier-mediated transport of thyroid hormones through the
rat blood–brain barrier. Primary role of albumin-bound hormone. Endocrinology,
105:605–612, 1979.
247. M. W. Bradbury. Transport across the blood–brain barrier. In E. A. Neuwelt, ed.,
Implications of the Blood–Brain Barrier and its Manipulation. Plenum, New York, pp.
119–136, 1989.
248. B. V. Zlokovic, D. J. Begley, D. G. Chain. Blood–brain barrier permeability to
dipeptides and their constituent amino acids. Brain Res., 271:65–71, 1983.
249. W. M. Pardridge, J. Eisenberg, J. Yang. Human blood–brain barrier insulin
receptor. J. Neurochem., 44:1771–1778, 1985.
250. A. Gjedde, M. Rasmussen. Blood–brain glucose transport in the conscious rat:
Comparison of the intravenous and intracarotid injection methods. J. Neurochem.,
35:1375–1381, 1980.
334 Nuclear Medicine Physics
251. W. M. Pardridge. Brain metabolism: A perspective from the blood–brain barrier.
Physiol. Rev., 63:1481–1535, 1983.
252. Q. R. Smith, Y. Takasato, D. J. Sweeney et al. Regional cerebrovascular transport
of leucine as measured by the in situ brain perfusion technique. J. Cereb. Blood
Flow Metab., 5:300–311, 1985.
253. M. E. Phelps, S.-C. Huang, E. J. Hoffman et al. Cerebral extraction of N-13 ammo-
nia: its dependence on cerebral blood flow and capillary permeability–surface
area product. Stroke, 12:607–619, 1981.
254. J. E. Cremer, L. Braun, W. H. Oldendorf. Changes during development in trans-
port processes of the blood–brain barrier. Biochim. Biophys. Acta, 448:633–637,
1975.
255. A. Gjedde,C. Crone.Inductionprocessesin blood–braintransfer of ketone bodies
during starvation. Am. J. Physiol., 229:1165–1169, 1975.
256. J. H. James, J. Escourrou, J. E. Fischer. Blood–brain neutral amino acid trans-
port activity is increased after portacaval anastomosis. Science, 200:1395–1397,
1978.
257. A. M. Mans, J. F. Biebuyck, R. A. Hawkins. Ammonia selectively stimulates neu-
tral amino acid transport across blood–brain barrier. Am. J. Physiol., 245:C74–C77,
1983.
258. G. S. Sarna, M. W. B. Bradbury, J. E. Cremer, et al. Brain metabolism and specific
transport at the blood–brain barrier after porto-caval anastomosis in the rat. Brain
Res., 160:69–83, 1979.
259. R. B. Schwartz, P. A. Carvalho, E. Alexander III, J. S. Loeffler, R. Folkerth, B. L.
Holman. Radiation necrosis versus high-grade recurrent glioma: Differentiation
by using dual-isotope SPECT with
201
Tl and
99m
Tc-HMPAO. AJNR, 12:1187–
1192, 1992.
260. D. C. Costa, S. Gacinovic, R. F. Miller. Radionuclide brain imaging in immuno-
deficiency syndrome (AIDS). Q. J. Nucl. Med., 39:243–249, 1995.
261. D. C. Costa. A Study of the First
99Tc
m
-labelled Radiopharmaceutical for the Inves-
tigation of Cerebral Blood Flow in Man. PhD Thesis, University of London,
1989.
262. G. Orefice, L. Celentano, M. Scaglione, M. Davoli, S. Striano. Radioisotopic
cisternography in benign intracranial hypertension of young obese women. A
seven-case study and pathogenetic suggestions. Acta Neurol (Napoli), 14:39–50,
1992.
263. M. Primeau, L. Carrier, P. C. Milette, R. Chartrand, D. Picard, M. Picard. Spinal
cerebrospinal fluid leak demonstrated by radioisotopic cisternography. Clin.
Nucl. Med., 13:701–703, 1988.
264. E. De Robertis. Ultrastructure and cytochemistry of the synaptic region. Science,
156:907–914, 1967.
265. G. D. Pappas. Ultrastructural basis of synaptic neurotransmission. In D. B. Tower,
ed., The Nervous System, Vol. 1, Raven Press, New York, pp. 19–30, 1975.
266. C. W. Cotman, M. Nieto-Sampedro. Cell biology of synaptic plasticity. Science,
225:1287–1294, 1984.
267. R. L. Clem, T. Celikel, A. L. Barth. Ongoing in Vivo experience triggers synaptic
metaplastcity in the neocortex. Science, 319:101–104, 2008.
268. R. S. Feldman, J. S. Meyer L. F. Quenzer. Priciples of Neuropharmacology. Sinauer
Associates, Inc., Publishers, Sunderland, MA, 1997.
7
Systems in Nuclear Medicine
J. J. Pedroso de Lima and António Dourado
CONTENTS
7.1 Theory of Biological Systems: Introduction ..................336
7.1.1 Some Basic Systemic Concepts in Biology and Physiology . . 336
7.1.1.1 Systems, Elements, and Relations ..............336
7.1.1.2 Feedback and Self-Regulation .................337
7.1.1.3 Environment, Closed Systems, Open Systems, and
Homeostasis ..............................339
7.1.1.4 Entropy and Negentropy ....................340
7.1.1.5 Autopoiesis, Adaptation, and Cybernetics ........342
7.1.1.6 A Hierarchy of Systems .....................343
7.1.2 Mathematical Modeling and Analysis of Biological Systems:
Population Models ...............................345
7.1.2.1 The Growth of a Single Species’ Population ......345
7.1.2.2 The Logistic Equation .......................346
7.1.2.3 The Lotka–Volterra Predator–Prey Model ........348
7.1.2.4 A General Model for the Interaction of Two
Populations ..............................350
7.1.2.5 Modeling Epidemic Phenomena ...............351
7.1.2.6 Physiological Systems ......................352
7.1.2.7 Compartmental Analysis ....................357
7.1.2.8 State, State Space, and State Equations ..........358
7.1.2.9 How to Choose the State Variables .............364
7.1.2.10 Solution of the Linear State Equation in the Time
Domain .................................365
7.1.2.11 Nonlinear Systems, Singular Points, and
Linearization .............................370
7.2 Tracer Kinetics Modeling ................................387
7.2.1 Introduction ....................................387
7.2.2 Concepts in Tracer Kinetics Modeling .................388
7.2.3 Transport of Tracers and Localization Mechanisms .......392
7.2.4 Compartmental Models ...........................395
7.2.4.1 Single-Compartment Models and
Multicompartment Models ...................397
7.2.5 Tracers, Volumes, and Flows in Dilution Systems .........407
335
336 Nuclear Medicine Physics
7.2.5.1 The Stewart–Hamilton Principle ...............413
7.2.5.2 Volume Calculation ........................414
7.2.6 Distribution Systems and the Convolution Integral .......415
7.2.6.1 Determination of Flow ......................417
7.2.7 Regeneration of the Frequency Function of Transit Times by
Deconvolution ..................................418
7.2.8 Data Analysis and Models in PET Studies ..............422
7.2.9 Parametric Analysis ..............................427
7.2.10 Monte Carlo Simulation ...........................428
References ..............................................429
7.1 Theory of Biological Systems: Introduction
7.1.1 Some Basic Systemic Concepts in Biology and Physiology
In this chapter, we will study the basic notions of systems theory that are most
relevant to biological systems (including physiological ones). In the last few
decades, the methods for modeling and analyzing biological systems have
probably been one of the most relevant facets of the enormous development
of mathematical biology.
We will study population models, compartmental models, and some prin-
ciples of the kinetics involved in the system’s dynamics and interactions,
starting with some concepts of general systems theory that are very useful
for understanding biological systems.
7.1.1.1 Systems, Elements, and Relations
System is a concept that pervades our daily life and even our imagination.
However, it is not easy to define. First of all, a system is goal oriented. Second,
a system is a collection of components. We may tentatively define a system
as a set of related elements (components) interacting with one another in an
orderly way to enable them to attain a goal.
Generally, we say that there is a relation between two elements, E1 and E2,
if the behavior of E1 influences the behavior of E2 and if the behavior of E2
influences the behavior of E1. Reciprocity is the essence of a relation.
Each element has a set of characteristic properties (color, size, weight,
temperature, etc.) that define its attributes. An attribute of an element is a
characteristic quality such as intensity, speed of communication, etc. In the
study of biological systems (and of systems in general), the evolution of the
attributes over time is fundamental, because this is what expresses the sys-
tem’s dynamics and it is through this evolution that the system attains its
goals.
Systems in Nuclear Medicine 337
If we think of an element as being some phenomenon of the natural or social
world, or its abstract representation, having some attributes that evolve as a
consequence of its own behavior in the system to which it belongs we have
a definition of a system that is sufficiently general to embrace the aim of our
study: biological and physiological systems. A system is said to be dynamic
if its attributes change over time.
There are two key concepts in this definition of a system: relation and
interaction. There is no system without them. They build it up.
A simple unique animal or vegetal cell is a very complex system with a
large number of elements acting in a united and coordinated manner in order
to carry out the actions needed for the life and reproduction of the cell.
A working automobile contains thousands of elements of different natures:
fuel tank, engine, tires, electronic components, microprocessors, etc. Informa-
tion (from multiple sensors and actuators to and from the microprocessors),
mass (from fuel tank for the motor injectors), and energy (from the engine to
the wheels, e.g., the mechanical energy) establish a multiplicity of connections
among the automobile elements.
Mass, energy, and information are the three elements circulating in systems.
They are the three pillars of modern science. In biological systems, the infor-
mation circulates in the proteins and is in the genome. A relation between
two elements is established by the flow of materials, information, and energy
between them. So, communication is synonymous of relation, in this context.
Acell is a sophisticated and complex fabric, with many components subject
to a well-defined set of relations. The same components subjected to a differ-
ent set of relations may give rise to cells with different characteristics. Each
component has a specific mission, and the interaction among them, combin-
ing their missions, makes life possible. The exchange of mass (e.g., nutrients),
energy (e.g., heat), and information (through genes and proteins) are the basic
conditions for the existence of the cellular system as a whole.
7.1.1.2 Feedback and Self-Regulation
Our nervous system senses the skin temperature and sends this information
to the brain. The brain, knowing whether it is good or bad, sends the order to
produce more heat (by consuming more calories) or to cool (by perspiration)
to the appropriate organs. This is a regulating system similar to many others
that are part of our daily environment (air conditioning, speed regulator in
cars, temperature regulator in an oven, etc.), in which the essential element is
the flow of information about the state of the system to some device whose
mission is to keep the system in good condition. This is called feedback (from
output to input). Feedback allows self-regulation, as the capacity of the whole
system to keep itself in a viable state. The human body contains multiple
intricate feedback paths that enable the self-regulation of the different organs
and physiological systems essential to the preservation of life (sugar in blood,
acid–base equilibrium, etc.).