Annals of Nuclear Medicine

, Volume 32, Issue 5, pp 363–371 | Cite as

Quantitative evaluation of the tracer distribution in dopamine transporter SPECT for objective interpretation

  • Yu Iwabuchi
  • Tadaki Nakahara
  • Masashi Kameyama
  • Yoshitake Yamada
  • Masahiro Hashimoto
  • Yuji Ogata
  • Yohji Matsusaka
  • Mari Katagiri
  • Kazunari Itoh
  • Takashi Osada
  • Daisuke Ito
  • Hajime Tabuchi
  • Masahiro Jinzaki
Original Article



Quantification of the tracer distribution would add objectivity to the visual assessments of dopamine transporter (DAT) single photon emission computed tomography (SPECT) data. Our study aimed to evaluate the diagnostic utility of fractal dimension (FD) as a quantitative indicator of tracer distribution and compared with the conventional quantitative value: specific binding ratio (SBR). We also evaluated the utility of the combined index SBR/FD (SBR divided by FD).

Materials and methods

We conducted both clinical and phantom studies. In the clinical study, 150 patients including 110 patients with Parkinsonian syndrome (PS) and 40 without PS were enrolled. In the phantom study, we used a striatal phantom with the striatum chamber divided into two spaces, representing the caudate nucleus and putamen. The SBR, FD, and SBR/FD were calculated and compared between datasets for evaluating the diagnostic utility. Mann–Whitney test and receiver-operating characteristics (ROC) analysis were used for analysis.


ROC analysis revealed that the FD value had high diagnostic performance [the areas under the curve (AUC) = 0.943] and the combined use of SBR and FD (SBR/FD) delivered better results than the SBR alone (AUC, 0.964 vs 0.899; p < 0.001). The sensitivity, specificity, and accuracy, respectively, were 79.1, 85.0, and 80.7% with SBR, 84.5, 97.5, and 88.0% with FD, and 92.7, 87.5, and 91.3% with SBR/FD.


Our results confirmed that the FD value is a useful diagnostic index, which reflects the tracer distribution in DAT SPECT images. The combined use of SBR and FD was more useful than either used alone.


123I-Ioflupane 123I-FP-CIT DAT Fractal analysis Fractal dimension 



Parkinson’s disease


Positron emission tomography


Single photon emission computed tomography


Dopamine transporter


Parkinsonian syndrome


Dementia with Lewy body


Specific binding ratio


Volume of interest


Fractal dimension


Three-dimensional-fractal analysis


Non Parkinsonian syndrome




Ordered-subset expectation maximization


Intra-class correlation coefficient


Receiver-operating characteristics


Area under the ROC curve


Limit of agreement


Selective serotonin reuptake inhibitor



The authors thank the staff of the Division of Nuclear Medicine at the Department of Diagnostic Radiology, for their valuable support. We also thank Editage for English language editing.


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

For this type of study, formal consent is not required.


  1. 1.
    Vlaar AM, van Kroonenburgh MJ, Kessels AG, Weber WE. Meta-analysis of the literature on diagnostic accuracy of SPECT in parkinsonian syndromes. BMC Neurol. 2007;7:27.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    McKeith I, O’Brien J, Walker Z, Tatsch K, Booij J, Darcourt J, et al. Sensitivity and specificity of dopamine transporter imaging with 123I-FP-CIT SPECT in dementia with Lewy bodies: a phase III, multicentre study. Lancet Neurol. 2007;6:305–13.CrossRefPubMedGoogle Scholar
  3. 3.
    Kwak Y, Müller ML, Bohnen NI, Dayalu P, Seidler RD. Effect of dopaminergic medications on the time course of explicit motor sequence learning in Parkinson’s disease. J Neurophysiol. 2010;103:942–9.CrossRefPubMedGoogle Scholar
  4. 4.
    Tondeur MC, Hambye AS, Dethy S, Ham HR. Interobserver reproducibility of the interpretation of I-123 FP-CIT single-photon emission computed tomography. Nucl Med Commun. 2010;31:717–25.CrossRefPubMedGoogle Scholar
  5. 5.
    Tossici-Bolt L, Hoffmann SM, Kemp PM, Mehta RL, Fleming JS. Quantification of [123I]FP-CIT SPECT brain images: an accurate technique for measurement of the specific binding ratio. Eur J Nucl Med Mol Imaging. 2006;33:1491–9.CrossRefPubMedGoogle Scholar
  6. 6.
    Varrone A, Dickson JC, Tossici-Bolt L, Sera T, Asenbaum S, Booij J, et al. European multicentre database of healthy controls for [123I] FP-CIT SPECT (ENC-DAT): age-related effects, gender differences and evaluation of different methods of analysis. Eur J Nucl Med Mol Imaging. 2013;40:213–27.CrossRefPubMedGoogle Scholar
  7. 7.
    Nonokuma M, Kuwabara Y, Hida K, Tani T, Takano K, Yoshimitsu K. Optimal ROI setting on the anatomically normalized I-123 FP-CIT images using high-resolution SPECT. Ann Nucl Med. 2016;30:637–44.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Nagao M, Murase K, Yasuhara Y, Ikezoe J. Quantitative analysis of pulmonary emphysema: three-dimensional fractal analysis of single-photon emission computed tomography images obtained with a carbon particle radioaerosol. Am J Roentgenol. 1998;171:1657–63.CrossRefGoogle Scholar
  9. 9.
    Nagao M, Murase K, Ichiki T, Sakai S, Yasuhara Y, Ikezoe J. Quantitative analysis of technegas SPECT: evaluation of regional severity of emphysema. J Nucl Med. 2000;41:590–5.PubMedGoogle Scholar
  10. 10.
    Nagao M, Murase K. Measurement of heterogeneous distribution on Technegas SPECT images by three-dimensional fractal analysis. Ann Nucl Med. 2002;16:369–76.CrossRefPubMedGoogle Scholar
  11. 11.
    Nagao M, Murase K, Kikuchi T, Ikeda M, Nebu A, Fukuhara R, et al. Fractal analysis of cerebral blood flow distribution in Alzheimer’s disease. J Nucl Med. 2001;42:1446–50.PubMedGoogle Scholar
  12. 12.
    Nagao M, Sugawara Y, Ikeda M, Fukuhara R, Hokoishi K, Murase K, et al. Heterogeneity of cerebral blood flow in frontotemporal lobar degeneration and Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2004;31:162–8.CrossRefPubMedGoogle Scholar
  13. 13.
    Nagao M, Sugawara Y, Ikeda M, et al. Heterogeneity of posterior limbic perfusion in very early Alzheimer’s disease. Neurosci Res. 2006;55:285–91.CrossRefPubMedGoogle Scholar
  14. 14.
    Södoferlund TA, Dickson JC, Prvulovich E, Ben-Haim S, Kemp P, Booij J, et al. Value of semiquantitative analysis for clinical reporting of 123I-2-β-carbomethoxy-3β-(4-iodopenyl)-N-(3-fluoropropyl)nortropane SPECT studies. J Nucl Med. 2013;54:714–22.CrossRefGoogle Scholar
  15. 15.
    Mäkinen E, Joutsa J, Johansson J, Mäki M, Seppänen M, Kaasinen V. Visual versus automated analysis of [I-123]FP-CIT SPECT scans in parkinsonism. J Neural Transm (Vienna). 2016;123:1309–18.CrossRefGoogle Scholar
  16. 16.
    Booij J, Dubroff J, Pryma D, Yu J, Agarwal R, Lakhani P, et al. Diagnostic performance of the visual reading of 123I-Ioflupane SPECT images when assessed with or without quantification in patients with movement disorders or dementia. J Nucl Med 2017. (Epub ahead of print).Google Scholar
  17. 17.
    Nicastro N, Garibotto V, Allali G, Assal F, Burkhard PR. Added value of combined semi-quantitative and visual [123I]FP-CIT SPECT analyses for the diagnosis of dementia with Lewy Bodies. Clin Nucl Med. 2017;42(2):e96–e102.CrossRefPubMedGoogle Scholar
  18. 18.
    Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307–10.CrossRefGoogle Scholar
  20. 20.
    DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRefPubMedGoogle Scholar
  21. 21.
    Michallek F, Dewey M. Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review. Eur Radiol. 2014;24:60–9.CrossRefPubMedGoogle Scholar
  22. 22.
    Kuikka JT, Tiihonen J, Karhu J, Bergström KA, Räsänen P. Fractal analysis of striatal dopamine re-uptake sites. Eur J Nucl Med. 1997;24:1085–90.PubMedGoogle Scholar
  23. 23.
    Kuikka JT, Yang J, Karhu J, Laitinen T, Tupala E, Hallikainen T, et al. Imaging the structure of the striatum: a fractal approach to SPECT image interpretation. Physiol Meas. 1998;19:367–74.CrossRefPubMedGoogle Scholar
  24. 24.
    Kuikka JT, Tiihonen J, Bergström KA, Karhu J, Räsänen P, Eronen M. Abnormal structure of human striatal dopamine re-uptake sites in habitually violent alcoholic offenders: a fractal analysis. Neurosci Lett. 1998;253:195–7.CrossRefPubMedGoogle Scholar
  25. 25.
    Bolt L, Fleming JS, Kemp PM. The 3D fractal dimension of DaTSCAN Images. Nucl Med Commun. 2006;27:296 (abstract).CrossRefGoogle Scholar
  26. 26.
    Bolt L, Fleming JS, Kemp PM. Quantifying DaTSCAN TM images- a comparison of region-of-interest and fractal analysis. Eur J Nucl Med Mol Imaging. 2006;33:S98 (abstract).Google Scholar
  27. 27.
    Yokoyama K, Imabayashi E, Sumida K, Sone D, Kimura Y, Sato N, et al. Computed-tomography-guided anatomic standardization for quantitative assessment of dopamine transporter SPECT. Eur J Nucl Med Mol Imaging. 2017;44:366–72.CrossRefPubMedGoogle Scholar
  28. 28.
    Kim JS, Cho H, Choi JY, Lee SH, Ryu YH, Lyoo CH, et al. Feasibility of computed tomography-guided methods for spatial normalization of dopamine transporter positron emission tomography image. PLoS One. 2015;10(7):e0132585. Scholar
  29. 29.
    Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G. Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology. 2016;86:566 – 76.CrossRefPubMedGoogle Scholar
  30. 30.
    Dickson JC, Tossici-Bolt L, Sera T, Booij J, Ziebell M, Morbelli S, et al. The impact of reconstruction and scanner characterisation on the diagnostic capability of a normal database for [123I]FP-CIT SPECT imaging. EJNMMI Res. 2017;7:10. (Epub 2017 Jan 24).CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2018

Authors and Affiliations

  • Yu Iwabuchi
    • 1
  • Tadaki Nakahara
    • 1
  • Masashi Kameyama
    • 1
    • 2
  • Yoshitake Yamada
    • 1
  • Masahiro Hashimoto
    • 1
  • Yuji Ogata
    • 1
  • Yohji Matsusaka
    • 1
  • Mari Katagiri
    • 1
  • Kazunari Itoh
    • 1
  • Takashi Osada
    • 3
  • Daisuke Ito
    • 3
  • Hajime Tabuchi
    • 4
  • Masahiro Jinzaki
    • 1
  1. 1.Department of Diagnostic RadiologyKeio University School of MedicineTokyoJapan
  2. 2.Department of Diagnostic RadiologyTokyo Metropolitan Geriatric Hospital and Institute of GerontologyTokyoJapan
  3. 3.Department of NeurologyKeio University School of MedicineTokyoJapan
  4. 4.Department of NeuropsychiatryKeio University School of MedicineTokyoJapan

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