Evaluation of Non-Invasive Methods for (R)-[<SUP>11</SUP>C]PK11195 PET Image Quantification in Multiple Sclerosis

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Tipo de produção
article
Data de publicação
2024
DOI
Scopus
Web of Science
PubMed
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Título do Volume
Editora
MDPI
Autores
SCHUCK, Phelipi N.
ARAUJO, Adriel S. de
Citação
JOURNAL OF IMAGING, v.10, n.2, article ID 39, 14p, 2024
Projetos de Pesquisa
Unidades Organizacionais
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Resumo
This study aims to evaluate non-invasive PET quantification methods for (R)-[C-11]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[C-11]PK11195 PET and magnetic resonance images. The total volume of distribution (VT) and distribution volume ratio (DVR) were measured in the gray matter, white matter, caudate nucleus, putamen, pallidum, thalamus, cerebellum, and brainstem using AIF, the image-derived input function (IDIF) from the carotid arteries, and pseudo-reference regions from supervised clustering analysis (SVCA). Uptake differences between MS and HC groups were tested using statistical tests adjusted for age and sex, and correlations between the results from the different quantification methods were also analyzed. Significant DVR differences were observed in the gray matter, white matter, putamen, pallidum, thalamus, and brainstem of MS patients when compared to the HC group. Also, strong correlations were found in DVR values between non-invasive methods and AIF (0.928 for IDIF and 0.975 for SVCA, p < 0.0001). On the other hand, (R)-[C-11]PK11195 uptake could not be differentiated between MS patients and HC using VT values, and a weak correlation (0.356, p < 0.0001) was found between VTAIF and VTIDIF. Our study shows that the best alternative for AIF is using SVCA for reference region modeling, in addition to a cautious and appropriate methodology.
Palavras-chave
TSPO, neuroinflammation, kinetic modelling, quantification, image-derived input function
URI
https://observatorio.fm.usp.br/handle/OPI/58846
Referências
  1. Banati RB, 2000, BRAIN, V123, P2321, DOI 10.1093/brain/123.11.2321
  2. Boellaard R., 2008, 2008 IEEE NUCL SCI S, P5400
  3. Croteau E, 2010, EUR J NUCL MED MOL I, V37, P1539, DOI 10.1007/s00259-010-1443-z
  4. de Souza AM, 2021, NEURAL REGEN RES, V16, P2494, DOI 10.4103/1673-5374.313062
  5. DEPIERRO AR, 1995, IEEE T MED IMAGING, V14, P132, DOI 10.1109/42.370409
  6. Finkelsztejn A, 2009, ARQ NEURO-PSIQUIAT, V67, P1071, DOI 10.1590/S0004-282X2009000600021
  7. Giannetti P, 2014, NEUROBIOL DIS, V65, P203, DOI 10.1016/j.nbd.2014.01.018
  8. Gousias IS, 2008, NEUROIMAGE, V40, P672, DOI 10.1016/j.neuroimage.2007.11.034
  9. Hammers A, 2003, HUM BRAIN MAPP, V19, P224, DOI 10.1002/hbm.10123
  10. Hunter CRRN, 2016, MED PHYS, V43, P1829, DOI 10.1118/1.4943565
  11. Jucaite A, 2012, EJNMMI RES, V2, DOI 10.1186/2191-219X-2-15
  12. Kang Y, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0201289
  13. Kaunzner UW, 2019, BRAIN, V142, P133, DOI 10.1093/brain/awy296
  14. Kaunzner UW, 2017, MULT SCLER RELAT DIS, V15, P27, DOI 10.1016/j.msard.2017.04.008
  15. Kropholler MA, 2006, J CEREBR BLOOD F MET, V26, P1431, DOI 10.1038/sj.jcbfm.9600289
  16. Lammertsma AA, 2017, J NUCL MED, V58, P1019, DOI 10.2967/jnumed.116.188029
  17. Logan J, 1996, J CEREBR BLOOD F MET, V16, P834, DOI 10.1097/00004647-199609000-00008
  18. Logan J, 2000, NUCL MED BIOL, V27, P661, DOI 10.1016/S0969-8051(00)00137-2
  19. Passamonti L, 2017, BRAIN, V140, P781, DOI 10.1093/brain/aww340
  20. Pitombeira MS, 2022, EUR J NUCL MED MOL I, V49, P4551, DOI 10.1007/s00259-022-05899-2
  21. Politis M, 2012, NEUROLOGY, V79, P523, DOI 10.1212/WNL.0b013e3182635645
  22. Qi JY, 2002, 2002 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, PROCEEDINGS, P413
  23. Rissanen E, 2018, NEUROL-NEUROIMMUNOL, V5, DOI 10.1212/NXI.0000000000000443
  24. Rissanen E, 2014, J NUCL MED, V55, P939, DOI 10.2967/jnumed.113.131698
  25. Rizzo G, 2019, J CEREBR BLOOD F MET, V39, DOI 10.1177/0271678X17742004
  26. Samkoff L.M., 2014, Multiple sclerosis and CNS inflammatory disorders
  27. Schubert J, 2021, EUR J NUCL MED MOL I, V49, P257, DOI 10.1007/s00259-021-05309-z
  28. Stokholm MG, 2017, LANCET NEUROL, V16, P789, DOI 10.1016/S1474-4422(17)30173-4
  29. Sucksdorff M, 2017, J NUCL MED, V58, P1646, DOI 10.2967/jnumed.116.183020
  30. Turkheimer FE, 2007, J NUCL MED, V48, P158
  31. van der Weijden CWJ, 2023, EUR J NUCL MED MOL I, V50, P1636, DOI 10.1007/s00259-022-06057-4
  32. Wang GB, 2020, IEEE T RADIAT PLASMA, V4, P663, DOI [10.1109/TRPMS.2020.3025086, 10.1109/trpms.2020.3025086]
  33. Wimberley C, 2021, EUR J NUCL MED MOL I, V49, P246, DOI 10.1007/s00259-021-05248-9
  34. Yaqub M, 2012, J CEREBR BLOOD F MET, V32, P1600, DOI 10.1038/jcbfm.2012.59
  35. Zanotti-Fregonara P, 2012, NUCL MED COMMUN, V33, P982, DOI 10.1097/MNM.0b013e328356185c
  36. Zanotti-Fregonara P, 2009, J CEREBR BLOOD F MET, V29, P1825, DOI 10.1038/jcbfm.2009.93

Coleções
Artigos e Materiais de Revistas Científicas - FM/MDR