Skip to main content
Log in

Multimodal molecular 3D imaging for the tumoral volumetric distribution assessment of folate-based biosensors

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The aim of this study was to characterize the in vivo volumetric distribution of three folate-based biosensors by different imaging modalities (X-ray, fluorescence, Cerenkov luminescence, and radioisotopic imaging) through the development of a tridimensional image reconstruction algorithm. The preclinical and multimodal Xtreme imaging system, with a Multimodal Animal Rotation System (MARS), was used to acquire bidimensional images, which were processed to obtain the tridimensional reconstruction. Images of mice at different times (biosensor distribution) were simultaneously obtained from the four imaging modalities. The filtered back projection and inverse Radon transformation were used as main image-processing techniques. The algorithm developed in Matlab was able to calculate the volumetric profiles of 99mTc-Folate-Bombesin (radioisotopic image), 177Lu-Folate-Bombesin (Cerenkov image), and FolateRSense™ 680 (fluorescence image) in tumors and kidneys of mice, and no significant differences were detected in the volumetric quantifications among measurement techniques. The imaging tridimensional reconstruction algorithm can be easily extrapolated to different 2D acquisition-type images. This characteristic flexibility of the algorithm developed in this study is a remarkable advantage in comparison to similar reconstruction methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Wang DS, Dake MD, Park JM, Kuo MD (2009) Molecular imaging: a primer for interventionalists and imagers. J Vasc Interv Radiol 20:1405–1423

    CAS  Google Scholar 

  2. Hoffman RM (2002) In vivo imaging of metastatic cancer with fluorescent proteins. Cell Death Differ 9(8):786–789. https://doi.org/10.1038/sj.cdd.4401077

    Article  PubMed  CAS  Google Scholar 

  3. Liu H, Ren G, Miao Z, Zhang X, Tang X, Han P et al (2010) Molecular optical imaging with radioactive probes. PLoS One 5(12):e14484. https://doi.org/10.1371/journal.pone.0014484

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Stuker F, Ripoll J, Rudin M (2011) Fluorescence molecular tomography: principles and potential for pharmaceutical research. Pharmaceutics 3(4):229–274. https://doi.org/10.3390/pharmaceutics3020229

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Rao J, Dragulescu-Andrasi A, Yao H (2007) Fluorescence imaging in vivo: recent advances. Curr Opin Biotechnol 18(1):17–25. https://doi.org/10.1016/j.copbio.2007.01.003

    Article  PubMed  CAS  Google Scholar 

  6. Ocak M, Gillman AG, Bresee J, Zhang L, Vlad AM, Müller C, Schibli R, Edwards WB, Anderson CJ, Gach HM (2015) Folate receptor-targeted multimodality imaging of ovarian cancer in a novel syngeneic mouse model. Mol Pharm 12(2):542–553. https://doi.org/10.1021/mp500628g

    Article  PubMed  CAS  Google Scholar 

  7. Aranda-Lara L, Ferro-Flores G, Ramírez F de M, Ocampo-García B, Santos-Cuevas C, Díaz-Nieto L et al. (2016) Improved radiopharmaceutical based on 99mTc-Bombesin–folate for breast tumour imaging. Nucl Med Commun [Internet]. 37:377–86. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00006231-201604000-00007

  8. Santos-Cuevas CL, Ferro-Flores G, Arteaga de Murphy C, Ramírez FDM, Luna-Gutiérrez MA, Pedraza-López M et al (2009) Design, preparation, in vitro and in vivo evaluation of 99mTc-N2S2-Tat(49-57)-bombesin: a target-specific hybrid radiopharmaceutical. Int J Pharm 375(1-2):75–83. https://doi.org/10.1016/j.ijpharm.2009.04.018

    Article  PubMed  CAS  Google Scholar 

  9. Kim SM, Choi N, Hwang S, Yim MS, Lee JS, Lee SM, Cho G, Ryu EK (2013) Folate receptor-specific positron emission tomography imaging with folic acid-conjugated tissue inhibitor of metalloproteinase-2. Bull Kor Chem Soc 34(11):3243–3248. https://doi.org/10.5012/bkcs.2013.34.11.3243

    Article  CAS  Google Scholar 

  10. Kwekkeboom DJ, Mueller-Brand J, Paganelli G, Anthony LB, Pauwels S, Kvols LK et al (2005) Overview of results of peptide receptor radionuclide therapy with 3 radiolabeled somatostatin analogs. J Nucl Med 46(Suppl 1):62S–66S

    PubMed  CAS  Google Scholar 

  11. Chu Z, La Sance K, Blanco V, Kwon C-H, Kaur B, Frederick M et al (2014) In vivo optical imaging of brain tumors and arthritis using fluorescent SapC-DOPS nanovesicles. J Vis Exp [Internet]. 12:1–7. Available from: http://www.jove.com/video/51187/in-vivo-optical-imaging-brain-tumors-arthritis-using-fluorescent-sapc

  12. Aranda-Lara L, Ferro-Flores G, Azorin-Vega E, Ramirez FM, Jimenez-Mancilla N, Ocampo-Garcia B et al (2016) Synthesis and evaluation of Lys1(alpha,gamma-Folate)Lys3(177Lu-DOTA)-Bombesin(1–14) as a potential theranostic radiopharmaceutical for breast cancer. Appl Radiat Isot. Elsevier 107:214–219. https://doi.org/10.1016/j.apradiso.2015.10.030

    Article  PubMed  CAS  Google Scholar 

  13. Kelemen LE (2006) The role of folate receptor alpha in cancer development, progression and treatment: cause, consequence or innocent bystander? Int J Cancer 119(2):243–250. https://doi.org/10.1002/ijc.21712

    Article  PubMed  CAS  Google Scholar 

  14. Teng L, Xie J, Teng L, Lee RJ (2012) Clinical translation of folate receptor-targeted therapeutics. Expert Opin Drug Deliv 9:901–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22663189

  15. Mitra A, Renukuntla J, Shah S, Boddu SHS, Vadlapudi A, Vadlapatla R, et al (2015) Functional characterization and expression of folate receptor-α in T47D human breast cancer cells. Drug Dev. Ther 6:52. Available from: http://www.ddtjournal.org/text.asp?2015/6/2/52/162441

  16. Sancho V, Di Florio A, Moody TW, Jensen RT (2011) Bombesin receptor-mediated imaging and cytotoxicity: review and current status. Curr Drug Deliv 8(1):79–134. https://doi.org/10.2174/156720111793663624

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Dalm SU, Martens JWM, Sieuwerts a. M, van Deurzen CHM, Koelewijn SJ, de Blois E, et al (2015) In vitro and in vivo application of radiolabeled gastrin-releasing peptide receptor ligands in breast cancer. J Nucl Med 56:752–7. Available from: http://jnm.snmjournals.org/cgi/doi/10.2967/jnumed.114.153023

  18. Sasser ATA, Orton SP, Leevy MW (2014) Multimodal in vivo fluorescen, luminescent and X-ray imaging in preclinical studies of inflammation and immunobiology 2009–10

  19. Bufkin K, Univversity W, Leevy M, Mentor PD (2015) Multimodal imaging trials with zebrafish specimens. 1:1–5

  20. Martí-Bonmatí L, Sopena R, Bartumeus P, Sopena P (2010) Multimodality imaging techniques. Contrast Media Mol Imaging 5:180–9. Available from: http://doi.wiley.com/10.1002/cmmi.393

  21. Magota K, Kubo N, Kuge Y, Nishijima K, Zhao S, Tamaki N (2011) Performance characterization of the Inveon preclinical small-animal PET/SPECT/CT system for multimodality imaging. Eur J Nucl Med Mol Imaging 38:742–752. https://doi.org/10.1007/s00259-010-1683-y

    Article  PubMed  Google Scholar 

  22. Hwang JY, Wachsmann-Hogiu S, Ramanujan VK, Ljubimova J, Gross Z, Gray HB et al (2012) A multimode optical imaging system for preclinical applications in vivo: Technology development, multiscale imaging, and chemotherapy assessment. Mol Imaging Biol 14:431–442. https://doi.org/10.1007/s11307-011-0517-z

    Article  PubMed  PubMed Central  Google Scholar 

  23. Chapon C, Jackson JS, Aboagye EO, Herlihy AH, Jones WA, Bhakoo KK (2009) An in vivo multimodal imaging study using MRI and PET of stem cell transplantation after myocardial infarction in rats. Mol. Imaging Biol 11:31–38. https://doi.org/10.1007/s11307-008-0174-z

    Article  PubMed  Google Scholar 

  24. Paproski RJ, Li Y, Barber Q, Lewis JD, Campbell RE, Zemp R (2015) Validating tyrosinase homologue melA as a photoacoustic reporter gene for imaging Escherichia coli. J Biomed Opt 20:106008. Available from: http://biomedicaloptics.spiedigitallibrary.org/article.aspx?. https://doi.org/10.1117/1.JBO.20.10.106008

  25. Doney E, Van Avermaete T, Chapman S, Waldeck J, Leevy WM (2013) Application note # AP0128 Jun 2013 Planar imaging of 99m Tc labeled SPECT probes in living mice using the In-Vivo Xtreme platform with radioisotopic phosphor Screen. 1–5

  26. Sumi NJ, Lima E, Pizzonia J, Orton SP, Craveiro V, Joo W, et al (2014) Murine model for non-invasive imaging to detect and monitor ovarian cancer recurrence. J. Vis. Exp. [Internet]. e51815. Available from: http://www.jove.com/video/51815/murine-model-for-non-invasive-imaging-to-detect-monitor-ovarian

  27. Hu GHG, Li HLH, Yao JYJ, Bai JBJ (2008) Fluorescent optical imaging of small animals using filtered back-projection 3D surface reconstruction method. 2008 Int Conf Biomed Eng Informatics 2:1000–1004

    Google Scholar 

  28. Kuo C, Coquoz O, Troy TL, Xu H, Rice BW (2007) Three-dimensional reconstruction of in vivo bioluminescent sources based on multispectral imaging. J Biomed Opt 12(2):024007. https://doi.org/10.1117/1.2717898

    Article  PubMed  CAS  Google Scholar 

  29. Song JS, Lee JM, Sohn JY, Yoon J-H, Han JK, Choi BI (2015) Hybrid iterative reconstruction technique for liver CT scans for image noise reduction and image quality improvement: evaluation of the optimal iterative reconstruction strengths. Radiol. Med 120:259–267. https://doi.org/10.1007/s11547-014-0441-9

    Article  PubMed  Google Scholar 

  30. Koch W, Suessmair C, Tatsch K, Pöpperl G (2011) Iterative reconstruction or filtered backprojection for semi-quantitative assessment of dopamine D2 receptor SPECT studies? Eur. J. Nucl. Med. Mol. Imaging 38:1095–1103. https://doi.org/10.1007/s00259-011-1737-9

    Article  PubMed  CAS  Google Scholar 

  31. Zeng GL (2010) Medical image reconstruction: a conceptual tutorial. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 125–173. https://doi.org/10.1007/978-3-642-05368-9_6

    Book  Google Scholar 

  32. Kikuchi S, Matsuya A, Yamaguchi M, Ohyama N (1996) Microscopic computed tomography based on generalized analytic reconstruction from discrete samples. Opt Rev 3:22–28. https://doi.org/10.1007/s10043-996-0022-9

    Article  Google Scholar 

  33. Aguirre J, Sisniega A, Ripoll J, Desco M, Vaquero JJ (2008) Design and development of a co-planar fluorescence and X-ray tomograph. IEEE Nucl Sci Symp Conf Rec 5412–3

  34. Ducros N, Bassi A, Valentini G, Canti G, Arridge S, D’Andrea C (2013) Fluorescence molecular tomography of an animal model using structured light rotating view acquisition. J Biomed Opt 18(2):020503. https://doi.org/10.1117/1.JBO.18.2.020503

    Article  CAS  Google Scholar 

  35. Liu X, Wang D, Bai J (2009) Fluorescence molecular tomography with optimal radon transform based surface reconstruction. Conf Proc IEEE Eng Med Biol Soc 2009:1404–1407. https://doi.org/10.1109/IEMBS.2009.5334178

    Article  PubMed  Google Scholar 

  36. Butz T. Fourier transformation for pedestrians. Cham: Springer International Publishing; 2015. p. 173–81. Available from: https://doi.org/10.1007/978-3-319-16985-9_7

  37. Jähne B (1995) Digital image processing: concepts, algorithms, and scientific applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 231–252. https://doi.org/10.1007/978-3-662-03174-2_13

    Book  Google Scholar 

  38. Prabhat P, Arumugam S, Madan VK (2012) Filtering in filtered backprojection computerized tomography. Proc NCNTE-2012 4–7

  39. Kendziorra C, Meyer H, Dewey M (2015) Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. PLoS one [internet]. Public Libr Sci 9:1–12. https://doi.org/10.1371/2Fjournal.pone.0116103

  40. Hautière N, Tarel J-P, Aubert D, Dumont É (2008) Blind Contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. [Internet]. 27:87–95. Available from: http://www.ias-iss.org/ojs/IAS/article/view/834

  41. Valencia-Murillo JF (2014) Poveda-Sendales DA, Valencia-Vargas DF. Evaluating the impact of image preprocessing on iris segmentation. Tecno Lógicas 17:31–41

    Article  Google Scholar 

  42. Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510. https://doi.org/10.1109/83.826787

    Article  PubMed  CAS  Google Scholar 

  43. Ogoda M, Hishinuma K, Yamada M, Shimura K (1997) Unsharp masking technique using multiresolution analysis for computed radiography image enhancement. J Digit Imaging [Internet]. 10:185–9. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3452800&tool=pmcentrez&rendertype=abstract

  44. Suter SK, Ma B, Entezari A (2014) Advances in visual computing: 10th International Symposium, ISVC 2014, Las Vegas, NV, USA, December 8–10, 2014, Proceedings, Part I. In: Bebis G, Boyle R, Parvin B, Koracin D, McMahan R, Jerald J, et al., editors. Cham: Springer International Publishing. p. 313–22. Available from: https://doi.org/10.1007/978-3-319-14249-4_30

  45. Tomayko MM, Reynolds CP (1989) Determination of subcutaneous tumor size in athymic (nude) mice. Cancer Chemother. Pharmacol 24:148–154. https://doi.org/10.1007/BF00300234

    Article  PubMed  CAS  Google Scholar 

  46. Schumann S, Liu L, Tannast M, Bergmann M, Nolte L-P, Zheng G (2013) An integrated system for 3D hip joint reconstruction from 2D X-rays: a preliminary validation study. Ann Biomed Eng 41:2077–2087. https://doi.org/10.1007/s10439-013-0822-6

    Article  PubMed  Google Scholar 

  47. Arranz A, Rudin M, Zaragoza C, Ripoll J (2015) Methods in mouse atherosclerosis. In: Andrés V, Dorado B, editors. New York, NY: Springer New York. p. 367–76. Available from: https://doi.org/10.1007/978-1-4939-2929-0_27

  48. Backfrieder W, Hanel R, Diemling M, Lorang T, Kettenbach J, Imhof H (2001) Digital (R) evolution in radiology. In: Hruby W, editor. Vienna: Springer Vienna p. 131–9. Available from: https://doi.org/10.1007/978-3-7091-3707-9_16

  49. Asaithambi N, Kayalvizhi R, Selvi W. Proceedings of the International Conference on Soft Computing Systems: ICSCS 2015, Volume 1. In: Suresh PL, Panigrahi KB, editors. New Delhi: Springer India; 2016. p. 415–25. Available from: https://doi.org/10.1007/978-81-322-2671-0_40

  50. Chin PTK, Welling MM, Meskers SCJ, Valdes Olmos RA, Tanke H, Van Leeuwen FWB (2013) Optical imaging as an expansion of nuclear medicine: Cerenkov-based luminescence vs fluorescence-based luminescence. Eur J Nucl Med Mol Imaging 40(8):1283–1291. https://doi.org/10.1007/s00259-013-2408-9

    Article  PubMed  CAS  Google Scholar 

  51. Lee YC, Fullerton GD, Goins BA (2015) Comparison of multimodality image-based volumes in preclinical tumor models using In-Air micro-CT image volume as reference tumor volume. Open J. Med. Imaging [Internet]. 05:117–32. Available from: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=59462&#abstract

  52. van Driel PB a. a., van de Giessen M, Boonstra MC, Snoeks TJ a., Keereweer S, Oliveira S, et al. (2014) Characterization and evaluation of the artemis camera for fluorescence-guided cancer surgery. Mol. Imaging Biol. [Internet]. 17:413–23. Available from: http://link.springer.com/10.1007/s11307-014-0799-z

  53. Ferro-flores G, Ocampo-garcía BE, Santos-cuevas CL, María F De, Azorín-vega EP, Meléndez-alafort L (2015) Theranostic radiopharmaceuticals based on gold nanoparticles labeled with 177 Lu and conjugated to peptides 150–9

  54. Jiménez-Mancilla N, Ferro-Flores G, Santos-Cuevas C, Ocampo-García B, Luna-Gutiérrez M, Azorín-Vega E, et al (2013) Multifunctional targeted therapy system based on (99m) Tc/(177) Lu-labeled gold nanoparticles-Tat(49–57)-Lys(3)-bombesin internalized in nuclei of prostate cancer cells. J. Labelled Comp. Radiopharm. [Internet]. 56:663–71. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25196028

  55. Shrivastava A, Ding H, Kothandaraman S, Wang S-H, Gong L, Williams M et al (2014) A high-affinity near-infrared fluorescent probe to target bombesin receptors. Mol Imaging Biol 16:661–669. https://doi.org/10.1007/s11307-014-0727-2

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are grateful for the support of the Mexican National Council of Science and Technology (CONACYT-SEP-CB-2014-01-242443 and CONACyT-PDCPN-2015-01-1040) and to the National Polytechnic Institute (IPN-SIPCOFAA-2015-0344). This research was carried out as part of the activities of the “Laboratorio Nacional de Investigación y Desarrollo de Radiofármacos, CONACyT.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Clara L. Santos-Cuevas.

Ethics declarations

The study was approved by the Institutional Ethical Committee for the Care and Use of Laboratory Animals (“Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán”).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramírez-Nava, G.J., Santos-Cuevas, C.L., Chairez, I. et al. Multimodal molecular 3D imaging for the tumoral volumetric distribution assessment of folate-based biosensors. Med Biol Eng Comput 56, 1135–1148 (2018). https://doi.org/10.1007/s11517-017-1755-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-017-1755-2

Keywords

Navigation