Autors: Tsvetkova, D. S., Georgieva, V. M.
Title: COMPARISON OF VARIOUS REGISTRATION METHODS ON MRI AND PET/PSMA MEDICAL IMAGES OF THE PROSTATE GLAND
Keywords:

Abstract: Image registration is an essential task in medical image analysis and has been a subject of ongoing research for several decades. Multimodal medical image registration plays a crucial role in the fusion of images, particularly in detecting and treatment of prostate cancer. Progress in diverse prostate imaging methods has significantly helped in tumor detection, patient risk assessment, and overall healthcare quality. The fusion of MRI/PET-PSMA imaging is a novel technique, that holds significant promise by combining biological and morphological data for oncological diagnosis, particularly in prostate cancer patients. Nevertheless, the lack of MRI/PET systems underscores the need for an efficient approach to merge separate PET-PSMA and MRI images acquired from the same patient. This paper offers a comparison of various techniques regarding the registration of MRI and PET/PSMA medical images of the prostate gland. The primary goal is to contribute to the advancement of medical registration methods, particularly those that improve the diagnosis of prostate diseases.

References

  1. D. Tsvetkova, V. Georgieva, V. Hadzhiyska, and Y. Gramatikov, "Review of multimodal medical image fusion techniques and their application in prostate cancer," in Proceeding of 17th International Conference on Communications, Electromagnetic and Medical Applications (CEMA), pp.16-20, ISSN:1314-2100, 2023.
  2. A. Khan, C. M. Moore, and M. Minhaj Siddiqui, "Prostate MRI and image quality: The urologist's perspective," Eur. J. Radiol., vol. 170, p. 111255, Jan. 2024, doi: 10.1016/j.ejrad.2023.111255.
  3. A. P. Sharma et al., "Accuracy of combined multi-parametric MRI and PSMA PET-CT in diagnosing localized prostate cancer: newer horizons for a biopsy-free pathway," Eur. J. Hybrid Imaging, vol. 7, no. 1, p. 24, Nov. 2023, doi: 10.1186/s41824-023-00182-5.
  4. M. V. G. Amaral, M. T. Daher, and G. R. F. Bertolini, "Effects of adding the Kinesio Taping method to a combined exercise training program for soccer players with instability of the ankle: A randomized controlled trial," Clinics, vol. 73, Suppl. 1, p. e586s, 2018, doi: 10.6061/clinics/2018/e586s.
  5. L. Cereser, L. Evangelista, G. Giannarini, and R. Girometti, "Prostate MRI and PSMA-PET in the Primary Diagnosis of Prostate Cancer," Diagnostics, vol. 13, no. 16, p. 2697, 2023, doi: 10.3390/diagnostics13162697.
  6. I. Sonni et al., "Head-to-Head Comparison of 68Ga-PSMA-11 PET/CT and mpMRI with a Histopathology Gold Standard in the Detection, Intraprostatic Localization, and Determination of Local Extension of Primary Prostate Cancer: Results from a Prospective Single-Center Imaging Trial," J. Nucl. Med., vol. 63, no. 6, pp. 847-854, Jun. 2022, doi: 10.2967/jnumed.121.262398.
  7. D. L. G. Hill, P. G. Batchelor, M. Holden, and D. J. Hawkes, "Medical image registration," Phys. Med. Biol., vol. 46, pp. R1-R45, 2001.
  8. Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu, and X. Yang, "Deep learning in medical image registration: A review," Phys. Med. Biol., vol. 65, p. 20TR01, 2020.
  9. K. Rohr, H. Stiehl, R. Sprengel, T. Buzug, J. Weese, and M. Kuhn, "Landmark-based elastic registration using approximating thin-plate splines," IEEE Trans. Med. Imaging, vol. 20, pp. 526-534, 2001.
  10. K. Rohr, M. Fornefett, and H. Stiehl, "Spline-based elastic image registration: Integration of landmark errors and orientation attributes," Comput. Vis. Image Underst., vol. 90, pp. 153-168, 2003.
  11. M. Ferrant et al., "Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model," IEEE Trans. Med. Imaging, vol. 20, pp. 1384-1397, 2001.
  12. F. El-Gamal, M. Elmogy, and A. Atwan, "Current trends in medical image registration and fusion," Egypt. Inform. J., vol. 17, no. 1, pp. 99-124, 2016.
  13. T. Liu, D. Shen, and C. Davatzikos, "Deformable registration of cortical structures via hybrid volumetric and surface warping," Neuroimage, vol. 22, pp. 1790-1801, 2004.
  14. V. Mani and Dr. Arivazhagan, "Survey of Medical Image Registration," J. Biomed. Eng. Technol., vol. 1, no. 2, pp. 8-25, 2013.
  15. D. L. G. Hill, P. G. Batchelor, M. Holden, and D. J. Hawkes, "Medical image registration," Phys. Med. Biol., vol. 46, pp. R1-R45, 2001.
  16. Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu, and X. Yang, "Deep learning in medical image registration: A review," Phys. Med. Biol., vol. 65, p. 20TR01, 2020.
  17. D. Yang et al., "Technical note: Dirart–a software suite for deformable image registration and adaptive radiotherapy research," Med. Phys., vol. 38, no. 1, pp. 67-77, 2011, doi: 10.1118/1.3521468.
  18. D. Yang, H. Li, D. A. Low, J. O. Deasy, and I. El Naqa, "A fast inverse consistent deformable image registration method based on symmetric optical flow computation," Phys. Med. Biol., vol. 53, no. 21, pp. 6143-6165, 2008, doi: 10.1088/0031-9155/53/21/017.
  19. T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, "Diffeomorphic demons: Efficient non-parametric image registration," Neuroimage, vol. 45, no. 1, Suppl. 1, pp. S61-S72, 2009.
  20. B. B. Avants et al., "A reproducible evaluation of ants similarity metric performance in brain image registration," Neuroimage, vol. 54, no. 3, pp. 2033-2044, 2011, doi: 10.1016/j.neuroimage.2010.09.025.
  21. S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, "Elastix: a toolbox for intensity-based medical image registration," [no journal specified].
  22. F. Darzi and T. Bocklitz, "A Review of Medical Image Registration for Different Modalities," Bioengineering, vol. 11, no. 8, p. 786, Aug. 2024. doi: 10.3390/bioengineering11080786
  23. X. Cheng, L. Zhang, and Y. Zheng, "Deep similarity learning for multimodal medical images," Comput. Methods Biomech. Biomed. Eng. Imaging Vis., vol. 6, no. 3, pp. 248-252, 2018, doi: 10.1080/21681163.2015.1135299.
  24. M. Simonovsky, B. Gutirrez-Becker, D. Mateus, N. Navab, and N. Komodakis, "A deep metric for multimodal registration," in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, Springer International Publishing, pp. 10-18.
  25. R. Liao et al., "An artificial agent for robust image registration," ArXiv, vol. abs/1611.10336, 2016.
  26. J. F. Fan, X. H. Cao, E. A. Yap, and D. G. Shen, "Birnet: Brain image registration using dual-supervised fully convolutional networks," Med. Image Anal., vol. 54, pp. 193-206, 2019, doi: 10.1016/j.media.2019.03.006.
  27. Z. Jiang, F. F. Yin, Y. Ge, and L. Ren, "A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration," Phys. Med. Biol., 2019, doi: 10.1088/1361-6560/ab5da0.
  28. C. Qin et al., "Unsupervised deformable registration for multi-modal images via disentangled representations," in Information Processing in Medical Imaging, Ipmi 2019, vol. 11492, pp. 249-261, 2019, doi: 10.1007/978-3-030-20351-1_19.
  29. K. A. J. Eppenhof and J. P. W. Pluim, "Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks," J. Med. Imaging, vol. 5, no. 2, 2018, doi: 10.1117/1.JMI.5.2.024003.
  30. X. L. Liu, D. S. Jiang, M. N. Wang, and Z. J. Song, "Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks," Med. Biol. Eng. Comput., vol. 57, no. 5, pp. 1037-1048, 2019, doi: 10.1007/s11517-018-1924-y.
  31. J. H. Song, "Methods for evaluating image registration," PhD dissertation, Univ. of Iowa, Iowa City, IA, USA, 2017. doi: 10.17077/etd.v0vailob.
  32. M. Fitzpatrick and J. B. West, "The Distribution of Target Registration Error in Rigid-Body Point-Based Registration," IEEE Transactions on Medical Imaging, vol. 20, no. 9, pp. 917-927, Sept. 2001. doi: 10.1109/42.952729.
  33. K. H. Zou et al., "Statistical validation of image segmentation quality based on a spatial overlap index," Academic Radiology, vol. 11, no. 2, pp. 178-189, Feb. 2004. doi: 10.1016/s1076-6332(03)00671-8. PMCID: PMC1415224.
  34. R. Karthik, R. Menaka, A. Johnson, and S. Anand, "Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects," Computer Methods and Programs in Biomedicine, vol. 197, p. 105728, 2020. doi: 10.1016/j.cmpb.2020.105728.

Issue

Proceedings of International Conference on Communications, Electromagnetic and Medical Applications, CEMA, pp. 16-21, 2025, Greece, ISSN 13142100

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus