Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                

Abdominal motion tracking with free-breathing XD-GRASP acquisitions using spatio-temporal geodesic trajectories

https://doi.org/10.1007/s11517-021-02477-w ·

Journal: Medical & Biological Engineering & Computing, 2022, № 2, p. 583-598

Publisher: Springer Science and Business Media LLC

Authors:

  1. Rihab Mansour
  2. Liset Vazquez Romaguera
  3. Catherine Huet
  4. Ahmed Bentridi
  5. Kim-Nhien Vu
  6. Jean-Sébastien Billiard
  7. Guilllaume Gilbert
  8. An Tang
  9. Samuel Kadoury

Funders

  1. nserc
  2. medteq
  3. fonds de recherche du québec - santé

List of references

  1. Balter JM, Ten Haken RK, Lawrence TS, Lam KL, Robertson JM (1996) Uncertainties in CT-based radiation therapy treatment planning associated with patient breathing. Int J Radiat Oncol Biol Phys 36(1):167–174
    https://doi.org/10.1016/S0360-3016(96)00275-1
  2. Sotiras A, Davatzikos C, Paragios N (2013) Deformable medical image registration: a survey. IEEE Trans Med Imaging 32(7):1153–1190
    https://doi.org/10.1109/TMI.2013.2265603
  3. Mutic S and Dempsey JF (2014) The ViewRay system: magnetic resonance–guided and controlled radiotherapy. In Seminars in Radiation Oncology 24(3): 196–199). WB Saunders
    https://doi.org/10.1016/j.semradonc.2014.02.008
  4. Paganelli C, Summers P, Gianoli C, Bellomi M, Baroni G, Riboldi M (2017) A tool for validating MRI-guided strategies: a digital breathing CT/MRI phantom of the abdominal site. Med Biol Eng Comput 55:2001–2014
    https://doi.org/10.1007/s11517-017-1646-6
  5. Mzenda B, Hosseini-Ashrafi M, Palmer A, Liu H, Brown DJ (2010) A simulation technique for computation of the dosimetric effects of setup, organ motion and delineation uncertainties in radiotherapy. Med Biol Eng Comput 48:661–669
    https://doi.org/10.1007/s11517-010-0616-z
  6. Wojcieszynski AP, Rosenberg SA, Brower JV, Hullett CR, Geurts MW, Labby ZE, Bassetti MF (2016) Gadoxetate for direct tumor therapy and tracking with real-time MRI-guided stereotactic body radiation therapy of the liver. Radiother Oncol 118(2):416–418
    https://doi.org/10.1016/j.radonc.2015.10.024
  7. Miranda A, Staelens S, Stroobants S, Verhaeghe J (2019) Estimation of and correction for finite motion sampling errors in small animal PET rigid motion correction. Med Biol Eng Comput 57:505–518
    https://doi.org/10.1007/s11517-018-1899-8
  8. Zhang Q, Pevsner A, Hertanto A, Hu YC, Rosenzweig KE, Ling CC, Mageras GS (2007) A patient-specific respiratory model of anatomical motion for radiation treatment planning. Med Phys 34(12):4772–4781
    https://doi.org/10.1118/1.2804576
  9. Garau N, Via R, Meschini G, Lee D, Keall P, Riboldi M, Paganelli C (2019) A ROI-based global motion model established on 4DCT and 2D cine-MRI data for MRI-guidance in radiation therapy. Phys Med Biol 64(4):045002
    https://doi.org/10.1088/1361-6560/aafcec
  10. Stemkens B, Paulson ES, Tijssen RH (2018) Nuts and bolts of 4D-MRI for radiotherapy. Phys Med Biol 63(21):21TR01
    https://doi.org/10.1088/1361-6560/aae56d
  11. Tryggestad E, Flammang A, Han-Oh S, Hales R, Herman J, McNutt T, Wong J (2013) Respiration-based sorting of dynamic MRI to derive representative 4D-MRI for radiotherapy planning. Med Phys 40(5):051909
    https://doi.org/10.1118/1.4800808
  12. Tong Y, Udupa JK, Ciesielski KC, Wu C, McDonough JM, Mong DA, Campbell RM Jr (2017) Retrospective 4D MR image construction from free-breathing slice acquisitions: a novel graph-based approach. Med Image Anal 35:345–359
    https://doi.org/10.1016/j.media.2016.08.001
  13. Romaguera LV, Olofsson N, Plantefève R, Lugez E, De Guise J, Kadoury S (2019) Automatic self-gated 4D-MRI construction from free-breathing 2D acquisitions applied on liver images. Int J Comput Assist Radiol Surg 14(6):933–944
    https://doi.org/10.1007/s11548-019-01941-1
  14. Baumgartner CF, Gomez A, Koch LM, Housden JR, Kolbitsch C, McClelland JR and King AP (2015). Self-aligning manifolds for matching disparate medical image datasets. In International Conference on Information Processing in Medical Imaging (pp. 363–374). Springer, Cham
    https://doi.org/10.1007/978-3-319-19992-4_28
  15. Clough JR, Balfour DR, Marsden PK, Prieto C, Reader AJ and King AP (2018) MRI slice stacking using manifold alignment and wave kernel signatures. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 319–323). IEEE
    https://doi.org/10.1109/ISBI.2018.8363583
  16. Han F, Zhou Z, Du D, Gao Y, Rashid S, Cao M, Shaverdian N, Hegde JV, Steinberg M, Lee P, Raldow A, Low DA, Sheng K, Yang Y, Hu P (2018) Respiratory motion-resolved, self-gated 4D-MRI using rotating Cartesian K-space (ROCK): initial clinical experience on an MRI-guided radiotherapy system. Radiother Oncol 127(3):467–473
    https://doi.org/10.1016/j.radonc.2018.04.029
  17. Küstner T, Fuin N, Hammernik K, Bustin A, Qi H, Hajhosseiny R, Prieto C (2020) CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions. Sci Rep 10(1):1–13
    https://doi.org/10.1038/s41598-020-70551-8
  18. Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, Otazo R (2014) Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 72(3):707–717
    https://doi.org/10.1002/mrm.24980
  19. Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R (2016) XD-GRASP: golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 75(2):775–788
    https://doi.org/10.1002/mrm.25665
  20. Stemkens B, Prins FM, Bruijnen T, Kerkmeijer LG, Lagendijk JJ, van den Berg CA, Tijssen RH (2019) A dual-purpose MRI acquisition to combine 4D-MRI and dynamic contrast-enhanced imaging for abdominal radiotherapy planning. Phys Med Biol 64(6):06NT02
    https://doi.org/10.1088/1361-6560/ab0295
  21. de Senneville BD, Cardiet CR, Trotier AJ, Ribot EJ, Lafitte L, Facq L, Miraux S (2020) Optimizing 4D abdominal MRI: image denoising using an iterative back-projection approach. Phys Med Biol 65(1):015003
    https://doi.org/10.1088/1361-6560/ab563e
  22. Mansour R, Antonacci AT, Bilodeau L, Romaguera LV, Cerny M, Huet C, Kadoury S (2020) Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence. Phys Med Biol 65(8):085004
    https://doi.org/10.1088/1361-6560/ab78be
  23. Schiratti JB, Allassonniere S, Colliot O and Durrleman S (2015) Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Neural Information Processing Systems (No. 28)
  24. Boumal N, Absil PA (2011) A discrete regression method on manifolds and its application to data on SO (n). IFAC Proceedings Volumes 44(1):2284–2289
    https://doi.org/10.3182/20110828-6-IT-1002.00542
  25. Usman M, Atkinson D, Odille F, Kolbitsch C, Vaillant G, Schaeffter T, Prieto C (2013) Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magn Reson Med 70(2):504–516
    https://doi.org/10.1002/mrm.24463
  26. Fessler JA, Sutton BP (2003) Nonuniform fast Fourier transforms using min-max interpolation. IEEE Trans Signal Process 51(2):560–574
    https://doi.org/10.1109/TSP.2002.807005
  27. Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med: An Official Journal of the International Society for Magnetic Resonance in Medicine 58(6):1182–1195
    https://doi.org/10.1002/mrm.21391
  28. Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2009) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196–205
    https://doi.org/10.1109/TMI.2009.2035616
  29. Benovoy M, Jacobs M, Cheriet F, Dahdah N, Arai AE, Hsu LY (2017) Robust universal nonrigid motion correction framework for first-pass cardiac MR perfusion imaging. J Magn Reson Imaging 46(4):1060–1072
    https://doi.org/10.1002/jmri.25659
  30. Jacobs M, Benovoy M, Chang LC, Arai AE, Hsu LY (2016) Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson 18(1):1–11
    https://doi.org/10.1186/s12968-016-0239-0
  31. Martinez JA, Moulin K, Yoo B, Shi Y, Kim HJ, Villablanca PJ, Ennis DB (2020) Evaluation of a workflow to define low specific absorption rate MRI protocols for patients with active implantable medical devices. J Magn Reson Imaging 52(1):91–102
    https://doi.org/10.1002/jmri.27044
  32. van de Lindt T, Sonke JJ, Nowee M, Jansen E, van Pelt V, van der Heide U, Fast M (2018) A self-sorting coronal 4D-MRI method for daily image guidance of liver lesions on an MR-LINAC. Int J Radiat Oncol Biol Phys 102(4):875–884
    https://doi.org/10.1016/j.ijrobp.2018.05.029
  33. Tarroni G, Tersi L, Corsi C, Stagni R (2012) Prosthetic component segmentation with blur compensation: a fast method for 3D fluoroscopy. Med Biol Eng Comput 50:631–640
    https://doi.org/10.1007/s11517-012-0884-x
  34. Yamamoto T, Langner U, Loo BW Jr, Shen J, Keall PJ (2008) Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients. Int J Radiat Oncol Biol Phys 72(4):1250–1258
    https://doi.org/10.1016/j.ijrobp.2008.06.1937
  35. Romaguera LV, Mezheritsky T, Mansour R, Carrier JF and Kadoury S (2021) Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy. Medical Image Analysis, 102250
    https://doi.org/10.1016/j.media.2021.102250
  36. Mafi M, Moghadam SM (2020) Real-time prediction of tumor motion using a dynamic neural network. Med Biol Eng Comput 58(3):529–539
    https://doi.org/10.1007/s11517-019-02096-6
About this publication
Number of citations 0
Number of works in the list of references 36
Journal indexed in Scopus Yes
Journal indexed in Web of Science Yes

Пошук