Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Registration between 2D and 3D Ultrasound Images to Track Liver Blood Vessel Movement

Author(s): Kohji Masuda*, Taichi Shimizu, Takumi Nakazawa and Yoshihiro Edamoto

Volume 19, Issue 10, 2023

Published on: 14 October, 2022

Article ID: e200922208978 Pages: 11

DOI: 10.2174/1573405618666220920114813

Price: $65

Abstract

Background: For the accurate positioning of surgical tools, conventional intraoperative navigation systems have been developed to recognize the relationship between target positions and the tools. However, since an internal organ is deformed during the operation, registration between realtime two-dimensional (2D) ultrasound images and three-dimensional (3D) CT or MRI images is not always effective. Therefore, this study developed image registration between 2D and 3D ultrasound images considering deformation for tracking target vessel movement in the liver.

Methods: 3D ultrasound image was obtained in advance with 3D coordinates, including the target vessel. Then real-time 2D images and ultrasound probe position were simultaneously acquired using a 3D position sensor. We applied multiple image resolution registration, where rapid and fine optimizations can be expected at higher and lower levels, respectively. Meanwhile, the gradient descent method was adopted for the optimization, which determines the relative arrangements to obtain maximum similarity between 2D and 3D images. We experimentally established resolution level parameters using a phantom before applying it to track liver blood vessel movements in a normal healthy subject.

Results: Comparing the 2D images and the registered images, although the approach has some limitations in tracking large displacement, we confirmed that the cross-section of the target blood vessel was clearly visualized.

Conclusion: This method has the potential for an ultrasound therapy targeting blood vessels under natural respiration conditions.

Keywords: Three-dimensional ultrasound image, gradient descend method, multiple image resolutions, hepatic artery, body motion, ultrasound images.

Graphical Abstract

[1]
Hayes C. Cellular immunotherapies for cancer. Ir J Med Sci 2021; 190(1): 41-57.
[http://dx.doi.org/10.1007/s11845-020-02264-w] [PMID: 32607912]
[2]
Waldman AD, Fritz JM, Lenardo MJ. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat Rev Immunol 2020; 20(11): 651-68.
[http://dx.doi.org/10.1038/s41577-020-0306-5] [PMID: 32433532]
[3]
Enosawa S, Yamazaki T, Kohsaka H, Tokiwa T. Repopulation of human origin hepatocyte progenitor-like cell line, THLE-5b, in the SCID mouse liver under p21-mediated cell growth-arresting conditions. Cell Transplant 2012; 21(2-3): 447-52.
[http://dx.doi.org/10.3727/096368911X605358] [PMID: 22793052]
[4]
Song HW, Lee HS, Kim SJ, et al. Sonazoid-Conjugated Natural Killer Cells for Tumor Therapy and Real-Time Visualization by Ultrasound Imaging. Pharmaceutics 2021; 13(10): 1689.
[http://dx.doi.org/10.3390/pharmaceutics13101689] [PMID: 34683982]
[5]
Woudstra L, Krijnen PAJ, Bogaards SJP, et al. Development of a new therapeutic technique to direct stem cells to the infarcted heart using targeted microbubbles: StemBells. Stem Cell Res (Amst) 2016; 17(1): 6-15.
[http://dx.doi.org/10.1016/j.scr.2016.04.018] [PMID: 27186654]
[6]
Emmens RW, Oedayrajsingh-Varma M, Woudstra L, et al. A comparison in therapeutic efficacy of several time points of intravenous StemBell administration in a rat model of acute myocardial infarction. Cytotherapy 2017; 19(1): 131-40.
[http://dx.doi.org/10.1016/j.jcyt.2016.10.004] [PMID: 27856230]
[7]
Oitate R, Otsuka T, Seki M, et al. Acoustic field sweeping for active induction of bubble-surrounded T-cells. Jpn J Appl Phys 2018; 57(7S1): 07LF10.
[http://dx.doi.org/10.7567/JJAP.57.07LF10]
[8]
Chikaarashi T, Watanabe S, Miyamoto Y, et al. Experimental study of ultrasound retention of bubble-surrounded cells under various conditions of acoustic field and flow velocity. Jpn J Appl Phys 2022; 61.
[http://dx.doi.org/10.35848/1347-4065/ac54f9]
[9]
Masuda K, Otsuka T, Seki M, et al. Experimental study for active control of bubble-surrounded cells by acoustic radiation force with considering optimal production and cell viability. IEEE Int’l Ultrasonics Symp. P1-B3-8, 2018
[http://dx.doi.org/10.1109/ULTSYM.2018.8579669]
[10]
Ito Y, Saito T, Watanabe S, et al. Validation of damage on vascular endothelial cells under ultrasound exposure according to adhered situation of bubbles. Jpn J Appl Phys 2022.
[http://dx.doi.org/10.35848/1347-4065/ac4d61]
[11]
Saito T, Seki M, Nozaki K, et al. Evaluation of damage on vascular endothelial cells under exposure of burst wave with presence of lipid bubbles. IEEE Int’l Ultrasonics Symp 1123.2020;
[http://dx.doi.org/10.1109/IUS46767.2020.9251363]
[12]
Seki M, Otsuka T, Oitate R, et al. Viability validation of therapeutic cells according to surrounded amount of microbubbles and ultrasound exposure condition. Jpn J Applied Physics 2019; 58.
[http://dx.doi.org/10.7567/1347-4065/ab19ab]
[13]
Katai T, Yasuda I, Watanabe K, et al. Three-dimensional extension of blood vessel network by combining multiple ultrasound volumes from different directions. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2019: 5824-7.
[http://dx.doi.org/10.1109/EMBC.2019.8856647] [PMID: 31947176]
[14]
Masuda K, Yamashita T, Katai T, et al. Reconstruction of threedimensional blood vessel network using multiple ultrasound volumes constructed by weighted fusion between B-mode and Doppler-mode. IEEE Int’l Ultrasonics Symp C7-7. 2017.
[http://dx.doi.org/10.1109/ULTSYM.2017.8091868]
[15]
Onogi S, Phan TH, Mochizuki T, Masuda K. Automatic doppler volume fusion of 3D ultrasound using Point-based registration of shared bifurcation points. Adv Biomed Eng 2015; 4(0): 27-34.
[http://dx.doi.org/10.14326/abe.4.27]
[16]
Onogi S, Wu J, Yoshida T, Masuda K. Patient-mounted robot for 2D ultrasound probe scanning using McKibben artificial muscles. Adv Biomed Eng 2014; 3(0): 130-8.
[http://dx.doi.org/10.14326/abe.3.130]
[17]
Onogi S, Irisawa S, Natsume K, Koda R, Masuda K. Position control of ultrasound transducer by parallel link robot for ultrasonic therapy in blood vessel. Adv Biomed Eng 2013; 2(0): 117-23.
[http://dx.doi.org/10.14326/abe.2.117]
[18]
Sofuni A, Itoi T, Itokawa F, et al. Real-time virtual sonography visualization and its clinical application in biliopancreatic disease. World J Gastroenterol 2013; 19(42): 7419-25.
[http://dx.doi.org/10.3748/wjg.v19.i42.7419] [PMID: 24259973]
[19]
Nakano S, Yoshida M, Fujii K, et al. Real-time virtual sonography, a coordinated sonography and MRI system that uses magnetic navigation, improves the sonographic identification of enhancing lesions on breast MRI. Ultrasound Med Biol 2012; 38(1): 42-9.
[http://dx.doi.org/10.1016/j.ultrasmedbio.2011.10.005] [PMID: 22137178]
[20]
Miyata A, Arita J, Shirata C, et al. Quantitative assessment of the accuracy of real-time virtual sonography for liver surgery. Surg Innov 2020; 27(1): 60-7.
[http://dx.doi.org/10.1177/1553350619875301] [PMID: 31516065]
[21]
Uematsu T, Takahashi K, Nishimura S, et al. Real-time virtual sonography examination and biopsy for suspicious breast lesions identified on MRI alone. Eur Radiol 2016; 26(4): 1064-72.
[http://dx.doi.org/10.1007/s00330-015-3892-z] [PMID: 26135000]
[22]
Oliveira FPM, Tavares JMRS. Medical image registration: A review. Comput Methods Biomech Biomed Engin 2014; 17(2): 73-93.
[http://dx.doi.org/10.1080/10255842.2012.670855] [PMID: 22435355]
[23]
Liu J, Singh G, Al’Aref S, et al. Image registration in medical robotics and intelligent systems: Fundamentals and applications. Adv Intell Syst 2019; 1: 1900048.
[http://dx.doi.org/10.1002/aisy.201900048]
[24]
Song G, Han J, Zhao Y, Wang Z, Du H. A review on medical image registration as an optimization problem. Curr Med Imaging Rev 2017; 13(3): 274-83.
[http://dx.doi.org/10.2174/1573405612666160920123955] [PMID: 28845149]
[25]
Xu J, Noo F. Convex optimization algorithms in medical image reconstruction-in the age of AI. Phys Med Biol 2022; 67: 07TR01.
[http://dx.doi.org/10.1088/1361-6560/ac3842] [PMID: 34757943]
[26]
Tian Q, Wu Y, Ren X, Razmjooy N. A New optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm. Biomed Signal Process Control 2021; 68.
[http://dx.doi.org/10.1016/j.bspc.2021.102761]
[27]
Hu A, Razmjooy N. Brain tumor diagnosis based on metaheuristics and deep learning. Int J Imaging Syst Technol 2021; 31(2): 657-69.
[http://dx.doi.org/10.1002/ima.22495]
[28]
Challis JH. Quaternions as a solution to determining the angular kinematics of human movement. BMC Biomed Eng 2020; 2(1): 5.
[http://dx.doi.org/10.1186/s42490-020-00039-z] [PMID: 32903359]
[29]
Özdemir M. The roots of a split quaternion. Appl Math Lett 2009; 22(2): 258-63.
[http://dx.doi.org/10.1016/j.aml.2008.03.020]
[30]
Hart JC, Francis GK, Kauffman LH. Visualizing quaternion rotation. ACM Trans Graph 1994; 13(3): 256-76.
[http://dx.doi.org/10.1145/195784.197480]
[31]
Di Stefano L, Mattoccia S, Tombari F. ZNCC-based template matching using bounded partial correlation. Pattern Recognit Lett 2005; 26(14): 2129-34.
[http://dx.doi.org/10.1016/j.patrec.2005.03.022]
[32]
Mori S, Kumagai M, Miki K, Fukuhara R, Haneishi H. Development of fast patient position verification software using 2D-3D image registration and its clinical experience. J Radiat Res (Tokyo) 2015; 56(5): 818-29.
[http://dx.doi.org/10.1093/jrr/rrv032] [PMID: 26081313]
[33]
Lin C, Li Y, Xu G, Cao Y. Optimizing ZNCC calculation in binocular stereo matching. Signal Process Image Commun 2017; 52: 64-73.
[http://dx.doi.org/10.1016/j.image.2017.01.001]
[34]
Bukovsky I, Homma N. An approach to stable gradient-descent adaptation of higher order neural units. IEEE Trans Neural Netw Learn Syst 2017; 28(9): 2022-34.
[http://dx.doi.org/10.1109/TNNLS.2016.2572310] [PMID: 27295693]
[35]
El Mouatasim A. Fast gradient descent algorithm for image classification with neural networks. Signal Image Video Process 2020; 14(8): 1565-72.
[http://dx.doi.org/10.1007/s11760-020-01696-2]
[36]
Zhang Y, Wang Y, Zhang C. Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction. Ultrasonics 2012; 52(8): 1046-55.
[http://dx.doi.org/10.1016/j.ultras.2012.08.012] [PMID: 22986153]
[37]
Nandish S, Prabhu G, Rajagopal KV. Multiresolution image registration for multimodal brain images and fusion for better neurosurgical planning. Biomed J 2017; 40(6): 329-38.
[http://dx.doi.org/10.1016/j.bj.2017.09.002] [PMID: 29433836]
[38]
Weiming Wang, Jing Qin, Yim-Pan Chui, Pheng-Ann Heng. A multiresolution framework for ultrasound image segmentation by combinative active contours. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013: 1144-7.
[http://dx.doi.org/10.1109/EMBC.2013.6609708] [PMID: 24109895]
[39]
Tsantis S, Spiliopoulos S, Skouroliakou A, Karnabatidis D, Hazle JD, Kagadis GC. Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction. Med Phys 2014; 41(7): 072903.
[http://dx.doi.org/10.1118/1.4883815] [PMID: 24989413]

Rights & Permissions Print Cite
Article Metrics
43
2
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy