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International Journal of Computer Assisted Radiology and Surgery

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01-09-2019 | Ultrasound | Original Article

Ultrasound simulation with deformable and patient-specific scatterer maps

Authors: Rastislav Starkov, Lin Zhang, Michael Bajka, Christine Tanner, Orcun Goksel

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 9/2019

Abstract

Purpose

Ray-tracing-based simulations model ultrasound (US) interactions with a custom geometric anatomical model, where US texture can be emulated via real-time point-spread function convolutions of a tissue scatterer representation. Such scatterer representations for realistic appearance are difficult to parameterize or model manually and do not respond to volumetric deformations such as those caused with tissue compression by the probe. Herein we utilize brightness mode (B-mode) estimated scatterer maps for ray tracing and propose to enhance the realism of ray-tracing-based simulations by incorporating dynamic speckle patterns that change compliant with tissue deformation.

Methods

In this work, we realistically simulate US texture deformations in the scatterer domain via back-projection of ray segments into a nominal state before sampling during simulation runtime. We estimate scatterer maps from background in vivo images using a pretrained generative adversarial network.

Results

We demonstrated our proposed scatterer estimation and runtime background fusion method on simulated transvaginal US scans of detailed surface-based foetal models. We show the viability of modelling deformations in the scatterer domain at interactive frame rates of 28 frames per second. A quantitative and a qualitative evaluations indicated improved realism in comparison to the state of the art.

Conclusions

Transferring a background image in a scatterer representation enables us to capture anatomical content in a physical space, in which deformations can be incorporated physically consistently before convolving with a US point-spread function during simulation runtime. This then uses the same imaging model on both the background and the hand-crafted models leading to a consistent and seamless compounding of contents in the scatterer space.

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Maul H, Scharf A, Baier P, Wüstemann M, Günter H, Gebauer G, Sohn C (2004) Ultrasound simulators: experience with the SonoTrainer and comparative review of other training systems. Ultrasound Obstet Gynecol 24(5):581–585CrossRefPubMed

Ehricke H (1998) SONOSim3D: a multimedia system for sonography simulation and education with an extensible case database. Eur J Ultrasound 7(3):225–300CrossRefPubMed

Arkhurst W, Pommert A, Richter E, Frederking H, Kim S-I, Schubert R, Höhne KH (2001) A virtual reality training system for pediatric sonography. Proc Int Congr Ser 1230:483–487CrossRef

Tahmasebi AM, Abolmaesumi P, Hashtrudi-Zaad K (2007) A haptic-based ultrasound training/examination system (HUTES). In: Procedings of IEEE international conference on robotics and automation (ICRA), pp 3130–3131

Sclaverano S, Chevreau G, Vadcard L, Mozer P, Troccaz J (2009) BiopSym: a simulator for enhanced learning of ultrasound-guided prostate biopsy. Stud Health Technol Inform 142:301–306PubMed

Goksel O, Salcudean SE (2009) B-mode ultrasound image simulation in deformable 3-D medium. IEEE Trans Med Imaging 28(11):1657–1669CrossRefPubMed

Reichl T, Passenger J, Acosta O, Salvado O (2009) Ultrasound goes GPU: real-time simulation using CUDA. In: Proceedings of SPIE medical imaging, p 726116

Gao H, Choi HF, Claus P, Boonen S, Jaecques S, Van Lenthe GH, Van der Perre G, Lauriks W, D’Hooge J (2009) A fast convolution-based methodology to simulate 2-D/3-D cardiac ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control 56(2):404–409CrossRefPubMed

Bürger B, Bettinghausen S, Radle M, Hesser J (2013) Real-time GPU-based ultrasound simulation using deformable mesh models. IEEE Trans Med Imaging 32(3):609–618CrossRefPubMed

10.

Mattausch O, Goksel O (2016) Monte-Carlo ray tracing for realistic ultrasound training simulation. In: Proceedings of the eurographics workshop visual computing biomedicine (EG VCBM), pp 173–181

11.

Mattausch O, Makhinya M, Goksel O (2018) Realistic ultrasound simulation of complex surface models using interactive Monte-Carlo path tracing. Comput Graph Forum 37(1):202–213CrossRef

12.

Tanner C, Starkov R, Bajka M, Goksel O (2018) Framework for fusion of data- and model-based approaches for ultrasound simulation. In: Proceedings of MICCAI, pp 332–339CrossRef

13.

Flach B, Makhinya M, Goksel O (2016) PURE: panoramic ultrasound reconstruction by seamless stitching of volumes. In: Proceedings of MICCAI workshop simulation and synthesis in medical imaging (SASHIMI), pp 75–84CrossRef

14.

Mattausch O, Goksel O (2018) Image-based reconstruction of tissue scatterers using beam steering for ultrasound simulation. IEEE Trans Med Imag 37(3):767–780CrossRef

15.

Al Bahou A, Tanner C, Goksel O (2019) SCATGAN for reconstruction of ultrasound scatterers using generative adversarial networks. In: Proceedings of IEEE international symposium Biomedical Imaging (ISBI), accepted (also arXiv:1902.00469)

16.

Starkov R, Tanner C, Bajka M, Goksel O (2019) Ultrasound simulation with animated anatomical models and on-the-fly fusion with real images via path-tracing. Comput Graph 82:44–52CrossRef

17.

Kajiya JT (1986) The rendering equation. ACM SIGGRAPH Comput Graph 20(4):143–150CrossRef

18.

Mattausch O, Ren E, Bajka M, Vanhoey K, Goksel O (2017) Comparison of texture synthesis methods for content generation in ultrasound simulation for training. In: Proceedings of SPIE Med Imaging, p 1013523

19.

Bamber JC, Dickinson RJ (1980) Ultrasonic B-scanning: a computer simulation. Phys Med Biol 25(3):463CrossRefPubMed

20.

Jensen J (2004) Simulation of advanced ultrasound systems using field II. Proc IEEE Int Symp Biomed Imaging 1:636–639

21.

Mattausch O, Goksel O (2015) Scatterer reconstruction and parametrization of homogeneous tissue for ultrasound image simulation. In: Proceedings of IEEE engineering medicine and biology conference (EMBC), pp 6350–6353

22.

Müller M, Stam J, James D, Thürey N (2008) Real time physics: class notes. In: Proceedings of ACM SIGGRAPH classes, pp 88:1–88:90

23.

Petrinec K (2013) Patient-specific interactive ultrasound image simulation with soft-tissue deformation. Ph.D. thesis, University of California

24.

Zikic D, Wein W, Khamene A, Clevert D-A, Navab N (2006) Fast deformable registration of 3D-ultrasound data using a variational approach. In: Proceedings of MICCAI, pp 915–923CrossRef

25.

Virga S, Göbl R, Baust M, Navab N, Hennersperger C (2018) Use the force: deformation correction in robotic 3D ultrasound. Int J Comput Assist Radiol Surg 13(5):619–627CrossRefPubMed

26.

Flach B, Makhinya M, Goksel O (2016) Model-based compensation of tissue deformation during data acquisition for interpolative ultrasound simulation. In: Proceedings of IEEE international symposium biomedical imaging (ISBI)

27.

Selmi S-Y, Promayon E, Sarrazin J, Troccaz J (2014) 3D interactive ultrasound image deformation for realistic prostate biopsy simulation. In: Proceedings of biomedical simulation, pp 122–130CrossRef

28.

Bro-Nielsen M (1998) Finite element modeling in surgery simulation. Proc IEEE 86(3):490–503CrossRef

29.

Clark JH (1976) Hierarchical geometric models for visible surface algorithms. Commun ACM 19(10):547–554CrossRef

30.

Stich M, Friedrich H, Dietrich A (2009) Spatial splits in bounding volume hierarchies. In: Proceedings of high-perform graph (HPG), pp 7–13

31.

Wald I, Boulos S, Shirley P (2007) Ray tracing deformable scenes using dynamic bounding volume hierarchies. ACM Trans Graph. https://doi.org/10.1145/1189762.1206075

32.

Karras T, Aila T (2013) Fast parallel construction of high-quality bounding volume hierarchies. In: Proceedings of high-perform graph (HPG), pp 89–99

33.

Lext J, Akenine-Möller T (2001) Towards rapid reconstruction for animated ray tracing. In: Proceedings of eurograph short present, pp 311–318

34.

Loughna P, Chitty L, Evans T, Chudleigh T (2009) Fetal size and dating: charts recommended for clinical obstetric practice. Ultrasound 17(3):160–166CrossRef

35.

Rubin SM, Whitted T (1980) A 3-dimensional representation for fast rendering of complex scenes. ACM SIGGRAPH Comput Graph 14(3):110–116CrossRef

36.

Faure F, Duriez C, Delingette H, Allard J, Gilles B, Marchesseau S, Talbot H, Courtecuisse H, Bousquet G, Peterlik I, Cotin S (2012) Sofa: a multi-model framework for interactive physical simulation. In: Soft tissue biomechanical modeling for computer assisted surgery. Springer, Berlin, pp 283–321

Title: Ultrasound simulation with deformable and patient-specific scatterer maps
Authors: Rastislav Starkov
Lin Zhang
Michael Bajka
Christine Tanner
Orcun Goksel
Publication date: 01-09-2019
Publisher: Springer International Publishing
Keywords: Ultrasound
Ultrasound
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 9/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-02054-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Ultrasound simulation with deformable and patient-specific scatterer maps

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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