Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods

Abstract

Background

A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed.

Purpose

To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration.

Material and Methods

Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test.

Results

Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3–16 s] vs. 32 s [range, 21–38 s]; P < 0.001] and complete (median, 34.0 s [range, 26–66 s] vs. 47.5 s [range, 32–90]; P = 0.001] image fusion. Registration error of Positioning auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0–84.6 mm] vs. 18.2 mm [6.7–73.4 mm]; P = 0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7–9.9 mm] vs. 5.8 mm [range, 2.0–13.0 mm]; P = 0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2–3] vs. 1 [range, 1–2]; P = 0.012].

Conclusion

Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.

Keywords

Fusion imaging ultrasonography computed tomography (CT)auto-registration liver

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References

Lee

Rhim

Cha

et al.

Planning US for percutaneous radiofrequency ablation of small hepatocellular carcinomas (1–3 cm): value of fusion imaging with conventional US and CT/MR images. J Vasc Interv Radiol 2013; 24: 958–965.

Liu

Liang

et al.

Microwave ablation assisted by a real-time virtual navigation system for hepatocellular carcinoma undetectable by conventional ultrasonography. Eur J Radiol 2012; 81: 1455–1459.

Song

Lee

Rhim

et al.

Fusion imaging-guided radiofrequency ablation for hepatocellular carcinomas not visible on conventional ultrasound. Am J Roentgenol 2013; 201: 1141–1147.

Lee

Rhim

Cha

et al.

Percutaneous radiofrequency ablation of hepatocellular carcinoma: fusion imaging guidance for management of lesions with poor conspicuity at conventional sonography. Am J Roentgenol 2012; 198: 1438–1444.

Mauri

Cova

De Beni

et al.

Real-time US-CT/MRI image fusion for guidance of thermal ablation of liver tumors undetectable with US: results in 295 cases. Cardiovasc Intervent Radiol 2015; 38: 143–151.

Kim

Han

et al.

Detection of hepatocellular carcinoma: gadoxetic acid-enhanced 3-dimensional magnetic resonance imaging versus multi-detector row computed tomography. J Comput Assist Tomogr 2009; 33: 844–850.

Lee

Kim

Park

et al.

Targeted sonography for small hepatocellular carcinoma discovered by CT or MRI: factors affecting sonographic detection. Am J Roentgenol 2010; 194: W396–400.

Minami

Kudo

. Ultrasound fusion imaging of hepatocellular carcinoma: a review of current evidence. Dig Dis 2014; 32: 690–695.

Lee

Lim

Kim

et al.

Percutaneous sonographically guided radio frequency ablation of hepatocellular carcinoma: causes of mistargeting and factors affecting the feasibility of a second ablation session. J Ultrasound Med 2011; 30: 607–615.

10.

Cha

Lee

Kim

et al.

Assessing patients with hepatocellular carcinoma meeting the Milan criteria: Is liver 3 tesla MR with gadoxetic acid necessary in addition to liver CT?

J Magn Reson Imaging 2014; 39: 842–852.

11.

Wein

Brunke

Khamene

et al.

Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 2008; 12: 577–585.

12.

Wein

Khamene

Clevert

et al.

Simulation and fully automatic multimodal registration of medical ultrasound. Med Image Comput Comput Assist Interv 2007; 10: 136–143.

13.

Nam

Kang

Lee

et al.

Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching. Phys Med Biol 2012; 57: 69–91.

14.

Mauri

De Beni

Forzoni

et al.

Virtual navigator automatic registration technology in abdominal application. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 5570–5574.

15.

Kadoury

Zagorchev

Wood

et al.

A model-based registration approach of preoperative MRI with 3D ultrasound of the liver for Interventional guidance procedures. 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE 2012, pp. 952–955.

16.

Kim

Lee

Cha

et al.

Automatic registration between real-time ultrasonography and pre-procedural magnetic resonance images: a prospective comparison between two registration methods by liver surface and vessel and by liver surface only. Ultrasound Med Biol 2016; 42: 1627–1636.

17.

Kadoury S, Wood BJ, Venkatesan AM, et al. Graph-based surface extraction of the liver using locally adaptive priors for multimodal interventional image registration. SPIE Medical Imaging 2012:83161O.

18.

Frangi A, Niessen W, Vincken K, et al. Multiscale vessel enhancement filtering. In: Wells W, Colchester A, Delp S, eds. Medical Image Computing and Computer-Assisted Interventation — MICCAI’98. Berlin, Heidelberg: Springer, 1998:130–137.

19.

Bradley

Roth

. Adaptive thresholding using the integral image. Journal of Graphics, GPU, and Game Tools 2007; 12: 13–21.

20.

Roth

. Ray casting for modeling solids. Comput Gr. Image Process 1982; 18: 109–144.

21.

Level Otsu

. A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 1979; 9: 62–66.

22.

Hwang

Y-K

Y-T

Kim

J-B

et al.

One click 3D ultrasound to MR registration. 2014 IEEE International Conference on Consumer Electronics (ICCE). IEEE 2014, pp. 270–273.

23.

Kang

Engelke

Kalender

. A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. Medical Imaging, IEEE Transactions on 2003; 22: 586–598.

24.

Hartley

Zisserman

. Multiple view geometry in computer vision, Cambridge: Cambridge University Press, 2003.

25.

Suramo

Päivänsalo

Myllylä

. Cranio-caudal movements of the liver, pancreas and kidneys in respiration. Acta Radiol Diagn 1983; 25: 129–131.