Home Evaluation of an Electromagnetic Image-Fusion Navigation System for Biopsy of Small Lesions
Post
Cancel

Evaluation of an Electromagnetic Image-Fusion Navigation System for Biopsy of Small Lesions

Posted Jan 31, 2013 Updated Apr 10, 2023
By Liat Appelbaum MD , Luigi Solbiati MD , Jacob Sosna MD , Yzhak Nissenbaum BS , Nathalie Greenbaum BS , S. Nahum Goldberg MD
10 min read

Purpose

To evaluate the accuracy of a novel combined electromagnetic (EM) navigation/image fusion system for biopsy of small lesions.

Materials and Methods

Using ultrasound (US) guidance, metallic (2 × 1 mm) targets were imbedded in the paraspinal muscle ( n = 28), kidney ( n = 18), and liver ( n = 4) of four 55- to 65-kg pigs. Baseline helical computed tomography (CT) imaging (Brilliance; Philips) identified these biopsy targets and six and nine cutaneous fiducial markers. CT data were imported into a MyLab Twice system (Esaote, Genoa, Italy) for CT/US image fusion. After verification of successful image fusion, baseline registration error and respiratory motion error were assessed by documenting deviation of the US and CT position of the targets in real time. Biopsy targeting was subsequently performed under conditions of normal respiratory using 15-cm 16G eTrax needles (Civco). To mimic the conditions of poor US visualization, only reconstructed CT information was displayed during biopsy. Accuracy of targeting was measured by repeat CT scanning as the distance of the needle tip to the target center. Targeting accuracy of free-hand vs. guided technique, and electromagnetic (EM) sensor positioning (ie, on the hub or within the needle stylus tip) were evaluated.

Results

In muscle, needle registration error was 0.9 ± 1.2 mm and respiratory motion error 4.0 ± 1.0 mm. Target accuracy was 4.0 ± 3.2 mm when an EM sensor was imbedded in the needle tip. Yet, with the EM sensor back on the needle hub, greater targeting accuracy was achieved using an US guide (3.2 ± 1.6 mm) vs. freehand (5.7 ± 3.2 mm, P = .04). For kidney, registration error was 1.8 ± 1.7 mm and respiratory motion error 4.9 ± 1.0 mm. For the deeper kidney targets, target accuracy was 4.4 ± 3.2 mm with a tip EM sensor, which was an improvement over the hub EM sensor positioning (9.3 ± 4.6 mm; P < .01). An additional source of fusion error was noted for liver. Beyond 17 ± 1 mm of respiratory motion, targets were observed to move >3 cm with US transducer/needle compression resulting in 14 ± 1.4 mm targeting accuracy.

Conclusions

A combined image-fusion/EM tracking platform can provide a high degree of needle placement accuracy (<5 mm) when targeting small lesions. Results fall within accuracy of respiratory error; with best results obtained by incorporating an EM sensor into the tip of the biopsy system.

Introduction

Precise device placement and positioning are the basis of every successful image-guided biopsy, tumor ablation, and most other interventional radiology procedures . Usually, this is accomplished using imaging guidance (computed tomography [CT], ultrasound [US], magnetic resonance imaging [MRI], or fluoroscopy), and depends on the operator’s ability and experience. Of these, US is often considered the ideal tool for daily practice in most solid organs because it enables real-time monitoring, is readily available, mobile, and lacks ionizing radiation. Yet, use of this modality is not always possible because of limitations in visualization of the target or surrounding critical structures . An experienced sonographer can often overcome less than ideal visualization at US by incorporating mental registration of the anatomic information from offline modalities (such as contrast-enhanced CT, MRI, and positron emission tomography-CT). Yet, as procedures get more complex, synthesis of all this requisite information becomes more challenging. This in turn has spawned the development of various devices/algorithms to assist in real-time, on-screen image fusion .

An additional approach to address this issue is navigation, electromagnetic (EM) tracking, can potentially provide spatial navigation information in real-time based on reconstruction of, and interaction with, datasets of previously acquired images . This can enable therapeutic and monitoring devices to be accurately positioned with use of an EM navigation system alone, without the need to continually perform imaging of the patient.

Get Radiology Tree app to read full this article<

Materials and methods

Get Radiology Tree app to read full this article<

Experimental Study Overview

Get Radiology Tree app to read full this article<

Figure 1, The MyLab Twice fusion-navigation system: guided biopsy of a paraspinal muscle target with labeling of the system components. US, ultrasound; VTrax, manufacturer's name for the EM system.

Get Radiology Tree app to read full this article<

Animal Model

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Figure 2, Image fusion using superficial fiducial markers. (a) Superficial fiducial markers placed on the animal's shaved skin ( arrows ). (b) Three-dimensional (3D) computed tomography (CT) reconstruction of the animal and the fiducial markers is obtained. (c) Each marker is manually registered in the 3D work space using an electromagnetic marker to achieve fusion. (d) After marking, computation of the fiducial localization error between the spatial marking and CT reconstruction is portrayed. Green shows good alignment (ie, root mean square registration error <5 mm), and red shows poor alignment (ie, error > 1 cm) necessitating further iterative verification of positioning by repeat marking.

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Figure 3, Target placement and appearance. (a) Small gold seeds are used as targets. (b) Insertion of one of the small seeds into the kidney. The needle is well seen throughout its tract ( arrowheads ), as is the seed ( arrow ). (c) Target in the paraspinal muscle ( arrow ). (c) Axial computed tomography showing both metallic targets in the kidney ( white arrow ) and muscle ( black arrow ).

Get Radiology Tree app to read full this article<

Biopsy Navigation System

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Figure 4, Screen display and measurement error. (a) The screen display enables projections of ultrasound, and computed tomography (CT) in multiple different projections. (b,c) Magnified views of the left-upper part of the image. The ultrasound image is projected over the CT image with sufficient translucency to visualize the position of the target by both ultrasound ( black arrow ) and CT ( white arrow ). (b) The image is captured with maximum separation in the apparent position of the target (6 mm). This distance varied with respiration and enabled the determination of potential respiratory error. (c) Depicts the minimum (2 mm) distance between the CT reconstructed target and the true position at real-time ultrasound, allowing determination of registration error. (d) During targeting, the ultrasound projection is adjust to permit targeting based upon only the CT projections of the navigation system. The intersection between the straight green and yellow lines represents the position of the needle tip and here shows successful targeting.

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

CT Data Acquisition

Get Radiology Tree app to read full this article<

Biopsy Methodology

Get Radiology Tree app to read full this article<

Study Procedure

Get Radiology Tree app to read full this article<

Figure 5, Targeting accuracy evaluation. After achieving successful targeting, the animal is again scanned by computed tomography. Multiple reformats were performed to determine the distance between the target and the needle tip—axial (a) and sagittal (b) —and define targeting accuracy.

Get Radiology Tree app to read full this article<

Study Endpoints

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Statistical Analysis

Get Radiology Tree app to read full this article<

Results

Get Radiology Tree app to read full this article<

Paraspinal Muscle

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Kidney

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Liver

Get Radiology Tree app to read full this article<

Figure 6, Navigation assisted targeting in the swine liver. (a) Fused computed tomography (CT)/ultrasound image, revealing a respiratory error of 17 mm. (b) Postprocedure CT image confirms wide targeting discrepancy, largely attributed to this respiratory error.

Figure 7, Effect of compression on the swine liver. Baseline image with the ultrasound transducer placed lightly on the subcostal anterior abdominal wall (a) vs. a scan with moderate compression (b) . These reveal several centimeters of interval movement internal structures including the portal vein ( white arrows ). Reduced distance from the portal vein to the liver surface ( black arrows ) highlights the marked degree of compressibility observed for this organ. This displacement or “compression error” in the normal liver is also likely to magnify targeting errors when using virtual navigation.

Get Radiology Tree app to read full this article<

Discussion

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

References

  • 1. Goldberg S.N., Grassi C.J., Cardella J.F., et. al.: Image-guided tumor ablation: standardization of terminology and reporting criteria. Society of Interventional Radiology Technology Assessment Committee; International Working Group on Image-Guided Tumor Ablation. Radiology 2005; 235: pp. 728-739.

  • 2. Appelbaum L., Sosna J., Nissenbaum Y., et. al.: Electromagnetic navigation system for CT-guided biopsy of small lesions. AJR Am J Roentgenol 2011; 196: pp. 1194-2000.

  • 3. Iagnocco A., Perella C., D’Agostino M.A., et. al.: Magnetic resonance and ultrasonography real-time fusion imaging of the hand and wrist in osteoarthritis and rheumatoid arthritis. Rheumatology (Oxford) 2011; 50: pp. 1409-1413.

  • 4. Yu X., Liu F., Liang P., et. al.: Microwave ablation assisted by a computerised tomography-ultrasonography fusion imaging system for liver lesions: an ex vivo experimental study. Int J Hypertherm 2011; 27: pp. 172-179.

  • 5. Krücker J., Xu S., Venkatesan A., et. al.: Clinical utility of real-time fusion guidance for biopsy and ablation. J Vasc Interv Radiol 2011; 22: pp. 515-524.

  • 6. Wood B.J., Zhang H., Durrani A., et. al.: Navigation with electromagnetic tracking for interventional radiology procedures: a feasibility study. J Vasc Interv Radiol 2005; 16: pp. 493-505.

  • 7. Seiler P.G., Blattman H., Kirsch R.K., et. al.: A novel tracking technique for the continuous precise measurement of tumour positions in conformal radiotherapy. Phys Med Biol 2000; 45: pp. N103-N110.

  • 8. Frantz D.D., Wiles A.D., Leis S.E., et. al.: Accuracy assessment protocols for electromagnetic tracking systems. Phys Med Biol 2003; 48: pp. 2241-2251.

  • 9. Solomon S.B., White P., Wiener C.M., et. al.: Three-dimensional CT-guided bronchoscopy with a real time electromagnetic position sensor. Chest 2000; 118: pp. 1783-1787.

  • 10. Solomon S.B., Dickfield T., Calkins H.: Real-time cardiac catheter navigation on three-dimensional CT images. J Intervent Cardiac Electrophysiol 2003; 8: pp. 27-36.

  • 11. Solomon S.B., Magee C.A., Acker D.E., et. al.: Experimental nonfluoroscopic placement of inferior vena cava filters: use of an electromagnetic navigation system with previous CT data. J Vasc Intervent Radiol 1999; 10: pp. 92-95.

  • 12. Solomon S.B., Magee C., Acker D.E., et. al.: TIPS placement in swine, guided by electromagnetic real-time needle tip localization displayed on previously acquired 3-D CT. Cardiovasc Intervent Radiol 1999; 22: pp. 411-414.

  • 13. Pinto P.A., Chung P.H., Rastinehad A.R., et. al.: Magnetic resonance imaging/ultrasound fusion guided prostate biopsy improves cancer detection following transrectal ultrasound biopsy and correlates with multiparametric magnetic resonance imaging. J Urol 2011; 186: pp. 1281-1285.

  • 14. Banovac F., Cheng P., Campos-Nanez E., et. al.: Radiofrequency ablation of lung tumors in swine assisted by a navigation device with preprocedural volumetric planning. J Vasc Interv Radiol 2010; 21: pp. 122-129.

  • 15. Buckner C.A., Venkatesan A., Locklin J.K., et. al.: Real-time sonography with electromagnetic tracking navigation for biopsy of a hepatic neoplasm seen only on arterial phase computed tomography. J Ultrasound Med 2011; 30: pp. 253-256.

  • 16. Venkatesan A.M., Kadoury S., Abi-Jaoudeh N., et. al.: Real-time FDG PET guidance during biopsies and radiofrequency ablation using multimodality fusion with electromagnetic navigation. Radiology 2011; 260: pp. 848-856.

  • 17. Mueller P.R., vanSonnenberg E.: Interventional radiology in the chest and abdomen. N Engl J Med 1990; 322: pp. 1364-1374.

  • 18. Liu F.Y., Yu X.L., Liang P., et. al.: Microwave ablation assisted by a real-time virtual navigation system for hepatocellular carcinoma undetectable by conventional ultrasonography. Eur J Radiol 2012; 81: pp. 1455-1459.

  • 19. Minami Y., Kitai S., Kudo M.: Treatment response assessment of radiofrequency ablation for hepatocellular carcinoma: usefulness of virtual CT sonography with magnetic navigation. Eur J Radiol 2012; 81: pp. e277-e280.

  • 20. Nakai M., Sato M., Sahara S., et. al.: Radiofrequency ablation assisted by real-time virtual sonography and CT for hepatocellular carcinoma undetectable by conventional sonography. Cardiovasc Intervent Radiol 2009; 32: pp. 62-69.

  • 21. Krucker J., Xu S., Glossop N., et. al.: Electromagnetic tracking for thermal ablation and biopsy guidance: clinical evaluation of spatial accuracy. J Vasc Interv Radiol 2007; 18: pp. 1141-1150.

  • 22. Wood B.J., Zhang H., Durrani A., et. al.: Navigation with electromagnetic tracking for interventional radiology procedures: a feasibility study. J Vasc Interv Radiol 2005; 16: pp. 493-505.

  • 23. Santos R.S., Gupta A., Ebright M.I., et. al.: Electromagnetic navigation to aid radiofrequency ablation and biopsy of lung tumors. Ann Thorac Surg 2010; 89: pp. 265-268.

  • 24. Timmerman R.D., Park C., Kavanagh B.D.: The North American experience with stereotactic body radiation therapy in non-small cell lung cancer. J Thorac Oncol 2007; 2: pp. S101-S112.

Academic Radiology, Volume 20, Issue 2
Journals General Radiology
This post is licensed under CC BY 4.0 by the author.
Recently Updated
Contents