Utilizing ultrasound as a surface digitization tool in image guided liver surgery

Miller, Kristen; Ondrake, Janet E.; Pheiffer, Thomas S.; Simpson, Amber L.; Miga, Michael I.

doi:10.1117/12.912373

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The second aspect is that there are other methods besides an LRS device, which can be used to provide the necessary surface measurement. For example, some have proposed to use a tracked ultrasound probe itself as a surface digitizer by swabbing the surface [18], and others have proposed to use stereoscopic surgical cameras to construct the organ surface [19]. A third consideration is that standardized surface acquisition is likely on the horizon for image-guided surgery systems.…”

Section: Discussionmentioning

confidence: 99%

Toward a generic real-time compression correction framework for tracked ultrasound

Pheiffer

Miga

2015

Int J CARS

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Purpose Tissue compression during ultrasound imaging leads to error in the location and geometry of subsurface targets during soft tissue interventions. We present a novel compression correction method, which models a generic block of tissue and its subsurface tissue displacements resulting from application of a probe to the tissue surface. The advantages of the new method are that it can be realized independent of preoperative imaging data and is capable of near-video framerate compression compensation for real-time guidance. Methods The block model is calibrated to the tip of any tracked ultrasound probe. Intraoperative digitization of the tissue surface is used to measure the depth of compression and provide boundary conditions to the biomechanical model of the tissue. The tissue displacement field solution of the model is inverted to nonrigidly transform the ultrasound images to an estimation of the tissue geometry prior to compression. This method was compared to a previously developed method using a patient-specific model and within the context of simulation, phantom, and clinical data. Results Experimental results with gel phantoms demonstrated that the proposed generic method reduced the mock tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 2.1 ± 0.7 mm and reduced mock tumor centroid alignment error from 7.6 ± 2.6 to 2.6 ± 1.1 mm. The method was applied to a clinical case and reduced the in vivo tumor margin MHD error from 5.4 ± 0.1 to 2.9 ± 0.1 mm, and the centroid alignment error from 7.2 ± 0.2 to 3.8 ± 0.4 mm. Conclusions The correction method was found to effectively improve alignment of ultrasound and tomographic images and was more efficient compared to a previously proposed correction.

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Section: Discussionmentioning

confidence: 99%

Toward a generic real-time compression correction framework for tracked ultrasound

Pheiffer

Miga

2015

Int J CARS

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“…The position of the US points in tracker space is given by an electromagnetic (EM) tracker attached to the probe and the US calibration. Surface points are digitised as in, 15 since the probe is in direct contact with the liver during acquisition. Considering a LUS sweep of n images, the surface "corners" of image 1 and image n {p 1 ...p 4 } are matched to a set {p 1 ...p 4 } on CT space (See Figure 3.).…”

Section: Ultrasound Image Processingmentioning

confidence: 99%

Breathing motion compensated registration of laparoscopic liver ultrasound to CT

et al. 2017

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Laparoscopic Ultrasound (LUS) is regularly used during laparoscopic liver resection to locate critical vascular structures. Many tumours are iso-echoic, and registration to pre-operative CT or MR has been proposed as a method of image guidance. However, factors such as abdominal insufflation, LUS probe compression and breathing motion cause deformation of the liver, making this task far from trivial. Fortunately, within a smaller local region of interest a rigid solution can suffice. Also, the respiratory cycle can be expected to be consistent. Therefore, in this paper we propose a feature-based local rigid registration method to align tracked LUS data with CT while compensating for breathing motion. The method employs the Levenberg-Marquardt Iterative Closest Point (LMICP) algorithm, registers both on liver surface and vessels and requires two LUS datasets, one for registration and another for breathing estimation. Breathing compensation is achieved by fitting a 1D breathing model to the vessel points. We evaluate the algorithm by measuring the Target Registration Error (TRE) of three manually selected landmarks of a single porcine subject. Breathing compensation improves accuracy in 77% of the measurements. In the best case, TRE values below 3mm are obtained. We conclude that our method can potentially correct for breathing motion without gated acquisition of LUS and be integrated in the surgical workflow with an appropriate segmentation.

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Utilizing ultrasound as a surface digitization tool in image guided liver surgery

Cited by 2 publications

References 5 publications

Toward a generic real-time compression correction framework for tracked ultrasound

Toward a generic real-time compression correction framework for tracked ultrasound

Breathing motion compensated registration of laparoscopic liver ultrasound to CT

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