Vessel-Based Non-Rigid Registration of MR/CT and 3D Ultrasound for Navigation in Liver Surgery

Lange, Thomas; Eulenstein, Sebastian; Hünerbein, M.; Schlag, P.M.

doi:10.3109/10929080309146058

Cited by 66 publications

(41 citation statements)

References 21 publications

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“…VOLUSON) but these are far more expensive. Tracked US has previously (Peterhans 2010, Lange 2003) been used as a tool for liver surgery navigation due to its ability to resolve blood vessels and some types of tumors. This technique is used in both open and laparoscopic cases.…”

Section: Intraoperative Usmentioning

confidence: 99%

“…The results of these studies suggest that there can be improvements to registration by accounting for nonrigid deformations. Lange (2003) published a study on using b-splines to deform and match preoperative segmented blood vessels to match intraoperative US detected vessels. Their algorithm was able to match about 30 vessel segments with only 1-2 mismatches per patient.…”

Section: Liver Deformation Registrationmentioning

confidence: 99%

See 1 more Smart Citation

Registration of liver images to minimally invasive intraoperative surface and subsurface data

Wu¹,

Rucker²,

Conley³

et al. 2014

SPIE Proceedings

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Section: Intraoperative Usmentioning

confidence: 99%

Section: Liver Deformation Registrationmentioning

confidence: 99%

Registration of liver images to minimally invasive intraoperative surface and subsurface data

Wu¹,

Rucker²,

Conley³

et al. 2014

SPIE Proceedings

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“…11 However, in addition to the challenge of accurately determining the boundary conditions for the complex shape of the organs and/or complex interaction forces between the region of interest (ROI) and surrounding tissues, which is required by FEM, FEM suffers from the problem of slow convergence. In the case of liver surgery, Lange et al 12 reported that registration accuracy in the range of 3 mm with a manual interaction time of about 10 min is reasonable for clinical application. During radiotherapy treatment, it is highly desirable to achieve an image registration accuracy of better than 3 mm at the treatment target.…”

Section: Introductionmentioning

confidence: 99%

Deformable image registration for tissues with large displacements

et al. 2017

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Abstract. Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients' movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of 1.87 AE 0.87 mm and an average centerline distance error of 1.28 AE 0.78 mm. The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.

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“…3 Since these methods use freehand US and assume a large coverage of the liver, they can not be applied in laparoscopy given the restricted probe movement and field of view. As an alternative to freehand US, other authors register 3D US, either by using reconstructed vessels as features 4,5 or by using segmented liver surface and vessels. Figure 1.…”

Section: Introductionmentioning

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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Vessel-Based Non-Rigid Registration of MR/CT and 3D Ultrasound for Navigation in Liver Surgery

Abstract: The proposed registration method is fast enough for clinical application in liver surgery. Initial accuracy results are promising and must be further evaluated, particularly in the operating room.

Cited by 66 publications

References 21 publications

Registration of liver images to minimally invasive intraoperative surface and subsurface data

Registration of liver images to minimally invasive intraoperative surface and subsurface data

Deformable image registration for tissues with large displacements

Breathing motion compensated registration of laparoscopic liver ultrasound to CT

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