Biomechanically Driven Registration of Pre- to Intra-Operative 3D Images for Laparoscopic Surgery

Oktay, Ozan; Zhang, Li; Mansi, Tommaso; Mountney, Peter; Mewes, Philip W.; Nicolau, Stéphane; Soler, Luc; Chefd’hotel, Christophe

doi:10.1007/978-3-642-40763-5_1

Cited by 30 publications

(27 citation statements)

References 12 publications

(22 reference statements)

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“…Based on the work of, 2 Oktay et al 25 proposed to combine the intra-operative data acquired from CTscans with a biomechanical simulation of gas insufflation to accurately perform the registration. Although this method provides accurate registration, it relies on intra-operative scans, which are currently not available during clinical routines.…”

Section: Introductionmentioning

confidence: 99%

Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery

Plantefève

Peterlík²,

Haouchine³

et al. 2015

Ann Biomed Eng

105

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During the minimally-invasive liver surgery, only the partial surface view of the liver is usually provided to the surgeon via the laparoscopic camera. Therefore, it is necessary to estimate the actual position of the internal structures such as tumors and vessels from the pre-operative images. Nevertheless, such task can be highly challenging since during the intervention, the abdominal organs undergo important deformations due to the pneumoperitoneum, respiratory and cardiac motion and the interaction with the surgical tools. Therefore, a reliable automatic system for intra-operative guidance requires fast and reliable registration of the pre- and intra-operative data. In this paper we present a complete pipeline for the registration of pre-operative patient-specific image data to the sparse and incomplete intra-operative data. While the intra-operative data is represented by a point cloud extracted from the stereo-endoscopic images, the pre-operative data is used to reconstruct a biomechanical model which is necessary for accurate estimation of the position of the internal structures, considering the actual deformations. This model takes into account the patient-specific liver anatomy composed of parenchyma, vascularization and capsule, and is enriched with anatomical boundary conditions transferred from an atlas. The registration process employs the iterative closest point technique together with a penalty-based method. We perform a quantitative assessment based on the evaluation of the target registration error on synthetic data as well as a qualitative assessment on real patient data. We demonstrate that the proposed registration method provides good results in terms of both accuracy and robustness w.r.t. the quality of the intra-operative data.

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Section: Introductionmentioning

confidence: 99%

Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery

Plantefève

Peterlík²,

Haouchine³

et al. 2015

Ann Biomed Eng

105

View full text Add to dashboard Cite

show abstract

“…Realistic organ deformation using a mass-spring vessels model have been done [6], unfortunately it required a manual initialization. Preoperative and simulated intraoperative (random noise and downsampling) CT image were registered using a biomechanical insufflation model and an intensity based optimization [7]. However the insufflation induced deformation might not work in the general case and the intensity based optimization is affected by real CBCT poor image quality.…”

Section: Introductionmentioning

confidence: 99%

Biomechanics-based graph matching for augmented CT-CBCT

2018

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Purpose: Augmenting intraoperative cone beam computed tomography (CBCT) images with preoperative computed tomography (CT) data in the context of imageguided liver therapy is proposed. The expected benefit is an improved visualization of tumor(s), vascular system and other internal structures of interest. Method: An automatic elastic registration based on matching of vascular trees extracted from both the preoperative and intraoperative images is presented. Although methods dedicated to non-rigid graph matching exist, they are not efficient when large intraoperative deformations of tissues occur, as is the case during the liver surgery. The contribution is an extension of the graph matching algorithm using Gaussian process regression (GPR) [1]: First, an improved GPR matching is introduced by imposing additional constraints during the matching when the number of hypothesis is large; like the original algorithm, this extended version does not require a manual initialization of matching. Second, a fast biomechanical model is employed to make the method capable of handling large deformations. Results: The proposed automatic intraoperative augmentation is evaluated on both synthetic and real data. It is demonstrated that the algorithm is capable of handling large deformations, thus being more robust and reliable than previous approaches. Moreover, the time required to perform the elastic registration is compatible with the intraoperative navigation scenario. Conclusion: A biomechanics-based graph matching method, which can handle large deformations and augment intraoperative CBCT, is presented and evaluated.

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“…Although this work is validated by porcine data, there is no evaluation using human data. Oktay et al 25 utilized their method to align pre-and intraoperative three-dimensional images.…”

Section: Introductionmentioning

confidence: 99%

Pneumoperitoneum simulation based on mass-spring-damper models for laparoscopic surgical planning

Nimura

Hayashi

et al. 2015

J. Med. Imag

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Abstract. Laparoscopic surgery, which is one minimally invasive surgical technique that is now widely performed, is done by making a working space (pneumoperitoneum) by infusing carbon dioxide (CO 2 ) gas into the abdominal cavity. A virtual pneumoperitoneum method that simulates the abdominal wall and viscera motion by the pneumoperitoneum based on mass-spring-damper models (MSDMs) with mechanical properties is proposed. Our proposed method simulates the pneumoperitoneum based on MSDMs and Newton's equations of motion. The parameters of MSDMs are determined by the anatomical knowledge of the mechanical properties of human tissues. Virtual CO 2 gas pressure is applied to the boundary surface of the abdominal cavity. The abdominal shapes after creation of the pneumoperitoneum are computed by solving the equations of motion. The mean position errors of our proposed method using 10 mmHg virtual gas pressure were 26.9 AE 5.9 mm, and the position error of the previous method proposed by Kitasaka et al. was 35.6 mm. The differences in the errors were statistically significant (p < 0.001, Student's t -test). The position error of the proposed method was reduced from 26.9 AE 5.9 to 23.4 AE 4.5 mm using 30 mmHg virtual gas pressure. The proposed method simulated abdominal wall motion by infused gas pressure and generated deformed volumetric images from a preoperative volumetric image. Our method predicted abdominal wall deformation by just giving the CO 2 gas pressure and the tissue properties. Measurement of the visceral displacement will be required to validate the visceral motion.

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Biomechanically Driven Registration of Pre- to Intra-Operative 3D Images for Laparoscopic Surgery

Cited by 30 publications

References 12 publications

Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery

Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery

Biomechanics-based graph matching for augmented CT-CBCT

Pneumoperitoneum simulation based on mass-spring-damper models for laparoscopic surgical planning

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