Assessment of Intraoperative Liver Deformation During Hepatic Resection: Prospective Clinical Study

Registration accuracy comparing different rendering techniques on local vs external virtual 3D liver model reconstruction for vascular landmark setting by intraoperative ultrasound in augmented reality navigated liver resection

PURPOSE

Augmented reality navigation in liver surgery still faces technical challenges like insufficient registration accuracy. This study compared registration accuracy between local and external virtual 3D liver models (vir3DLivers) generated with different rendering techniques and the use of the left vs right main portal vein branch (LPV vs RPV) for landmark setting. The study should further examine how registration accuracy behaves with increasing distance from the ROI.

METHODS

Retrospective registration accuracy analysis of an optical intraoperative 3D navigation system, used in 13 liver tumor patients undergoing liver resection/thermal ablation.

RESULTS

109 measurements in 13 patients were performed. Registration accuracy with local and external vir3DLivers was comparable (8.76 ± 0.9 mm vs 7.85 ± 0.9 mm; 95% CI = -0.73 to 2.55 mm; p = 0.272). Registrations via the LPV demonstrated significantly higher accuracy than via the RPV (6.2 ± 0.85 mm vs 10.41 ± 0.99 mm, 95% CI = 2.39 to 6.03 mm, p < 0.001). There was a statistically significant positive but weak correlation between the accuracy (dFeature) and the distance from the ROI (dROI) (r = 0.298; p = 0.002).

CONCLUSION

Despite basing on different rendering techniques both local and external vir3DLivers have comparable registration accuracy, while LPV-based registrations significantly outperform RPV-based ones in accuracy. Higher accuracy can be assumed within distances of up to a few centimeters around the ROI.

Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery

BACKGROUND

Image-guidance promises to make complex situations in liver interventions safer. Clinical success is limited by intraoperative organ motion due to ventilation and surgical manipulation. The aim was to assess influence of different ventilatory and operative states on liver motion in an experimental model.

METHODS

Liver motion due to ventilation (expiration, middle, and full inspiration) and operative state (native, laparotomy, and pneumoperitoneum) was assessed in a live porcine model (n = 10). Computed tomography (CT)-scans were taken for each pig for each possible combination of factors. Liver motion was measured by the vectors between predefined landmarks along the hepatic vein tree between CT scans after image segmentation.

RESULTS

Liver position changed significantly with ventilation. Peripheral regions of the liver showed significantly higher motion (maximal Euclidean motion 17.9 ± 2.7 mm) than central regions (maximal Euclidean motion 12.6 ± 2.1 mm, p < 0.001) across all operative states. The total average motion measured 11.6 ± 0.7 mm (p < 0.001). Between the operative states, the position of the liver changed the most from native state to pneumoperitoneum (14.6 ± 0.9 mm, p < 0.001). From native state to laparotomy comparatively, the displacement averaged 9.8 ± 1.2 mm (p < 0.001). With pneumoperitoneum, the breath-dependent liver motion was significantly reduced when compared to other modalities. Liver motion due to ventilation was 7.7 ± 0.6 mm during pneumoperitoneum, 13.9 ± 1.1 mm with laparotomy, and 13.5 ± 1.4 mm in the native state (p < 0.001 in all cases).

CONCLUSIONS

Ventilation and application of pneumoperitoneum caused significant changes in liver position. Liver motion was reduced but clearly measurable during pneumoperitoneum. Intraoperative guidance/navigation systems should therefore account for ventilation and intraoperative changes of liver position and peripheral deformation.

Efficient knowledge distillation for liver CT segmentation using growing assistant network

Segmentation has been widely used in diagnosis, lesion detection, and surgery planning. Although deep learning (DL)-based segmentation methods currently outperform traditional methods, most DL-based segmentation models are computationally expensive and memory inefficient, which are not suitable for the intervention of liver surgery. To address this issue, a simple solution is to make a segmentation model very small for the fast inference time, however, there is a trade-off between the model size and performance. In this paper, we propose a DL-based real-time 3-D liver CT segmentation method, where knowledge distillation (KD) method, known as knowledge transfer from teacher to student models, is incorporated to compress the model while preserving the performance. Because it is well known that the knowledge transfer is inefficient when the disparity of teacher and student model sizes is large, we propose a growing teacher assistant network (GTAN) to gradually learn the knowledge without extra computational cost, which can efficiently transfer knowledge even with the large gap of teacher and student model sizes. In our results, dice similarity coefficient of the student model with KD improved 1.2% (85.9% to 87.1%) compared to the student model without KD, which is a similar performance of the teacher model using only 8% (100k) parameters. Furthermore, with a student model of 2% (30k) parameters, the proposed model using the GTAN improved the dice coefficient about 2% compared to the student model without KD, and the inference time is 13 ms per a 3-D image. Therefore, the proposed method has a great potential for intervention in liver surgery as well as in many real-time applications.

Research on registration and navigation technology of augmented reality for ex-vivo hepatectomy

PURPOSE

The application of augmented reality technology to the partial hepatectomy procedure has high practical significance, but the existing augmented reality navigation system has major drawbacks in the display and registration methods, which result in low precision. The augmented reality surgical navigation system proposed in this study has been improved in the above two aspects, which can significantly improve the surgical accuracy.

METHODS

The use of optical see-through head-mounted displays for imaging displays can prevent doctors from reconstructing the patient's two-dimensional image information in their minds and reduce the psychological burden of doctors. In the registration process, the biomechanical properties of the liver are introduced, and a non-rigid registration method based on biomechanics is proposed and realized by a meshless algorithm. In addition, this study uses the moving grid algorithm to carry out clinical experiments on ex-vivo pig liver for experimental verification.

RESULTS

The mark-based interactive registration error is 4.21 ± 1.6 mm, and the registration error is reduced after taking the biomechanical properties of the liver into account, which is - 0.153 ± 0.398 mm. The cutting error of the liver model is 0.159 ± 0.292 mm. In addition, with the aid of the navigation system proposed in this paper, the experiment of ex-vivo pig liver cutting was completed with an error of - 1.164 ± 0.576 mm.

CONCLUSIONS

As a proof-of-concept study, the augmented reality navigation system proposed in this study improves the traditional image-guided surgery in terms of display and registration methods, and the feasibility of the system is verified by ex-vivo pig liver experiments. Therefore, the navigation system has a certain guiding significance for clinical surgery.

Morphologic Change of In Vivo Porcine Liver Under 13 mm Hg Pneumoperitoneum Pressure

BACKGROUND

Clinically, the total and residual liver volume must be accurately calculated before major hepatectomy. However, liver volume might be influenced by pneumoperitoneum during surgery. Changes in liver volume change also affect the accuracy of simulation and augmented reality navigation systems, which are commonly first validated in animal models. In this study, the morphologic changes in porcine livers in vivo under 13 mm Hg pneumoperitoneum pressure were investigated.

MATERIALS AND METHODS

Twenty male pigs were scanned with contrast-enhanced computed tomography without pneumoperitoneum and with 13 mm Hg pneumoperitoneum pressure.

RESULTS

The surface area and volume of the liver and the vascular diameter of the aortic lumen, inferior vena cava lumen, and portal vein lumen were measured. There were statistically significant differences in the surface area and volume of the liver (P=0.000), transverse diameter of the portal vein (P=0.038), longitudinal diameter of the inferior vena cava (P=0.033), longitudinal diameter of the portal vein (P=0.036), vascular cross-sectional area of the inferior vena cava (P=0.028), and portal vein (P=0.038) before and after 13 mm Hg pneumoperitoneum pressure.

CONCLUSIONS

This study indicated that the creation of pneumoperitoneum at 13 mm Hg pressure in a porcine causes liver morphologic alterations affecting the area and volume, as well as the diameter of a blood vessel.

A novel use of biomechanical model-based deformable image registration (DIR) for assessing colorectal liver metastases ablation outcomes

Purpose:

Colorectal cancer is the third most common form of cancer in the United States, and up to 60% of these patients develop liver metastasis. While hepatic resection is the curative treatment of choice, only 20% of patients are candidates at the time of diagnosis. While percutaneous thermal ablation (PTA) has demonstrated 24%–51% overall 5-year survival rates, assurance of sufficient ablation margin delivery (5 mm) can be challenging, with current methods of 2D distance measurement not ensuring 3D minimum margin. We hypothesized that biomechanical model-based deformable image registration (DIR) can reduce spatial uncertainties and differentiate local tumor progression (LTP) patients from LTP-free patients.

Methods:

We retrospectively acquired 30 patients (16 LTP and 14 LTP-free) at our institution who had undergone PTA and had a contrast-enhanced pre-treatment and post-ablation CT scan. Liver, disease, and ablation zone were manually segmented. Biomechanical model-based DIR between the pre-treatment and post-ablation CT mapped the gross tumor volume onto the ablation zone and measured 3D minimum delivered margin (MDM). An in-house cone-tracing algorithm determined if progression qualitatively collocated with insufficient 5 mm margin achieved.

Results:

Mann–Whitney U test showed a significant difference (p < 0.01) in MDM from the LTP and LTP-free groups. A total of 93% (13/14) of patients with LTP had a correlation between progression and missing 5 mm of margin volume.

Conclusions:

Biomechanical DIR is able to reduce spatial uncertainty and allow measurement of delivered 3D MDM. This minimum margin can help ensure sufficient ablation delivery, and our workflow can provide valuable information in a clinically useful timeframe.

Laparoscopic augmented reality registration for oncological resection site repair

PURPOSE

Resection site repair during laparoscopic oncological surgery (e.g. laparoscopic partial nephrectomy) poses some unique challenges and opportunities for augmented reality (AR) navigation support. This work introduces an AR registration workflow that addresses the time pressure that is present during resection site repair.

METHODS

We propose a two-step registration process: the AR content is registered as accurately as possible prior to the tumour resection (the primary registration). This accurate registration is used to apply artificial fiducials to the physical organ and the virtual model. After the resection, these fiducials can be used for rapid re-registration (the secondary registration). We tested this pipeline in a simulated-use study with [Formula: see text] participants. We compared the registration accuracy and speed for our method and for landmark-based registration as a reference.

RESULTS

Acquisition of and, thereby, registration with the artificial fiducials were significantly faster than the initial use of anatomical landmarks. Our method also had a trend to be more accurate in cases in which the primary registration was successful. The accuracy loss between the elaborate primary registration and the rapid secondary registration could be quantified with a mean target registration error increase of 2.35 mm.

CONCLUSION

This work introduces a registration pipeline for AR navigation support during laparoscopic resection site repair and provides a successful proof-of-concept evaluation thereof. Our results indicate that the concept is better suited than landmark-based registration during this phase, but further work is required to demonstrate clinical suitability and applicability.

CBCT-based navigation system for open liver surgery: Accurate guidance toward mobile and deformable targets with a semi-rigid organ approximation and electromagnetic tracking of the liver

Purpose

The surgical navigation system that provides guidance throughout the surgery can facilitate safer and more radical liver resections, but such a system should also be able to handle organ motion. This work investigates the accuracy of intraoperative surgical guidance during open liver resection, with a semi‐rigid organ approximation and electromagnetic tracking of the target area.

Methods

The suggested navigation technique incorporates a preoperative 3D liver model based on diagnostic 4D MRI scan, intraoperative contrast‐enhanced CBCT imaging and electromagnetic (EM) tracking of the liver surface, as well as surgical instruments, by means of six degrees‐of‐freedom micro‐EM sensors.

Results

The system was evaluated during surgeries with 35 patients and resulted in an accurate and intuitive real‐time visualization of liver anatomy and tumor's location, confirmed by intraoperative checks on visible anatomical landmarks. Based on accuracy measurements verified by intraoperative CBCT, the system’s average accuracy was 4.0 ± 3.0 mm, while the total surgical delay due to navigation stayed below 20 min.

Conclusions

The electromagnetic navigation system for open liver surgery developed in this work allows for accurate localization of liver lesions and critical anatomical structures surrounding the resection area, even when the liver was manipulated. However, further clinically integrating the method requires shortening the guidance‐related surgical delay, which can be achieved by shifting to faster intraoperative imaging like ultrasound. Our approach is adaptable to navigation on other mobile and deformable organs, and therefore may benefit various clinical applications.

Image-guided minimally invasive endopancreatic surgery using a computer-assisted navigation system

BACKGROUND

Minimally invasive endopancreatic surgery (EPS), performing a pancreatic resection from inside the pancreatic duct, has been proposed as an experimental alternative to duodenum-preserving pancreatic head resection in benign diseases such as chronic pancreatitis, but is complicated by difficult spatial orientation when trying to reach structures of interest. This study assessed the feasibility and potential benefits of image-guided EPS using a computer-assisted navigation system in artificial pancreas silicon model.

METHODS

A surgical navigation system displayed a 3D reconstruction of the original computed tomography (CT) scan and the endoscope in relation to a selected target structure. In a first step, different surface landmark (LM)-based and intraparenchymal LM-based approaches for image-to-physical space registration were evaluated. The accuracy of registration was measured as fiducial registration error (FRE). Subsequently, intrapancreatic lesions (n = 8) that were visible on preoperative imaging, but not on the endoscopic view, were targeted with a computer-assisted, image-guided endopancreatic resection technique in pancreas silicon models. After each experiment, a CT scan was obtained for measurement of the shortest distance from the resection cavity to the centre of the lesion.

RESULTS

Intraparenchymal LM registration [FRE 2.24 mm (1.40-2.85)] was more accurate than surface LM registration [FRE 3.46 mm (2.25-4.85); p = 0.035], but not more accurate than combined registration of intraparenchymal and surface LM [FRE 2.46 mm (1.60-3.35); p = 0.052]. Using image-guided EPS, six of seven lesions were successfully targeted. The median distance from the resection cavity to the centre of the lesion on CT was 1.52 mm (0-2.4). In one pancreas, a lesion could not be resected due to the fragility of the pancreas model.

CONCLUSION

Image-guided minimally invasive EPS using a computer-assisted navigation system enabled successful targeting of pancreatic lesions that were invisible on the endoscopic image, but detectable on preoperative imaging. In the clinical setting, this tool could facilitate complex minimally invasive and robotic pancreatic procedures.

A deformable model for navigated laparoscopic gastrectomy based on finite elemental method

Background: Accurate registration for surgical navigation of laparoscopic surgery is highly challenging due to vessel deformation. Here, we describe the design of a deformable model with improved matching accuracy by applying the finite element method (FEM).Material and methods: ANSYS software was used to simulate an FEM model of the vessel after pull-up based on laparoscopic gastrectomy requirements. The central line of the FEM model and the central line of the ground truth were drawn and compared. Based on the material and parameters determined from the animal experiment, a perigastric vessel FEM model of a gastric cancer patient was created, and its accuracy in a laparoscopic gastrectomy surgical scene was evaluated.Results: In the animal experiment, the FEM model created with Ogden foam material exhibited better results. The average distance between the two central lines was 6.5mm, and the average distance between their closest points was 3.8 mm. In the laparoscopic gastrectomy surgical scene, the FEM model and the true artery deformation demonstrated good coincidence.Conclusion: In this study, a deformable vessel model based on FEM was constructed using preoperative CT images to improve matching accuracy and to supply a reference for further research on deformation matching to facilitate laparoscopic gastrectomy navigation.

Multimodal image registration for liver radioembolization planning and patient assessment

Purpose

Multimodal imaging plays a key role in patient assessment and treatment planning in liver radioembolization. It will reach its full potential for convenient use in combination with deformable image registration methods. A registration framework is proposed for multimodal liver image registration of multi-phase CT, contrast-enhanced late-phase T1, T2, and DWI MRI sequences.

Methods

A chain of four pair-wise image registrations based on a variational registration framework using normalized gradient fields as distance measure and curvature regularization is introduced. A total of 103 cases of 35 patients was evaluated based on anatomical landmarks and deformation characteristics.

Results

Good anatomical correspondence and physical plausibility of the deformation fields were attained. The global mean landmark errors vary from 3.20 to 5.36 mm, strongly influenced by low resolved images in z-direction. Moderate volume changes are indicated by mean minimum and maximum Jacobian determinants of 0.44 up to 1.88. No deformation foldings were detected. The mean average divergence of the deformation fields range from 0.08 to 0.16 and the mean harmonic energies vary from 0.08 to 0.58.

Conclusion

The proposed registration solutions enable the combined use of information from multimodal imaging and provide an excellent basis for patient assessment and primary planning for liver radioembolization.

Prediction of Local Tumor Progression after Radiofrequency Ablation (RFA) of Hepatocellular Carcinoma by Assessment of Ablative Margin Using Pre-RFA MRI and Post-RFA CT Registration

Objective

To evaluate the clinical impact of using registration software for ablative margin assessment on pre-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) and post-RFA computed tomography (CT) compared with the conventional side-by-side MR-CT visual comparison.

Materials and Methods

In this Institutional Review Board-approved prospective study, 68 patients with 88 hepatocellulcar carcinomas (HCCs) who had undergone pre-RFA MRI were enrolled. Informed consent was obtained from all patients. Pre-RFA MRI and post-RFA CT images were analyzed to evaluate the presence of a sufficient safety margin (≥ 3 mm) in two separate sessions using either side-by-side visual comparison or non-rigid registration software. Patients with an insufficient ablative margin on either one or both methods underwent additional treatment depending on the technical feasibility and patient's condition. Then, ablative margins were re-assessed using both methods. Local tumor progression (LTP) rates were compared between the sufficient and insufficient margin groups in each method.

Results

The two methods showed 14.8% (13/88) discordance in estimating sufficient ablative margins. On registration software-assisted inspection, patients with insufficient ablative margins showed a significantly higher 5-year LTP rate than those with sufficient ablative margins (66.7% vs. 27.0%, p = 0.004). However, classification by visual inspection alone did not reveal a significant difference in 5-year LTP between the two groups (28.6% vs. 30.5%, p = 0.79).

Conclusion

Registration software provided better ablative margin assessment than did visual inspection in patients with HCCs who had undergone pre-RFA MRI and post-RFA CT for prediction of LTP after RFA and may provide more precise risk stratification of those who are treated with RFA.

Augmented reality technology for preoperative planning and intraoperative navigation during hepatobiliary surgery: A review of current methods

BACKGROUND

Augmented reality (AR) technology is used to reconstruct three-dimensional (3D) images of hepatic and biliary structures from computed tomography and magnetic resonance imaging data, and to superimpose the virtual images onto a view of the surgical field. In liver surgery, these superimposed virtual images help the surgeon to visualize intrahepatic structures and therefore, to operate precisely and to improve clinical outcomes.

DATA SOURCES

The keywords "augmented reality", "liver", "laparoscopic" and "hepatectomy" were used for searching publications in the PubMed database. The primary source of literatures was from peer-reviewed journals up to December 2016. Additional articles were identified by manual search of references found in the key articles.

RESULTS

In general, AR technology mainly includes 3D reconstruction, display, registration as well as tracking techniques and has recently been adopted gradually for liver surgeries including laparoscopy and laparotomy with video-based AR assisted laparoscopic resection as the main technical application. By applying AR technology, blood vessels and tumor structures in the liver can be displayed during surgery, which permits precise navigation during complex surgical procedures. Liver transformation and registration errors during surgery were the main factors that limit the application of AR technology.

CONCLUSIONS

With recent advances, AR technologies have the potential to improve hepatobiliary surgical procedures. However, additional clinical studies will be required to evaluate AR as a tool for reducing postoperative morbidity and mortality and for the improvement of long-term clinical outcomes. Future research is needed in the fusion of multiple imaging modalities, improving biomechanical liver modeling, and enhancing image data processing and tracking technologies to increase the accuracy of current AR methods.

Fast elastic registration of soft tissues under large deformations

A fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patient's position, the resection plan based on the pre-operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone-beam CT, provides the information about the intra-operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre-operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra-operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co-rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi-synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface-matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra-operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state-of-the art intensity-based registration technique in terms of accuracy and performance.

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n = 13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods. An important feature of the approach is that it centers on combining workflow-realistic data acquisition and surgical deformations that are appropriate in behavior and magnitude. Using the approach, we investigate volumetric target registration error (TRE) with both current rigid IGLS and our improved nonrigid registration methods. Additionally, we introduce a spatial data resampling approach to mitigate the workflow-sensitive sampling problem. Using our human-to-phantom approach, TRE after routine rigid registration was 10.9 ± 0.6 mm with a signed closest point distance associated with residual surface fit in the range of ±10 mm, highly representative of open liver resections. After applying our novel resampling strategy and improved deformation correction method, TRE was reduced by 51%, i.e., a TRE of 5.3 ± 0.5 mm. This paper reported herein realizes a novel tractable approach for the validation of image-to-physical registration methods and demonstrates promising results for our correction method.

Deformable image registration for tissues with large displacements

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 [Formula: see text] and an average centerline distance error of [Formula: see text]. 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.

An automatic registration method for pre- and post-interventional CT images for assessing treatment success in liver RFA treatment

PURPOSE

In image-guided radio frequency ablation for liver cancer treatment, pre- and post-interventional CT images are typically used to verify the treatment success of the therapy. In current clinical practice, the tumor zone in the diagnostic, preinterventional images is mentally or manually mapped to the ablation zone in the post-interventional images to decide success of the treatment. However, liver deformation and differences in image quality as well as in texture of the ablation zone and the tumor area make the mental or manual registration a challenging task. Purpose of this paper is to develop an automatic framework to register the pre-interventional image to the post-interventional image.

METHODS

The authors propose a registration approach enabling a nonrigid deformation of the tumor to the ablation zone, while keeping locally rigid deformation of the tumor area. The method was evaluated on CT images of 38 patient datasets from Erasmus MC. The evaluation is based on Dice coefficients of the liver segmentation on both the pre-interventional and post-interventional images, and mean distances between the liver segmentations. Additionally, residual distances after registration between corresponding landmarks and local mean surface distance in the images were computed.

RESULTS

The results show that rigid registration gives a Dice coefficient of 87.9%, a mean distance of the liver surfaces of 5.53 mm, and a landmark error of 5.38 mm, while non-rigid registration with local rigid deformation has a Dice coefficient of 92.2%, a mean distance between the liver segmentation boundaries near the tumor area of 3.83 mm, and a landmark error of 2.91 mm, where a part of this error can be attributed to the slice spacing in the authors' CT images.

CONCLUSIONS

This method is thus a promising tool to assess the success of RFA liver cancer treatment.

Evaluation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound

Soft-tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface-based metrics, and subsurface validation has largely been performed via phantom experiments. The proposed method involves the analysis of two deformation-correction algorithms for open hepatic image-guided surgery systems via subsurface targets digitized with tracked intraoperative ultrasound (iUS). Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration and for use in retrospective deformation-correction algorithms. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. Mean closest-point distances between the feature contours delineated in the iUS images and corresponding three-dimensional anatomical model generated from preoperative tomograms were computed to quantify the extent to which the deformation-correction algorithms improved registration accuracy. The results for six patients, including eight anatomical targets, indicate that deformation correction can facilitate reduction in target error of [Formula: see text].

A Novel Ultrasound-Based Registration for Image-Guided Laparoscopic Liver Ablation

Background Patient-to-image registration is a core process of image-guided surgery (IGS) systems. We present a novel registration approach for application in laparoscopic liver surgery, which reconstructs in real time an intraoperative volume of the underlying intrahepatic vessels through an ultrasound (US) sweep process. Methods An existing IGS system for an open liver procedure was adapted, with suitable instrument tracking for laparoscopic equipment. Registration accuracy was evaluated on a realistic phantom by computing the target registration error (TRE) for 5 intrahepatic tumors. The registration work flow was evaluated by computing the time required for performing the registration. Additionally, a scheme for intraoperative accuracy assessment by visual overlay of the US image with preoperative image data was evaluated. Results The proposed registration method achieved an average TRE of 7.2 mm in the left lobe and 9.7 mm in the right lobe. The average time required for performing the registration was 12 minutes. A positive correlation was found between the intraoperative accuracy assessment and the obtained TREs. Conclusions The registration accuracy of the proposed method is adequate for laparoscopic intrahepatic tumor targeting. The presented approach is feasible and fast and may, therefore, not be disruptive to the current surgical work flow.

A clinically applicable laser-based image-guided system for laparoscopic liver procedures

PURPOSE

Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario.

METHODS

In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process.

RESULTS

Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of [Formula: see text].

CONCLUSIONS

We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.

Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy

Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

The role of intraoperative ultrasonography in the diagnosis and management of focal hepatic lesions

Intraoperative ultrasonography (IOUS) has been widely utilized in hepatic surgery both as a diagnostic technique and in the course of treatment. Since IOUS involves direct-contact imaging of the target organ, it can provide high spatial resolution without interference from the surrounding structures. Therefore, IOUS may improve the detection, characterization, localization, and local staging of hepatic tumors. IOUS is also a real-time imaging modality capable of providing interactive information and valuable guidance in a range of procedures. Recently, contrast-enhanced IOUS, IOUS elastography, and IOUS-guided hepatic surgery have attracted increasing interest and are expected to lead to the broader implementation of IOUS. Herein, we review the various applications of IOUS in the diagnosis and management of focal hepatic lesions.

Direct volume manipulation for visualizing intraoperative liver resection process

This paper introduces a new design and application for direct volume manipulation for visualizing the intraoperative liver resection process. So far, interactive volume deformation and resection have been independently handled due to the difficulty of representing elastic behavior of volumetric objects. Our framework models global shape editing and discontinuous local deformation by merging proxy geometry encoding and displacement mapping. A local-frame-based elastic model is presented to allow stable editing of the liver shape including bending and twisting while preserving the volume. Several tests using clinical CT data have confirmed the developed software and interface can represent the intraoperative state of liver and produce local views of reference vascular structures, which provides a "road map of vessels" that are key features when approaching occluded tumors during surgery.

A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data

In open abdominal image-guided liver surgery, sparse measurements of the organ surface can be taken intraoperatively via a laser-range scanning device or a tracked stylus with relatively little impact on surgical workflow. We propose a novel nonrigid registration method which uses sparse surface data to reconstruct a mapping between the preoperative CT volume and the intraoperative patient space. The mapping is generated using a tissue mechanics model subject to boundary conditions consistent with surgical supportive packing during liver resection therapy. Our approach iteratively chooses parameters which define these boundary conditions such that the deformed tissue model best fits the intraoperative surface data. Using two liver phantoms, we gathered a total of five deformation datasets with conditions comparable to open surgery. The proposed nonrigid method achieved a mean target registration error (TRE) of 3.3 mm for targets dispersed throughout the phantom volume, using a limited region of surface data to drive the nonrigid registration algorithm, while rigid registration resulted in a mean TRE of 9.5 mm. In addition, we studied the effect of surface data extent, the inclusion of subsurface data, the trade-offs of using a nonlinear tissue model, robustness to rigid misalignments, and the feasibility in five clinical datasets.

[Specific considerations in living-donor liver transplantation]

INTRODUCTION

Due to the limited number of cadaver donors, adult living liver donor transplantation became an alternative for liver transplantation. During living liver donor transplantation, the safety and uncomplicated recovery of the donor are as important as the appropriate volume and weight of the donated graft. The middle hepatic vein causes a significant dilemma, due to the special anatomical position. The reconstruction of the middle hepatic vein branches supplying S5, S8 is suggested when the anatomically right liver lobe is transplanted.

AIM

The aim of the present study was to investigate the requirements of the reconstruction of middle hepatic vein and to give an accurate description about the discrepancy between the portal vein in- and outflow.

METHOD

The authors analyzed the liver anatomic characteristics of 130 donors undergoing living liver donor transplantation with the use of MeVis software. The so-called porto-hepatic disparity index (shift) was introduced.

RESULTS

The right hepatic vein was dominant in 64.6% of all cases, while the left hepatic vein was never observed to be dominant. The territories of V5 and V8 were responsible for the 33.2±8.9% of the right hepatic lobe area. The correlation between portal venous territory and vein dominancy were as follows: R2 = 0.7811 in the left liver lobe; R² = 0.5463 in the area of middle hepatic vein and R² = 0.5843 in the case of the right hepatic vein. The average value of the shift was 28.2%.

CONCLUSIONS

The differences among the pattern of portal in- and hepatic outflow is an important issue that should be taken into consideration when deciding the necessity for reconstruction of the middle hepatic vein.

Preliminary study of a novel method for conveying corrected image volumes in surgical navigation

BACKGROUND

Commercial image-guided surgery systems rely on the fundamental assumption that preoperative medical images represent the physical state of the patient in the operating room. The guidance display typically consists of a three-dimensional (3D) model derived from medical images and three orthogonal views of the imaging data. A challenging question in image-guided surgery is: what happens when the images used in the guidance display no longer correspond to the current geometric state of the anatomy and guidance information is still desirable?

METHODS

We modify the conventional display with two techniques for incorporating a displacement field from a finite-element model into the guidance display and present a preliminary study of the effect of our method on performance with a simple surgical task. The topic of this paper is methods for conveying the computational model solution, not the model itself. To address the integration of the computational model solution into the display, a novel method of applying the deformation to the tool tip was developed, which quickly corrects for deformation but also maintains the pristine nature of the preoperative images. We compare the proposed technique to an existing method that applies the deformation field to the image volume.

RESULTS

A pilot study compared mean performance with our method of applying the deformation to the tool tip and the conventional technique. These methods were statistically similar with respect to accuracy of localization (p < 0.05) and amount of time (p < 0.05) required for localization of the target.

CONCLUSIONS

These results suggest that our new technique can be used in place of the computationally expensive task of deforming the image volume, without affecting the time or accuracy of the surgical task. Most notably, our work addresses the problem of incorporating deformation correction into the guidance display and offers a first step toward understanding its effect on surgical performance.

Image-Guided Abdominal Surgery and Therapy Delivery

Image-Guided Surgery has become the standard of care in intracranial neurosurgery providing more exact resections while minimizing damage to healthy tissue. Moving that process to abdominal organs presents additional challenges in the form of image segmentation, image to physical space registration, organ motion and deformation. In this paper, we present methodologies and results for addressing these challenges in two specific organs: the liver and the kidney.

Organ surface deformation measurement and analysis in open hepatic surgery: method and preliminary results from 12 clinical cases

The incidence of soft tissue deformation has been well documented in neurosurgical procedures and is known to compromise the spatial accuracy of image-guided surgery systems.Within the context of image-guided liver surgery (IGLS), no detailed method to study and analyze the observed organ shape change between preoperative imaging and the intra-operative presentation has been developed. Contrary to the studies of deformation in neurosurgical procedures, the majority of deformation in IGLS is imposed prior to resection and due to laparotomy and mobilization. As such, methods of analyzing the organ shape change must be developed to use the intra-operative data (e.g. laser range scan (LRS) surfaces) acquired with the organ in its fully deformed shape. To achieve this end we use a signed closest point distance deformation metric computed after rigid alignment of the intra-operative LRS data with organ surfaces generated from the preoperative tomograms. The rigid alignment between the intra-operative LRS surfaces and pre-operative image data was computed with a feature weighted surface registration algorithm. In order to compare the deformation metrics across patients, an inter-patient non-rigid registration of the pre-operative CT images was performed. Given the inter-patient liver registrations, an analysis was performed to determine the potential similarities in the distribution of measured deformation between patients for which similar procedures had been performed. The results of the deformation measurement and analysis indicates the potential for soft tissue deformation to compromise surgical guidance information and suggests a similarity in imposed deformation among similar procedure types.