A study of porcine liver motion during respiration for improving targeting in image-guided needle placements

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.

Coarse-to-fine registration and time-intensity curves constraint for liver DCE-MRI synthesis

Image registration plays a crucial role in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), used as a fundamental step for the subsequent diagnosis of benign and malignant tumors. However, the registration process encounters significant challenges due to the substantial intensity changes observed among different time points, resulting from the injection of contrast agents. Furthermore, previous studies have often overlooked the alignment of small structures, such as tumors and vessels. In this work, we propose a novel DCE-MRI registration framework that can effectively align the DCE-MRI time series. Specifically, our DCE-MRI registration framework consists of two steps, i.e., a de-enhancement synthesis step and a coarse-to-fine registration step. In the de-enhancement synthesis step, a disentanglement network separates DCE-MRI images into a content component representing the anatomical structures and a style component indicating the presence or absence of contrast agents. This step generates synthetic images where the contrast agents are removed from the original images, alleviating the negative effects of intensity changes on the subsequent registration process. In the registration step, we utilize a coarse registration network followed by a refined registration network. These two networks facilitate the estimation of both the coarse and refined displacement vector fields (DVFs) in a pairwise and groupwise registration manner, respectively. In addition, to enhance the alignment accuracy for small structures, a voxel-wise constraint is further conducted by assessing the smoothness of the time-intensity curves (TICs). Experimental results on liver DCE-MRI demonstrate that our proposed method outperforms state-of-the-art approaches, offering more robust and accurate alignment results.

[Status Quo of Surgical Navigation]

Surgical navigation, also referred to as computer-assisted or image-guided surgery, is a technique that employs a variety of methods - such as 3D imaging, tracking systems, specialised software, and robotics to support surgeons during surgical interventions. These emerging technologies aim not only to enhance the accuracy and precision of surgical procedures, but also to enable less invasive approaches, with the objective of reducing complications and improving operative outcomes for patients. By harnessing the integration of emerging digital technologies, surgical navigation holds the promise of assisting complex procedures across various medical disciplines. In recent years, the field of surgical navigation has witnessed significant advances. Abdominal surgical navigation, particularly endoscopy, laparoscopic, and robot-assisted surgery, is currently undergoing a phase of rapid evolution. Emphases include image-guided navigation, instrument tracking, and the potential integration of augmented and mixed reality (AR, MR). This article will comprehensively delve into the latest developments in surgical navigation, spanning state-of-the-art intraoperative technologies like hyperspectral and fluorescent imaging, to the integration of preoperative radiological imaging within the intraoperative setting.

Partial Respiratory Motion Compensation for Abdominal Extracorporeal Boiling Histotripsy Treatments With a Robotic Arm

Extracorporeal boiling histotripsy (BH), a noninvasive method for mechanical tissue disintegration, is getting closer to clinical applications. However, the motion of the targeted organs, mostly resulting from the respiratory motion, reduces the efficiency of the treatment. Here, a practical and affordable unidirectional respiratory motion compensation method for BH is proposed and evaluated in ex vivo tissues. The BH transducer is fixed on a robotic arm following the motion of the skin, which is tracked using an inline ultrasound imaging probe. In order to compensate for system lags and obtain a more accurate compensation, an autoregressive motion prediction model is implemented. BH pulse gating is also implemented to ensure targeting accuracy. The system is then evaluated with ex vivo BH treatments of tissue samples undergoing motion simulating breathing with the movement of amplitudes between 5 and 10 mm, the frequency between 16 and 18 breaths/min, and a maximum speed of 14.2 mm/s. Results show a reduction of at least 89% of the value of the targeting error during treatment while only increasing the treatment time by no more than 1%. The lesions obtained by treating with the motion compensation were close in size and affected area to the no-motion case, whereas lesions obtained without the compensation were often incomplete and had larger affected areas. This approach to motion compensation could benefit extracorporeal BH and other histotripsy methods in clinical translation.

Liver DCE-MRI registration based on sparse recovery of contrast agent curves

PURPOSE

Dynamic contrast-enhanced MRI (DCE-MRI) registration is a challenging task because of the effect of remarkable intensity changes caused by contrast agent injections. Unrealistic deformation usually occurs by using traditional intensity-based algorithms. To alleviate the effect of contrast agent on registration, we proposed a DCE-MRI registration strategy and investigated the registration performance on the clinical DCE-MRI time series of liver.

METHOD

We reconstructed the time-intensity curves of the contrast agent through sparse representation with a predefined dictionary whose columns were the time-intensity curves with high correlations with respect to a preselected contrast agent curve. After reshaping 1D-reconstructed contrast agent time-intensity curves into a 4D contrast agent time series, we aligned the original time series to the reconstructed contrast agent time series through traditional free-form deformation (FFD) registration scheme combined with a residual complexity (RC) similarity and an iterative registration strategy. This study included the DCE-MRI time series of 20 patients with liver cancer.

RESULTS

Qualitatively, the time-cut images and subtraction images of different registration methods did not obviously differ. Quantitatively, the mean (standard deviation) of temporal intensity smoothness of all the patients achieved 54.910 (18.819), 54.609 (18.859), and 53.391 (19.031) in FFD RC, RDDR, Zhou et al.'s method and the proposed method, respectively. The mean (standard deviation) of changes in the lesion volume were 0.985 (0.041), 0.983 (0.041), 0.981 (0.046), and 0.989 (0.036) in FFD RC, RDDR, Zhou et al.'s method and the proposed method.

CONCLUSION

Our proposed method would be an effective registration strategy for DCE-MRI time series, and its performance was comparable with that of three advanced registration methods.

Effects of laparoscopy, laparotomy, and respiratory phase on liver volume in a live porcine model for liver resection

Background

Hepatectomy, living donor liver transplantations and other major hepatic interventions rely on precise calculation of the total, remnant and graft liver volume. However, liver volume might differ between the pre- and intraoperative situation. To model liver volume changes and develop and validate such pre- and intraoperative assistance systems, exact information about the influence of lung ventilation and intraoperative surgical state on liver volume is essential.

Methods

This study assessed the effects of respiratory phase, pneumoperitoneum for laparoscopy, and laparotomy on liver volume in a live porcine model. Nine CT scans were conducted per pig (N = 10), each for all possible combinations of the three operative (native, pneumoperitoneum and laparotomy) and respiratory states (expiration, middle inspiration and deep inspiration). Manual segmentations of the liver were generated and converted to a mesh model, and the corresponding liver volumes were calculated.

Results

With pneumoperitoneum the liver volume decreased on average by 13.2% (112.7 ml ± 63.8 ml, p < 0.0001) and after laparotomy by 7.3% (62.0 ml ± 65.7 ml, p = 0.0001) compared to native state. From expiration to middle inspiration the liver volume increased on average by 4.1% (31.1 ml ± 55.8 ml, p = 0.166) and from expiration to deep inspiration by 7.2% (54.7 ml ± 51.8 ml, p = 0.007).

Conclusions

Considerable changes in liver volume change were caused by pneumoperitoneum, laparotomy and respiration. These findings provide knowledge for the refinement of available preoperative simulation and operation planning and help to adjust preoperative imaging parameters to best suit the intraoperative situation.

Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration

Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A "correlation-weighted" constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients' map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency.

Experimental and Preliminary Clinical Study of Real-Time Registration in Liver Tumors During Respiratory Motion Based on a Multimodality Image Navigation System

Purpose:

To develop a fusion imaging system that combines ultrasound and computed tomography for real-time tumor tracking and to validate the accuracy of performing registration via this approach during a specific breathing phase.

Materials and Methods:

The initial part of the experimental study was performed using iodized oil injection in pig livers and was focused on determining the accuracy of registration. Eight points (A1-4 and B1-4) at different positions and with different target sizes were selected as target points. During respiratory motion, we used our self-designed system to perform the procedure either with (experimental group, E) or without (control group, C) the respiratory monitoring module. The registration errors were then compared between the 2 groups and within group E. The second part of this study was designed as a preliminary clinical study and was performed in 18 patients. Screening was performed to determine the combination of points on the body surface that provided the highest sensitivity to respiratory motion. Registration was performed either with (group E) or without (group C) the respiratory monitoring module. Registration errors were compared between the 2 groups.

Results:

In part 1 of this study, there were fewer registration errors at each point in group E than at the corresponding points in group C (P < .01). In group E, there were more registration errors at points A1 and B1 than at the other points (P < .05). There was no significant difference in registration errors among the remaining points. During part 2 of the study, there was a significant difference in the registration errors between the 2 groups (P < .01).

Conclusions:

Real-time fusion registration is feasible and can be accurately performed during respiratory motions when using this system.

Multi-Operational Selective Computer-Assisted Targeting of hepatocellular carcinoma-Evaluation of a novel approach for navigated tumor ablation

Objective

To facilitate precise local ablation of hepatocellular carcinoma (HCC) in a setting of combined ablation and transarterial chemoembolization (TACE), we evaluated accuracy and efficiency of a novel technique for navigated positioning of ablation probes using intrahepatic tumor referencing and electromagnetic (EM) guidance, in a porcine model.

Methods

An angiographic wire with integrated EM reference sensor at its tip was inserted via a transarterial femoral access and positioned in the vicinity of artificial liver tumors. The resulting offset distance between the tumor center and the intrahepatic endovascular EM reference was calculated. Subsequently, EM tracked ablation probes were inserted percutaneously and navigated toward the tumor center, relying on continuous EM guidance via the intrahepatic reference. Targeting accuracy was assessed as the Euclidean distance between the tip of the ablation probe and the tumor center (Target Positioning Error, TPE). Procedural efficiency was assessed as time efforts for tumor referencing and tumor targeting.

Results

In 6 animals, 124 targeting measurements were performed with an offset distance < 30 mm (clinically most feasible position), resulting in a mean TPE of 2.9 ± 1.6 mm. No significant correlation between the TPE and different intrahepatic offset distances (range 21 to 61 mm, n = 365) was shown as long as the EM reference was placed within the liver. However, the mean TPE increased when placing the EM reference externally on the animal skin (p < 0.01). TPE was similar when targeting under continuous ventilation or in apnea (p = 0.50). Mean time for tumor referencing and navigated targeting was 6.5 ± 3.8 minutes and 14 ± 8 seconds, respectively.

Conclusion

The proposed technique allows precise and efficient navigated positioning of ablation probes into liver tumors in the animal model. We introduce a simple approach suitable for combined ablation and TACE of HCC in a single treatment session.

Three-Dimensional Liver Surgery Simulation: Computer-Assisted Surgical Planning with Three-Dimensional Simulation Software and Three-Dimensional Printing<sup/>

To perform accurate hepatectomy without injury, it is necessary to understand the anatomical relationship among the branches of Glisson's sheath, hepatic veins, and tumor. In Japan, three-dimensional (3D) preoperative simulation for liver surgery is becoming increasingly common, and liver 3D modeling and 3D hepatectomy simulation by 3D analysis software for liver surgery have been covered by universal healthcare insurance since 2012. Herein, we review the history of virtual hepatectomy using computer-assisted surgery (CAS) and our research to date, and we discuss the future prospects of CAS. We have used the SYNAPSE VINCENT medical imaging system (Fujifilm Medical, Tokyo, Japan) for 3D visualization and virtual resection of the liver since 2010. We developed a novel fusion imaging technique combining 3D computed tomography (CT) with magnetic resonance imaging (MRI). The fusion image enables us to easily visualize anatomic relationships among the hepatic arteries, portal veins, bile duct, and tumor in the hepatic hilum. In 2013, we developed an original software, called Liversim, which enables real-time deformation of the liver using physical simulation, and a randomized control trial has recently been conducted to evaluate the use of Liversim and SYNAPSE VINCENT for preoperative simulation and planning. Furthermore, we developed a novel hollow 3D-printed liver model whose surface is covered with frames. This model is useful for safe liver resection, has better visibility, and the production cost is reduced to one-third of a previous model. Preoperative simulation and navigation with CAS in liver resection are expected to help planning and conducting a surgery and surgical education. Thus, a novel CAS system will contribute to not only the performance of reliable hepatectomy but also to surgical education.

Lipiodol versus diaphragm in 4D-CBCT-guided stereotactic radiotherapy of hepatocellular carcinomas

PURPOSE

The purpose of this work was to investigate the potential of lipiodol as a direct tumor surrogate alternative to the diaphragm surrogate on four-dimensional cone-beam computed tomography (4D-CBCT) image guidance for stereotactic radiotherapy of hepatocellular carcinomas.

METHODS

A total of 29 hepatocellular carcinomas (HCC) patients treated by stereotactic radiotherapy following transarterial chemoembolization (TACE) with homogeneous or partial defective lipiodol retention were included. In all, 4-7 pretreatment 4D-CBCT scans were selected for each patient. For each scan, either lipiodol or the diaphragm was used for 4D registration. Resulting lipiodol/diaphragm motion ranges and position errors relative to the reconstructed midventilation images were analyzed to obtain the motion variations, and group mean (ΔM), systematic (Σ), and random (σ) errors of the treatment setup.

RESULTS

Of the lipiodolized tumors, 55 % qualified for direct localization on the 4D-CBCT. Significant correlations of lipiodol and diaphragm positions were found in the left-right (LR), craniocaudal (CC), and anteroposterior (AP) directions. ΔM and σ obtained with lipiodol and diaphragm were similar, agreed to within 0.5 mm in the LR and AP, and 0.3 mm in the CC directions, and Σ differed by 1.4 (LR), 1.1 (CC), and 0.6 (AP) mm. Variations of diaphragm motion range > 5 mm were not observed with lipiodol and in one patient with diaphragm. The margin required for the tumor prediction error using the diaphragm surrogate was 6.7 (LR), 11.7 (CC), and 4.1 (AP) mm.

CONCLUSION

Image-guidance combining lipiodol with 4D-CBCT enabled accurate localization of HCC and thus margin reduction. A major limitation was the degraded lipiodol contrast on 4D-CBCT.

Strategy for accurate liver intervention by an optical tracking system

Image-guided navigation for radiofrequency ablation of liver tumors requires the accurate guidance of needle insertion into a tumor target. The main challenge of image-guided navigation for radiofrequency ablation of liver tumors is the occurrence of liver deformations caused by respiratory motion. This study reports a strategy of real-time automatic registration to track custom fiducial markers glued onto the surface of a patient's abdomen to find the respiratory phase, in which the static preoperative CT is performed. Custom fiducial markers are designed. Real-time automatic registration method consists of the automatic localization of custom fiducial markers in the patient and image spaces. The fiducial registration error is calculated in real time and indicates if the current respiratory phase corresponds to the phase of the static preoperative CT. To demonstrate the feasibility of the proposed strategy, a liver simulator is constructed and two volunteers are involved in the preliminary experiments. An ex-vivo porcine liver model is employed to further verify the strategy for liver intervention. Experimental results demonstrate that real-time automatic registration method is rapid, accurate, and feasible for capturing the respiratory phase from which the static preoperative CT anatomical model is generated by tracking the movement of the skin-adhered custom fiducial markers.

Virtual liver resection: computer-assisted operation planning using a three-dimensional liver representation

In liver surgery, understanding the complicated liver structures and a detailed evaluation of the functional liver remnant volume are essential to perform safe surgical procedures. Recent advances in imaging technology have enabled operation planning using three-dimensional (3D) image-processing software. Virtual liver resection systems provide (1) 3D imaging of liver structures, (2) detailed volumetric analyses based on portal perfusion, and (3) quantitative estimates of the venous drainage area, enabling the investigation of uncharted fields that cannot be examined using a conventional two-dimensional modality. The next step in computer-assisted liver surgery is the application of a virtual hepatectomy to real-time operations. However, the need for a precise alignment between the preoperative imaging data and the intraoperative situation remains to be adequately addressed, since the liver is subject to deformation and respiratory movements during the surgical procedures. We expect that the practical application of a navigation system for transferring the preoperative planning to real-time operations could make liver surgery safer and more standardized in the near future.