Local incompressible registration for liver ablation surgery assessment

MRI non-rigid registration with tumor contraction correction for ablative margin assessment after microwave ablation of hepatocellular carcinomas

Objective. This study aims to develop and assess a tumor contraction model, enhancing the precision of ablative margin (AM) evaluation after microwave ablation (MWA) treatment for hepatocellular carcinomas (HCCs).Approach. We utilize a probabilistic method called the coherent point drift algorithm to align pre-and post-ablation MRI images. Subsequently, a nonlinear regression method quantifies local tumor contraction induced by MWA, utilizing data from 47 HCC with viable ablated tumors in post-ablation MRI. After automatic non-rigid registration, correction for tumor contraction involves contracting the 3D contour of the warped tumor towards its center in all orientations.Main results. We evaluate the performance of our proposed method on 30 HCC patients who underwent MWA. The Dice similarity coefficient between the post-ablation liver and the warped pre-ablation livers is found to be 0.95 ± 0.01, with a mean corresponding distance between the corresponding landmarks measured at 3.25 ± 0.62 mm. Additionally, we conduct a comparative analysis of clinical outcomes assessed through MRI over a 3 month follow-up period, noting that the AM, as evaluated by our proposed method, accurately detects residual tumor after MWA.Significance. Our proposed method showcases a high level of accuracy in MRI liver registration and AM assessment following ablation treatment. It introduces a potentially approach for predicting incomplete ablations and gauging treatment success.

Augmented Reality Navigation System for Biliary Interventional Procedures With Dynamic Respiratory Motion Correction

OBJECTIVE

Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation.

METHODS

We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion.

RESULTS

Our system is respectively evaluated and achieved an average alignment error of 0.75 ± 0.17 mm and 2.79 ± 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 ± 0.15 mm and an average fusion error of 1.67 ± 0.34 mm after correction.

CONCLUSION

Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation.

SIGNIFICANCE

This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.

Study on the Microwave Ablation Effect of Inflated Porcine Lung

(1) Background: Microwave ablation (MWA) has an efficient killing effect on primary and metastatic lung cancer. However, the treatment effect will be affected by the air in the lung, which makes it very difficult to accurately predict and control the ablation area; (2) Methods: In this paper, in vitro experiments combined with simulations are used to study the microwave ablation area of inflated porcine lung. The in vitro experiment is divided into inflated group and deflated group, combined with different ablation power (40 W, 50 W, 60 W) and ablation time (100 s, 200 s, 300 s) for experiment, each power and time combination are repeated five times. A total of 90 ablation experiments were performed. The simulation experiment uses COMSOL Multiphysics software to simulate the microwave ablation area of the inflated lung; (3) Results and Conclusions: When the ablation power is 40 W, 50 W, and 60 W, the average long diameter of the deflated group are 20.8–30.9%, 7.6–22.6%, 10.4–19.8% larger than those of the inflated group, respectively; the average short diameter of the deflated group is 24.5–41.4%, 31.6–45.7%, 27.3–42.9% larger than that of the inflated group. The results show that the ablation area of inflated lung is smaller than deflated lung, which is mainly due to the smaller ablation short diameter.

Dynamic changes in liver volume calculated using a three-dimensional visualization system after microwave ablation of hepatocellular carcinomas

OBJECTIVES

To investigate the changes in liver volume and function after microwave ablation (MWA) of hepatocellular carcinomas (HCCs).

MATERIALS AND METHODS

We retrospectively analyzed 76 patients with 106 nodules who underwent MWA for HCCs ≤5 cm between January 2015 and September 2017. Liver and ablation volumes were calculated using a three-dimensional visualization system on MRI. Multiple regression analysis was used to estimate the association between the ablation volume and liver volume changes. Deformable image registration (DIR) was performed to confirm the influence of liver volume changes on curative effect evaluation after ablation.

RESULTS

The initial liver and tumor volumes were 1262.1±259.91 cm³ (range: 864.9∼1966.8) and 2.5 cm³ (interquartile range [IQR]: 1.3∼8.8), respectively. Compared to the initial liver volumes, the entire live volume (ELV) increased by 10.1%±8.93% (range: -4.9%∼46.68%) on the 3rd day after ablation. Subsequently, it recovered to initial level at the 3rd month and maintained its level during the 1-year follow-up. The median total ablation volume was 34.9 cm³ (IQR: 20.4∼65.4) on the 3rd day after ablation, which decreased by 71.2% (IQR: 57.4%∼78.1%) one year after ablation. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (T-Bil) peaked within 3 days after MWA and recovered to normal within 1 month. The ablation volume proportion of the ELV was an independent risk factor for the increase in the ELV and AST, ALT, and T-Bil levels within 3 days after ablation. When DIR was conducted to fuse ablation zone and tumor, the reshaped tumor volumes were enlarged by 40% because of the increase in ELV.

CONCLUSIONS

MWA of HCCs based on the Milan criteria could induce temporary increases in ELV and RLV within 3 days after ablation, but both parameters recovered to initial levels 3 months after ablation. This indicates that MWA of early-stage HCCs would not lead to liver volume loss and could potentially protect liver function. The liver cannot be treated as an incompressible organ after ablation, and the appropriate deformation constraint should be designed for DIR to evaluate ablation margin accurately. This article is protected by copyright. All rights reserved.

Differential Imaging of Liver Tumors before and after Microwave Ablation with Electrode Displacement Elastography

Liver cancer is a leading cause of cancer-related deaths; however, primary treatment options such as surgical resection and liver transplant may not be viable for many patients. Minimally invasive image-guided microwave ablation (MWA) provides a locally effective treatment option for these patients with an impact comparable to that of surgery for both cancer-specific and overall survival. MWA efficacy is correlated with accurate image guidance; however, conventional modalities such as B-mode ultrasound and computed tomography have limitations. Alternatively, ultrasound elastography has been used to demarcate post-ablation zones, yet has limitations for pre-ablation visualization because of variability in strain contrast between cancer types. This study attempted to characterize both pre-ablation tumors and post-ablation zones using electrode displacement elastography (EDE) for 13 patients with hepatocellular carcinoma or liver metastasis. Typically, MWA ablation margins of 0.5-1.0 cm are desired, which are strongly correlated with treatment efficacy. Our results revealed an average estimated ablation margin inner quartile range of 0.54-1.21 cm with a median value of 0.84 cm. These treatment margins lie within or above the targeted ablative margin, indicating the potential to use EDE for differentiating index tumors and ablated zones during clinical ablations. We also obtained a high correlation between corresponding segmented cross-sectional areas from contrast-enhanced computed tomography, the current clinical gold standard, when compared with EDE strain images, with r2 values of 0.97 and 0.98 for pre- and post-ablation regions.

Multiple feature-based portal vein classification for liver segment extraction

PURPOSE

The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third-order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments.

METHODS

In this paper, a multiple feature-based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third-order branches of PV are obtained. The liver segments are eventually obtained through the third-order branches.

RESULTS

The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state-of-the-art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third-order branches, eight liver segments correspond to the different functional areas are precisely extracted.

CONCLUSIONS

The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery.

Divergence-Free Fitting-Based Incompressible Deformation Quantification of Liver

Liver is an incompressible organ that maintains its volume during the respiration-induced deformation. Quantifying this deformation with the incompressible constraint is significant for liver tracking. The constraint can be accomplished with retaining the divergence-free field obtained by the deformation decomposition. However, the decomposition process is time-consuming, and the removal of non-divergence-free field weakens the deformation. In this study, a divergence-free fitting-based registration method is proposed to quantify the incompressible deformation rapidly and accurately. First, the deformation to be estimated is mapped to the velocity in a diffeomorphic space. Then, this velocity is decomposed by a fast Fourier-based Hodge-Helmholtz decomposition to obtain the divergence-free, curl-free, and harmonic fields. The curl-free field is replaced and fitted by the obtained harmonic field with a translation field to generate a new divergence-free velocity. By optimizing this velocity, the final incompressible deformation is obtained. Moreover, a deep learning framework (DLF) is constructed to accelerate the incompressible deformation quantification. An incompressible respiratory motion model is built for the DLF by using the proposed registration method and is then used to augment the training data. An encoder-decoder network is introduced to learn appearance-velocity correlation at patch scale. In the experiment, we compare the proposed registration with three state-of-the-art methods. The results show that the proposed method can accurately achieve the incompressible registration of liver with a mean liver overlap ratio of 95.33%. Moreover, the time consumed by DLF is nearly 15 times shorter than that by other methods.

Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration

Aim: To assess the ablative margin (AM) after microwave ablation (MWA) for hepatocellular carcinoma (HCC) with a deep learning-based deformable image registration (DIR) technique and analyze the relation between the AM and local tumor progression (LTP).

Patients and Methods: From November 2012 to April 2019, 141 consecutive patients with single HCC (diameter ≤ 5 cm) who underwent MWA were reviewed. Baseline characteristics were collected to identify the risk factors for the determination of LTP after MWA. Contrast-enhanced magnetic resonance imaging scans were performed within 1 month before and 3 months after treatment. Complete ablation was confirmed for all lesions. The AM was measured based on the margin size between the tumor region and the deformed ablative region. To correct the misalignment, DIR between images before and after ablation was achieved by an unsupervised landmark-constrained convolutional neural network. The patients were classified into two groups according to their AMs: group A (AM ≤ 5 mm) and group B (AM > 5 mm). The cumulative LTP rates were compared between the two groups using Kaplan–Meier curves and the log-rank test. Multivariate analyses were performed on clinicopathological variables to identify factors affecting LTP.

Results: After a median follow-up period of 28.9 months, LTP was found in 19 patients. The mean tumor and ablation zone sizes were 2.3 ± 0.9 cm and 3.8 ± 1.2 cm, respectively. The mean minimum ablation margin was 3.4 ± 0.7 mm (range, 0–16 mm). The DIR technique had higher AUC for 2-year LTP without a significant difference compared with the registration assessment without DL (P = 0.325). The 6-, 12-, and 24-month LTP rates were 9.9, 20.6, and 24.8%, respectively, in group A, and 4.0, 8.4, and 8.4%, respectively, in group B. There were significant differences between the two groups (P = 0.011). Multivariate analysis showed that being >65 years of age (P = 0.032, hazard ratio (HR): 2.463, 95% confidence interval (CI), 1.028–6.152) and AM ≤ 5 mm (P = 0.010, HR: 3.195, 95% CI, 1.324–7.752) were independent risk factors for LTP after MWA.

Conclusion: The novel technology of unsupervised landmark-constrained convolutional neural network-based DIR is feasible and useful in evaluating the ablative effect of MWA for HCC.