Automatic Radiofrequency Ablation Planning for Liver Tumors With Multiple Constraints Based on Set Covering

Multi-stage automatic and rapid ablation and needle trajectory planning method for CT-guided percutaneous liver tumor ablation

BACKGROUND

Computer-assisted planning methods have increasingly contributed to preoperative ablation planning; however, these methods cannot automatically obtain the final optimal solution within a short time and are rarely validated in practice, greatly limiting their clinical applicability.

PURPOSE

We aimed to propose a full-automatic multi-stage ablation and needle trajectory planning method for CT-guided percutaneous liver ablation to attain the final optimal plans under multiple clinical constraints rapidly.

METHODS

Our proposed method integrates the ablation zone planning fulfilling complete tumor coverage and critical structure avoidance while reaching a trade-off between ablation number and healthy tissue damage, and needle trajectory planning under multiple clinical constraints. Our needle trajectory planning determines feasible skin entry regions based on hard constraints, where the multi-objective optimization (MOO) considering soft constraints is performed using the Pareto Optimality and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the final optimal solution. The performance of our proposed method was evaluated on 30 tumors of various characteristics from 23 patients and clinically validated in five clinical cases.

RESULTS

Our proposed method achieved 99.8% treatment zone coverage and 40.5% ablation efficiency without involving critical structures, and completely satisfied multiple clinical constraints in all needle trajectory planning results. The average planning time was 23.6 s for tumors of different sizes. All the plans were considered clinically acceptable by the doctors' evaluation. Our method achieved complete tumor coverage without complications in clinical case validation.

CONCLUSION

Our proposed planning method can generate a final optimal plan satisfying multiple clinical constraints within a short time, potentially facilitating preoperative planning for hepatic tumor ablation.

How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.

The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy

PURPOSE

Magnetic resonance (MR) imaging targeted prostate cancer (PCa) biopsy enables precise sampling of MR-detected lesions, establishing its importance in recommended clinical practice. Planning for the ultrasound-guided procedure involves pre-selecting needle sampling positions. However, performing this procedure is subject to a number of factors, including MR-to-ultrasound registration, intra-procedure patient movement and soft tissue motions. When a fixed pre-procedure planning is carried out without intra-procedure adaptation, these factors will lead to sampling errors which could cause false positives and false negatives. Reinforcement learning (RL) has been proposed for procedure plannings on similar applications such as this one, because intelligent agents can be trained for both pre-procedure and intra-procedure planning. However, it is not clear if RL is beneficial when it comes to addressing these intra-procedure errors.

METHODS

In this work, we develop and compare imitation learning (IL), supervised by demonstrations of predefined sampling strategy, and RL approaches, under varying degrees of intra-procedure motion and registration error, to represent sources of targeting errors likely to occur in an intra-operative procedure.

RESULTS

Based on results using imaging data from 567 PCa patients, we demonstrate the efficacy and value in adopting RL algorithms to provide intelligent intra-procedure action suggestions, compared to IL-based planning supervised by commonly adopted policies.

CONCLUSIONS

The improvement in biopsy sampling performance for intra-procedure planning has not been observed in experiments with only pre-procedure planning. These findings suggest a strong role for RL in future prospective studies which adopt intra-procedure planning. Our open source code implementation is available here .

Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints and fuzzy optimization

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience.Approach.To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning.Main results.To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs.Significance.The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure.

High-security automatic path planning of radiofrequency ablation for liver tumors

BACKGROUND AND OBJECTIVE

Radiofrequency ablation (RFA) is an effective method for the treatment of liver tumors. Preoperative path planning, which plays a crucial role in RFA treatment, requires doctors to have significant experience and ability. Specifically, correct and highly active preoperative path planning should ensure the safety of the whole puncturing process, complete ablation of tumors and minimal damage to healthy tissues.

METHODS

In this paper, a high-security automatic multiple puncture path planning method for liver tumors is proposed, in which the optimization of the ablation number, puncture number, target positions and puncture point positions subject to comprehensive clinical constraints are studied. In particular, both the safety of the puncture path and the distribution of ablation ellipsoids are taken into consideration. The influence of each constraint on the safety of the whole puncturing process is discussed in detail. On this basis, the efficiency of the planning method is obviously improved by simplifying the computational data and optimized variables. In addition, the performance and adaptability of the proposed method to large and small tumors are compared and summarized.

RESULTS

The proposed method is evaluated on 10 liver tumors of various geometric characteristics from 7 cases. The test results show that the average path planning time and average ablation efficiency are 41.4 s and 60.19%, respectively. For tumors of different sizes, the planning results obtained from the proposed method have similar healthy tissue coverage. Through the clinical evaluation of doctors, the planning results meet the needs of RFA for liver tumors.

CONCLUSIONS

The proposed method can provide reasonable puncture paths in RFA planning, which is beneficial to ensure the safety and efficiency of liver tumor ablation.

A heuristic method for rapid and automatic radiofrequency ablation planning of liver tumors

PURPOSE

Preprocedural planning is a key step in radiofrequency ablation (RFA) treatment for liver tumors, which is a complex task with multiple constraints and relies heavily on the personal experience of interventional radiologists, and existing optimization-based automatic RFA planning methods are very time-consuming. In this paper, we aim to develop a heuristic RFA planning method to rapidly and automatically make a clinically acceptable RFA plan.

METHODS

First, the insertion direction is heuristically initialized based on tumor long axis. Then, the 3D RFA planning is divided into insertion path planning and ablation position planning, which are further simplified into 2D by projections along two orthogonal directions. Here, a heuristic algorithm based on regular arrangement and step-wise adjustment is proposed to implement the 2D planning tasks. Experiments are conducted on patients with liver tumors of different sizes and shapes from multicenter to evaluate the proposed method.

RESULTS

The proposed method automatically generated clinically acceptable RFA plans within 3 min for all cases in the test set and the clinical validation set. All RFA plans of our method achieve 100% treatment zone coverage without damaging the vital organs. Compared with the optimization-based method, the proposed method reduces the planning time by dozens of times while generating RFA plans with similar ablation efficiency.

CONCLUSION

The proposed method demonstrates a new way to rapidly and automatically generate clinically acceptable RFA plans with multiple clinical constraints. The plans of our method are consistent with the clinical actual plans on almost all cases, which demonstrates the effectiveness of the proposed method and can help reduce the burden on clinicians.

Path planning for percutaneous lung biopsy based on the loose-Pareto and adaptive heptagonal optimization method

Lung cancer has is highly prevalent worldwide and is the leading cause of cancer-related deaths. In the clinic, a biopsy sample of the lesion is taken to determine whether a lung mass is benign or malignant. CT-guided percutaneous lung biopsy is a minimally invasive intervention and is commonly used to diagnose lung cancer. Path planning before surgery plays a crucial role in percutaneous lung biopsy. Traditionally, path planning for lung biopsy is performed manually by physicians based on CT images of the patient, which demands knowledge and extensive clinical experience of the operating physicians. In this work, a computer-assisted path planning system for percutaneous lung biopsy is proposed based on clinical objectives. Five constraints are presented to remove unqualified skin entry points and determine a feasible entry region based on clinical criteria. Inspired by the Pareto principle and the concept of geometric weighting, the loose-Pareto and adaptive heptagonal optimization (LPHO) method is introduced to plan the optimal puncture path. CT images of 29 patients were collected from Zigong First People's Hospital. Retrospective experiments and test experiments were conducted to evaluate the effectiveness of the algorithm. The planning paths obtained using the proposed method were clinically feasible for 89.7% of patients, demonstrating the applicability and robustness of the system in surgical path planning for lung biopsy.

Integrated Treatment Planning in Percutaneous Microwave Ablation of Lung Tumors

Microwave ablation (MWA) is a clinically widespread minimally invasive treatment method for lung tumors. Preoperative planning plays a vital role in MWA therapy. However, previous planning methods are far from satisfactory in clinical practice because they only one-sidedly consider the surgical path or energy parameters of an MWA surgery. In this paper, we propose a novel planning model with a computational model of thermal damage to integrally optimize both the surgical path and energy parameters. To ensure the model can be solved in a reasonable time, we elaborate a search space reducing strategy based on clinical constraints. Simulation and ex vivo experimental results were compared with an average mean absolute error of 0.82 K and an average root mean square error of 1.01 K. Our planning model was evaluated on clinical data, and the experimental results demonstrate the effectiveness of our model.

Complications Risk Assessment and Imaging Findings of Thermal Ablation Treatment in Liver Cancers: What the Radiologist Should Expect

One of the major fields of application of ablation treatment is liver tumors. With respect to HCC, ablation treatments are considered as upfront treatments in patients with early-stage disease, while in colorectal liver metastases (CLM), they can be employed as an upfront treatment or in association with surgical resection. The main prognostic feature of ablation is the tumor size, since the goal of the treatment is the necrosis of all viable tumor tissue with an adequate tumor-free margin. Radiofrequency ablation (RFA) and microwave ablation (MWA) are the most employed ablation techniques. Ablation therapies in HCC and liver metastases have presented a challenge to radiologists, who need to assess response to determine complication-related treatment. Complications, defined as any unexpected variation from a procedural course, and adverse events, defined as any actual or potential injury related to the treatment, could occur either during the procedure or afterwards. To date, RFA and MWA have shown no statistically significant differences in mortality rates or major or minor complications. To reduce the rate of major complications, patient selection and risk assessment are essential. To determine the right cost-benefit ratio for the ablation method to be used, it is necessary to identify patients at high risk of infections, coagulation disorders and previous abdominal surgery interventions. Based on risk assessment, during the procedure as part of surveillance, the radiologists should pay attention to several complications, such as vascular, biliary, mechanical and infectious. Multiphase CT is an imaging tool chosen in emergency settings. The radiologist should report technical success, treatment efficacy, and complications. The complications should be assessed according to well-defined classification systems, and these complications should be categorized consistently according to severity and time of occurrence.

Method for puncture trajectory planning in liver tumors thermal ablation based on NSGA-III

BACKGROUND

Thermal ablation of liver tumors is a conventional mode for treating liver tumors. In order to reduce the damage to normal tissue endangered by thermal ablation, the physician needs to plan the puncture path before surgery.

OBJECTIVE

In this paper, a puncture trajectory planning method for thermal ablation of liver tumor based on NSGA-III is proposed. This method takes the clinical hard constraints and soft constraints into account.

METHOD

The feasible puncture region is solved by the hard constraints, and after that the pareto front points are obtained under the soft constraints. When accessing the feasible puncture region, an adaptive morphological closing operation method based on K-means algorithm is adopted to process the spherical angle binary image of obstacles that might be encountered in the puncture process. RANSAC is performed to fit the tangent plane of liver surface when calculating the angle between the puncture trajectory and liver surface. In order to evaluate the puncture path obtained by this method, 6 tumors are selected as experimental subjects, and Hausdorff distance and Overlap Rate of Pareto front points with manually recommend points are calculated respectively.

RESULTS

The average value of Hausdorff distance is 24.91 mm, and the mean value of the overlap rate is 86.43%.

CONCLUSION

The proposed method can provide high safety and clinical practice of the puncture route.

A novel parallel overlapping mode for complete ablation of large benign thyroid nodules in a single-session radiofrequency ablation

BACKGROUND

Radiofrequency ablation (RFA) has been widely applied in patients with benign thyroid nodules (BTNs), and complete ablation in a single-session treatment brings great benefits to patients. While how the ablation should be planned and performed to achieve complete ablation in a single-session treatment in large BTNs remains unknown.

PURPOSE

To determine a more suitable ablation strategy for sufficient treatment in a single-session treatment.

MATERIALS AND METHODS

This retrospective study included 108 BTNs receiving RFA treatment. These patients were divided into two groups: group A using one insertion point with a fan-shaped overlapping mode and group B using multiple insertion points with a novel parallel overlapping mode. All the treatments used a hydrodissection approach and moving-shot technique. Contrast-enhanced ultrasonography (CEUS) was used to guide the supplementary ablation. Follow-ups were performed at 1, 3, 6 and 12 months. The rates of supplementary ablation, initial ablation ratio (IAR), the rates of complete ablation (CAR), treatment effects and complications between the two groups were compared.

RESULTS

The group B had larger treated nodules (10.2ml vs 6.4ml, P<0.001) than group A, while group B had a lower rate of supplementary ablation (21.6% vs 75.4%, P<0.001), especially in the BTNs with craniocaudal diameters ≥30mm (22.0% vs 100%, P<0.001). With the assistance of supplementary ablation, both groups achieved similar IAR (100% vs 100%, P=0.372) and CAR (94.7% vs 94.1%, P=1.000). Two groups showed similar VRRs at 12-month follow-up (77.9% vs 77.5%, P=0.894) and similar rates of complications (3.5% vs 2.0%, P=1.000).

CONCLUSIONS

Needle placement using the multiple insertion points with a novel parallel overlapping mode would be easier to achieve complete ablation with less supplementary ablation, especially in large nodules.

Versatile multi-constrained planning for thermal ablation of large liver tumors

The surgical planning of large hepatic tumor ablation remains a challenging task that relies on fulfilling multiple medical constraints, especially for the ablation based on configurations of multiple electrodes. The placement of the electrodes to completely ablate the tumor as well as their insertion trajectory to their final position have to be planned to cause as little damage to healthy anatomical structures as possible to allow a fast rehabilitation. In this paper, we present a novel, versatile approach for the computer-assisted planning of multi-electrode thermal ablation of large liver tumors based on pre-operative CT data with semantic annotations. This involves both the specification of the number of required electrodes and their distribution to adequately ablate the tumor region without damaging too much healthy tissue. To determine the insertion trajectory of the electrodes to their final position, we additionally incorporate a series of medical constraints into our optimization, which allows a global analysis where obstacles such as bones are taken into account and damage to healthy tissue is mitigated. Compared with the state-of-the-art method, our method achieves compact ablation regions without relying on assumptions on a potential needle path for optimal global search and, hence, is suitable for guiding clinicians through the planning of the tumor ablation. We also demonstrate the feasibility of our approach in various experiments of clinical data and demonstrate that our approach not only allows completely ablating the tumor region but also reducing the damage of healthy tissue in comparison to the previous state-of-the-art method.

Microwave ablation of lung tumors: A probabilistic approach for simulation-based treatment planning

PURPOSE

Microwave ablation (MWA) is a clinically established modality for treatment of lung tumors. A challenge with existing application of MWA, however, is local tumor progression, potentially due to failure to establish an adequate treatment margin. This study presents a robust simulation-based treatment planning methodology to assist operators in comparatively assessing thermal profiles and likelihood of achieving a specified minimum margin as a function of candidate applied energy parameters.

METHODS

We employed a biophysical simulation-based probabilistic treatment planning methodology to evaluate the likelihood of achieving a specified minimum margin for candidate treatment parameters (i.e., applied power and ablation duration for a given applicator position within a tumor). A set of simulations with varying tissue properties was evaluated for each considered combination of power and ablation duration, and for four different scenarios of contrast in tissue biophysical properties between tumor and normal lung. A treatment planning graph was then assembled, where distributions of achieved minimum ablation zone margins and collateral damage volumes can be assessed for candidate applied power and treatment duration combinations. For each chosen power and time combination, the operator can also visualize the histogram of ablation zone boundaries overlaid on the tumor and target volumes. We assembled treatment planning graphs for generic 1, 2, and 2.5 cm diameter spherically shaped tumors and also illustrated the impact of tissue heterogeneity on delivered treatment plans and resulting ablation histograms. Finally, we illustrated the treatment planning methodology on two example patient-specific cases of tumors with irregular shapes.

RESULTS

The assembled treatment planning graphs indicate that 30 W, 6 min ablations achieve a 5-mm minimum margin across all simulated cases for 1-cm diameter spherical tumors, and 70 W, 10 min ablations achieve a 3-mm minimum margin across 90% of simulations for a 2.5-cm diameter spherical tumor. Different scenarios of tissue heterogeneity between tumor and lung tissue revealed 2 min overall difference in ablation duration, in order to reliably achieve a 4-mm minimum margin or larger each time for 2-cm diameter spherical tumor.

CONCLUSIONS

An approach for simulation-based treatment planning for microwave ablation of lung tumors is illustrated to account for the impact of specific geometry of the treatment site, tissue property uncertainty, and heterogeneity between the tumor and normal lung.

Multiple objective planning for thermal ablation of liver tumors

PURPOSE

Preoperative treatment planning is key to ensure successful thermal ablation of liver tumors. The planning aims to minimize the number of electrodes required for complete ablation and the damage to the surrounding tissues while satisfying multiple clinical constraints. This is a challenging multiple objective planning problem, in which the trade-off between different objectives must be considered.

METHODS

We propose a novel method to solve the multiple objective planning problem, which combines the set cover-based model and Pareto optimization. The set cover-based model considers multiple clinical constraints and generates several clinically feasible treatment plans, among which the Pareto optimization is performed to find the trade-off between different objectives.

RESULTS

We evaluated the proposed method on 20 tumors of 11 patients in two different situations used in common thermal ablation approaches: with and without the pull-back technique. Pareto optimal plans were found and verified to be clinically acceptable in all cases, which can find the trade-off between the number of electrodes and the damage to the surrounding tissues.

CONCLUSION

The proposed method performs well in the two different situations we considered: with or without the pull-back technique. It can generate Pareto optimal plans satisfying multiple clinical constraints. These plans consider the trade-off between different planning objectives.

Geometrically variable three-dimensional ultrasound for mechanically assisted image-guided therapy of focal liver cancer tumors

PURPOSE

Image-guided focal ablation procedures are first-line therapy options in the treatment of liver cancer tumors that provide advantageous reductions in patient recovery times and complication rates relative to open surgery. However, extensive physician training is required and image guidance variabilities during freehand therapy applicator placement limit the sufficiency of ablation volumes and the overall potential of these procedures. We propose the use of three-dimensional ultrasound (3D US) to provide guidance and localization of therapy applicators, augmenting current ablation therapies without the need for specialized procedure suites. We have developed a novel scanning mechanism for geometrically variable 3D US images, a mechanical tracking system, and a needle applicator insertion workflow using a custom needle applicator guide for targeted image-guided procedures.

METHODS

A three-motor scanner was designed to use any commercially available US probe to generate accurate, consistent, and geometrically variable 3D US images. The designed scanner was mounted on a counterbalanced stabilizing and mechanical tracking system for determining the US probe orientation, which was assessed using optical tracking. Further exploiting the utility of the motorized scanner, an image-guidance workflow was developed that moved the probe to any identified target within an acquired 3D US image. The complete 3D US guidance system was used to perform mock targeted interventional procedures on a phantom by selecting a target in a 3D US image, navigating to the target, and performing needle insertion using a custom 3D-printed needle applicator guide. Registered postinsertion 3D US images and cone-beam computed tomography (CBCT) images were used to evaluate tip targeting errors when using the motors, tracking system, or mixed navigation approaches. Two 3D US image geometries were investigated to assess the accuracy of a small-footprint tilt approach and a large field-of-view hybrid approach for a total of 48 targeted needle insertions. 3D US image quality was evaluated in a healthy volunteer and compared to a commercially available matrix array US probe.

RESULTS

A mean positioning error of 1.85 ± 1.33 mm was observed when performing compound joint manipulations with the mechanical tracking system. A combined approach for navigation that incorporated the motorized movement and the in-plane tracking system corrections performed the best with a mean tip error of 3.77 ± 2.27 mm and 4.27 ± 2.47 mm based on 3D US and CBCT images, respectively. No significant differences were observed between hybrid and tilt image acquisition geometries with all mean registration errors ≤1.2 mm. 3D US volunteer images resulted in clear reconstruction of clinically relevant anatomy.

CONCLUSIONS

A mechanically tracked system with geometrically variable 3D US provides a utility that enables enhanced applicator guidance, placement verification, and improved clinical workflow during focal liver tumor ablation procedures. Evaluations of the tracking accuracy, targeting capabilities, and clinical imaging feasibility of the proposed 3D US system, provided evidence for clinical translation. This system could provide a workflow for improving applicator placement and reducing local cancer recurrence during interventional procedures treating liver cancer and has the potential to be expanded to other abdominal interventions and procedures.