Planning and Navigation for Percutaneous Ablations

Project Goals

Two challenges are mostly clinical concerns in tumor ablation — the size of the tumor and accessibility to the probes. Multiple overlapping ablations need to be planned to cover irregular and oversize tumors through a series of single probe ablations. In the meantime, the planned ablations should be accessible by the needle-based probe and should avoid critical healthy tissue. Manual treatment planning and execution is dependent on the operator’s experience and relies on a trial and error approach, which is error-prone and time-consuming without the assistance of planning and navigation. To address these challenges, we focus on an automated planning and navigation system for percutaneous radio-frequency ablations, particularly for liver tumor ablation. The planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization formulation.

Collaborators:

Bioengineering Initiative, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, Washington, DC
Imaging Science and Information Systems (ISIS) Center, Department of Radiology, Georgetown University Hospital
Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School

Approaches

Towards semi-automatic treatment planning for image-guided surgical interventions, we develop a systematic approach to the needle-based ablation placement task, including pre-operative planning algorithms and intra-operative tracking and navigation approaches.

The overall system concept is shown in Fig. 1, with the key components including treatment optimization, treatment evaluation, and surgical navigation. Specifically, the planning workstation implements patient-specific modeling through segmentation, margin addition, optimization, and plan evaluation as illustrated in Fig. 2.

Semi-automatic segmentation based on geodesic active contour method is used to identify the key structures including: the tumor; structures that should not be traversed such as the ribs, liver vasculature, and adjacent critical anatomical structures, collectively referred to as a no-fly-zone; and surgeon preferred entry points. Additional margins are created for tumor tissue, ablation margin, and critical tissue, which includes safety margins that should be avoided. This is realized by applying a binary image morphological operator, dilation, to the segmented tumor and critical structures. The margin creation process can be described by the following morphological dilation operation.

The flowchart in Fig. 2 describes the optimal planning and the evaluation modules. A semiautomatic treatment planning module for optimized probe placement is developed to guide the RFA ablation probe. For a given irregular liver tumor, the solution of a mathematical optimization problem provides 1) optimized probe trajectories, 2) location of multiple overlapping ablations in order to cover the tumor, and 3) a tumor-free margin, while avoiding the no-fly zone. Hence, the treatment planning is a multiple-objective optimization problem guided by these five clinical considerations:

  • Minimize the number of ablations. Fewer ablations mean shorter treatment times and less chance for complications.
  • Limit the number of probe insertions. This reduces the perforations to the liver capsule decreasing the chances of intraperitoneal haemorrhage.
  • Probe trajectory constraints. The model includes physical constraints imposed by ribs, vessels, and other organs which restrict possible trajectories.
  • Irregular shaped tumor coverage. The optimization uses segmented tumor data from patients and does not pre-suppose a particular tumor shape. This makes this planning method more general.
  • Minimize unnecessary damage to healthy tissue while fully covering the tumor and margin.

The optimization module uses integer programming techniques to model and solve the planning problem. Considering a voxelized tumor region, the possible choices for trajectories and ablations are represented by binary decision variables and the clinical constraints are modeled algebraically using linear inequalities. Aiming at optimizing multiple measures of RFA planning performance simultaneously, we present a decomposition approach that solves this decision problem by repeatedly solving two integer programming models. Initially, a set of entry points is specified by the clinician and each entry point is tested for feasibility in avoiding direct puncture of critical structures to the tumor. Then, for each feasible entry point we define the following two optimization models: the Minimal Trajectories Integer Program (MTIP) to find a minimal number of trajectories necessary to cover the tumor, and the Minimal Ablations Integer Program (MAIP) to find a minimal number of ablations along the selected trajectories necessary to cover the tumor. In each of these integer programs we employ a weighted formulation to reduce healthy tissue damage, while keeping as main objective the minimization of the number of trajectories and ablations that are needed to guarantee coverage of the tumor and safety margin.

Results and Remarks

The planning module yielded 100% coverage over the large tumor using multiple ablations and can generate multiple feasible plans with evaluation parameters for physicians to choose. Both numerical evaluation and visual evaluation can be performed to determine the execution plan from those candidates. The number of trajectories and ablations are reduced to a minimum at the same time. In our previous approach for planning ablations for lung tumors, we only generated an “optimal” solution, which removed the specific perspective of the interventionalist. We now provide the physician with multiple feasible plans which satisfy to some degree the optimization requirements. This is a cooperative approach to planning in which the computational burden is automated, and the clinician selects from a small set of plans which satisfy the clinical criteria such as maximum number of trajectories, maximum number of ablations, overlap of ablation spheres, etc. This approach yields comprehensive and clinically feasible planning results. Given the requirement of 100% coverage on the tumor, the over-ablation rate is found relevant to the size and shape of the tumor, the size of ablation probe and the maximum number of ablations.

The navigation module based on electromagnetic tracking system is susceptible to interference from the CT scanner. In earlier phantom studies on the CT table directly, the fiducial registration error was up to 10 mm, which is too large for accurate targeting. Once we moved the phantom to a metal-free environment the fiducial registration error could be decreased to 1 mm and yield accurate targeting performance. For this reason, in our animal study the swine was moved to a table in the CT room away from the CT gantry, where we were able to obtain a registration error from 3.6 to 3.9 mm for several trials. This makes the postoperative CT evaluation difficult for each ablation, as the animal cannot be moved back to CT and moved out for performing the subsequent planned ablations without potentially changing its position relative to the V-trough. The final targeting error is difficult to evaluate as the planned trajectory cannot be mapped to the postoperative image coordinate system. Instead, we measure the distance from the probe to the tumor margin region surface in 3D-Slicer and found the distance from the probe to the closest tumor surface was approximately 5 mm. For the future study, a pre-operative image to post-operative image registration method can be developed to overcome this limit in ablation evaluation.
According to the planning results and evaluation results on the second ablation, we show the feasibility of semiautomatic planning and navigation procedures overseen by the radiologist. The presented ablation planning and navigation approach provides a comprehensive solution for treating large tumors using RFA, while keeping the physician in the loop. The planning system uses a patient specific model and an optimization approach to produce potential plans which satisfy multiple clinical criteria to certain degrees. The clinicians then select the plan which they judge to be most appropriate. The navigation system provides the precise guidance required to carry out the plan, which currently is all but impossible to do using the standard free hand technique

To summarize, a new treatment planning and navigation system was developed for liver tumor ablations, particularly for multiple overlapping radiofrequency ablations. The treatment planning is composed of needle-like probe trajectory planning and overlapping ablation planning. Multiple-objective optimization for probe insertions incorporates both clinical and technical constraints. Additional validation is required prior to introducing our system into a clinical trial. Systematic evaluations were presented to check the candidate plans by both statistical measures and visualization. The presented semiautomatic planning and guidance method can be applied to tumor ablation in other organs using the proposed techniques. In its current form the system in combination with a phantom can also be used as a training aid for interventional radiologists.

People Involved

Hongliang Ren
Enrique Campos-Nanez
Ziv Yaniv
Filip Banovac
Hernan Abeledo
Nobuhiko Hata
Kevin Cleary

Publications

[1] Ren, H.; Campos-Nanez, E.; Yaniv, Z.; Banovac, F.; Hata, N. & Cleary, K. Treatment Planning and Image Guidance for Radiofrequency Ablations of Large Tumors IEEE Transactions on Information Technology in Biomedicine (IEEE Journal of Biomedical and Health Informatics), 2013

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