Ablation Planning in Computer-Assisted Interventions

Project Goals

Tumor ablation is the removal of tumor tissue and is considered as one type of minimally invasive interventions. It can be performed using techniques like cryoablation, high-intensity focused ultrasound (HIFU), and radiofrequency ablation (RFA). These techniques rely on minimally invasive principles to ablate tumor tissues, without having to directly expose the target regions to the environment. It has been widely noted that the success of a tumor ablation procedure hinges greatly on its pre-operative planning, which is often assisted by computational interventions. The proposed ablation planning system in this paper focuses mainly on the radiofrequency ablation (RFA) of hepatic tumors. This project is to develop computational optimization algorithms to plan optimal ablation delivery. Ablation planning systems are necessary to model the 3D interventional environments, identify feasible needle insertion trajectories and deploy ablating electrodes, while avoiding many critical structures.


Genetic Algorithm (GA) was used as it can be designed to consider the multi-objective nature of a tumor ablation planning system. The proposed ablation planning system is designed based on the following objectives: to achieve complete tumor coverage; and to minimize the number of ablations, number of needle trajectories and healthy tissue damage. These objectives are taken into account using an optimization method, Genetic Algorithm (GA). GA is capable of generating many solutions within a defined search space, and these solutions can be selected to undergo evolution based on a quantified value given by a fitness function. An exponential weight-criterion fitness function is used to represent the multiple objectives such as the number of ablation spheres, the number of trajectories, the covariance, and the coverage volume.

Current Results

The proposed mathematical protocol to determine the range of ablation spheres required to achieve complete tumor coverage is feasible to be used as a reference in the context of tumor ablation planning. The following figure shows how tumor coverage changed when trajectory optimization was considered: 0% tumor coverage (top), 100% tumor coverage with [ablation radius]=15 and [number of spheres]=3 (orange spheres) (bottom).


  • Ren, H.; Guo, W.; Ge, S. S. & Lim, W. Coverage Planning in Computer-Assisted Ablation Based On Genetic Optimization Computers in Biology and Medicine, in press, 2014
  • Lim, W. & Ren, H. Cognitive Planning Based on Genetic Algorithm in Computer-Assisted Interventions CIS-RAM 2013, 6th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the 6th IEEE International Conference on Robotics, Automation and Mechatronics (RAM), 2013

People Involved

FYP Student: Wan Cheng LIM
Graduate Student: Weian GUO
Advisor: Dr. Hongliang REN


[1] C. Baegert, C. Villard, P. Schreck, L. Soler, and A. Gangi, “Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors,” Computer Aided Surgery, 12(2): pp. 82-90, March, 2007.
[2] Z. Yaniv, P. Cheng, E. Wilson, T. Popa, D. Lindisch, E. Campos-Nanez, H. Abeledo, V. Watson, and F. Banovac, “Needle-Based Interventions With the Image-guided Surgery Toolkit (IGSTK): From Phantoms to Clinical Trials,” IEEE Trans. on Biomedical Engineering, vol. 57, no. 4, April, 2010.
[3] G. D. Dodd, M. C. Soulen, R. A. Kane, T. Livraghi, W. R. Lees, Y. Yamashita, A. R. Gillams, O. I. Karahan, H. Rhim. “Minimally invasive treatment of malignant hepatic tumors: At the threshold of a major breakthrough,” RadioGraphics, vol. 20, no. 1, January-February, 2000.
[4] C. Rieder, T. Kroger, C. Schumann, and H. K. Hahn, “GPU-Based Real-Time Approximation of the Ablation Zone for Radiofrequency Ablation”, IEEE Trans. On Visualization and Computer Graphics, vol. 17, no. 12, pp. 1812-1821, December, 2011.

Related Poster

BN5209 Neurosensors and Signal Processing AY12/13

BN5209 Neurosensors and Signal Processing Semester 2, 2012/2013


Time period: 15-Jan-13 To 10-May-13
Lecture Time:

  • Tuesday: 4:30 pm – 6:30 pm (SINAPSE)
  • Thursday: 4:30 pm – 6:30 pm (SINAPSE)


  • Week 1: Mon 14 Jan – Fri 18 Jan 2013
    Introduction to the Course and Introduction to Neurosciences, Neurophysiology
  • Week 2: Mon 21 Jan – Fri 25 Jan 2013
    Neural recording methods: Microelectrodes, MEMS, optical neuro sensors
    (Notes) (Quiz)
  • Week 3: Mon 28 Jan – Fri 1 Feb 2013
    Neural recording methods: Neural circuits, amplifiers, telemetry, stimulation
    (Notes)(Quiz 1, Quiz 2)
  • Week 4: Mon 4 Feb – Fri 8 Feb 2013
    Introduction of Signal Processing (Notes)
  • Week 5: Mon 11 Feb – Fri 15 Feb 2013
    Neural signals (basic science) – action potentials (spikes) and analysis
  • Week 6: Mon 18 Feb – Fri 22 Feb 2013
    Neural signals (clinical applications)- EEG, evoked potentials
  • Week 7: Mon 4 Mar – Fri 8 Mar 2013
    Brain machine interfaces (Notes)
  • Week 8: 11 Mar – Fri 15 Mar 2013
    Multiple Dimensional Signal Processing (Notes) (Quiz)
  • Week 9: Mon 18 Mar – Fri 22 Mar 2013
    Neuroimaging and Neurosurgery (Notes)
  • Week 10: Mon 25 Mar – Fri 29 Mar 2013
    Optical imaging: Cellular (microscopy), In Vivo (speckle, Photoacoustic, OCT)
  • Week 11: Mon 1 Apr – Fri 5 Apr 2013
    Neurosurgical systems and Image Processing
  • Week 12: Mon 8 Apr – Fri 12 Apr 2013
    Applications of neural interfaces (peripheral and central cortical)
  • Week 13: Mon 15 Apr – Fri 19 Apr 2013
    Project Reports/presentations

Course Projects

Please log in dropbox to view the materials.
1. EEG for brain state monitoring
2. EEG/EMG Feature Identification during Elbow Flexion/Extension


This module teaches students the advanced neuroengineering principles ranging from basic neuroscience introduction to neurosensing technology as well as advanced signal processing techniques.  Major topics include: introduction to neurosciences, neural recording methods, neural circuits, amplifiers, telemetry, stimulation, sensors for measuring the electric field and magnetic field of the brain in relation to brain activities, digitization of brain activities, neural signal processing, brain machine interfaces, neurosurgical systems and applications of neural interfaces. The module is designed for students at Master and PhD levels in Engineering, Science and Medicine.


Basic probability
Basic circuits
Linear algebra (matrix/vector)
Matlab or other programming
Recommended Textbooks: Neural Engineering, Edited by Bin He


The majority of the course will be in lecture-tutorial format. Some advanced topics will be in the formats of seminar and research presentations.


In Class Quizzes (10 for 20% grade)
Take Home Tests (2 for 50% or Exam)
Labs/Projects (3 for 30%)

IVLE Registration and Information


Lectures and Guest Lectures


BN5209 Neurosensors and Signal Processing (10/11)

Course information from IVLE – Integrated Virtual Learning Environment

Module Code BN5209 (2010/2011 Semester 2)
Description This module teaches students the electrical and magnetic field of the human brain in relation to the brain activities and methods for sensing the electrical and magnetic field of human brain in relation to brain activities. Major topics include: the electric and magnetic field of the brain in relation to brain activities, sensors for measuring the electric field and magnetic field of the brain in relation to brain activities, digitization of brain activities – neural waves, characterization of neural waves, neural power map and neural matrix brain activity pattern recognition using neural power map and neural matrix, and applications of brain activity monitoring. The module is designed for students at Master and PhD levels in Engineering, Science and Medicine.
Module Credit 4
Workload 2-0-1-0-7

More Information can be found from IVLE – Integrated Virtual Learning Environment.

BN2203 Introduction to Bioengineering Design

Course information from IVLE – Integrated Virtual Learning Environment

Module Code BN2203
Module Title Introduction to Bioengineering Design
Description This module introduces the students to the basic elements for design of medical devices through a hands-on design project performed in teams. Examples of engineering analysis and design are applied to representative topics in bioengineering, such as biomechanics, bioinstrumentation, biomaterials, biotechnology, and related areas. Topics include: identification of the technological needs, design methodology, evaluation of costs and benefits, quality of life and ethical considerations.
Module Credit 4
Workload 1.5-1-0-4.5-3
Prerequisites BIE Stage 2 standing

More Information can be found from IVLE – Integrated Virtual Learning Environment.

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.


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


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


[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


3D Ultrasound Tracking and Servoing of Tubular Surgical Robots


[Pediatric Cardiac Bioengineering Lab of Children’s Hospital Boston, Harvard Medical School, USA]
[Philips Research]


Ultrasound imaging is a useful modality for guiding minimally invasive interventions due to its portability and safety. In cardiac surgery, for example, real-time 3D ultrasound imaging is being investigated for guiding repairs of complex defects inside the beating heart. Substantial difficulty can arise, however, when surgical instruments and tissue structures are imaged simultaneously to achieve precise manipulations. This research project includes: (1) the development of echogenic instrument coatings, (2) the design of passive instrument markers, and (3) the development of algorithms for instrument tracking and servoing. For example, a family of passive markers has been developed by which the position and orientation of a surgical instrument can be determined from a single 3D ultrasound volume using simple image processing. Marker-based estimates of instrument pose can be used in augmented reality displays or for image-based servoing.
For example, a family of passive markers has been developed by which the position and orientation of a surgical instrument can be determined from a single 3D ultrasound volume using simple image processing. Marker-based estimates of instrument pose can be used in augmented reality displays or for image-based servoing. The design principles for marker shapes ensure imaging system and measurement uniqueness constraints are met. Error analysis is used to guide marker design and to establish a lower bound on measurement uncertaintanty. Experimental evaluation of marker designs and tracking algorithms demonstrate a tracking accuracy of 0.7 mm in position and 0.075 rad in orientation.
Another example is to investigate the problem of automatic curve pattern detection from 3D ultrasound images, because many surgical instruments are curved along the distal end during operation, such as continuum tube robot, and catheter insertion etc. We propose a two-stage approach to decompose the six parameter constant-curvature curve estimation problem into a two stage parameter estimation problems: 3D spatial plane detection and 2D circular pattern detection. The algorithm includes an image-preprocessing pipeline, including thresholding, denoising, connected component analysis and skeletonization, for automatically extracting the curved robot from ultrasound volumetric images. The proposed method can also be used for spatial circular or arc pattern recognition from other volumetric images such as CT and MRI.
Additional related information at [Pediatric Cardiac Bioengineering Lab of Children’s Hospital Boston, Harvard Medical School]

Surgical Tracking System for Laparoscopic Surgery

ERC-CISST, LCSR Lab of Johns Hopkins University, USA
Fraunhofer Germany (FhG)

Laparoscopic surgery poses a challenging problem for a real-time intra-body navigation system: how to keep tracking the surgical instruments inside the human body intra-operatively. This project aims to develop surgical tracking technology that is accurate, robust against environmental disturbances, and does not require line-of-sight. The current approach is to combine electromagnetic and inertial sensing. Sensor fusion methods are proposed for a hybrid tracking system that incorporates a miniature inertial measurement unit and an electromagnetic navigation system, in order to obtain continuous position and orientation information, even in the presence of metal objects.
Additional information at [SMARTS Lab of Johns Hopkins University]

Biomedical Application of Wireless Heterogeneous Sensor Networks

We study a typical heterogeneous network, a Wireless Biomedical Sensor Network (WBSN), as it consists of various types of biosensors to monitor different physiological parameters. WBSN will help to enhance medical services with its unique advantages in long-term monitoring, easy network deployment, wireless connections, and ambulatory capabilities. The network protocol plays an important role in carrying out the medical and healthcare services. Many unique challenges exist in WBSN design for medical and healthcare services, including extensive optimization problems in network protocol design to deal with power scheduling and radiation absorption concerns. Concerning these issues, we present a systematic solution to the wireless biomedical sensor network in our project named MediMesh. We develop a prototypical test-bed for medical and healthcare applications and evaluate the radiation absorption effects and efficiency. A lightweight network protocol is proposed, taking into consideration of the radiation absorption effects and communication overhead. After data acquisition from the sink stations, a data publishing system based on web service technology is implemented for typical medical and healthcare monitoring services in a hospital or home environment.

Automated tracking of pallets in warehouses based on asymmetric ultrasound observation models

We consider the use of wireless sensor networks to automatically track “perceptive pallets” of materials in ware-houses for the purpose of monitoring volumetric and spatial constraints. A combination of radio frequency and ultrasound chirping produces position estimates that are noisy and prone to error. To address this, we measure and characterize the ultrasound response from standard “Cricket” wireless sensor motes and beacons. We develop a non-parametric particle filtering approach to estimate trajectories of moving motes and introduce two asymmetric observation models that incorporate measured cardioid-shaped response patterns of ultrasound.

Collaborator: Automation Lab of Professor Ken Goldberg, EECS UC Berkeley