Teo Popa

Needle-Based Interventions With the Image-Guided Surgery Toolkit (IGSTK): From Phantoms to Clinical Trials

We present three image-guided navigation systems developed for needle-based interventional radiology procedures, using the open source image-guided surgery toolkit (IGSTK). The clinical procedures we address are vertebroplasty, RF ablation of large lung tumors, and lung biopsy. In vertebroplasty, our system replaces the use of fluoroscopy, reducing radiation exposure to patient and physician. We evaluate this system using a custom phantom and compare the results obtained by a medical student, an interventional radiology fellow, and an attending physician. In RF ablation of large lung tumors, our system provides an automated interventional plan that minimizes damage to healthy tissue and avoids critical structures, in addition to accurate guidance of multiple electrode insertions. We evaluate the systems performance using an animal model. Finally, in the lung biopsy procedure, our system replaces the use of computed tomographic (CT) fluoroscopy, reducing radiation exposure to patient and physician, while at the same time enabling oblique trajectories which are considered challenging under CT fluoroscopy. This system is currently being used in an ongoing clinical trial at Georgetown University Hospital and was used in three cases.

Radiofrequency ablation of lung tumors in swine assisted by a navigation device with preprocedural volumetric planning.

PURPOSE To develop an image guidance system that incorporates volumetric planning of spherical ablations and electromagnetic tracking of radiofrequency (RF) electrodes during insertion. MATERIALS AND METHODS Simulated tumors were created in three live swine by percutaneously injecting agar nodules into the lung. A treatment plan was devised for each tumor with optimization software to solve the planning problem. The desired output was the minimum number of overlapping ablation spheres necessary to ablate each tumor and the margin. The insertion plan was executed with use of the electromagnetic tracking system that guided the insertion of the probe into precomputed locations. After a 72-hour survival period, animals were killed and histopathologic sections of the tissue were examined for cell viability and burn pattern analysis. RESULTS A planning algorithm to spherically cover the tumors and the margin was computed. Electromagnetic tracking allowed successful insertion of the instrument, and impedance roll-off was reached in all ablations. Depending on their size, the tumors and the tumor margins were successfully covered with two to four ablation spheres. The image registration error was 1.0 mm +/- 0.64. The overall error of probe insertion was 9.4 mm +/- 3.0 (N = 8). Analysis of histopathologic sections confirmed successful ablations of the tissue. CONCLUSIONS Computer-assisted RF ablation planning and electromagnetically tracked probe insertion were successful in three swine, validating the feasibility of electromagnetic tracking-assisted tumor targeting. Image misregistration caused by respiratory motion and tissue deformation contributed to the overall error of probe insertion.

ITK implementation of deformable registration methods for time-varying (4D) imaging data

Dynamic or 4D images (in which a section of the body is repeatedly imaged in order to capture physiological motion) are becoming increasingly important in medicine. These images are especially critical to the field of image-guided therapy, because they enable treatment planning that reflects the realistic motion of the therapy target. Although it is possible to acquire static images and deform them based on generalized assumptions of normal motion, such an approach does not account for variability in the individual patient. To enable the most effective treatments, it is necessary to be able to image each patient and characterize their unique respiratory motion, but software specifically designed around the needs of 4D imaging is not widely available. We have constructed an open source application that allows a user to manipulate and analyze 4D image data. This interface can load DICOM images into memory, reorder/rebin them if necessary, and then apply deformable registration methods to derive the respiratory motion. The interface allows output and display of the deformation field, display of images with the deformation field as an overlay, and tables and graphs of motion versus time. The registration is based on the open source Insight Toolkit (ITK) and the interface is constructed using the open source GUI tool FLTK, which will make it easy to distribute and extend this software in the future.

Transbronchial needle aspiration with a new electromagnetically-tracked TBNA needle

Transbronchial needle aspiration (TBNA) is a common method used to collect tissue for diagnosis of different chest diseases and for staging lung cancer, but the procedure has technical limitations. These limitations are mostly related to the difficulty of accurately placing the biopsy needles into the target mass. Currently, pulmonologists plan TBNA by examining a number of Computed Tomography (CT) scan slices before the operation. Then, they manipulate the bronchoscope down the respiratory track and blindly direct the biopsy. Thus, the biopsy success rate is low. The diagnostic yield of TBNA is approximately 70 percent. To enhance the accuracy of TBNA, we developed a TBNA needle with a tip position that can be electromagnetically tracked. The needle was used to estimate the bronchoscopes tip position and enable the creation of corresponding virtual bronchoscopic images from a preoperative CT scan. The TBNA needle was made with a flexible catheter embedding Wang Transbronchial Histology Needle and a sensor tracked by electromagnetic field generator. We used Aurora system for electromagnetic tracking. We also constructed an image-guided research prototype system incorporating the needle and providing a user-friendly interface to assist the pulmonologist in targeting lesions. To test the feasibility of the accuracy of the newly developed electromagnetically-tracked needle, a phantom study was conducted in the interventional suite at Georgetown University Hospital. Five TBNA simulations with a custom-made phantom with a bronchial tree were performed. The experimental results show that our device has potential to enhance the accuracy of TBNA.

High dynamic range (HDR) virtual bronchoscopy rendering for video tracking

In this paper, we present the design and implementation of a new rendering method based on high dynamic range (HDR) lighting and exposure control. This rendering method is applied to create video images for a 3D virtual bronchoscopy system. One of the main optical parameters of a bronchoscopes camera is the sensor exposure. The exposure adjustment is needed since the dynamic range of most digital video cameras is narrower than the high dynamic range of real scenes. The dynamic range of a camera is defined as the ratio of the brightest point of an image to the darkest point of the same image where details are present. In a video camera exposure is controlled by shutter speed and the lens aperture. To create the virtual bronchoscopic images, we first rendered a raw image in absolute units (luminance); then, we simulated exposure by mapping the computed values to the values appropriate for video-acquired images using a tone mapping operator. We generated several images with HDR and others with low dynamic range (LDR), and then compared their quality by applying them to a 2D/3D video-based tracking system. We conclude that images with HDR are closer to real bronchoscopy images than those with LDR, and thus, that HDR lighting can improve the accuracy of image-based tracking.

Creation of 4D imaging data using open source image registration software

4D images (3 spatial dimensions plus time) using CT or MRI will play a key role in radiation medicine as techniques for respiratory motion compensation become more widely available. Advance knowledge of the motion of a tumor and its surrounding anatomy will allow the creation of highly conformal dose distributions in organs such as the lung, liver, and pancreas. However, many of the current investigations into 4D imaging rely on synchronizing the image acquisition with an external respiratory signal such as skin motion, tidal flow, or lung volume, which typically requires specialized hardware and modifications to the scanner. We propose a novel method for 4D image acquisition that does not require any specific gating equipment and is based solely on open source image registration algorithms. Specifically, we use the Insight Toolkit (ITK) to compute the normalized mutual information (NMI) between images taken at different times and use that value as an index of respiratory phase. This method has the advantages of (1) being able to be implemented without any hardware modification to the scanner, and (2) basing the respiratory phase on changes in internal anatomy rather than external signal. We have demonstrated the capabilities of this method with CT fluoroscopy data acquired from a swine model.