Ameet Kumar Jain

FTRAC--a robust fluoroscope tracking fiducial.

C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional (3D) information. A major obstacle in fluoroscopic reconstruction is discerning the pose of the x-ray image, in 3D space. Optical/magnetic trackers tend to be prohibitively expensive, intrusive and cumbersome in many applications. We present single-image-based fluoroscope tracking (FTRAC) with the use of an external radiographic fiducial consisting of a mathematically optimized set of ellipses, lines, and points. This is an improvement over contemporary fiducials, which use only points. The fiducial encodes six degrees of freedom in a single image by creating a unique view from any direction. A nonlinear optimizer can rapidly compute the pose of the fiducial using this image. The current embodiment has salient attributes: small dimensions (3×3×5cm); need not be close to the anatomy of interest; and accurately segmentable. We tested the fiducial and the pose recovery method on synthetic data and also experimentally on a precisely machined mechanical phantom. Pose recovery in phantom experiments had an accuracy of 0.56mm in translation and 0.33° in orientation. Object reconstruction had a mean error of 0.53mm with 0.16mm STD. The method offers accuracies similar to commercial tracking systems, and appears to be sufficiently robust for intraoperative quantitative C-arm fluoroscopy. Simulation experiments indicate that the size can be further reduced to 1×1×2cm, with only a marginal drop in accuracy.

A rapid calibration method for registration and 3D tracking of ultrasound images using spatial localizer

Conventional freehand 3D ultrasound (US) is a complex process, involving calibration, scanning, processing, volume reconstruction, and visualization. Prior to calibration, a position sensor is attached to the probe for tagging each image with its position and orientation in space; then calibration process is performed to determine the spatial transformation of the scan plan with respect to the position sensor. Finding this transformation matrix is a critical, but often underrated task in US-guided surgery. The purpose of this study is to enhance previously known calibration methods by introducing a novel calibration fixture and process. The proposed phantom is inexpensive, easy to construct, easy to scan, while yielding more data points per image than previously known designs. The processing phase is semi-automated, allowing for fast processing of a massive amount of data, which in turn increases accuracy by reducing human errors.

Matching and reconstruction of brachytherapy seeds using the Hungarian algorithm (MARSHAL).

Intraoperative dosimetric quality assurance in prostate brachytherapy critically depends on discerning the three-dimensional (3D) locations of implanted seeds. The ability to reconstruct the implanted seeds intraoperatively will allow us to make immediate provisions for dosimetric deviations from the optimal implant plan. A method for seed reconstruction from segmented C-arm fluoroscopy images is proposed. The 3D coordinates of the implanted seeds can be calculated upon resolving the correspondence of seeds in multiple x-ray images. We formalize seed-matching as a combinatorial optimization problem, which has salient features: (a) extensively studied solutions by the computer science community; (b) proof for the nonexistence of any polynomial time exact algorithm; and (c) a practical pseudo-polynomial algorithm that mostly runs in O(N3) time using any number of images. We prove that two images are insufficient to correctly match the seeds, while a third image renders the matching problem to be of nonpolynomial complexity. We utilize the special structure of the problem and propose a pseudopolynomial time algorithm. Using three presegmented images, matching and reconstruction of brachytherapy seeds using the Hungarian algorithm achieved complete matching in simulation experiments; and 98.5% in phantom experiments. 3D reconstruction error for correctly matched seeds has a mean of 0.63mm, and 0.9mm for incorrectly matched seeds. The maximum seed reconstruction error in each implant was typically around 1.32mm. Both on synthetic data and in phantom experiments, matching rate and reconstruction error achieved using presegmented images was found to be sufficient for prostate brachytherapy. The algorithm is extendable to deal with arbitrary number of images without any loss in speed or accuracy. The algorithm is sufficiently generic to provide a practical solution to any correspondence problem, across different imaging modalities and features.

C-arm tracking and reconstruction without an external tracker

For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence and reconstruction, without the use of optical/electromagnetic trackers or precision-made fiducial fixtures. Our method uses randomly distributed unknown points in the imaging volume, either naturally present or induced by randomly sticking beads or other simple markers in the image pace. After these points are segmented, a high dimensional non-linear optimization computes all unknown parameters for calibration, C-arm pose, correspondence and reconstruction. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.9 degrees and a 3D reconstruction error of 0.8 mm, with an 80 region of convergence for both the AP and lateral axes. The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation and does not interfere with the workspace.

A non-disruptive technology for robust 3d tool tracking for ultrasound-guided interventions

In the past decade ultrasound (US) has become the preferred modality for a number of interventional procedures, offering excellent soft tissue visualization. The main limitation however is limited visualization of surgical tools. A new method is proposed for robust 3D tracking and US image enhancement of surgical tools under US guidance. Small US sensors are mounted on existing surgical tools. As the imager emits acoustic energy, the electrical signal from the sensor is analyzed to reconstruct its 3D coordinates. These coordinates can then be used for 3D surgical navigation, similar to current day tracking systems. A system with real-time 3D tool tracking and image enhancement was implemented on a commercial ultrasound scanner and 3D probe. Extensive water tank experiments with a tracked 0.2mm sensor show robust performance in a wide range of imaging conditions and tool position/orientations. The 3D tracking accuracy was 0.36 +/- 0.16mm throughout the imaging volume of 55 degrees x 27 degrees x 150mm. Additionally, the tool was successfully tracked inside a beating heart phantom. This paper proposes an image enhancement and tool tracking technology with sub-mm accuracy for US-guided interventions. The technology is non-disruptive, both in terms of existing clinical workflow and commercial considerations, showing promise for large scale clinical impact.

REDMAPS: Reduced-Dimensionality Matching for Prostate Brachytherapy Seed Reconstruction

The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland. Intraoperative localization of the implanted seeds provides potential for dose evaluation and optimization during therapy. A reduced-dimensionality matching algorithm for prostate brachytherapy seed reconstruction (REDMAPS) that uses multiple X-ray fluoroscopy images obtained from different poses is proposed. The seed reconstruction problem is formulated as a combinatorial optimization problem, and REDMAPS finds a solution in a clinically acceptable amount of time using dimensionality reduction to create a smaller space of possible solutions. Dimensionality reduction is possible since the optimal solution has approximately zero cost when the poses of the acquired images are known to be within a small error. REDMAPS is also formulated to address the “hidden seed problem” in which seeds overlap on one or more observed images. REDMAPS uses a pruning algorithm to avoid unnecessary computation of cost metrics and the reduced problem is solved using linear programming. REDMAPS was first evaluated and its parameters tuned using simulations. It was then validated using five phantom and 21 patient datasets. REDMAPS was successful in reconstructing the seeds with an overall seed matching rate above 99% and a reconstruction error below 1 mm in less than 5 s.

3D TEE Registration with X-Ray Fluoroscopy for Interventional Cardiac Applications

Live 3D trans-esophageal echocardiography (TEE) and X-ray fluoroscopy provide complementary imaging information for guiding minimally invasive cardiac interventions. X-ray fluoroscopy is most commonly used for these procedures due to its excellent device visualization. However, its challenges include the 2D projection nature of the images and poor soft tissue contrast, both of which are addressed by the use of live 3D TEE imaging. We propose to integrate 3D TEE imaging with X-ray fluoroscopy, providing the capability to co-visualize both the interventional devices and cardiac anatomy, by accurately registering the images using an electro-magnetic tracking system. Phantom trials validating the proposed registration scheme indicate an average accuracy of 2.04 mm with a standard deviation of 0.59 mm. In the future, this system may benefit the guidance and navigation of interventional cardiac procedures such as mitral valve repair or patent foramen ovale closure.

C-arm calibration – is it really necessary?

C-arm fluoroscopy is modelled as a perspective projection, the parameters of which are estimated through a calibration procedure. It has been universally accepted that precise intra-procedural calibration is a prerequisite for accurate quantitative C-arm fluoroscopy guidance. Calibration, however, significantly adds to system complexity, which is a major impediment to clinical practice. We challenge the status quo by questioning the assumption that precise intra-procedural calibration is really necessary. We derived theoretical bounds for the sensitivity of 3D measurements to mis-calibration. Experimental results corroborated the theory in that mis-calibration in the focal spot by as much as 50 mm still allows for tracking with an accuracy of 0.5 mm in translation and 0.65o in rotation, and such mis-calibration does not impose any additional error on the reconstruction of small objects.

Intra-operative 3D guidance in prostate brachytherapy using a non-isocentric C-arm

Intra-operative guidance in Transrectal Ultrasound (TRUS) guided prostate brachytherapy requires localization of inserted radioactive seeds relative to the prostate. Seeds were reconstructed using a typical C-arm, and exported to a commercial brachytherapy system for dosimetry analysis. Technical obstacles for 3D reconstruction on a non-isocentric C-arm included pose-dependent C-arm calibration; distortion correction; pose estimation of C-arm images; seed reconstruction; and C-arm to TRUS registration. In precision-machined hard phantoms with 40-100 seeds, we correctly reconstructed 99.8% seeds with a mean 3D accuracy of 0.68 mm. In soft tissue phantoms with 45-87 seeds and clinically realistic 15 degrees C-arm motion, we correctly reconstructed 100% seeds with an accuracy of 1.3 mm. The reconstructed 3D seed positions were then registered to the prostate segmented from TRUS. In a Phase-1 clinical trial, so far on 4 patients with 66-84 seeds, we achieved intra-operative monitoring of seed distribution and dosimetry. We optimized the 100% prescribed iso-dose contour by inserting an average of 3.75 additional seeds, making intra-operative dosimetry possible on a typical C-arm, at negligible additional cost to the existing clinical installation.

Prostate Brachytherapy Seed Reconstruction With Gaussian Blurring and Optimal Coverage Cost

Intraoperative dosimetry in prostate brachytherapy requires localization of the implanted radioactive seeds. A tomosynthesis-based seed reconstruction method is proposed. A three-dimensional volume is reconstructed from Gaussian-blurred projection images and candidate seed locations are computed from the reconstructed volume. A false positive seed removal process, formulated as an optimal coverage problem, iteratively removes ldquoghostrdquo seeds that are created by tomosynthesis reconstruction. In an effort to minimize pose errors that are common in conventional C-arms, initial pose parameter estimates are iteratively corrected by using the detected candidate seeds as fiducials, which automatically ldquofocusesrdquo the collected images and improves successive reconstructed volumes. Simulation results imply that the implanted seed locations can be estimated with a detection rate of ges97.9% and ges99.3% from three and four images, respectively, when the C-arm is calibrated and the pose of the C-arm is known. The algorithm was also validated on phantom data sets successfully localizing the implanted seeds from four or five images. In a Phase-1 clinical trial, we were able to localize the implanted seeds from five intraoperative fluoroscopy images with 98.8% (STD=1.6) overall detection rate.