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Myocardial infarction (MI) is one of the leading causes of death in the world. Small animal studies have shown that stem-cell therapy offers dramatic functional improvement post-MI. An endomyocardial catheter injection approach to therapeutic agent delivery has been proposed to improve efficacy through increased cell retention. Accurate targeting is critical for reaching areas of greatest therapeutic potential while avoiding a life-threatening myocardial perforation. Multimodal image fusion has been proposed as a way to improve these procedures by augmenting traditional intra-operative imaging modalities with high resolution pre-procedural images. Previous approaches have suffered from a lack of real-time tissue imaging and dependence on X-ray imaging to track devices, leading to increased ionizing radiation dose. In this paper, we present a new image fusion system for catheter-based targeted delivery of therapeutic agents. The system registers real-time 3D echocardiography, magnetic resonance, X-ray, and electromagnetic sensor tracking within a single flexible framework. All system calibrations and registrations were validated and found to have target registration errors less than 5 mm in the worst case. Injection accuracy was validated in a motion enabled cardiac injection phantom, where targeting accuracy ranged from 0.57 to 3.81 mm. Clinical feasibility was demonstrated with in-vivo swine experiments, where injections were successfully made into targeted regions of the heart.

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C-arm fluoroscopy images are frequently used for qualitative assessment of prostate brachytherapy. Three-dimensional seed reconstruction from C-arm images is necessary for intraoperative dosimetry and quantitative assessment. Seed reconstruction requires accurately known C-arm poses. We propose to measure the C-arm rotation angles and computationally compensate for inevitable C-arm motion to compute the pose. We compensate the translational motions of a C-arm, such as oscillation, sagging and wheel motion using a three-level optimization algorithm and obviate the need for full pose tracking using external trackers or fiducials. We validated our approach on simulated and 100 clinical data sets from 10 patients and gained on average, a seed matching rate of 98.5%, projection error of 0.33 mm (STD=0.21 mm) and computation time of 19.8s per patient, which must be considered as clinically excellent results. We also show that without motion compensation the reconstruction is likely to fail.

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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.

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PURPOSE: To evaluate a prototypical system of dynamic intraoperative dosimetry for prostate brachytherapy using registered ultrasound and fluoroscopy (RUF) with a nonisocentric C-arm (GE OEC, Salt Lake City, UT) and to compare intraoperative dosimetry of RUF as well as ultrasound-based seed localization (USD) with Day 0 CT dosimetry. METHODS: Seed positions were independently determined using RUF and USD. RUF uses a radio-opaque fiducial for registration to ultrasound and 3-dimensional reconstruction of seeds relative to prostate using nonisocentric C-arm fluoroscopy. Postimplant CT was performed on Day 0. Squared differences between dosimetric measures for RUF vs. CT and USD vs. CT were calculated and mean squared differences evaluated. Paired t test was used to evaluate which method was more closely aligned with CT. Accuracies of USD and RUF compared with CT were estimated using a nonparametric approach. RESULTS: Six patients were treated and compared with USD. RUF identified areas of underdosage intraoperatively in all patients and median 5 additional seeds were placed. In 40 of 42 measures, RUF was equally or more closely correlated with CT than USD. USD showed statistically significant variation from CT for 6 of 7 parameters compared with 1 of 7 parameters for RUF. Mean squared differences from CT were significantly smaller for RUF in 4 of 7 parameters compared with USD. CONCLUSIONS: Dynamic intraoperative dosimetry is possible with a conventional nonisocentric C-arm. Compared with an USD method, RUF-based intraoperative dosimetry was more closely aligned with immediate postimplant CT. RUF identified areas of underdosage, which were not detected using USD.

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Targeted stem cell therapy offers great potential for the repair of infarcted cardiac tissue following heart attack. Safe delivery of stem-cells via catheter based interventions remains a challenge. A multi-modal image fusion approach has been considered for safe targeting of myocardial infarct border zones. In this paper we present an apparatus and method for measuring the accuracy of catheter-based injections using a multi-modal image fusion system. We also present results of the accuracy of our image fusion system under varying levels of cardio-respiratory motion.

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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 "ghost" 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 "focuses" the collected images and improves successive reconstructed volumes. Simulation results imply that the implanted seed locations can be estimated with a detection rate of > or = 97.9% and > or = 99.3% 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.

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In prostate brachytherapy, x-ray fluoroscopy has been used for intra-operative dosimetry to provide qualitative assessment of implant quality. More recent developments have made possible 3D localization of the implanted radioactive seeds. This is usually modeled as an assignment problem and solved by resolving the correspondence of seeds. It is, however, NP-hard, and the problem is even harder in practice due to the significant number of hidden seeds. In this paper, we propose an algorithm that can find an optimal solution from multiple projection images with hidden seeds. It solves an equivalent problem with reduced dimensional complexity, thus allowing us to find an optimal solution in polynomial time. Simulation results show the robustness of the algorithm. It was validated on 5 phantom and 18 patient datasets, successfully localizing the seeds with detection rate of > or = 97.6% and reconstruction error of < or = 1.2 mm. This is considered to be clinically excellent performance.

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X-ray fluoroscopy is widely used for intra-operative dosimetry in prostate brachytherapy. Three-dimensional locations of the implanted radioactive seeds can be calculated from multiple X-ray images upon resolving the correspondence of seeds. This is usually modeled as an assignment problem that is NP-hard. We propose an algorithm that allows us to derive an equivalent problem of reduced dimensionality based on practical observation that the optimal solution has almost zero cost if the C-arm pose is known. The reduced problem is efficiently solved by linear programming in polynomial time. Additionally, our method solves the hidden seeds problem. Simulation results demonstrate that the implanted seeds can be localized with a matching rate of ≥ 98.8 % and reconstruction error of ≤ 0.37 mm using three images with hidden seeds in a few seconds when the pose of the C-arm is known.

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We have developed a tomosynthesis-based radioactive seed localization method for prostate brachytherapy. In contrast to the projection image-based matching approach, our method does not involve explicit segmentation of seeds and can recover hidden seeds. Modified distance map images are computed from a limited number of x-ray projection images, and are backprojected to reconstuct a 3-D volume of interest. Candidate seed locations are extracted from the reconstructed volume and false positive seeds are eliminated by solving an optimal geometry coverage problem. The simulation results indicate that the implanted seed locations can be estimated from three or four images depending on the number of seeds if the pose of a C-arm is known. The algorithm was validated using phantom and clinical patient data.

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C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional information, attributable to the difficulty in obtaining the pose of X-ray images in 3D space. We propose a unified mathematical framework to address the issues of intra-operative pose estimation, correspondence and reconstruction, using simple elliptic curves. In contrast to other fiducial-based tracking methods, our method uses a single ellipse to constrain 5 out of 6 degrees of freedom of C-arm pose, along with randomly distributed unknown points in the imaging volume (either naturally present or induced by randomly placed beads or other markers in the image space) from two images/views to completely recover the C-arm pose. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.51° and 0.12° STD. The method appears to be sufficiently accurate and appealing for many clinical applications, since it uses a simple elliptic fiducial coupled with patient information and has very minimal interference with the workspace.

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We developed a discrete tomography method for prostate implant reconstructions using only a limited number of X-ray projection images. A 3D voxel volume is reconstructed by back-projection and using distance maps generated from the projection images. The true seed locations are extracted from the voxel volume while false positive seeds are eliminated using a novel optimal geometry coverage model. The attractive feature of our method is that it does not require exact seed segmentation of the X-ray images and it yields near 100% correct reconstruction from only six images with an average reconstruction accuracy of 0.86 mm (std = 0.46mm).

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