Virtual dye angiography: flow visualization for MRI-guided interventions.

In magnetic resonance imaging-guided cardiovascular interventional procedures, it is valuable to be able to visualize blood flow immediately and interactively in selected regions. In particular, it is useful to assess normal or pathological communications between specific heart chambers and vessels. Phase-contrast velocity mapping is not suitable for this purpose as it requires too much data and is not capable of determining directly if blood originating in one location travels to a nearby location. This article presents a novel flow visualization method called virtual dye angiography that enables visualization of blood flow analogous to selective catheter angiography. The method uses two-dimensional radio frequency pulses to achieve interactive, intermittent, targeted saturation of a localized region of the blood pool. The flow of the saturated spins is observed directly on real-time images or, in an enhanced manner, using ECG synchronized background subtraction. The modular nature of the technique allows for easy and seamless integration into a real-time, interactive imaging system with minimal overhead. We present initial results in animals and in a healthy human volunteer.

Robust automatic rigid registration of MRI and X-ray using external fiducial markers for XFM-guided interventional procedures.

PURPOSE: In X-ray fused with MRI, previously gathered roadmap MRI volume images are overlaid on live X-ray fluoroscopy images to help guide the clinician during an interventional procedure. The incorporation of MRI data allows for the visualization of soft tissue that is poorly visualized under X-ray. The widespread clinical use of this technique will require fully automating as many components as possible. While previous use of this method has required time-consuming manual intervention to register the two modalities, in this article, the authors present a fully automatic rigid-body registration method. METHODS: External fiducial markers that are visible under these two complimentary imaging modalities were used to register the X-ray images with the roadmap MR images. The method has three components: (a) The identification of the 3D locations of the markers from a full 3D MR volume, (b) the identification of the 3D locations of the markers from a small number of 2D X-ray fluoroscopy images, and (c) finding the rigid-body transformation that registers the two point sets in the two modalities. For part (a), the localization of the markers from MR data, the MR volume image was thresholded, connected voxels were segmented and labeled, and the centroids of the connected components were computed. For part (b), the X-ray projection images, produced by an image intensifier, were first corrected for distortions. Binary mask images of the markers were created from the distortion-corrected X-ray projection images by applying edge detection, pattern recognition, and image morphological operations. The markers were localized in the X-ray frame using an iterative backprojection-based method which segments voxels in the volume of interest, discards false positives based on the previously computed edge-detected projections, and calculates the locations of the true markers as the centroids of the clusters of voxels that remain. For part (c), a variant of the iterative closest point method was used to find correspondences between and register the two sets of points computed from MR and X-ray data. This knowledge of the correspondence between the two point sets was used to refine, first, the X-ray marker localization and then the total rigid-body registration between modalities. The rigid-body registration was used to overlay the roadmap MR image onto the X-ray fluoroscopy projections. RESULTS: In 35 separate experiments, the markers were correctly registered to each other in 100% of the cases. When half the number of X-ray projections was used (10 X-ray projections instead of 20), the markers were correctly registered in all 35 experiments. The method was also successful in all 35 experiments when the number of markers was (retrospectively) halved (from 16 to 8). The target registration error was computed in a phantom experiment to be less than 2.4 mm. In two in vivo experiments, targets (interventional devices with pointlike metallic structures) inside the heart were successfully registered between the two modalities. CONCLUSIONS: The method presented can be used to automatically register a roadmap MR image to X-ray fluoroscopy using fiducial markers and as few as ten X-ray projections.

Visualization of active devices and automatic slice repositioning ("SnapTo") for MRI-guided interventions.

The accurate visualization of interventional devices is crucial for the safety and effectiveness of MRI-guided interventional procedures. In this paper, we introduce an improvement to the visualization of active devices. The key component is a fast, robust method ("CurveFind") that reconstructs the three-dimensional trajectory of the device from projection images in a fraction of a second. CurveFind is an iterative prediction-correction algorithm that acts on a product of orthogonal projection images. By varying step size and search direction, it is robust to signal inhomogeneities. At the touch of a key, the imaged slice is repositioned to contain the relevant section of the device ("SnapTo"), the curve of the device is plotted in a three-dimensional display, and the point on a target slice, which the device will intersect, is displayed. These features have been incorporated into a real-time MRI system. Experiments in vitro and in vivo (in a pig) have produced successful results using a variety of single- and multichannel devices designed to produce both spatially continuous and discrete signals. CurveFind is typically able to reconstruct the device curve, with an average error of approximately 2 mm, even in the case of complex geometries.

Shear-based fast hierarchical backprojection for parallel-beam tomography.

We introduce a family of fast algorithms for 2-D parallel-beam tomographic backprojection. They aggregate the projections in a hierarchical structure involving the shearing and addition of sparsely sampled images. The algorithms achieve a computational cost of O(N(2) log P), when backprojecting an N x N pixel image from P projections. The algorithms provide a systematic means, guided by a Fourier-domain interpretation, to adjust and optimize the tradeoff between computational cost and accuracy. In an example with N = 512 and P = 1458 the algorithms provide high accuracy, with more than an order of magnitude reduction in operation counts.