Stroke is one of the leading causes of death in the U.S. The treatment of stroke often involves vascular interventions in which devices are guided to the intervention site often through tortuous vessels based on two-dimensional (2-D) angiographic images. Three dimensional (3-D) vascular information may facilitate these procedures. Methods have been proposed for the self-calibrating determination of 3-D vessel trees from biplane and multiple plane images and the geometric relationships between the views (imaging geometries). For the biplane analysis, four or more corresponding points must be identified in the biplane images. For the multiple view technique, multiple vessels must be indicated and only the translation vectors relating the geometries are calculated. We have developed methods for the calculation of the 3-D vessel data and the full transformations relating the multiple views (rotations and translations) obtained during interventional procedures, and the technique does not require indication of corresponding points, but only the indication of a single vessel, e.g., the vessel of interest. Multiple projection views of vessel trees are obtained and transferred to the analysis computer. The vessel or vessels of interest are indicated by the user. Using the initial imaging geometry determined from the gantry information, 3-D vessel centerlines are calculated using the indicated centerlines in pairs of images. The imaging geometries are then iteratively adjusted and 3-D centerlines recalculated until the root-mean-square (rms) difference between the calculated 3-D centerlines is minimized. Simulations indicate that the 3-D centerlines can be accurately determined (to within 1 mm) even for errors in indication of the vessel endpoints as large as 5 mm. In phantom studies, the average rms difference between the pairwise calculated 3-D centerlines is approximately 7.5 mm prior to refinement (i.e., using the gantry information alone), whereas the average rms difference is usually below 1 mm after refinement. Accuracies and reliabilities of better than 1 mm were also determined by comparing centerlines determined using multiview and rotational angiography reconstruction and clinical data sets. These results indicate that the multiview approach will provide accurate and reliable 3-D centerlines for indicated vessel(s) without increasing the dose to the patient.
The new Multi-View Reconstruction (MVR) method for generating 3D vascular images was evaluated experimentally. The MVR method requires only a few digital subtraction angiographic (DSA) projections to reconstruct the 3D model of the vessel object compared to 180 or more projections for standard CBCT. Full micro-CBCT datasets of a contrast filled carotid vessel phantom were obtained using a Microangiography (MA) detector. From these datasets, a few projections were selected for use in the MVR technique. Similar projection views were also obtained using a standard x-ray image intensifier (II) system. A comparison of the 2D views of the MVRs (MA and II derived) with reference micro-CBCT data, demonstrated best agreement with the MA MVRs, especially at the curved part of the phantom. Additionally, the full 3D MVRs were compared with the full micro-CBCT 3D reconstruction resulting for the phantom with the smallest diameter (0.75 mm) vessel, in a mean centerline deviation from the micro-CBCT derived reconstructions of 29 μm for the MA MVR and 48 μm for the II MVR. The comparison implies that an MVR may be substituted for a full micro-CBCT scan for evaluating vessel segments with consequent substantial savings in patient exposure and contrast media injection yet without substantial loss in 3D image content. If a high resolution system with MA detector is used, the improved resolution could be well suited for endovascular image guided interventions where visualization of only a small field of view (FOV) is required.
Purpose: The sensitivity of a new 3D Multi‐View Reconstruction (MVR) angiography technique to the projection angles used is evaluated by comparing 3D centerlines calculated from combinations of three projections acquired from two imaging systems with that from micro‐Cone Beam CT (μCBCT), which is taken as truth. Method and Materials: A 3D centerline of a contrast‐filled carotid vessel phantom was reconstructed from image data acquired using a custom‐made μCBCT system with a microangiographic (MA) detector (45 μm pixels, 4.5 cm field‐of‐view (FOV)). Projection images of the same phantom were also acquired using the MA and an image intensifier (II) detector system (120 μm pixels, 4.5 in FOV) on a C‐arm x‐ray unit. The MVR technique was used to compute 3D centerlines for 12 combinations of projection angles. Each 3D MVR centerline was aligned with the μCBCT “true” 3D centerline using a Procrustes technique, and a root‐mean‐square (RMS) deviation was calculated. Results: The average RMS deviation for the MA‐MVR centerlines is 25 μm with a standard deviation of 3 μm over the 12 different projection‐angle combinations, whereas the average RMS deviation for the II‐MVR centerlines is 41 μm with a standard deviation of 4 μm over these same combinations. The RMS deviation as a percent of the internal vessel diameter, 0.75 mm, is 3.3% for the MA and 5.5% for the II and appears to be independent of view selection. Conclusion: For the MVR technique, the improved resolution of the MA resulted in improved centerline determination compared to the II system. For both detectors, the selection of a particular projection set had little effect on the RMS centerline deviation. The low RMS deviations for both detectors indicate that the MVR technique can provide accurate 3D centerlines. (Partial support from NIH Grants R01‐NS43924, R01‐EB02873, R01‐HL52567, R01‐EB02916, and Toshiba Medical Systems Corporation).
These results indicate that this technique will provide more reliable vessel centerlines in the clinical setting without requiring additional acquisitions or increasing dose to the patient.
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