Determinants of image quality of rotational angiography for on-line assessment of frame geometry after transcatheter aortic valve implantation

To study the determinants of image quality of rotational angiography using dedicated research prototype software for motion compensation without rapid ventricular pacing after the implantation of four commercially available catheter-based valves. Prospective observational study including 179 consecutive patients who underwent transcatheter aortic valve implantation (TAVI) with either the Medtronic CoreValve (MCS), Edward-SAPIEN Valve (ESV), Boston Sadra Lotus (BSL) or Saint-Jude Portico Valve (SJP) in whom rotational angiography (R-angio) with motion compensation 3D image reconstruction was performed. Image quality was evaluated from grade 1 (excellent image quality) to grade 5 (strongly degraded). Distinction was made between good (grades 1, 2) and poor image quality (grades 3–5). Clinical (gender, body mass index, Agatston score, heart rate and rhythm, artifacts), procedural (valve type) and technical variables (isocentricity) were related with the image quality assessment. Image quality was good in 128 (72 %) and poor in 51 (28 %) patients. By univariable analysis only valve type (BSL) and the presence of an artefact negatively affected image quality. By multivariate analysis (in which BMI was forced into the model) BSL valve (Odds 3.5, 95 % CI [1.3–9.6], p = 0.02), presence of an artifact (Odds 2.5, 95 % CI [1.2–5.4], p = 0.02) and BMI (Odds 1.1, 95 % CI [1.0–1.2], p = 0.04) were independent predictors of poor image quality. Rotational angiography with motion compensation 3D image reconstruction using a dedicated research prototype software offers good image quality for the evaluation of frame geometry after TAVI in the majority of patients. Valve type, presence of artifacts and higher BMI negatively affect image quality

Exact And Efficient Cone-Beam Reconstruction Algorithm For A Short-Scan Circle Combined With Various Lines

X-ray 3D rotational angiography based on C-arm systems has become a versatile and established tomographic imaging modality for high contrast objects in interventional environment. Improvements in data acquisition, e.g. by use of flat panel detectors, will enable C-arm systems to resolve even low-contrast details. However, further progress will be limited by the incompleteness of data acquisition on the conventional short-scan circular source trajectories. Cone artifacts, which result from that incompleteness, significantly degrade image quality by severe smearing and shading. To assure data completeness a combination of a partial circle with one or several line segments is investigated. A new and efficient reconstruction algorithm is deduced from a general inversion formula based on 3D Radon theory. The method is theoretically exact, possesses shift-invariant filtered backprojection (FBP) structure, and solves the long object problem. The algorithm is flexible in dealing with various circle and line configurations. The reconstruction method requires nothing more than the theoretically minimum length of scan trajectory. It consists of a conventional short-scan circle and a line segment approximately twice as long as the height of the region-of-interest. Geometrical deviations from the ideal source trajectory are considered in the implementation in order to handle data of real C-arm systems. Reconstruction results show excellent image quality free of cone artifacts. The proposed scan trajectory and reconstruction algorithm assure excellent image quality and allow low-contrast tomographic imaging with C-arm based cone-beam systems. The method can be implemented without any hardware modifications on systems commercially available today

Automatic Extraction of 3D Dynamic Left Ventricle Model from 2D Rotational Angiocardiogram

Abstract. In this paper, we propose an automatic method to directly extract 3D dynamic left ventricle (LV) model from sparse 2D rotational angiocardiogram (each cardiac phase contains only five projections). The extracted dynamic model provides quantitative cardiac function for analysis. The overlay of the model onto 2D real-time fluoroscopic images provides valuable visual guidance during cardiac intervention. Though containing severe cardiac motion artifacts, an ungated CT reconstruction is used in our approach to extract a rough static LV model. The initialized LV model is projected onto each 2D projection image. The silhouette of the projected mesh is deformed to match the boundary of LV blood pool. The deformation vectors of the silhouette are back-projected to 3D space and used as anchor points for thin plate spline (TPS) interpolation of other mesh points. The proposed method is validated on 12 synthesized datasets. The extracted 3D LV meshes match the ground truth quite well with a mean point-to-mesh error of 0.51 ± 0.11mm. The preliminary experiments on two real datasets (included a patient and a pig) show promising results too.

4-D Motion Field Estimation by Combined Multiple Heart Phase Registration (CMHPR) for Cardiac C-arm Data

In interventional cardiology three-dimensional anatomical and functional information of the cardiac chambers would have an important impact on diagnosis and therapy. With the technology of C-arm CT it is possible to reconstruct intraprocedural 3-D images from angiographic projection data. In order to generate accurate and artifact-free reconstructions from dynamic cardiac projections, the motion needs to be taken into account. We present the novel Combined Multiple Heart Phase Registration (CMHPR) method. CMHPR is an iterative motion estimation and compensation algorithm that uses projection data acquired during a single C-arm sweep. Filtered-backprojection (FBP) volumes from electrocardiogram (ECG)-gated data are reconstructed for different motion states of the heart. According to an unknown 4-D motion vector field the ECG-gated FBP images are deformed and accumulated to a sum volume for representing the status of a particular heart phase. In an iterative optimization procedure the 4-D motion vector field is computed by registering the sum volume to a reference volume of the same heart phase. The negative normalized cross correlation (NCC) of both volumes is used as a cost function. In this paper, the reference image is generated using the prior image constrained compressed sensing (PICCS) algorithm combined with the improved total variation (iTV). First preliminary experiments on clinical porcine data sets show promising results. CMHPR reduces streak artifacts and simultaneously preserves sharp edges without producing the artificial comic-like appearance of the PICCS + iTV reference volume.status: publishe