Fusion of angiography and intravascular ultrasound in vivo: establishing the absolute 3-D frame orientation

Data fusion of biplane angiography and intravascular ultrasound (IVUS) facilitates geometrically correct reconstruction of coronary vessels. The locations of IVUS frames along the catheter pullback trajectory can be identified, however the IVUS image orientations remain ambiguous. An automated approach to determination of correct IVUS image orientation in three-dimensional space is reported. Analytical calculation of the catheter twist is followed by statistical optimization determining the absolute IVUS image orientation. The fusion method was applied to data acquired in patients undergoing routine coronary intervention, demonstrating the feasibility and good performance of our approach.

Towards a geometrically correct 3-D reconstruction of tortuous coronary arteries based on biplane angiography and intravascular ultrasound

At present, 3-D reconstructions of coronary vessels are generated from intravascular ultrasound (IVUS) by stacking up ECG-gated segmented IVUS frames of a pullback sequence. This simplified approach always results in straight vessel reconstructions and, therefore, gives an incorrect representation of tortuous coronary arteries. A more realistic reconstruction of tortuous vessels may be obtained by data fusion with biplane angiography. The 3-D course of the vessel is first derived from the angiograms and then combined with the segmented IVUS images. In this paper, we focus on two problems associated with the data fusion method: The definition of the pullback path and the estimation of the IVUS catheter twist during pullback. A robust algorithm for calculation of tortuosity-induced catheter twist is reported that is based on sequential triangulation of the 3-D pullback path. The method is analyzed with computer simulations and validated in helical vessel phantoms. A largely automated data fusion approach is proposed and applied to tortuous coronary arteries in cadaveric pig hearts.

Objective methods for optimizing JPEG compression of coronary angiographic images

Digital angiographic images contain a significant amount of redundancy as well as some irrelevant information and noise. Therefore, it is possible to reduce the number of bits required to represent an image considerably. The lossy JPEG standard may be used provided that no significant diagnostic information is lost. As implemented in presently available hard- and software in most cases the luminance quantization table (LQT) is applied for gray level images, which may only be scaled by a so-called quality factor. The questions arise whether it is possible and worthwhile to specify quantization tables for the particular characteristics of angiograms. To assess the quality performance quantitatively, global numerical quality measures and evaluations based on Hosaka-plots were performed. Those diagrams compare the errors introduced into areas of different local activity. By the newly introduced weighting of these errors with the relative occupancy of the respective classes of activity the results got more reproducible. The blocking and blurring effects introduced by lossy JPEG compression could be compared objectively. Two new quantization tables were derived from the transfer function of the angiographic X-ray system, the modulation transfer quantization table (MTQT) and the star pattern quantization table (SPQT). Both tables guarantee that the blurring of sharp edges is minimized so that no deterioration around a coronary lesion occurs. Based on the signal-to-noise ratio, the overall quality performance is the same as for the LQT. A general relation between the bit rate of the compressed image and the quality factor has been determined for images of high local activity and normally scaled coronary angiographic images (512 x 512).