During an Intravascular Ultrasound (IVUS) intervention, a catheter with an ultrasound transducer is introduced in the body through a blood vessel and then pulled back to image a sequence of vessel cross-sections. Unfortunately, there is no 3D information about the position and orientation of these cross-section planes. To position the IVUS images in space, some researchers have proposed complex stereoscopic procedures relying on biplane angiography to get two X-ray image sequences of the IVUS transducer trajectory along the catheter. We have elaborated a much simpler algorithm to recover the transducer 3D trajectory with only a single view X-ray image sequence. The known pullback distance of the transducer during the IVUS intervention is used as an a priori to perform this task. Considering that biplane system are difficult to operate and rather expensive and uncommon in hospitals; this simple pose estimation algorithm could lead to an affordable and useful tool to better assess the 3D shape of vessels investigated with IVUS.
In order to complete a thorough examination of a patient heart muscle, physicians practice two common invasive procedures: the ventriculography, which allows the determination of the ejection fraction, and the coronarography, giving among other things, information on stenosis of arteries. We propose a method that allows the determination of a contraction index similar to ejection fraction, using only single-plane coronarography. Our method first reconstructs in 3D, selected points on the angiogram, using a 3D model devised from data published by Dodge ea. ['88, '92]. We then follow the point displacements through a complete heart contraction cycle. The objective function, minimizing the RMS distances between the angiogram and the model, relies on affine transformations, i.e. translation, rotation and isotropic scaling. We validate our method on simulated projections using cases from Dodge data. In order to avoid any bias, a leave-one-out strategy was used, which excludes the reference case when constructing the 3D coronary heart model. The simulated projections are created by transforming the reference case, with scaling, translation and rotation transformations, and by adding random 3D noise for each frame in the contraction cycle. Comparing the true scaling parameters to the reconstructed sequence, our method is quite robust (R2=96.6%, P<1%), even when noise error level is as high as 1 cm. Using 10 clinical cases we then proceeded to reconstruct the contraction sequence for a complete cardiac cycle starting at end-diastole. A simple heart contraction mathematical model permitted us to link the measured ejection fraction of the different cases to the maximum heart contraction amplitude (R2=57%, P<1%) determined by our method.