The present embodiments relate to assessing a haemodynamic parameter.
Today, angiography is a well-established medical imaging technique. Coronary angiography, for example, is considered the gold standard for anatomical assessment of coronary artery disease. Nonetheless, coronary angiography has some intrinsic limitations and shortcomings. One of the most significant limitations of coronary angiography is an inability to accurately assess the physiological significance of lesions. Subject-specific physiology modeling may address this limitation. The approach includes determining the geometry features of the patient-specific arterial tree, and subsequent use of a computational model designed to predict functional diagnostic indices, such as FFR, as a function of the geometry.
US 2014/0058715A1 discloses a method and a system for non-invasive functional assessment of a coronary artery stenosis. Therein, patient-specific anatomical measurements of coronary arteries are extracted from medical image data acquired during a rest state of the patient. Based thereon, patient-specific rest state boundary conditions of a model of coronary circulation are calculated. Additionally, patient-specific hyperemic boundary conditions of the model are calculated based on the rest boundary conditions and a model for simulated hyperaemia. A hyperemic blood flow and pressure across a stenosis region are then simulated using the model of coronary circulation and the hyperemic boundary conditions to, finally, calculate a fractional flow reserve of the stenosis region.
US 2017/0032097A1 discloses a method and a system for enhancing medical image-based blood flow computations using invasively acquired physiological measurements. A patient-specific anatomical model of the patient's vessels is generated from non-invasively acquired medical image data. A computational blood flow model is then personalized using the invasive physiological measurements. One or more haemodynamic quantities of interest are then computed using this personalized computational blood flow model.
In standard coronary angiography, a limited number of 2D projections of vessels or vessel segments of interest are acquired, for example, through x-ray imaging. A combination of a pair of 2D images or views of the same vessel taken from different angles or angulations generally allows or enables a representation of the vessel in 3D space. The representation in 3D space may include or consist of a 3D anatomically accurate geometrical model, to which the following description may generically refer as a 3D reconstruction. Nonetheless, a 3D representation of, for example, a vascular tree may include or consist of, for example, a tree of branching centerlines of vessels or vessel segments, with each centerline point being associated to a radius or diameter value representing a local size of the respective vessel or vessel segment. Equivalently, within the scope of the present embodiments, the vascular tree of interest may be represented by a collection of features of a 3D geometry, such as a number of branches, a length of each branch, an average caliber of the branch, a shape of a cross-section of each branch, etc.
Since coronary angiography is typically concerned with and therefore focused on very specific parts or features, such as a stenosis, a respective field of view in each 2D image or 2D projection is optimized for visualization of this exact part or feature of the diseased vessel. In many cases, this limits the possibility of reconstructing or characterizing neighboring vessels to obtain a complete representation or reconstruction or model of the vessel tree surrounding an imaged feature of interest, such as a stenosis. In typical sets of angiographic images, the neighboring, presumably healthy, vessels may not be clearly visible, for example, due to a complex geometry of the vessel tree and/or foreshortening or overlaps with other anatomical structures. In these cases or conditions, it is not possible to fully represent or reconstruct the complete 3D geometry and topology of the vessel tree or arterial tree.
In some cases, additional images, such as previously acquired anatomical scans of the same patient, may provide additional data to enable the 3D representation of the complete vessel tree. If there is sufficient overlap between the angiographic images and the additional images or data, a single comprehensive 3D representation may be achieved by combining all available data. The required additional data is, however, not reliably available for every patient. Therefore, to fully represent or reconstruct the vessel tree in a region of interest, additional images would have to be acquired at the cost of additional exposure of the patient to radiation and contrast medium as well as additional expenditure of time and money.
Even if additional images are available or acquired, it may be impossible or extremely difficult to combine all data into a single complete and correct 3D representation. One difficulty lies in the needed registration between different datasets, which, for example, may have been acquired at different points in time and/or using different imaging equipment. If, for example, the patient has been moved or repositioned between the respective acquisitions of the different datasets, corresponding representations may be obtained in different coordinate systems. In this case, combining the respective data or models into a single cohesive and consistent 3D model may, at least for practical purposes, be impossible without proper position tracking at all times.