QFR (3.0)

“QFR is delivered as a standalone software package which is installed and running on a server system in the server room of the cathlab or the hospital. The server offers all functionalities that are required to work with the quantitative measurement in X-ray Angiographic (XA) patient studies supported by the QFR device.”

“The QFR device calculates the QFR value based on an anatomical model which is the result of a 3D reconstruction using the 2D contours obtained from two angiographic projections with angles ≥25° apart. The algorithm involves three key steps: (1) Vessel Selection, (2) Contours Detection, and (3) QFR Analysis: 1. Vessel Selection: Angiograms are pre-classified by a deep learning model, identifying main epicardial vessels such as RCA, LAD, and LCx. The user then chooses the segment for analysis, and the software automatically selects end-diastolic image frames. This selection is either based on the patient's electrocardiogram when available or performed by the software using a deep learning model. 2. Contours Detection. First, the system runs another deep learning model for coronary vessel segmentation as input to identify anatomical corresponding points on both projections for automatic correction of the system distortions introduced by the isocenter offset and the respiration-induced heart motion. Selection is now supported by an AI/ML model and manual correction (for angiographic series selection and start/end points). Selection is now supported by a combination of an Al/ML model and an analytical algorithm using the ECG data and manual correction (for ED frame determination).”

“For all of these algorithmic improvements the user is able to review and correct the results before the QFR value is calculated.”

“Results have been summarized in the risk management report and cybersecurity assessment report.”