A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

Systems and methods for fully automated anatomical analysis of an anatomical structure are provided to facilitate pre-operative planning. The computerized method may include obtaining a plurality of images, e.g., MSCT images, of patient-specific cardiovascular anatomy, and analyzing the MSCT images with a trained artificial intelligence module to identify one or more anatomical landmarks and to construct a virtual three-dimensional model of the anatomical structure. For example, the trained artificial intelligence module may execute segmentation, point detection, curve detection, or plane detection deep learning modules, independently or in combination, to identify the anatomical landmarks. The method further may include deriving anatomical measurements of the one or more identified anatomical landmarks, and displaying the virtual three-dimensional model alongside the anatomical measurements of the one or more identified anatomical landmarks.

The present invention refers to the field of cardiac electrophysiology (EP) and, more specifically, to image-guided radio frequency ablation and pacemaker placement procedures. For those procedures, it is proposed to display the overlaid 2D navigation motions of an interventional tool intraoperatively obtained from the same projection angle for tracking navigation motions of an interventional tool during an image-guided intervention procedure while being navigated through a patient's bifurcated coronary vessel or cardiac chambers anatomy in order to guide e.g. a cardiovascular catheter to a target structure or lesion in a cardiac vessel segment of the patient's coronary venous tree or to a region of interest within the myocard. In such a way, a dynamically enriched 2D reconstruction of the patient's anatomy is obtained while moving the interventional instrument. By applying a cardiac and/or respiratory gating technique, it can be provided that the 2D live images are acquired during the same phases of the patient's cardiac and/or respiratory cycles. Compared to prior-art solutions which are based on a registration and fusion of image data independently acquired by two distinct imaging modalities, the accuracy of the two-dimensionally reconstructed anatomy is significantly enhanced.

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

A medical scan assisted review system is operable to receive, via a network, a medical scan for review. Abnormality data is generated by identifying a plurality of abnormalities in the medical scan by utilizing a computer vision model that is trained on a plurality of training medical scans. The abnormality data includes location data and classification data for each of the plurality of abnormalities. Text describing each of the plurality of abnormalities is generated based on the abnormality data. The abnormality data and the text is transmitted to a client device. A display device associated with the client device displays the abnormality data in conjunction with the medical scan via an interactive interface, and the display device further displays the text via the interactive interface.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

A method for calculating a heart parameter includes receiving a series of two-dimensional images of a heart, the series covering at least one heart cycle. The method includes calculating a volume of the heart in a first systole image based on an orientation of the heart in the first systole image and a segmentation of the heart in the first systole image, and a volume of the heart in a first diastole image based at least on an orientation of the heart in the first diastole image and a segmentation of the heart in the first diastole image; determining the heart parameter based at least on the volume of the heart in the first systole image and the volume of the heart in the first diastole image; determining a confidence score of the heart parameter; and displaying the heart parameter and the confidence score.

A filter method for reducing interferences of a measuring signal, caused by magnetic fields of a rotatable medical imaging system while measuring bioelectric signals in a differential voltage measuring system, the filter method including: capturing a frequency value of a rotation of a gantry of the rotatable medical imaging system; generating a virtual reference signal as a function of the frequency value captured; estimating, via an adaptive signal filter, an amplitude and a constant phase offset of an estimated interference signal, based upon the virtual reference signal generated and a measuring signal; and filtering the measuring signal with the adaptive signal filter by subtracting the estimated interference signal from the measuring signal. A filter apparatus is also described. Furthermore a voltage measuring system is described. Furthermore, a rotating medical imaging system is described.

An exemplary system, method and computer-accessible medium for detecting an anomaly(ies) in an anatomical structure(s) of a patient(s) includes receiving imaging information related to the anatomical structure(s) of the patient(s), classifying a feature(s) of the anatomical structure(s) based on the imaging information using a neural network (s), and detecting the anomaly(ies) based on data generated using the classification procedure.

A method and apparatus for calculating the blood flow rate in a coronary artery, an electronic device and a storage medium. The method for calculating the blood flow rate in a coronary artery comprises the following steps: S1, acquiring an angiography image of the coronary artery, segmenting the angiography image of the coronary artery by using deep learning, and obtaining segmented images of a main vessel (S1); S2, calculating the length of the main vessel in each segmented image frame on the basis of the segmented images of the main vessel (S2); and S3, obtaining the blood flow rate in the main vessel on the basis of the calculated change of the lengths of the main vessel with time (S3). By using the method and apparatus for calculating the blood flow rate in a coronary artery and the electronic device, the automation of the calculation of the blood flow rate in a coronary artery is achieved, the calculated blood flow rate in the coronary artery is more accurate, and the calculation method is simple.