A quantitative method for nuclear medicine heart image and an electronic device are provided. The method is adapted for multi-pinhole SPECT images or SPECT/CT images. The method includes a radionuclide physical decay correction, a scatter correction, a geometry distortion correction, a data truncation compensation, a tissue attenuation correction, an image space resolution recovery, a noise removal, a pixel value conversion, a myocardial blood flow quantitative calculation, an intra-scan patient movement correction and a blood flow condition evaluation. Accordingly, a quantitative SPECT reconstructed image of a heart is obtained, and an absolute quantization of the myocardial blood flow is calculated to measure the myocardial blood flow quantitatively according to the quantitative SPECT reconstructed image. In addition, a blood flow condition diagram according to a number of indicators is established, and a myocardial blood flow condition is evaluated according to a quantization result of myocardial blood flow and the blood flow condition diagram.

A method of determining the blood flow through coronary arteries comprises generating (S1) a 3D image data set of at least the coronary arteries and the myocardial muscle, generating (S2) a 3D marker data set of at least the myocardial muscle from a dual-energy or spectral 3D data set obtained after administration of a marker, said 3D marker data set indicating the amount of said marker contained within voxels of said myocardial muscle, subdividing (S3) the myocardial muscle into myocardial muscle segments, determining (S4) which coronary artery supplies the respective myocardial muscle segments, determining (S5) the volume of blood that flows into the respective myocardial muscle segments from said 3D marker data set, and determining (S6) the total volume of blood that flows into a coronary artery of interest by summing the volume of blood flowing into all myocardial muscle segments supplied by said coronary artery.

Optical coherence tomography (OCT) may be used to acquire cross-sectional or volumetric images of any specimen, including biological specimens such as the retina. Additional processing of the OCT data may be performed to generate images of features of interest. In some embodiments, these features may be in motion relative to their surroundings, e.g., blood in the retinal vasculature. The proposed invention describes a combination of images acquired by OCT, manual segmentations of these images by experts, and an artificial neural network for the automated segmentation and classification of features in the OCT images. As a specific example, the performance of the systems and methods described herein are presented for the automatic segmentation of blood vessels in images acquired with OCT angiography.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

A computer-implemented method of providing a temporal sequence of 3D angiographic images (110) representing a flow of a contrast agent through a region of interest (120), is provided. The method includes: inputting (S130) volumetric image data (130a, 130b), and a temporal sequence of 2D angiographic images (140) into a neural network (NN1); and generating (S140) the predicted temporal sequence of 3D angiographic images (110) representing the flow of the contrast agent through the region of interest (120) in response to the inputting.

A computer-implemented method can include generating centerlines of a patient's cardiovascular network, determining geometric features of the cardiovascular network based on the centerlines and a three-dimensional (3D) computer model of the cardiovascular network, constructing a lumped parameter network (LPN) of resistors corresponding to the cardiovascular network, and solving a system of equations corresponding to flow and pressure for the LPN model.

A system and method for performing image processing. The method includes receiving an image of a first modality and a real image of a second modality, the image of the first modality and the image of the second modality capturing respective images of a same subject, applying a first trained model to the image of the first modality to generate an artificial image mimicking the image of the second modality, applying a second trained model to the artificial image mimicking the image of the second modality and data of the image of the first modality, and outputting at least one conclusion regarding the generated artificial image.