Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

A neuroimaging-based approach to predict treatment response in mental disorders by acquiring and analysing brain PET dopamine measures from patients. The method uses a short, simplified protocol for [18F]FDOPA brain PET imaging adapted for clinical practice. Individual [18F]FDOPA brain PET data are then quantified with a fully-automated analysis pipeline to extract information on the dopamine function of the subject. This information coupled with clinical information is run through a prediction algorithm to identify those patients whose illness will not respond to conventional antipsychotics.

A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.

Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

Systems and methods are disclosed for simulating microvascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

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.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Aspects of the disclosure provide a method for denoising an image. The method can include receiving an acquired image from an image acquisition system, and processing the acquired image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the acquired image.

A method and system are provided for reconstructing a motion-compensated nuclear image of a subject, as well as an arrangement for method. The reconstruction method comprises receiving nuclear image data the acquiring a nuclear image, and a computer program for carrying out the for multiple motion states, reconstructing the data into an image for each motion state, and calculating a deformation vector field for each state for mapping the image onto a reference motion state. Calculating the deformation vector field comprises providing an initial vector field, defining at least one rigid region of the subject, incorporating that rigid region into the initial vector field, and calculating the deformation vector field with the incorporated rigid region. The method further comprises mapping the reconstructed image of each motion state onto the reference state using the deformation vector fields; and combining the mapped images into a motion-compensated nuclear image.