Abstract: Systems and methods for performing a medical imaging analysis task from PCCT (photon counting computed tomography) imaging data are provided. PCCT imaging data acquired from a PCCT imaging device is received. A plurality of PCCT virtual images is generated from the PCCT imaging data. A plurality of medical imaging analysis sub-tasks is performed based on the plurality of PCCT virtual images using a plurality of machine learning based networks. Results of the medical imaging analysis sub-tasks are combined to generate results of a medical imaging analysis task. The results of the medical imaging analysis task are output.

Abstract: Systems and methods for training a machine learning based network based on PCCT (photon counting computed tomography) imaging data. PCCT imaging data acquired from a PCCT imaging device is received. One or more PCCT virtual images are generated from the PCCT imaging data. A machine learning based network is trained for performing a medical imaging analysis task based on the one or more PCCT virtual images. The trained machine learning based network is output.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks comprises segmentation of reference walls of the vessel from the one or more input medical images and segmentation of lumen of the vessel from the one or more input medical images. Results of the plurality of vessel assessment tasks are output.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks comprises segmentation of reference walls of the vessel from the one or more input medical images and segmentation of lumen of the vessel from the one or more input medical images. Results of the plurality of vessel assessment tasks are output.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.

Abstract: A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

Abstract: A method for determining functional severity of a stenosis includes: (a) generating a simulated perfusion map from a calculated blood flow; (b) comparing the simulated perfusion map to a measured perfusion map to identify a degree of mismatch therebetween, the measured perfusion map representing perfusion in a patient; (c) modifying a parameter in a model used in calculating the blood flow when the degree of mismatch meets or exceeds a predefined threshold; (d) computing a hemodynamic quantity from the simulated perfusion map when the degree of mismatch is less than the predefined threshold, the hemodynamic quantity being indicative of the functional severity of the stenosis; and (e) displaying the hemodynamic quantity. Systems for determining functional severity of a stenosis are described.

Abstract: A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.

Abstract: A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

Abstract: A method and a system are disclosed for interactively creating and/or modifying a hollow organ representation on the basis of medical-technical image data of a hollow organ. An embodiment of the method includes providing the medical-technical image data together with a hollow organ course line representing the course of the hollow organ; providing a plurality of contour representations of a contour of the hollow organ representation along the hollow organ course line; receiving a command input for the input and/or modification of a selected contour representation and/or of the hollow organ course line; locally modifying (H) the contour of the hollow organ representation on the basis of the command input, taking into consideration a number of contour representations adjacent to the selected contour representation on at least one side along the hollow organ course line, using an automatic interpolating sweep algorithm.

Abstract: A method for determining functional severity of a stenosis includes: (a) generating a simulated perfusion map from a calculated blood flow; (b) comparing the simulated perfusion map to a measured perfusion map to identify a degree of mismatch therebetween, the measured perfusion map representing perfusion in a patient; (c) modifying a parameter in a model used in calculating the blood flow when the degree of mismatch meets or exceeds a predefined threshold; (d) computing a hemodynamic quantity from the simulated perfusion map when the degree of mismatch is less than the predefined threshold, the hemodynamic quantity being indicative of the functional severity of the stenosis; and (e) displaying the hemodynamic quantity. Systems for determining functional severity of a stenosis are described.