Abstract: Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.

Abstract: In personalized object creation, for implants, medical imaging is used to derive a model personalized to a patient. The model may be of a dynamic structure, such as part of the cardiovascular system, and is used to print the implant itself. The model may be used to print a mold to create the implant, a scaffold on which to grow tissue, and/or tissue itself. In another or additional approach, the medical imaging information is used to determine tissue properties. Differences in a material property of the anatomy is mapped to different materials used by a multi-material 3D printer, resulting in a printed object reflecting the size, shape, and/or other material property of the anatomy of the patient.

Abstract: A method and system for interactive patient-specific simulation of liver tumor ablation is disclosed. A patient-specific anatomical model of the liver and circulatory system of the liver is estimated from 3D medical image data of a patient. A computational domain is generated from the patient-specific anatomical model of the liver. Blood flow in the liver and the circulatory system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on a virtual ablation probe position and the simulated blood flow in the liver and the circulatory system of the liver by solving a bio-heat equation for each node on the level-set representation using a Lattice-Boltzmann method (LBM) implementation. Cellular necrosis in the liver is computed based on the simulated heat diffusion. Visualizations of a computed necrosis region and temperature maps of the liver are generated.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: Systems and methods are provided for identifying pathological changes in follow up medical images. Reference image data is acquired. Follow up image data is acquired. A deformation field is generated for the reference image data and the follow up data using a machine-learned network trained to generate deformation fields describing healthy, anatomical deformation between input reference image data and input follow up image data. The reference image data and the follow up image data are aligned using the deformation field. The co-aligned reference image data and follow up image data are analyzed for changes due to pathological phenomena.

Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.

Abstract: In order to reduce computation time and provide more accurate solutions for bi-directional, multi-modal image registration, a learning-based unsupervised multi-modal deformable image registration method that does not require any aligned image pairs or anatomical landmarks is provided. A bi-directional registration function is learned based on disentangled shape representation by optimizing a similarity criterion defined on both latent space and image space.

Abstract: For three-dimensional rendering, a machine-learnt model is trained to generate representation vectors for rendered images formed with different rendering parameter settings. The distances between representation vectors of the images to a reference are used to select the rendered image and corresponding rendering parameters that provides a consistency with the reference. In an additional or different embodiment, optimized pseudo-random sequences are used for physically-based rendering. The random number generator seed is selected to improve the convergence speed of the renderer and to provide higher quality images, such as providing images more rapidly for training compared to using non-optimized seed selection.

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: For measurement point determination in imaging with a medical scanner, the user selects a location on the image. Rather than using that location, an “intended” location corresponding to a local boundary or landmark represented in the image is identified. The medical scanner uses the simple user interface to more exactly determine points for measurement. One or more rays are cast from the user selected location. The actual location is found by examining data along the ray or rays. For 2D imaging, the rays are cast in the plane. For 3D imaging, the ray is cast along a view direction to find the depth. The intensities along the ray or around the ray are used to find the actual location, such as by application of a machine-learnt classifier to the limited region around the ray or by finding intensities along the ray relative to a threshold.

Abstract: A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: For soft tissue deformation prediction, a biomechanical or other tissue-related physics model is used to find an instantaneous state of the soft tissue. A machine-learned artificial neural network is applied to predict the position of volumetric elements (e.g., mesh node) from the instantaneous state. Since the machine-learned artificial neural network may predict quickly (e.g., in a second or less), the soft tissue position at different times or a further time given the instantaneous state is provided in real-time without the minutes of physics model computation. For example, a real-time, biomechanical solver is provided, allowing interaction with the soft tissue model, while still getting accurate results. The accuracy allows for generating images of a soft tissue with greater accuracy and/or the benefit of user interaction in real-time.

Abstract: For liver modeling from medical scan data, multiple modalities of imaging are used. By using multiple modalities of imaging in combination with generative modeling, a more comprehensive and informed assessment may be performed. The generative modeling may allow feedback of effects of proposed therapy on function of the liver. This feedback is used to update the liver function information based on the imaging. Based on the computerized modeling with information from various imaging modes, an output based on more comprehensive information and patient personalized modeling and feedback may be provided to assist the physician.

Abstract: For three-dimensional rendering, a machine-learnt model is trained to generate representation vectors for rendered images formed with different rendering parameter settings. The distances between representation vectors of the images to a reference are used to select the rendered image and corresponding rendering parameters that provides a consistency with the reference. In an additional or different embodiment, optimized pseudo-random sequences are used for physically-based rendering. The random number generator seed is selected to improve the convergence speed of the renderer and to provide higher quality images, such as providing images more rapidly for training compared to using non-optimized seed selection.

Abstract: Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.

Abstract: A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.