Abstract: In valve modeling from medical scan data, chordae are modeled as a dense structure. Rather than attempting to provide the same number of chordae (e.g., 25) as found in a human valve, hundreds or thousands of chordae connectors are used. Since solving for lengths of so many chordae may be computationally intensive, the lengths of only a few are solved, and the lengths of the rest of the chordae are derived from the lengths of the few.

Abstract: Systems and methods for computing uncertainty include generating a surface model of a target anatomical object from medical imaging data of a patient. Uncertainty is estimated at each of a plurality of vertices of the surface model. The uncertainty estimated at each of the plurality of vertices is visualized on the surface model.

Abstract: A method and system for registering ultrasound images and physiological models to x-ray fluoroscopy images is disclosed. A fluoroscopic image and an ultrasound image, such as a Transesophageal Echocardiography (TEE) image, are received. A 2D location of an ultrasound probe is detected in the fluoroscopic image. A 3D pose of the ultrasound probe is estimated based on the detected 2D location of the ultrasound probe in the fluoroscopic image. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image based on the estimated 3D pose of the ultrasound probe. The ultrasound image can then be projected into the fluoroscopic image using a projection matrix associated with the fluoroscopic image. A patient specific physiological model can be detected in the ultrasound image and projected into the fluoroscopic image.

Abstract: Anatomy, such as papillary muscle, is automatically detected (34) and/or detected in real-time. For automatic detection (34) of small anatomy, machine-learnt classification with spatial (32) and temporal (e.g., Markov) (34) constraints is used. For real-time detection, sparse machine-learnt detection (34) interleaved with optical flow tracking (38) is used.

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 first interface for reading image data of an anatomical region obtained by means of a medical imaging method is provided. A modeling module serves for establishing a volumetric biomechanical structure model of the anatomical region on the basis of the image data . Moreover, provision is made of a tracking module, couplable with a camera, for video-based registration of spatial gestures of a user. Furthermore, a simulation module, based on the biomechanical structure model, serves to assign a registered gesture to a simulated mechanical effect on the anatomical region , simulate a mechanical reaction of the anatomical region to the simulated mechanical effect, and modify the biomechanical structure model in accordance with the simulated mechanical reaction. Moreover, provision is made for a visualization module for the volumetric visualization of the biomechanical structure model.

Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.

Abstract: Systems and methods for computing uncertainty include generating a surface model of a target anatomical object from medical imaging data of a patient. Uncertainty is estimated at each of a plurality of vertices of the surface model. The uncertainty estimated at each of the plurality of vertices is visualized on the surface model.

Abstract: An entire volume is scanned. A sub-volume is separately scanned with different settings for beamforming parameters, allowing greater image quality for the sub-volume while providing context from the volume. The anatomy of interest is periodically detected, and the sub-volume shifted in position to cover the anatomy of interest, allowing for relatively continuous volume imaging with enhanced quality imaging of the sub-volume. Interleaving by volume and sub-volume slices may allow for optimization of relative frame rate and image quality. Different combinations between volume and sub-volume data for anatomy detection and display may provide for desired imaging while allowing the regular detection of the anatomy.

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: In valve modeling from medical scan data, chordae are modeled as a dense structure. Rather than attempting to provide the same number of chordae (e.g., 25) as found in a human valve, hundreds or thousands of chordae connectors are used. Since solving for lengths of so many chordae may be computationally intensive, the lengths of only a few are solved, and the lengths of the rest of the chordae are derived from the lengths of the few.

Abstract: Physically-constrained modeling of a heart is provided. Patient-specific data may be used to estimate heart anatomy locations. A model is applied to the data for estimation. For increased accuracy of estimation, the biomechanics of the heart, such as the valve, may be used to constrain the estimation. By applying a dynamic system between estimated anatomy locations of different times, the locations may be deformed or refined. The modeled heart and/or valve may be used to estimate hemodynamics. The resulting velocities or other motion information may be used to emulate ultrasound Doppler imaging for comparing with acquired ultrasound Doppler data. The comparison may validate the modeling.

Abstract: A regurgitant orifice of a valve is detected. The valve is detected from ultrasound data. An anatomical model of the valve is fit to the ultrasound data. This anatomical model may be used in various ways to assist in valvular assessment. The model may define anatomical locations about which data is sampled for quantification. The model may assist in detection of the regurgitant orifice using both B-mode and color Doppler flow data with visualization without the jet. Segmentation of a regurgitant jet for the orifice may be constrained by the model. Dynamic information may be determined based on the modeling of the valve over time.

Abstract: A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

Abstract: Valve treatment simulation is performed from patient specific imaging data for therapy planning. A model of the valve may be generated from the patient specific data automatically or with very minimal user indication of anatomy locations relative to an image. Any characteristics for the valve not extracted from images of the patient may be added to create a volumetric model. Added characteristics include chordae, such as chordae length and leaflet fiber direction. The characteristics may be adjusted based on user feedback and/or comparison with images of the patient. The effect of therapy on closure of the valve may be simulated from the model. For instance, mitral clip intervention is simulated on the patient-specific model. Valves are deformed according to the clip location. Valve closure is then simulated to predict effect of the therapy in terms of mitral regurgitation.

Abstract: A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.

Abstract: Heart valve operation is assessed with patient-specific medical diagnostic imaging data. To deal with the complex motion of the passive valve tissue, a hierarchal model is used. Rigid global motion of the overall valve, non-rigid local motion of landmarks of the valve, and surface motion of the valve are modeled sequentially. For the non-rigid local motion, a spectral trajectory approach is used in the model to determine location and motion of the landmarks more efficiently than detection and tracking. Given efficiencies in processing, more than one valve may be modeled at a same time. A graphic overlay representing the valve in four dimensions and/or quantities may be provided during an imaging session. One or more of these features may be used in combination or independently.

Abstract: A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step.

Abstract: A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

Abstract: A method and system for intraoperative guidance in an off-pump mitral valve repair procedure is disclosed. A plurality of patient-specific models of the mitral valve are generated, each from pre-operative image data obtained using a separate imaging modality. The pre-operative image data from the separate imaging modalities are fused into a common coordinate system by registering the plurality of patient-specific models. A model of the mitral valve is estimated in real-time in intraoperative image data using a fused physiological prior resulting from the registering of the plurality of patient-specific models.