Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Corporation

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Helene C Houle

Tommaso Mansi

Bogdan Georgescu

Dorin Comaniciu

Ingmar Voigt

Sasa Grbic

Razvan Ioan Ionasec

Razvan Ionasec

Bimba Rao

Ismayil M Guracar

Kutay F Ustuner

Ankur Kapoor

Yang Wang

Rickard C Loftman

Mihai Scutaru

Ali Kamen

Frank Sauer

Tiziano Passerini

Michael Scheuering

Rui Liao

Michael Suehling

Liexiang Fan

Estelle Camus

Viorel Mihalef

Noha Youssry El-Zehiry

Fernando Vega-Higuera

Dominik Neumann

Philipp Hoelzer

Chris Schwemmer

Huseyin Tek

Max Schoebinger

Seshadri Srinivasan

Julian Krebs

Charles Henri Florin

Alexander Brost

Sebastian Vogt

King Yuen Wong

Marcus Schreckenberg

Georg Schummers

Andrzej Milkowski

Saurabh Datta

Yue Zhang

Wayne J Gueck

Max Schöbinger

Bernhard Mumm

Abdoul Aziz Amadou

Christian Galuschky

Etienne Assoumou Mengue

Anand Vinod Tatpati

Codruta-Xenia Ene

Joel Mancina

Matthew Holladay

Matthias John

For quantification of blood flow by an ultrasound system, B-mode images generated with a multi-transmit, coherent image formation produce swirling or other speckle patterns in the blood regions. These patterns, as represented in specially formed B-mode images, are tracked over time to indicate two or three-dimensional velocity vectors of the blood at a B-mode resolution. Various visualizations may be provided at the same resolution, including the velocity flow field, flow direction, vorticity, vortex size, vortex shape, and/or divergence.

Quantification of blood flow with ultrasound B-mode imaging

For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Cardiac flow detection based on morphological modeling in medical diagnostic ultrasound imaging

A method, apparatus, and computer readable storage medium are provided herein for constructing a representation of an annular structure associated with an anatomical object. The method includes receiving three-dimensional image data of the anatomical object and detecting at least a first landmark point and a second landmark point on the annular structure. A plane positioned between the first landmark point and the second landmark point, and oriented in accordance with a predefined angular relationship to a line connecting the first landmark point and the second landmark point is determined. A third landmark point on the annular structure which lies in the plane is also detected and the representation of the annular structure is generated using at least the first landmark point, the second landmark point, and the third landmark point. The representation is then outputted.

Annular structure representation

Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.

Knowledge-based ultrasound image enhancement

A system and method for multi-modality fusion for 3D printing of a patient-specific organ model is disclosed. A plurality of medical images of a target organ of a patient from different medical imaging modalities are fused. A holistic mesh model of the target organ is generated by segmenting the target organ in the fused medical images from the different medical imaging modalities. One or more spatially varying physiological parameter is estimated from the fused medical images and the estimated one or more spatially varying physiological parameter is mapped to the holistic mesh model of the target organ. The holistic mesh model of the target organ is 3D printed including a representation of the estimated one or more spatially varying physiological parameter mapped to the holistic mesh model. The estimated one or more spatially varying physiological parameter can be represented in the 3D printed model using a spatially material property (e.g., stiffness), spatially varying material colors, and/or spatially varying material texture.

Multi-modality image fusion for 3D printing of organ morphology and physiology

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.

Personalized creation from medical imaging

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.

Measurement point determination in medical diagnostic imaging

A processor acquires image data from a medical imaging system. The processor generates a first model from the image data. The processor generates a computational model which includes cardiac electrophysiology and cardiac mechanics estimated from the first model. The processor performs tests on the computational model to determine outcomes for therapies. The processor overlays the outcome on an interventional image. Using interventional imaging, the first heart model may be updated/overlaid during the therapy to visualize its effect on a patient's heart.

Planning and guidance of electrophysiology therapies

Computerized characterization of cardiac wall motion is provided. Quantities for cardiac wall motion are determined from a four-dimensional (i.e., 3D+time) sequence of ultrasound data. A processor automatically processes the volume data to locate the cardiac wall through the sequence and calculate the quantity from the cardiac wall position or motion. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for initially locating the cardiac wall and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift.

Computerized characterization of cardiac motion in medical diagnostic ultrasound

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

Landmark detection with spatial and temporal constraints in medical imaging

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.

Valve regurgitant detection for echocardiography

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.

Method and system for advanced transcatheter aortic valve implantation planning

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.

Physically-constrained modeling of a heart in medical imaging

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Sunnyvale, CA, United States of America

Helene C Houle