Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Corporation

Charles Henri Florin

James P Williams

Romain Moreau-Gobard

Dorin Comaniciu

Tommaso Mansi

Sasa Grbic

Nikolaos Paragios

Bogdan Georgescu

Frank Sauer

Gareth Funka-Lea

Ali Khamene

Ingmar Voigt

James Williams

Terrence Chen

Michael Suehling

Stefan Kluckner

Razvan Ionasec

Helene C Houle

Philipp Hoelzer

Mikael Rousson

Juan Carlos Celi

Yuri Boykov

Barbara Ofstad

Christian Schaller

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

Multi-source, multi-type image registration is provided. Images are received from a plurality of image devices, and images are received from a medical imaging device. A pre-existing diagram of a probe of the medical imaging device is received. A four-dimensional model is determined based on the received images from the image devices. A pose of the probe of the medical imaging device is determined based on the pre-existing diagram of the probe and the received images from the image devices. The plurality of images from the medical imaging device are registered with the four-dimensional model based on a common coordinate system and the determined pose of the probe.

Multi-view, multi-source registration of moving anatomies and devices

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.

Systems and method for computation and visualization of segmentation uncertainty in medical images

A computer readable medium is provided embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target. The method including learning prior knowledge, providing volume data comprising the target, selecting a plurality of key contours of the image of the target, building a 3D spare model of the image of the target given the plurality of key contours, segmenting the image of the target given the 3D sparse model, and outputting a segmentation of the image of the target.

Sparse volume segmentation for 3D scans

A method and apparatus of visually depicting an organ, having the steps of choosing a predefined set features for analysis, the predefined set of features having distinguishing weak learners for an algorithm, wherein the predefined set of features and the weak learners chosen distinguish features of the organ desired to be represented, developing a strong classifier for the algorithm for the organ desired to be represented based upon the weak learners for the organ, one of conducing a body scan to produce a body scan data set and obtaining a body scan data set of information for a patient, applying the strong classifier and the algorithm to the body scan data set to develop a result of a representation of the organ and outputting the result of the step of applying of the strong classifier and the algorithm to the body scan data set to represent the organ.

Automatic organ detection using machine learning and classification algorithms

A method for fusing two digitized image datasets for 2-dimensional visualization of the heart, includes providing a first digitized image and a second digitized image, each image acquired from a different imaging modality and each comprising a plurality of intensities corresponding to a domain of points on a 3-dimensional grid, and wherein the first and second images have been registered, segmenting the heart in one of said digitized images, forming a 2-dimensional maximum intensity projection of a surface of said heart from each of said digitized images, and fusing said images by overlaying one 2-dimensional projection of said surface over the other 2-dimensional projection of said surface.

System and method for fused PET-CT visualization for heart unfolding

A system and method for tree structure based two-dimensional to three-dimensional registration are provided for receiving three-dimensional (3D) data indicative of vessels, segmenting the 3D data with a vessel tree, simulating two-dimensional (2D) data responsive to the segmented 3D data to form a simulated 2D image, receiving 2D data indicative of vessels, segmenting the received 2D data with a vessel tree, finding a distance transform of the segmented 2D data to form a 2D distance map image, considering a set of poses as state vectors with the corresponding probability computed using the similarity measure computation, re-sampling the pose vector to find a set of most probable poses and considering them as hypothesized poses, recomputing the projection and re-evaluating the probability of the hypothesized poses and updating the state vector until the optimal pose is found.

Tree structure based 2D to 3D registration

Disclosed is a method and apparatus for generating a two dimensional (2D) image of a structure (e.g., an organ) that has at least one pixel corresponding to at least one voxel of a three dimensional (3D) image of the structure. First, the surface of the structure in the 3D image is modeled by a geometrical volume such as an ellipsoid. Next, normal maximum intensity projection (MIP) rays are cast (i.e., projected) for voxels of the geometrical volume. The 2D image is then generated using the rays. The 2D image has at least one pixel that corresponds to at least one voxel of the 3D image.

Method and apparatus for generating a 2D image having pixels corresponding to voxels of a 3D image

A method of segmenting tubular structures in digital images includes selecting a point in an image of a tubular object to be segmented, defining an initial state of the selected point, initializing measurement weights, a conditional probability distribution and a prior probability distribution of a feature space of the initial state, sampling the feature space from the prior probability distribution, estimating a posterior probability distribution by summing sample measurements weighted by the measurement weights, and segmenting a cross section of the tubular object from the posterior probability distribution.

System and method for vascular segmentation by Monte-Carlo sampling

A method and related system for automatically and efficiently isolating the heart in Computer Tomography (CT) or Magnetic Resonance Imaging cardiac scans is disclosed. The method involves segmenting a heart within a set of volumetric data. In accordance with one aspect of the present invention, the set of volumetric data is processed to determine the minimum value of an energy function having a first term, a second term and a third term. The heart is segmented based on the processing of the set of volumetric data.

CT/MRI heart isolation using a graph cut algorithm

A method of segmenting tubular structures in digital images comprises providing a digitized image, selecting a point within an object for segmenting in the image, defining an initial state of the selected point in the object, performing an initial segmentation of a 2D cross section of the object based on the initial state, predicting a new state of said object about a new point that is a translation of said selected point along the object tangent, correcting said new state prediction based on a measurement of said new point in said image, and segmenting a 2D cross section of said object based on said new state.

System and method for Kalman filtering in vascular segmentation

A novel method is presented for detecting coronary arteries as well as other peripheral vessels of the heart. After finding the location of the myocardium through a segmentation method, such as a graph theoretic segmentation method, the method models the heart with a biaxial ellipsoid. For each point of the ellipsoid, a collection of intensities are computed that are normal to the surface. This collection is then filtered to detect the cardiovascular structures. Ultimately, vessel centerline points are detected using a vessel tracking method, and linked together to form a complete coronary artery tree.

Automatic coronary isolation using a n-MIP ray casting technique

A method for four-dimensional (4D) image verification in respiratory gated radiation therapy, includes: acquiring 4D computed tomography (CT) images, each of the 4D CT images representing a breathing phase of a patient and tagged with a corresponding time point of a first surrogate signal; acquiring fluoroscopic images of the patient under free breathing, each of the fluoroscopic images tagged with a corresponding time point of a second surrogate signal; generating digitally reconstructed radiographs (DRRs) for each breathing phase represented by the 4D CT images; generating a similarity matrix to assess a degree of resemblance in a region of interest between the DRRs and the fluoroscopic images; computing a compounded similarity matrix by averaging values of the similarity matrix across different time points of the breathing phase during a breathing period of the patient; determining an optimal time point synchronization between the DRRs and the fluoroscopic images by using the compounded similarity matrix; and acquiring a third surrogate signal and turning a treatment beam on or off according to the optimal time point synchronization.

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Trenton, NJ, United States of America

Charles Henri Florin