Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Biosense Webster, Inc.

Matthias John

Jan Boese

Dorin Comaniciu

Yefeng Zheng

Norbert Rahn

Sasa Grbic

Rui Liao

Razvan Ionasec

Peter Mountney

Gareth Funka-Lea

Alois Nöttling

Bogdan Georgescu

Shaohua Kevin Zhou

Ali Kamen

Chenyang Xu

Estelle Camus

Razvan Ioan Ionasec

Peng Wang

Yu Pang

Assaf Govari

Terrence Chen

Tommaso Mansi

Marcus Pfister

Vivek Kumar Singh

Scott Bradley Baker

Ingmar Voigt

Andreas Meyer

Assaf Preiss

Stefan Lautenschläger

Yiyong Sun

Yang Wang

Wolfgang Wein

Michelle Xiaohong Yan

Ulrich Hartung

Alois Noettling

Sarfaraz Hussein

Jing Feng Han

Yinxiao Liu

Christophe Duong

Herve Lombaert

Wei You

Meir Bartal

Stratis Tzoumas

Christian Gesell

Marily Sarmiento

Tianzhou Wang

Uwe Kirschstein

Volkmar Falk

Thomas Walther

Markus Kaiser

Murrill Michael Szucs

Robust calcification tracking is provided in fluoroscopic imagery. A patient with an inserted catheter is scanned over time. A processor detects the catheter in the patient from the scanned image data. The processor tracks the movement of the catheter. The processor also detects a structure represented in the data. The structure is detected as a function of movement with a catheter. The processor tracks the movement of the structure using sampling based on a previous location of the structure in the patient. The processor may output an image of the structure.

Robust calcification tracking in fluoroscopic imaging

A computer-implemented method for temporal up-to-date representation of a moving section of a body is provided. A first x-ray image data record is provided containing the moving section in a specific movement phase and a first 2D x-ray image and a 3D x-ray image superimposed and registered with one another. 2D x-ray images are repeatedly recorded each containing the moving section. 3D ultrasound images are repeatedly recorded each containing the moving section. Extended 2D x-ray images are created from the 2D x-ray images using the first x-ray images data record. The 3D ultrasound images are used as intermediaries for movement correction. The extended 2D images are displayed.

Method and X-ray device for temporal up-to-date representation of a moving section of a body, computer program product and data carrier

A method and system for autoregressive model based pigtail catheter motion prediction in a fluoroscopic image sequence is disclosed. Parameters of an autoregressive model are estimated based on observed pigtail catheter tip positions in a plurality of previous frames of a fluoroscopic image sequence. A pigtail catheter tip position in a current frame of the fluoroscopic image sequence is predicted using the fitted autoregressive model. The predicted pigtail catheter tip position can be used to constrain pigtail catheter tip detection in the current frame. The predicted pigtail catheter tip position may also be used to predict abnormal motion in the fluoroscopic image sequence.

Method and system for pigtail catheter motion prediction

A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

Method and system for model-based fusion of multi-modal volumetric images

A method and system for aorta segmentation in a 3D volume, such as a C-arm CT volume is disclosed. The aortic root is detected in the 3D volume using marginal space learning (MSL) based segmentation. The aortic arch is detected in the 3D volume using MSL based segmentation. The ascending aorta is tracked from the aortic root to the aortic arch in the 3D volume, and the descending aorta is tracked from the aortic arch in the 3D volume.

Method and system for automatic aorta segmentation

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.

Method and system for registration of ultrasound and physiological models to X-ray fluoroscopic images

A method for real-time fusion of a 2D cardiac ultrasound image with a 2D cardiac fluoroscopic image includes acquiring real time synchronized US and fluoroscopic images, detecting a surface contour of an aortic valve in the 2D cardiac ultrasound (US) image relative to an US probe, detecting a pose of the US probe in the 2D cardiac fluoroscopic image, and using pose parameters of the US probe to transform the surface contour of the aortic valve from the 2D cardiac US image to the 2D cardiac fluoroscopic image.

Image fusion for interventional guidance

A method and system for extracting a silhouette of a 3D mesh representing an anatomical structure is disclosed. The 3D mesh is projected to two dimensions. Silhouette candidate edges are generated in the projected mesh by pruning edges and mesh points based on topology analysis of the projected mesh. Each silhouette candidate edge that intersects with another edge in the projected mesh is split into two silhouette candidate edges. The silhouette is extracted using an edge following process on the silhouette candidate edges.

Method and system for efficient extraction of a silhouette of a 3D mesh

A method and system for device detection in a 2D medical image. In order to account for shape variation of a 3D object in a 2D imaging plane, a hierarchical tree-structured array of trained classifiers is used to detect a 3D object, such as a pigtail catheter in a 2D medical image, such as a fluoroscopic image. The hierarchical tree-structured array of trained classifiers increases a dimensionality of the search space with each hierarchical level, and as the search space is increased, the classification is split into object sub-classes using trained classifiers independently trained for each sub-class.

Method and system for device detection in 2D medical images

In a method and apparatus for automatic or semi-automatic segmentation of a 3D image data set, acquired by a medical imaging apparatus, of an examination region that includes an organ, the 3D image data set is provided to a computer/processor, which is also provided with information with designating the type of organ imaged in the examination region. The 3D image data set is automatically segmented in the computer/processor using a model-based segmentation algorithm, wherein the designated type of organ is used as a basis of the model. The 3D data set is also automatically or semi-automatically segmented using a greyscale value-based segmentation algorithm. At least one of the segmentation results is displayed.

Method and device for automatic or semi-automatic segmentation of a 3D image data set

A method and system for model based fusion pre-operative image data, such as computed tomography (CT), and intra-operative C-arm CT is disclosed. A first pericardium model is segmented in the pre-operative image data and a second pericardium model is segmented in a C-arm CT volume. A deformation field is estimated between the first pericardium model and the second pericardium model. A model of a target cardiac structure, such as a heart chamber model or an aorta model, extracted from the pre-operative image data is fused with the C-arm CT volume based on the estimated deformation field between the first pericardium model and the second pericardium model. An intelligent weighted average may be used improve the model based fusion results using models of the target cardiac structure extracted from pre-operative image data of patients other than a current patient.

Method and system for pericardium based model fusion of pre-operative and intra-operative image data for cardiac interventions

A method and system for model-based fusion of multi-modal volumetric images is disclosed. A first patient-specific model of an anchor anatomical structure is detected in a first medical image acquired using a first imaging modality, and a second patient-specific model of the anchor anatomical structure is detected in a second medical image acquired using a second imaging modality. A weighted mapping function is determined based on the first patient-specific model of the anchor anatomical structure and the second patient-specific model of the anchor anatomical structure using learned weights to minimize mapping error with respect to a target anatomical structure. The target anatomical structure from the first medical image to the second medical image using the weighted mapping function. In an application of this model-based fusion to transcatheter valve therapies, the trachea bifurcation is used as the anchor anatomical structure and the aortic valve is the target anatomical structure.

Method and system for model-based fusion of computed tomography and non-contrasted C-arm computed tomography

A method () to compensate for cardiac and respiratory motion in cardiac imaging during minimal invasive (e.g., trans-catheter) AVI procedures by image-based tracking () on fluoroscopic images.

Method of motion compensation for trans-catheter aortic valve implantation

A method and system for model-based fusion of pre-operative image data and intra-operative fluoroscopic images is disclosed. A fluoroscopic image and an ultrasound image are received. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image. Contours of an anatomical structure are detected in the ultrasound image, and a transformation is calculated between the ultrasound image and a pre-operative CT image based on the contours and a patient-specific physiological model extracted from the pre-operative CT image. A final mapping is determined between the CT image and the fluoroscopic image based on the transformation between the ultrasound image and physiological model and the mapping of the ultrasound image to the 3D coordinate system of the fluoroscopic image acquisition device. The CT image or the physiological model can then be projected into the fluoroscopic image.

Method and system for model based fusion on pre-operative computed tomography and intra-operative fluoroscopy using transesophageal echocardiography

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Nürnberg, Germany

Matthias John