Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

Jens Kaftan

Matthew David Kelly

Shaohua Kevin Zhou

Michal Sofka

Jingdan Zhang

Neil Birkbeck

Jerome Declerck

Jens Wetzl

Guenther Platsch

Julien Michel Yvon Willaime

Azadeh Firouzian

Antonios Makropoulos

Marin Kopaczka

Timo Kohlberger

In a method and apparatus for providing a visualization image and a reference image for a patient, with only one reconstruction being implemented, raw scan data from the patient are reconstructed into a visualization image, and the reference image is generated, without another reconstruction, by applying a filter to the visualization image to obtain a simulated reference image, using a value of a filter parameter that is specific for the patient. The patient-specific filter parameter is determined by comparing image-derived properties of an accessed, previously-reconstructed reference image and the reference image, and the filter parameter is adapted to reduce differences in the image-derived properties of the respective images.

Method and apparatus for calibration of medical image data

In a method and apparatus for automatic background region selection for lesion segmentation in medical images, a patient medical image dataset is loaded into a computer and an image region is delineated to obtain a segmentation containing a lesion. A background region is created from the segmentation representing the patient organ.

Automatic background region selection for lesion delineation in medical images

In a method and apparatus for generating an enhanced image for display from medical imaging data of a subject, a feature of interest in the imaging data elongated in at least one dimension is determined. A location of a line through the imaging data along the feature of interest is obtained, and a projection image along the line is generated from the imaging data.

Method and apparatus for generating an enhanced image from medical imaging data

In a method for improving alignment between representations of a same elongate feature in first and second images, the images are provided to a processor and the processor derives a centerline from image data representing an elongate anatomical feature in the first image, and derives a value of a metric indicating a present quality of alignment of the centerline with image data of the second image representing the same anatomic feature. The processor optimizes the metric to provide a rigid transform, and applies the rigid transform to the centerline or the second image to improve the alignment between the first and the second image with respect to the elongate feature.

Method and apparatus for registration of multimodal imaging data by using a center line to optimize quality of alignment

In a method and an apparatus to visualize nuclear medicine data from different modalities in a single image, image data sets acquired with different modalities are aligned with a data set representing corresponding anatomical data. The image data in each data set are segmented into separate regions, representing respective structures of interest, with reference to a segmentation derived from anatomical data. For each region, a corresponding segment of image data is selected from a selected image data set. The selected segments of the image data set are combined to generate a multi-fused image of the regions, by applying spatially dependent look-up tables to the multiple image data sets, thereby to determine whether each data set is hidden or displayed in each region.

Method for combining multiple image data sets into one multi-fused image

A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.

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Oxford, United Kingdom

Jens Kaftan