Siemens Medical Solutions Usa, Inc.

Eberhard Karls Universitaet Tuebingen Medizinische Fakultaet

Eberhard-karls-universität Tübingen Universitätsklinikum

Bernd J Pichler

Wolfgang Renz

Ralf Ladebeck

Matthias J Schmand

James Luke Corbeil

Nan Zhang

Hubertus Fischer

Ronald Nutt

Robert E Nutt

Günter Schnur

Christian Kesenheimer

Markus Vester

Ronald Grazioso

Florian Steinke

Guenter Schnur

Ron Grazioso

Bernhard Schölkopf

Anna Teske

Marcel Krueger

Klaus Schulze-Osthoff

Dominic Hildebrand

Florian Maier

Marcus Vester

Ramona Stumm

Matthias Hofmann

Jonathan Cotton

Kerstin Fuchs

APD-based PET modules are provided for use in combined PET/MR imaging. Each module includes a number of independent, optically isolated detectors. Each detector includes an array of scintillator (e.g. LSO) crystals read out by an array of APDs. The modules are positioned in the tunnel of a MR scanner. Simultaneous, artifact-free images can be acquired with the APD-based PET and MR system resulting in a high-resolution and cost-effective integrated PET/MR system.

Combined PET/MR imaging system and APD-based PET detector for use in simultaneous PET/MR imaging

The instant invention relates to novel compounds useful for visualizing cell senescence, their preparation and use. In particular, this invention relates to novel fucose and amino-quinoline derivatives useful as senescence traces and their preparation.

Senescence tracers

The present invention relates to novel halogenated benzoxazines, methods for their production, their use for the diagnosis of diseases and their use for the production of medicaments for the diagnosis of diseases, preferably dementia diseases and in particular the Alzheimer's disease, in humans and/or animals.

Halogenated benzoxazines and their use

Combined PET/MR imaging system and APD-based pet detector for use in simultaneous PET/MR imaging

It is disclosed a system and method () for determining a property map () of an object, particularly a human being, based on at least a first image (), particularly an magnetic resonance (MR) image, of the object. In the method (), a structure of reference pairs is defined in a first step (), wherein each reference pair (-) comprises at least two entries (). The first entry represents a property value, particularly an attenuation value. The second entry () preferably represents a group of image points () belonging together, which is extracted particularly from MR images () and comprises an interesting image point corresponding to the property value. In another step () of the method () a plurality of training pairs (-) is provided. A structure of the training pairs (-) corresponds to the structure of reference pairs, and the entries of respective training pairs (-) are known. In another step () of the method (), an assignment between the first entries and the other entries (-) of the training pairs (-) is determined by machine learning, thus allowing prediction of a property value () corresponding to an arbitrary point () of the first image ().

Method for determining a property map of an object, particularly of a living being, based on at least a first image, particularly a magnetic resonance image

Growing community of inventors

Scheyern, Germany

Bernd J Pichler