Siemens Healthcare Gmbh

Siemens Healthineers Ag

Markus Barth

Kieran O'Brien

Steffen Bollmann

Benedicte Marechal

Jin Jin

Francesco Cognolato

A training method for training neural networks to determine a magnetic susceptibility distribution of a sample may include: storing a simulated magnetic susceptibility map of the sample, generating a modified magnetic susceptibility map by combining an influence of one or more external magnetic susceptibility sources with the simulated magnetic susceptibility map and storing the modified magnetic susceptibility maps. The method may include generating a first training image by applying a quantitative susceptibility mapping model the modified magnetic susceptibility map and storing the first training image, applying the first neural network to the first image and a second neural network to an output of the first neural network and changing network parameters of the first and the second neural network depending on a deviation of an output of the second artificial neural network from the simulated magnetic susceptibility map.

Method and system for determining a magnetic susceptibility distribution

Techniques are disclosed to leverage the use of convolutional neural networks or similar machine learning algorithms to predict an underlying susceptibility distribution from MRI phase data, thereby solving the ill-posed inverse problem. These techniques include the use of Deep Quantitative Susceptibility 'DeepQSM' mapping, which uses a large amount of simulated susceptibility distributions and computes phase distribution using a unique forward solution. These examples are then used to train a deep convolutional neuronal network to invert the ill-posed problem.

Method and deep quantitative susceptibility mapping (QSM)

In a magnetic resonance method and apparatus, deficiencies in conventional masking in quantitative susceptibility mapping (QSM) are addressed by the inclusion of an additional step in the conventional QSM post-processing pipeline. In this additional step, atlas-based segmentation techniques, which have been developed for morphological applications such as T1w MPRAGE are used in order to provide the mask. This mask is then fed to the remainder of the QSM post-processing pipeline.

Growing community of inventors

St. Lucia, Australia

Markus Barth