Siemens Healthineers Ag

Siemens Healthcare Gmbh

Thomas Benkert

Marcel Dominik Nickel

Simon Arberet

Mariappan S Nadar

Mahmoud Mostapha

Boris Mailhe

Mario Zeller

David Grodzki

Berthold Kiefer

Robert Grimm

Vibhas S Deshpande

Fasil Gadjimuradov

Michael Bush

Marcel Nickel Dominik

Systems and methods for reconstruction for a medical imaging system. A scaling factor is used during the reconstruction process to adjust a step size of a gradient update. The adjustment of the step size of the gradient provides the ability to adjust a level of denoising by the reconstruction process.

Adjusted data consistency in deep learning reconstructions

A computer-implemented method for providing output data comprising an indication regarding the affliction of a patient with an infectious respiratory disease, the method comprises receiving magnetic resonance imaging data, the magnetic resonance imaging data acquired using a magnetic resonance imaging system, the magnetic resonance imaging data comprising a lung region of the patient; applying a trained function to the magnetic resonance imaging data to generate the output data, the trained function being based on an artificial neural network and the output data comprising the indication regarding the affliction of the patience with the infectious respiratory disease; and proving the output data.

Providing an indication regarding the affliction of a patient with an infectious respiratory disease based on magnetic resonance imaging data

Systems and methods for reconstruction for a medical imaging system. An adapter is used to adapt scan data so that different quantities of repetitions or directions may be used to train and implement a single multichannel backbone network.

Multichannel deep learning reconstruction of multiple repetitions

Techniques are provided for determining magnetic resonance images showing different contrasts in an examination. Magnetic resonance data for all magnetic resonance images are acquired using the same acquisition technique and the magnetic resonance images are reconstructed from their magnetic resonance data sets using at least one reconstruction algorithm. The reconstruction comprises at least one de-noising step. After acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and de-noising strengths for the de-noising step are chosen individually for each contrast depending on the respective at least one noise strength measure.

Computer-implemented method for determining magnetic resonance images showing different contrasts, magnetic resonance device, computer program and electronically readable storage medium

Various examples relate to SENSitivity Encoding (SENSE) reconstruction of Magnetic Resonance Imaging (MRI) images. Multiple coil sensitivity maps per coil of a receiver coil array are used, e.g., obtained from an Eigenvalue-based Spatially Constrained Iterative Reconstruction Technique (ESPIRIT) autocalibration protocol.

Multiple coil sensitivity maps of coils of a receiver array of a magnetic resonance imaging apparatus and sense reconstruction

For reconstruction, a machine-learned model is adapted to allow for reconstruction based on the repetitions available in some scanning. The reconstruction for one or more subsets is performed during the scanning. The machine-learned model is trained to reconstruction separately or independently for each repetition or to use information from previous repetitions without requiring waiting for completion of scanning. The reconstructed image may be displayed much more rapidly after completion of the acquisition since the reconstruction begins during the reconstruction.

Deep learning-based realtime reconstruction

In a method for generating combined image data based on first magnetic resonance (MR) data and second MR data, the first MR data and the second MR data are provided, the first MR data having been generated by a first actuation of a magnetic resonance device from an examination area of an examination object using a first sequence module, and the second MR data having been generated by a second actuation of the magnetic resonance device from the examination area of the examination object using the first sequence module, the first MR data and the second MR data are registered to one another to generate first registered MR data and second registered MR data; the first registered MR data and the second registered MR data are statistically combined to generate combined image data, and the combined image data is provided as an output in electronic form as a data file.

Methods and systems for generating combined image data based on MR data

For reconstruction in medical imaging using phase correction, a machine learning model is trained for reconstruction of an image. The reconstruction may be for a sequence without repetitions or may be for a sequence with repetitions. Where repetitions are used, rather than using just a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions may used to train the machine learning model. In either approach, a phase correction is applied in machine training. A phase map is extracted from output of the model in training or extracted from the ground truth of the training data. The phase correction, based on the phase map, is applied to the ground truth and/or the output of the model in training. The resulting machine-learned model may better reconstruct an image as a result of having been trained using phase correction.

Machine learning for medical image reconstruction with phase correction

For imaging an object subject to a cyclic motion, two or more imaging repetitions are carried out. Each of the imaging repetitions includes a sequence of equally spaced imaging events, wherein each imaging event has an event number, which corresponds to a respective predefined imaging parameter. A cycle duration of the cyclic motion is determined, a number of events per cycle is determined based on the cycle duration and a shift number is determined at least in part randomly. For a first imaging repetition, a starting number is determined depending on the number of events per cycle and the shift number. The first imaging repetition is carried out, wherein the respective sequence is started with an imaging event, whose event number is given by the starting number.

Imaging an object subjected to a cyclic motion

For reconstruction in medical imaging using a scan protocol with repetition, a machine learning model is trained for reconstruction of an image for each repetition. Rather than using a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions is used to train the machine learning model. This loss for reconstruction of one repetition based on aggregation of reconstructions for multiple repetitions is based on deep set-based deep learning. The resulting machine-learned model may better reconstruct an image from a given repetition and/or a combined image from multiple repetitions than a model learned from a loss per repetition.

Aggregation based on deep set learning in magnetic resonance reconstruction

Magnetic resonance imaging (MRI) image reconstruction using machine learning is described. A variational or unrolled deep neural network can be used in the context of an iterative optimization. In particular, a regularization operation can be based on a deep neural network. The deep neural network can take, as an input, an aliasing data structure being indicative of aliasing artifacts in one or prior images of the iterative optimization. The deep neural networks can be trained to suppress aliasing artifacts.

Magnetic resonance imaging reconstruction using machine learning

A computer-implemented method includes, based on scan data defining an input image, determining a reconstructed image using a reconstruction algorithm, and executing a data consistency operation for enforcing consistency between the input image and the reconstructed image. The data consistency operation includes using a norm ball projection that takes into account the available noise level information in order to automatically adjust the balance between the network prediction and the input measurements.

Noise adaptive data consistency in deep learning image reconstruction via norm ball projection

In a computer-implemented method of training a machine learning based processor, the processor can be trained to derive image data from signal data sets of multiple spin echo sequences. The trained processor can be configured to perform image processing for Magnetic Resonance Imaging (MRI) to derive the image data.

Trained image processing for diffusion weighted imaging and/or turbo spin echo sequences with focus on body applications

In a method for generation of a homogenization field suitable for homogenization of magnetic resonance data of an examination object, first magnetic resonance data from an examination region of the examination object is provided, a trained function is provided, a homogenization field is extracted by processing the first magnetic resonance data by way of the trained function, and the homogenization field is provided.

Generation of a homogenization field suitable for homogenization of magnetic resonance data

In a method for determining imaging parameters for a Magnetic Resonance (MR) image, a set of image sequence parameters of the imaging sequence is determined, a frequency offset of off-resonant tissue potentially present in the object under examination is determined, an allowed maximum position shift of the off-resonant tissue along a slice selection direction is determined, a rotation angle which leads to the allowed maximum shift for the off-resonant tissue is determined based on the determined set of image sequence parameters, and the determined rotation angle is provided to the MR imaging system to allow the MR imaging system to generate the MR image using the determined rotation angle in the imaging sequence.

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Thomas Benkert