Abstract: In the field of MRI, a model-based MRI image reconstruction technique is provided. The model-based reconstruction technique increases the performance of Time-of-Flight MRA. In a learning phase, a model is calculated from a sufficiently large set of data acquired at both low and high magnetic fields, using deep learning strategies. In a clinical phase, the model is applied to measured data generating high MR image quality.

Abstract: Techniques are provided for operating an MR scanner that comprises gradient coils configured to generate a field gradient along coordinate axes of a coil coordinate system. The techniques include receiving limit values concerning the field gradient in the coil coordinate system and an MR scan protocol. Expected orientations of a logical coordinate system are then determined and, for each one, a compliant value for a timing parameter is determined and stored in a database. The MR scanner is controlled to carry out an MR scan, and afterwards the orientation is set to a first orientation. A first value for the timing parameter according to the first orientation is determined based on the data stored in the database, and the MR scanner is controlled to carry out a scan sequence according to the first orientation and the first value.

Abstract: Embodiments provide a computer-implemented method for selecting thermal images for generating a temperature difference map through proton resonance frequency (PRF) thermometry, including: acquiring a set of baseline images prior to a thermal treatment of an organ of interest; identifying a subset of baseline images in a most stable motion state from the set of baseline images; averaging the subset of baseline images to generate a template image; determining an acceptance threshold based on an image similarity measure (ISM) between each of the set of baseline images and the template image; acquiring a set of thermal images during the thermal treatment; and selecting a subset of thermal images from the set of thermal images, wherein each of the subset of thermal images has the image similarity measure above the acceptance threshold.

Abstract: The invention relates to a method for optimizing an MR imaging sequence with a plurality of sequence blocks where gradient requirements of logical gradients switched along axes including a read, slice, and phase axis are determined. Based on the requirements, a reference gradient is determined among the logical gradients having a longest minimum duration relative to remaining logical gradients within the sequence block during which the reference gradient is switched on. A scaling factor is determined for each of the logical gradients. The scaling factors for the logical gradient factors are updated taking into account the determined stretch ratios, and optimized amplitudes are determined for the logical gradients based on the updated scaling factors.

Abstract: A method for phase correction in proton resonance frequency (PRF) thermometry application includes acquiring a series of magnetic resonance (MR) images comprising a first MR image and plurality of subsequent MR images depicting an anatomical area of interest. The MR images are acquired while tissue in the anatomical area of interest is undergoing a temperature change. Each subsequent MR image is registered to the first MR image to yield a plurality of registered images. A plurality of basis images are computed from the registered images using Principal Component Analysis (PCA). The basis images are used to remove motion-related phase changes from a second series of MR images, thereby yielding a motion corrected second series of MR images. One or more temperature difference maps are generated that depict a relative temperature change for the tissue in the anatomical area of interest based on the motion corrected second series.

Abstract: A method for decomposing noise into white and spatially correlated components during MR thermometry imaging includes acquiring a series of MR images of an anatomical object and generating a series of temperature difference maps of the anatomical object. The method further includes receiving a selection of a region of interest (ROI) within the temperature difference map and estimating total noise variance values depicting total noise variance in the temperature difference map. Each total noise variance value is determined using a random sampling of a pre-determined number of voxels from the ROI. A white noise component and a spatially correlated noise component of the total noise variance providing a best fit to the total noise variance values for all of the random samplings are identified. The white noise component and the spatially correlated noise component are displayed on a user interface.

Abstract: A method for decomposing noise into white and spatially correlated components during MR thermometry imaging includes acquiring a series of MR images of an anatomical object and generating a series of temperature difference maps of the anatomical object. The method further includes receiving a selection of a region of interest (ROI) within the temperature difference map and estimating total noise variance values depicting total noise variance in the temperature difference map. Each total noise variance value is determined using a random sampling of a pre-determined number of voxels from the ROI. A white noise component and a spatially correlated noise component of the total noise variance providing a best fit to the total noise variance values for all of the random samplings are identified. The white noise component and the spatially correlated noise component are displayed on a user interface.

Abstract: In the field of MRI, a model-based MRI image reconstruction technique is provided. The model-based reconstruction technique increases the performance of Time-of-Flight MRA. In a learning phase, a model is calculated from a sufficiently large set of data acquired at both low and high magnetic fields, using deep learning strategies. In a clinical phase, the model is applied to measured data generating high MR image quality.

Abstract: A method for phase correction in proton resonance frequency (PRF) thermometry application includes acquiring a series of magnetic resonance (MR) images comprising a first MR image and plurality of subsequent MR images depicting an anatomical area of interest. The MR images are acquired while tissue in the anatomical area of interest is undergoing a temperature change. Each subsequent MR image is registered to the first MR image to yield a plurality of registered images. A plurality of basis images are computed from the registered images using Principal Component Analysis (PCA). The basis images are used to remove motion-related phase changes from a second series of MR images, thereby yielding a motion corrected second series of MR images. One or more temperature difference maps are generated that depict a relative temperature change for the tissue in the anatomical area of interest based on the motion corrected second series.

Abstract: A method of obtaining analytical expressions for an optimized diffusion encoding gradient (DEG) waveform is disclosed. The method uses a constrained numerical optimization to obtain an optimal configuration of a DEG waveform. The optimization adjusts parameters for a waveform modeled as a finite set of square pulses to maximize an exact b-value equation, while maintaining a waveform shape that also compensates for motion. The optimal configuration is then verified using a waveform model of a set of trapezoidal pulses to obtain an optimal DEG waveform. Generally, the parameters describing the reduced set of trapezoidal pulses are reduced, thereby allowing the optimal DEG waveform to be expressed as closed-form analytical expressions. The analytical expressions simplify the derivation of optimal DEG waveforms for a range of diffusion imaging scanning parameters, thereby improving the quality and versatility of diffusion weighted MRI.

Abstract: A method of obtaining analytical expressions for an optimized diffusion encoding gradient (DEG) waveform is disclosed. The method uses a constrained numerical optimization to obtain an optimal configuration of a DEG waveform. The optimization adjusts parameters for a waveform modeled as a finite set of square pulses to maximize an exact b-value equation, while maintaining a waveform shape that also compensates for motion. The optimal configuration is then verified using a waveform model of a set of trapezoidal pulses to obtain an optimal DEG waveform. Generally, the parameters describing the reduced set of trapezoidal pulses are reduced, thereby allowing the optimal DEG waveform to be expressed as closed-form analytical expressions. The analytical expressions simplify the derivation of optimal DEG waveforms for a range of diffusion imaging scanning parameters, thereby improving the quality and versatility of diffusion weighted MRI.