Koninklijke Philips Corporation N.V.

Philips Gmbh

Adrianus Joseph Willibrordus Duijndam

Miha Fuderer

Michel Paul Jurriaan Jurrissen

Johannes Martinus Peeters

Peter Boernert

Holger Eggers

Kay Nehrke

Mariya Ivanova Doneva

Thomas Hendrik Rozijn

Franciscus Johannes Maria Benschop

A magnetic resonance (MR) imaging system includes a memory for storing machine executable instructions and preparation pulse sequence commands. The preparation pulse sequence commands are configured to control the system to acquire the preliminary MR data as a first data portion and a second data portion; to generate a first bipolar readout gradient during acquisition of the first portion; and to generate a second bipolar readout gradient during acquisition of the second portion, wherein the first bipolar readout gradient has an opposite polarity to the second bipolar gradient. The system is further configured to calculate a measured normalised phase correction quantity in image space using the first and second data portions; and fit a modeled phase correction to the measured phase error, wherein modeled phase correction is an exponential of a complex value multiplied by a phase error function that is spatially dependent.

Phase error correction for bipolar read out gradients

The invention provides for a magnetic resonance imaging system () for acquiring magnetic resonance data () from a subject (). The magnetic resonance imaging system comprises a processor () for controlling the magnetic resonance imaging system. The execution of the instructions causes the processor to control () the magnetic resonance imaging system with the pulse sequence data to acquire the magnetic resonance data. The pulse sequence data comprises commands for acquiring the magnetic resonance data using an n point Dixon magnetic resonance imaging method. The execution of the instructions causes the processor to construct () two phase candidate maps () using the magnetic resonance data according to the n point Dixon magnetic resonance imaging method; divide () each of the set of voxels into a set of object voxels (); identify () a set of boundary voxels () and interior voxels within the set of object voxels; create () a chosen phase candidate map () in the memory; select () a chosen phase map value for at least a portion of the set of boundary voxels in the chosen phase map from the two phase candidate maps by selecting the candidate phase map value which indicates the lowest fat to water ratio; and calculate () the phase map value of the object voxels according to a phase candidate selection algorithm.

Dixon magnetic resonance imaging using prior knowledge

An improved method of MR imaging of at least two chemical species having different MR spectra, such as water and fat, enables a precise quantification of water and fat or derived measures, such as a fat fraction. The method includes the steps of: a) generating two or more echo signals at different echo times by subjecting a body () placed in the examination volume of a MR device () to an imaging sequence of RF pulses and switched magnetic field gradients; b) acquiring the two or more echo signals; c) separating signal contributions of the at least two chemical species to the acquired echo signals on the basis of a signal model including the MR spectrum of each of the chemical species, the spatial variation of the main magnetic field in the examination volume, the effective transverse relaxation rate, and eddy current-induced phase errors. The eddy current-induced phase errors are estimated by a fitting procedure from the two or more echo signals.

MR imaging with dixon-type water/fat separation

A parallel magnetic resonance imaging system () includes at least one radio frequency (RF) coil () with a plurality of coil elements, a smart select unit (), a parallel imaging parameter unit (), and a sequence control (). The smart select unit (), from a pre-scan or prior scan of a subject with the at least one RF coil, constructs () a signal map and a plurality of noise maps based on different sets of reduction factors. The parallel imaging parameter unit () selects a set of reduction factors corresponding to a noise map which includes a highest signal-to-noise ratio (SNR). The sequence control () performs a magnetic resonance imaging scan of the subject based on the selected reduction factors.

Automatic optimization of parallel imaging acceleration parameters

A magnetic resonance imaging system () includes a denoising unit (), and a reconstruction unit (). The denoising unit () denoises a partial image and provides a spatially localized measure of a denoising effectivity. The reconstruction unit () iteratively reconstructs an output image from the received MR data processed with a Fast Fourier Transform (FFT), and in subsequent iterations includes the denoised partial image and the spatially localized measure of the denoising effectivity.

Iterative sense denoising with feedback

The invention relates to a method of MR imaging of at least a portion of a body () of a patient placed in an examination volume of an MR device (). The object of the invention is to provide an improved, i.e. faster, parallel imaging technique. The invention proposes to acquire a survey signal data set () at a low image resolution, which survey signal data set () includes MR signals received in parallel or successively via a volume RF coil () and via a set of array RF coils (). Spatial sensitivity profiles () of the array RF coils () are determined from the low resolution data. As a next step, a reference scan is performed in which a reference signal data set () is acquired at intermediate resolution solely via the array RF coils (). The spatial sensitivity profiles () of the array RF coils () are determined from the data acquired at intermediate resolution and from the spatial sensitivity profiles () determined before at low resolution. Finally, a diagnostic scan is performed, in which a diagnostic signal data set () is acquired in parallel via the array RF coils () at high resolution. The diagnostic signal data set () may be acquired in a sub-sampled fashion. A diagnostic MR image () is then reconstructed from the diagnostic signal data set () and from the spatial sensitivity profiles () determined at the intermediate resolution, for example by using the SENSE or SMASH algorithm.

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Eindhoven, Netherlands

Adrianus Joseph Willibrordus Duijndam