Patent classifications
G01R33/4822
Magnetic resonance imaging apparatus and magnetic resonance imaging method
A magnetic resonance imaging apparatus according to an embodiment includes sequence controlling circuitry and processing circuitry. The sequence controlling circuitry is configured to execute (i) a first pulse sequence in which a spatially selective Inversion recovery (IR) pulse and a spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (ii) a second pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed, while varying the first TI period, with respect to a plurality of first TI periods. The processing circuitry is configured to calculate a second TI period to be used in a third pulse sequence and a fourth pulse sequence, based on data obtained from the first pulse sequence and the second pulse sequence. The sequence controlling circuitry executes (iii) the third pulse sequence in which the spatially selective IR pulse and the spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (iv) the fourth pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed. The processing circuitry generates a magnetic resonance image of an imaged region based on data obtained from the third pulse sequence and the fourth pulse sequence.
Artefact reduction in magnetic resonance imaging
Techniques for compensating magnetic resonance imaging (MRI) data for artefacts caused by motion of a subject being imaged. The techniques include obtaining spatial frequency data obtained by using a magnetic resonance imaging (MRI) system to perform MRI on a patient, the spatial frequency data including first spatial frequency data and second spatial frequency data; determining a transformation using a first image obtained using the first spatial frequency data and a second image obtained using the second spatial frequency data; determining a residual spatial phase; correcting, using the transformation, second spatial frequency data and the residual spatial phase, to obtain corrected second spatial frequency data and a corrected residual spatial phase; and generating a magnetic resonance (MR) image using the corrected second spatial frequency data and the corrected residual spatial phase.
METHOD AND APPARATUS FOR RECONSTRUCTION OF MAGNETIC RESONANCE IMAGES WITH INCOMPLETE SAMPLING
A magnetic resonance (MR) image is created by executing an imaging sequence with an MR apparatus, wherein data in k-space are acquired using multiple receiving antennae, and reconstruction of all image points that correspond to all k-space points belonging to the imaging sequence takes place using a sensitivity profile of the receiving antennae in order to also take account of data at k-space points at positions at which no data were acquired. Data acquired at a number of positions of particular k-space points, the number of the particular k-space points being smaller than the number of all k-space points belonging to the imaging sequence. The aperture of each of the receiving antennae is configured such that, for acquisition of data at a respective k-space point, the spectral main lobe of the respective receiving antenna also extends over k-space points adjacent to the respective k-space point.
Artefact reduction in magnetic resonance imaging
Techniques of prospectively compensating for motion of a subject being imaged by an MRI system, the MRI system comprising a plurality of magnetics components including at least one gradient coil and at least one radio-frequency (RF) coil, the techniques comprising: obtaining first spatial frequency data and second spatial frequency data by operating the MRI system in accordance with a pulse sequence, wherein the pulse sequence is associated with a sampling path that includes at least two non-contiguous portions each for sampling a central region of k-space; determining a transformation using a first image obtained using the first spatial frequency data and a second image obtained using the second spatial frequency data; correcting the pulse sequence using the determined transformation to obtain a corrected pulse sequence; and obtaining additional spatial frequency data in accordance with the corrected pulse sequence.
SYSTEM AND METHOD FOR CONTROLLING PHYSIOLOGICAL NOISE IN FUNCTIONAL MAGNETIC RESONANCE IMAGING
A system and method is provided for controlling physiological-noise in functional magnetic resonance imaging using raw k-space data to extract physiological noise effects. The method can identify these effects when they are separable and directly reflects the artefactual effects on fMRI data, without the need for external monitoring or recording devices and to be compensated for via rigorous statistical analysis modeling of such noise sources. The physiological fluctuations may be treated as global perturbations presented around the origin point in a k-space 2D slice. Each k-space 2D slice may be acquired at a very short repetition time with an effective sampling rate to sample cardiac and respiratory rhythms through proper reordering and phase-unwarping techniques applied to the raw k-space data.
MAGNETIC RESONANCE IMAGING APPARATUS AND MAGNETIC RESONANCE IMAGING METHOD
A magnetic resonance imaging apparatus according to an embodiment includes sequence controlling circuitry and processing circuitry. The sequence controlling circuitry is configured to execute (i) a first pulse sequence in which a spatially selective Inversion recovery (IR) pulse and a spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (ii) a second pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed, while varying the first TI period, with respect to a plurality of first TI periods. The processing circuitry is configured to calculate a second TI period to be used in a third pulse sequence and a fourth pulse sequence, based on data obtained from the first pulse sequence and the second pulse sequence. The sequence controlling circuitry executes (iii) the third pulse sequence in which the spatially selective IR pulse and the spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (iv) the fourth pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed. The processing circuitry generates a magnetic resonance image of an imaged region based on data obtained from the third pulse sequence and the fourth pulse sequence.
MAGNETIC RESONANCE IMAGING APPARATUS
In one embodiment, a magnetic resonance imaging apparatus includes: a scanner that includes a static magnetic field magnet configured to generate a static magnetic field, a gradient coil configured to generate a gradient magnetic field, and a WB (Whole Body) coil configured to apply an RF pulse to an object; and processing circuitry. The processing circuitry is configured to: set (i) a pulse sequence in which a sequence element is repeated, the sequence element including at least an inversion pulse and (ii) a data acquisition sequence executed after a delay time from the inversion pulse; and cause the scanner to execute the pulse sequence by using virtual gating.
Magnetic resonance imaging using motion-compensated image reconstruction
The invention relates to a method of MR imaging of an object (10). It is an object of the invention to enable MR imaging in the presence of motion of the imaged object, wherein full use is made of the acquired MR signal and a high-quality MR image essentially free from motion artefacts is obtained. The method of the invention comprises the steps of: generating MR signals by subjecting the object (10) to an imaging sequence comprising RF pulses and switched magnetic field gradients; acquiring the MR signals as signal data over a given period of time (T); subdividing the period of time into a number of successive time segments (SO, S1, S2, . . . Sn); deriving a geometric transformation (DVF1, DVF2, . . . DVFn) in image space for each pair of consecutive time segments (S0, S1, S2, . . . Sn), which geometric transformation (DVF1, DVF2, . . . DVFn) reflects motion occurring between the two time segments of the respective pair; and reconstructing an MR image from the signal data, wherein a motion compensation is applied according to the derived geometric transformations (DVF1, DVF2, . . . DVFn). Moreover, the invention relates to an MR device (1) and to a computer program for an MR device (1).
Method and apparatus for generating a T1/T2 map
A method and apparatus for generating a T1 or T2 map for a three-dimensional (3D) image volume of a subject. The method includes acquiring first, second, and third 3D images of the image volume of the subject. Signal evolutions of voxels through the first to third 3D images by comparing voxel intensity levels of corresponding voxel locations in the first, second, and third 3D images. A simulation dictionary representing the signal evolutions for a number of different tissue parameter combinations is obtained. The T1 or T2 map is generated by comparing the determined signal evolutions to entries in the dictionary and by finding, for each of the determined signal evolutions, the entry in the dictionary that best matches the determined signal evolution.
Adaptive Reconstruction of Magnetic Resonance Images
The present disclosure relates to a method comprising: providing a trained machine learning model. The trained machine learning model is configured for reconstructing images from input data. The method comprises: receiving (201) a multidimensional array comprising M dimensional acquired data; determining (205) a subset of values of at least one K selected dimension of the array; for each value of the subset determining (207) a M−K dimensional array comprising the acquired data corresponding to the value, resulting in a set of M−1 dimensional arrays; inputting (209) the set of M−K dimensional arrays to the trained machine learning model, and receive a reconstructed image from the trained machine learning model.