A method for magnetic resonance imaging (MRI) may include cause, based on a pulse sequence, a magnetic resonance (MR) scanner to perform a scan on an object. The pulse sequence may include a steady-state sequence and an acquisition sequence that is different from the steady-state sequence. The steady-state sequence may correspond to a steady-state phase of the scan in which no MR data is acquired. The acquisition sequence may correspond to an acquisition phase of the scan in which MR data of the object is acquired. The method may also include generating one or more images of the object based on the MR data.

A system and method is provided for improved magnetic resonance fingerprinting (MRF) data dictionary matching using an MRF dictionary having entries with an inner product storing tissue properties.

A method for producing an MRI image includes the steps of acquiring lines of a volume under analysis using a steady-state gradient echo sequence in successive repetition times (TR); and applying an inhomogeneous magnetization transfer (ihMT) pre-saturation module (T.sub.MT) in each repetition time. The duration of the repetition times is greater than 20 milliseconds.

A magnetic resonance imaging system (100) acquires magnetic resonance data (142) from a subject (118) within a measurement zone (108). Pulse sequence commands (140) control the magnetic resonance imaging system to acquire the magnetic resonance data according to a magnetic resonance fingerprinting protocol. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to repeatedly generate an RF pulse train (300) and acquire the magnetic resonance data as multiple k-space traces. The machine executable instructions causes the processor to: sequentially acquire (200) the multiple k-space traces of magnetic resonance data by controlling the magnetic resonance imaging system with pulse sequence commands and calculate (202) the abundance of each of a set of predetermined substances for k-space traces that are acquired after a predetermined number of k-space traces of the multiple k-space traces has been acquired and the acquired magnetization has reached a steady state. The abundance of each of a set of predetermined substances is determined by comparing the magnetic resonance data with a steady state magnetic resonance fingerprinting dictionary (144) which contains a listing of calculated magnetic resonance signals in response to the RF pulse train for a set of predetermined substances.

Systems and methods for producing quantitative maps of a longitudinal relaxation parameter, such as a longitudinal relaxation time (T1), using magnetic resonance imaging (MRI) are described. More particularly, a pulse sequence and imaging method for cardiac phase-resolved myocardial T1 mapping are provided.

A method for obtaining a magnetic resonance imaging (MRI) image of an object is provided. The method includes applying a MRI sequence to a target area in the object, receiving magnetic resonance (MR) signals from the target area, acquiring, in one k-space strategy, a first set of k-space lines based on the MR signals while nuclear spins in the target area are in a transient state, acquiring, in another k-space strategy, a second set of k-space lines based on the MR signals while the nuclear spins in the target area are in a steady-state or a mixed state of the transient state and the steady-state, and reconstructing the MRI image based on the first set of k-space lines and the second set of k-space lines.

An MRI device for executing an imaging operation at least three times or more with a different combination of at least a repetition time and a flip angle in the same imaging sequence, includes: a receiving unit which receives information specifying an imaging target and a constraint condition relating to an imaging time or quantitative value accuracy; and a scan parameter set generation unit which calculates at least three or more scan parameter sets having a different combination of at least the repetition time and the flip angle on the basis of the constraint condition. The MRI device uses three or more scan parameter sets generated by the optimal scan parameter set generation unit and calculates quantitative values (T1, T2, and the like) of the imaging target from a plurality of images obtained by the imaging operation.

In a method for controlling a magnetic resonance imaging system as part of functional magnetic resonance imaging, a main magnetic field B0 is provided having a field strength of at most 1.4 tesla at a main field magnet system (4) of the magnetic resonance imaging system (1); and a measurement is performed as part of functional magnetic resonance imaging, wherein a measurement sequence (MS) is applied that has a longer echo time TE (e.g. longer than 100 ms).

A system and method for generating quantitative images of a subject using a nuclear magnetic resonance system. The method includes performing a navigator module to acquire navigator data, and performing an acquisition module during free breathing of the subject to acquire NMR data from the subject that contains one or more resonant species that simultaneously produce individual NMR signals in response to the acquisition module. The above steps are repeated to acquire data from a plurality of partitions across the volume. The navigator data is analyzed to determine if the NMR data meets a predetermined condition and if not, the above steps are repeated for at least an affected partition corresponding to NMR data that did not meet the predetermined condition. The NMR data is compared to a dictionary of signal evolutions to determine quantitative values for two or more parameters of the resonant species in the volume.

A method for creating a dictionary for a magnetic resonance fingerprinting (MRF) reconstruction includes training a semi-supervised learning system based on at least a set of MRF data and a set of control variables and generating a plurality of signal evolutions using the trained semi-supervised learning system. The method also includes generating an MRF dictionary using the plurality of signal evolutions generated using the trained semi-supervised learning system and storing the MRF dictionary in a memory. In one embodiment, the semi-supervised learning system is a MRF generative adversarial network (GAN).