Patent Description:
Characterizing tissue species using nuclear magnetic resonance ("NMR") can include identifying different properties of a resonant species (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation, proton density). Other properties like tissue types and super-position of attributes can also be identified using NMR signals. These properties and others may be identified simultaneously using magnetic resonance fingerprinting ("MRF"), which is described, as one example, by <NPL>.

Conventional magnetic resonance imaging ("MRI") pulse sequences include repetitive similar preparation phases, waiting phases, and acquisition phases that serially produce signals from which images can be made. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may produce a T1-weighted signal at a first echo time ("TE"), while a second pulse sequence may produce a T2-weighted signal at a second TE. These conventional pulse sequences typically provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation).

When conventional magnetic resonance ("MR") images are generated, they may be viewed by a radiologist and/or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2-weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration.

Unlike conventional MRI, MRF employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which the radio frequency ("RF") is applied. The signals from different resonant tissues will, however, be different and can be distinguished using MRF. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. Characterizing the resonant species may include identifying a material or tissue type, or may include identifying MR parameters associated with the resonant species. The "known" evolutions may be, for example, simulated evolutions calculated from physical principles and/or previously acquired evolutions. A large set of known evolutions may be stored in a dictionary.

Thus, MRF provides a framework that combines the transient state of the signal evolution with dictionary matching to achieve accurate and efficient multi-parameter maps. The original MRF design was based on a balanced steady state (bSSFP) acquisition, which is sensitive to T1, T2 and off-resonance (B0), and provides high signal to noise ratio (SNR). The signal intensity is dependent on the off-resonance frequency, which allows B-field mapping, but can also lead to banding artifacts in cases of significant B-field variations. A SSFP-MRF approach was proposed to eliminate banding artifacts, but it presents lower SNR and lack of B-field sensitivity.

As such, a need persists to overcome these challenges with traditional MRF acquisitions.

The present disclosure provides systems and methods for a balanced steady state free precession (bSSFP) based MRF framework, which allows for different phase cycling (phc-MRF). The phase cycling may be used to compensate for banding artifact induced by B0 inhomogeneity.

According to claim <NUM>, a method is provided for performing a magnetic resonance fingerprinting (MRF) study. The method includes performing phase-cycled balanced steady-state free precession (bSSFP) pulse sequences multiple times while cycling through radio frequency (RF) phase patterns of the phase-cycled bSSFP pulse sequences that differ across the multiple times to acquire multiple MRF datasets from a region of interest, ROI, in a subject, each MRF dataset formed by signal evolutions acquired by performing one of the phase-cycled bSSFP pulse sequences. The process also includes comparing the multiple MRF datasets with at least one dictionary of signal models, or templates, that have been generated for acquisition parameters of the phase-cycled bSSFP pulse sequences by performing a matching or pattern recognition technique for each voxel of the multiple MRF datasets to determine at least one matched value for at least one tissue property for each voxel for each of the multiple MRF datasets, and producing an aggregated dataset of the at least one tissue property by aggregating the matched values of the at least one tissue property from the voxels of all of the multiple MRF datasets; and producing at least one map of the at least one tissue property using the aggregated dataset of the at least one tissue property.

In an exemplary embodiment, cycling through the RF phase patterns of the phase-cycled bSSFP pulse sequences that differ across the multiple times includes performing a linear phase cycling with a phase increment (Δϕ) for each timepoint of π, <NUM>, π/<NUM> and 3π/<NUM> of the multiple times.

In another exemplary embodiment, performing the matching or pattern recognition technique for each voxel of the multiple MRF datasets includes comparing with a plurality of different dictionaries that are paired, one each, with the multiple MRF datasets.

According to another exemplary embodiment, performing the matching or pattern recognition technique for each voxel of the multiple MRF datasets includes comparing each of the MRF datasets with a respective MRF dictionary to generate a respective inner product.

In yet another exemplary embodiment, aggregating the indication of the at least one tissue property includes producing an average of the at least one tissue property determined for each of the multiple MRF datasets.

According to another exemplary embodiment, the method further comprises performing a search across the aggregated indication of the at least one tissue property to determine a maximum value of the at least one tissue property.

In another exemplary embodiment, the at least one map of the at least one tissue property includes at least one of a T1 map, a T2 map, and a B0 map.

According to another exemplary embodiment, the ROI in the subject is characterized by an inhomogeneous magnetic field.

According to claim <NUM>, a system is provided that includes a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject and a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field. The system also includes a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array and a computer system. The computer system is programmed to control the magnetic gradient system (<NUM>) and the RF system (<NUM>) to perform phase-cycled balanced steady-state free precession, bSSFP, pulse sequences multiple times while cycling through radio frequency, RF, phase patterns of the phase-cycled bSSFP pulse sequences that differ across the multiple times to acquire multiple MRF datasets from a region of interest, ROI, in a subject, each MRF dataset formed by signal evolutions acquired by performing one of the phase-cycled bSSFP pulse sequences. computer system is also programmed to compare the multiple MRF datasets with at least one dictionary of signal models, or templates, that have been generated for acquisition parameters of the phase-cycled bSSFP pulse sequences by performing a matching or pattern recognition technique for each voxel of the multiple MRF datasets to determine at least one matched value for at least one tissue property for each voxel for each of the multiple MRF datasets, produce an aggregated dataset of the at least one tissue property by aggregating the matched values of the at least one tissue property from the voxels of all of the multiple MRF datasets, and produce at least one map of the at least one tissue property using the aggregated dataset of the at least one tissue property.

In an exemplary embodiment, the computer system is further programmed to cycle through the RF phase patterns of the phase-cycled bSSFP pulse sequences that differ across the multiple times by performing a linear phase cycling with a phase increment Δϕ for each timepoint of π, <NUM>, π/<NUM> and 3π/<NUM> of the multiple times.

In another exemplary embodiment, the computer system is further programmed utilize a plurality of different dictionaries that are paired, one each, with the multiple MRF datasets to compare the multiple MRF datasets with the at least one MRF dictionary.

According to yet another exemplary embodiment, the computer system is further programmed to compare each of the MRF datasets with a respective MRF dictionary to generate a respective inner product when comparing the multiple MRF datasets with the MRF dictionary.

In another exemplary embodiment, the computer system is further programmed to produce an average of the at least one tissue property determined for each of the multiple MRF datasets to aggregate the indication of the at least one tissue property.

In yet another exemplary embodiment, the computer system is further programmed to perform a search across the aggregated indication of the at least one tissue property to determine a maximum value of the at least one tissue property.

According to another exemplary embodiment, the at least one map of the at least one tissue property includes at least one of a T1 map, a T2 map, and a B0 map.

In yet another exemplary embodiment, the ROI in the subject is characterized by an inhomogeneous magnetic field.

Magnetic resonance fingerprinting ("MRF") is a technique that facilitates mapping of tissue or other material properties based on random or pseudorandom measurements of the subject or object being imaged. In particular, MRF can be conceptualized as employing a series of varied "sequence blocks" of radio-frequency ("RF") pulses that produce different signal evolutions in different "resonant species" to which the RF pulses are applied. The term "resonant species," as used herein, refers to a material, such as water, fat, bone, muscle, soft tissue, and the like, that can be made to resonate using NMR. By way of illustration, when RF energy is applied to a volume that has both bone and muscle tissue, then both the bone and muscle tissue will produce a nuclear magnetic resonance ("NMR") signal; however, the "bone signal" represents a first resonant species and the "muscle signal" represents a second resonant species, and thus the two signals will be different. These different signals from different species can be collected simultaneously over a period of time to collect an overall "signal evolution" for the volume.

The measurements obtained in MRF techniques are achieved by varying the acquisition parameters from one repetition time ("TR") period to the next, which creates a time series of signals with varying contrast. Examples of acquisition parameters that can be varied include flip angle ("FA"), RF pulse phase, TR, echo time ("TE'), sampling patterns, and the like, such as by modifying one or more readout encoding gradients. The acquisition parameters are varied in a random manner, pseudorandom manner, or other manner that results in signals from different materials or tissues to be spatially incoherent, temporally incoherent, or both. For example, in some instances, the acquisition parameters can be varied according to a non-random or non-pseudorandom pattern that otherwise results in signals from different materials or tissues to be spatially incoherent, temporally incoherent, or both.

From these measurements, which as mentioned above may be random or pseudorandom, or may contain signals from different materials or tissues that are spatially incoherent, temporally incoherent, or both, MRF processes can be designed to map any of a wide variety of parameters. Examples of such parameters that can be mapped may include, but are not limited to, longitudinal relaxation time (T<NUM>), transverse relaxation time (T<NUM>), main or static magnetic field map (B<NUM> ), and proton density (ρ). MRF is generally described in <NPL> and <CIT> and Published <CIT>.

The data acquired with MRF techniques are compared with a dictionary of signal models, or templates, that have been generated for different acquisition parameters from magnetic resonance signal models, such as Bloch equation-based physics simulations. This comparison allows estimation of the physical parameters, such as those mentioned above. The comparison of the acquired signals to a dictionary is performed using a matching or pattern recognition technique. The parameters for the tissue or other material in a given voxel can be estimated to be the values that provide the best signal matching. For instance, the comparison of the acquired data with the dictionary can result in the selection of a signal vector, which may constitute a weighted combination of signal vectors, from the dictionary that best corresponds to the observed signal evolution. The selected signal vector includes values for multiple different quantitative parameters, which can be extracted from the selected signal vector and used to generate the relevant quantitative parameter maps.

The stored signals and information derived from reference signal evolutions may be associated with a potentially very large data space. The data space for signal evolutions can be partially described by: <MAT>
where SE is a signal evolution; NS is a number of spins; NA is a number of sequence blocks; NRF is a number of RF pulses in a sequence block; α is a flip angle; φ is a phase angle; Ri(α) is a rotation due to off resonance; RRFij(α,φ) is a rotation due to RF differences; R(G) is a rotation due to a magnetic field gradient; T<NUM> is a longitudinal, or spin-lattice, relaxation time; T<NUM> is a transverse, or spin-spin, relaxation time; D is diffusion relaxation; Ei(T<NUM>,T<NUM>,D) is a signal decay due to relaxation differences; and M<NUM> is the magnetization in the default or natural alignment to which spins align when placed in the main magnetic field.

While Ei(T<NUM>,T<NUM>,D) is provided as an example, in different situations, the decay term, Ei(T<NUM>,T<NUM>,D), may also include additional terms, Ei(T<NUM>,T<NUM>,D,. ) or may include fewer terms, such as by not including the diffusion relaxation, as Ei(T1,T<NUM>) or Ei(T<NUM>,T<NUM>,. Also, the summation on "j" could be replace by a product on "j".

The dictionary may store signals described by, <MAT>
where S<NUM> is the default, or equilibrium, magnetization; Si is a vector that represents the different components of magnetization, Mx, My, and Mz during the ith acquisition block; Ri is a combination of rotational effects that occur during the ith acquisition block; and Ei is a combination of effects that alter the amount of magnetization in the different states for the ith acquisition block. In this situation, the signal at the ith acquisition block is a function of the previous signal at acquisition block (i.e., the (i-<NUM>)th acquisition block). Additionally or alternatively, the dictionary may store signals as a function of the current relaxation and rotation effects and of previous acquisitions. Additionally or alternatively, the dictionary may store signals such that voxels have multiple resonant species or spins, and the effects may be different for every spin within a voxel. Further still, the dictionary may store signals such that voxels may have multiple resonant species or spins, and the effects may be different for spins within a voxel, and thus the signal may be a function of the effects and the previous acquisition blocks.

As described, MRF provides a framework for multiple tissue parameter mapping based on a single acquisition. This framework combines the transient state of the signal evolution with dictionary matching to achieve accurate and efficient multi-parameter maps. The original MRF design was based on a balanced steady state free precession (bSSFP) acquisition, which can be sensitive to T1, T2, and off-resonance (B0) and provide high signal to noise ratio (SNR). The signal intensity is dependent on the off-resonance frequency, which allows magnetic field mapping, but can also lead to banding artifacts in cases of significant magnetic field variations. A steady state free precession (SSFP) -MRF approach was proposed to eliminate banding artifacts, but it presents lower SNR and lack of magnetic field sensitivity. Thus, as will be described, the present disclosure presents an approach for MRF that is based on a bSSFP sequence, but that can control banding artifacts through cycling variation of the radiofrequency (RF) pulses phase (phc-MRF).

Referring to <FIG> a flowchart is provided for one implementation of a process in accordance with the present disclosure. To start, multiple phase-cycled bSSFP pulse sequence 10a, 10b, 10c, 10d may be used to acquire MRF data one after each other. In this non-limiting example, four repetitions of the pulse sequence are illustrated but more or less repetitions may be desired. Each acquisition 10a-10d can use one arm of a variable density spiral trajectory for each TR to acquire an image (timepoint). For each of the may timepoints (e.g., <NUM> timepoints) used to acquire all desired MRF data, the RF excitation flip angle (FA), the repetition time (TR), the RF pulse phase, spiral arm orientation, or other parameters can be varied to, thereby, acquire signal evolutions forming MRF data.

In one non-limiting example, referring to <FIG>, the four acquisitions can share the same FA and TR variations, as well as presenting an inversion pulse with, in this non-limiting example, a time of <NUM> prior to the acquisition. In this case, the RF pulse phase can change for each acquisition, as illustrated by the phase of the four initial pulses shown schematically in <FIG>. In this non-limiting example, the trajectory rotated <NUM>° at each timepoint for all acquisitions.

Referring again to <FIG>, the four acquisitions 10a-10d use a linear phase cycling with a phase increment (Δϕ) for each timepoint of π, <NUM>, π/<NUM> and 3π/<NUM> respectively. This phase increment, or offset, can have or form any of a variety of patterns. Four different dictionaries 12a-12d can be used for the four acquisitions 10a-10d. Each voxel of a given MRF dataset acquired from a given acquisition 10a-10d can be matched with the respective dictionary 12a-12c through, as a non-limiting example, L<NUM>-normalized matching, for example, an inner product (IP) 14a-14d. Then, the matched values from all four matchings can be used to form an aggregated dataset. For example, the aggregated dataset can be formed by averaging the matches 14a-14d at process block <NUM> to select the atom providing the highest average match at process block <NUM> and estimate T1, T2, and B0 maps, <NUM>, <NUM>, <NUM>. That is, one example of a matching can be performed by taking the inner product between the voxel signal and each dictionary and the entry in the dictionary that returns the highest value is considered to be the one that best represents the tissue properties, such that the respective T1, T2 and/or off-resonance values can be assigned to the voxel.

The L<NUM>-normalized match values being averaged at process block <NUM>, before looking for the highest value at process block <NUM>, yields the dictionary atom representing each voxel. Though variations on this implementation may be performed, this particular, non-limiting implementation enforces the prior knowledge that the atom to be selected to be the same for all four scans. The dictionary atom providing the maximum average matching is selected to retrieve the T1, T2, and B0 map <NUM>, <NUM>, <NUM>, of the relative voxel.

The above-described phc-MRF technique was tested on a healthy volunteer after informed consent in this IRB approved study and on an ISMRM/NIST MR system phantom. The acquisition were performed on a 3T system (Skyra, Siemens, Erlangen, Germany) and data were acquired with a <NUM> channel array. Images of 256x256 base resolution and <NUM> field of view were acquired. The volunteer acquisition was performed on a level of the brain close to the frontal sinus, where pronounced B0 inhomogeneity is present. The phantom acquisition was performed with a gradient applied along the x axis to generate a linearly varying frequency shift. The volunteer results were compared to the uncombined maps, while the phantom results were compared to the values provided by NIST.

The T1, T2, and B0 maps obtained from the volunteer using the above-described acquisition are shown in <FIG>. Specifically, the T1 maps are provided on the top row, T2 maps in the middle row, and B0 maps on the bottom row. Each acquisition is reflected in the four columns, with the phc-MRF maps provide din the rightmost column. While each acquisition provides maps which are suboptimal and presents banding artifacts, the combination of the four of them provides artifact-free maps through the entire FOV.

That is, from the B0 maps, one can notice the wide variation of off-resonance frequency due to the inhomogeneity induced by the tissue-air interface at the level of the sinus. Each of the single T1 maps presents artifacts in different region of the FOV, due to the different frequency response of each acquisition caused by the different RF pulses phase cycles. When combined in the reconstruction identified under the "phc-MRF" heading, nearly all banding artifacts are removed. The T2 maps of the individual acquisitions, instead, are of poor quality, due to the artifacts and the limited number of timepoints used for reconstruction. When the four acquisition are combined, though, good quality T2 maps are obtained.

The same maps obtained on the NIST phantom are shown in <FIG>. The linear gradient along the X axis generated vertical banding artifacts, which are clearly visible in the four singular scans, while the combined phc-MRF T1 and T2 maps are artifact free. The B0 phc-MRF maps shows the linear increase of frequency along the field of view. Notably that the deionized water surrounding the spheres has parameters outside the physiological range, were this MRF framework was not optimized for, thus affecting the matching accuracy especially for the B0 map. The inhomogeneity induced by the gradient caused extensive artifacts which were compensated in the combined reconstruction which provided T1 and T2 values matching with the standard values within physiological range, which are illustrated in <FIG>. That is, in <FIG>, average T1 and T2 values measured in the <NUM> spheres of the phantom from the phc-MRF reconstruction are illustrated. Good agreement between the measured values and the NIST reference values were obtained within the physiological range (T1<<NUM> and T2< <NUM>).

The results demonstrate that the MRF framework allows the combination of multiple different phase cycling to reduce the banding artifacts in a bSSFP-based acquisition. It is noted that each acquisition uses multiple timepoints that, collectively, may reach the total number of timepoints using in a standard acquisition originally (e.g., <NUM> timepoints per acquisition across multiple acquisitions versus <NUM> timepoints in a traditional acquisition), such that the total scan time efficiency is minimally affected. It is also contemplated that the multiple (e.g., four) acquisitions may be merged in a single acquisition.

Referring particularly now to <FIG>, an example of an MRI system <NUM> that can implement the methods described here is illustrated. The MRI system <NUM> includes an operator workstation <NUM> that may include a display <NUM>, one or more input devices <NUM> (e.g., a keyboard, a mouse), and a processor <NUM>. The processor <NUM> may include a commercially available programmable machine running a commercially available operating system. The operator workstation <NUM> provides an operator interface that facilitates entering scan parameters into the MRI system <NUM>. The operator workstation <NUM> may be coupled to different servers, including, for example, a pulse sequence server <NUM>, a data acquisition server <NUM>, a data processing server <NUM>, and a data store server <NUM>. The operator workstation <NUM> and the servers <NUM>, <NUM>, <NUM>, and <NUM> may be connected via a communication system <NUM>, which may include wired or wireless network connections.

The pulse sequence server <NUM> functions in response to instructions provided by the operator workstation <NUM> to operate a gradient system <NUM> and a radiofrequency ("RF") system <NUM>. Gradient waveforms for performing a prescribed scan are produced and applied to the gradient system <NUM>, which then excites gradient coils in an assembly <NUM> to produce the magnetic field gradients Gx, Gy, and Gz that are used for spatially encoding magnetic resonance signals. The gradient coil assembly <NUM> forms part of a magnet assembly <NUM> that includes a polarizing magnet <NUM> and a whole-body RF coil <NUM>.

RF waveforms are applied by the RF system <NUM> to the RF coil <NUM>, or a separate local coil to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil <NUM>, or a separate local coil, are received by the RF system <NUM>. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server <NUM>. The RF system <NUM> includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server <NUM> to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the whole-body RF coil <NUM> or to one or more local coils or coil arrays.

The RF system <NUM> also includes one or more RF receiver channels. An RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil <NUM> to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at a sampled point by the square root of the sum of the squares of the I and Q components: <MAT>
and the phase of the received magnetic resonance signal may also be determined according to the following relationship: <MAT>.

The pulse sequence server <NUM> may receive patient data from a physiological acquisition controller <NUM>. By way of example, the physiological acquisition controller <NUM> may receive signals from a number of different sensors connected to the patient, including electrocardiograph ("ECG") signals from electrodes, or respiratory signals from a respiratory bellows or other respiratory monitoring devices. These signals may be used by the pulse sequence server <NUM> to synchronize, or "gate," the performance of the scan with the subject's heart beat or respiration.

The pulse sequence server <NUM> may also connect to a scan room interface circuit <NUM> that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit <NUM>, a patient positioning system <NUM> can receive commands to move the patient to desired positions during the scan.

The digitized magnetic resonance signal samples produced by the RF system <NUM> are received by the data acquisition server <NUM>. The data acquisition server <NUM> operates in response to instructions downloaded from the operator workstation <NUM> to receive the real-time magnetic resonance data and provide buffer storage, so that data is not lost by data overrun. In some scans, the data acquisition server <NUM> passes the acquired magnetic resonance data to the data processor server <NUM>. In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server <NUM> may be programmed to produce such information and convey it to the pulse sequence server <NUM>. For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server <NUM>. As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system <NUM> or the gradient system <NUM>, or to control the view order in which k-space is sampled. In still another example, the data acquisition server <NUM> may also process magnetic resonance signals used to detect the arrival of a contrast agent in a magnetic resonance angiography ("MRA") scan. For example, the data acquisition server <NUM> may acquire magnetic resonance data and processes it in real-time to produce information that is used to control the scan.

The data processing server <NUM> receives magnetic resonance data from the data acquisition server <NUM> and processes the magnetic resonance data in accordance with instructions provided by the operator workstation <NUM>. Such processing may include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., iterative or backprojection reconstruction algorithms), applying filters to raw k-space data or to reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images.

Images reconstructed by the data processing server <NUM> are conveyed back to the operator workstation <NUM> for storage. Real-time images may be stored in a data base memory cache, from which they may be output to operator display <NUM> or a display <NUM>. Batch mode images or selected real time images may be stored in a host database on disc storage <NUM>. When such images have been reconstructed and transferred to storage, the data processing server <NUM> may notify the data store server <NUM> on the operator workstation <NUM>. The operator workstation <NUM> may be used by an operator to archive the images, produce films, or send the images via a network to other facilities.

The MRI system <NUM> may also include one or more networked workstations <NUM>. For example, a networked workstation <NUM> may include a display <NUM>, one or more input devices <NUM> (e.g., a keyboard, a mouse), and a processor <NUM>. The networked workstation <NUM> may be located within the same facility as the operator workstation <NUM>, or in a different facility, such as a different healthcare institution or clinic.

The networked workstation <NUM> may gain remote access to the data processing server <NUM> or data store server <NUM> via the communication system <NUM>. Accordingly, multiple networked workstations <NUM> may have access to the data processing server <NUM> and the data store server <NUM>. In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing server <NUM> or the data store server <NUM> and the networked workstations <NUM>, such that the data or images may be remotely processed by a networked workstation <NUM>.

The systems and methods described herein can be particularly useful in areas where the wide B0 inhomogeneity is present along the extended field of view, which makes the collection of bSSFP based images challenging due to the persistent presence of banding artifacts. The phc-MRF technique described herein is capable of compensating for banding artifacts. This a fundamental step towards an MRF acquisition suitable for all body applications where a bSSFP based approach provides high SNR and ability to map tissue parameters and static field inhomogeneity.

Claim 1:
A method for performing magnetic resonance fingerprinting, MRF, comprising:
performing phase-cycled balanced steady-state free precession, bSSFP, pulse sequences multiple times while cycling through radio frequency, RF, phase patterns of the phase-cycled bSSFP pulse sequences that differ across the multiple times to acquire multiple MRF datasets from a region of interest, ROI, in a subject, each MRF dataset formed by signal evolutions acquired by performing one of the phase-cycled bSSFP pulse sequences;
comparing the multiple MRF datasets with at least one dictionary of signal models, or templates, that have been generated for acquisition parameters of the phase-cycled bSSFP pulse sequences by performing a matching or pattern recognition technique for each voxel of the multiple MRF datasets to determine at least one matched value for at least one tissue property for each voxel for each of the multiple MRF datasets;
producing an aggregated dataset of the at least one tissue property by aggregating the matched values of the at least one tissue property from the voxels of all of the multiple MRF datasets; and
producing at least one map of the at least one tissue property using the aggregated dataset of the at least one tissue property.