CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of the filing date of European patent application no. EP 18184274.1, filed on Jul. 18, 2018, the contents of which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to magnetic resonance imaging (MRI) and, in particular, to techniques for generating setting parameters for an MRI sequence to be performed.

BACKGROUND

Medical imaging plays an important role in the diagnosis and treatment of diseases. One of the best known and established medical imaging procedures is magnetic resonance imaging (MRI). MRI uses setting parameters for an MRI sequence to be performed, and the present techniques to do so still have various drawbacks.

SUMMARY

Magnetic resonance imaging is typically used in radiology in order to form images of the anatomy and/or physiological processes in both healthy and diseased bodies. MRI apparatuses (also called “MRI data acquisition scanners,” “MRI scanners,” or simply “scanners”) use strong magnetic fields, for example stronger than 1 tesla (1 T), electric field gradients, and radio-frequency waves to generate images of body parts.

Magnetic resonance imaging images are usually generated in one or more MRI sequences. An MRI sequence consists of at least one, normally more, radio-frequency pulses (“RF pulses”) in accordance with setting parameters of the MRI apparatus.

Put simply, during an MRI procedure, a body is introduced into a static magnetic field generated by a magnet in the MRI apparatus. For this, this body is usually introduced into a tunnel of the MRI apparatus. In response to the strong magnetic field, the spins of the hydrogen nuclei are aligned in the body parallel to the magnetic field, wherein it is optionally possible for additional preliminaries to be performed in order to prepare for the magnetization of the hydrogen nuclei.

After this, the MRI apparatus emits a radio-frequency pulse. If this radio-frequency pulse is emitted at the Larmor frequency it is resonant: phase coherence is created in the precession of the hydrogen nuclei spins. The duration of the radio-frequency pulse is selected such that the spin magnetization of the hydrogen nuclei in the body is aligned perpendicular to the applied magnetic field. In a receiving coil of the MRI apparatus arranged in the vicinity of the body, an electric current is induced by the hydrogen nuclei spins that are still rotating (Larmor precession); this is also called a magnetic resonance signal (MR signal).

The MR signal fades over time due to two relaxation processes. The loss of coherence of the spin system attenuates the MR signal with a time constant, which is usually called the “transverse relaxation time” and designated “T2”. At the same time, the magnetization vector gradually relaxes back to its equilibrium alignment parallel to the magnetic field. This takes place with a time constant known as the “spin-lattice relaxation time” or “longitudinal relaxation time” and is designated “T1.”

Spatial resolution of the MR signal is generated by the use of a magnetic field gradient (i.e. a gradient of the magnetic field) that causes the hydrogen nuclei spins to precess at different locations (usually in different layers of the body) at a slightly different speed. This can be used in order to localize or trigger signal origins within the body.

The contrast in MRI images is usually due to the fact that different components of the body (in particular bone, tissue, etc.) generally have different relaxation times T1 and T2. Since this is particularly the case with soft tissue types, MRI images usually provide good resolution for such soft tissue. Hence, the selection of optimal MRI sequences (or, in other words, the performance of the MRI process with optimally-selected setting parameters) can maximize the contrast for tissue of interest or of a body part of interest (for example, a specific organ). This can improve the chances of successful diagnosis and treatment.

Generally, a plurality of MRI sequences is required in order to depict tissue adequately. A combination of MRI sequences (frequently together with specific locations and/or alignments or sectional planes) is frequently designated by an MRI protocol. A user of the MRI apparatus (frequently a radiologist) adapts the MRI sequences in order to provide the best possible imaging for a specific medical question.

An MRI sequence is usually described by a plurality of setting parameters comprising, partially as a function of the specific MRI sequence performed, for example: an echo time (TE), a repetition time (TR), a flip angle, a bandwidth of the radio-frequency pulse, a turbo factor (also called pulse-train length), a gradient during the radio-frequency pulse excitation (“spoiling gradient” or “crusher gradient”), etc.

Two decisive setting parameters are the repetition time (usually abbreviated to TR) and the echo time (usually abbreviated to TE), which are normally measured and reported in milliseconds (as, for example, in the code snippets reproduced below).

The echo time, TE, indicates the duration from the center of the radio-frequency pulse to the center of the echo, i.e. of the induced signal. For MRI sequences with a plurality of echoes between each radio-frequency pulse, it is possible for a plurality of echo times to be defined; these are usually designated TE1, TE2, TE3, etc.

The repetition time, TR, is the duration between mutually-corresponding sequential points of a recurrent series of radio-frequency pulses (or echoes).

The most frequently used MRI sequences include the so-called T1-weighted MRI sequence (“T1-weighting”), the so-called T2-weighted MRI sequence (“T2-weighting”), and the proton-density-weighted MRI sequence (“PD-weighting”).

With T1-weighting, the repetition time TR is usually short compared to the longest longitudinal relaxation time T1 of the tissue of interest, and the echo time TE is short in relation to the transverse relaxation time T2 of the tissue of interest. This reduces T2 contributions to the image contrast.

In the case of T2-weighting, the repetition time TR is usually long compared to the longitudinal relaxation time T1 of the tissue (in order to reduce T1 contributions), and the echo time TE is selected between the longest and the shortest transverse relaxation time T2 of the tissue of interest.

It is evident that a specific MRI sequence can be T1-weighted for a specific type of tissue and have another type of weighting for another type of tissue.

A proton-density-weighted MRI sequence can be achieved by using a long repetition time TR and a short echo time TE.

For a more detailed discussion of the technical background of MRI methods, reference is made to Brown (see reference [1]).

Specific MRI protocols exist for many diseases and/or body parts. To date, the creation of optimal MRI sequences and MRI protocols has required many years of practice and experience, in particular if contrast has to be optimized for a specific medical question (for example the presence of a tumor).

Since adequately trained staff are not always available, it is desirable to reduce the complexity of the operation of MRI facilities and the user interface of an MRI apparatus.

Known from the field of machine learning and artificial intelligence are generative adversarial nets (GAN), such as one discussed in Goodfellow (see reference [2]).

Goodfellow describes a generative adversarial net in which two networks are trained substantially simultaneously as adversaries: a generative part, i.e. a generative artificial neural network, and a discriminative part, i.e. a discriminative artificial neural network.

The generative artificial neural network is trained to generate its own data distribution based on an original data distribution (training data). The discriminative artificial neural network is trained to identify whether a specific dataset belongs to the original data distribution or to the data distribution generated by the generative artificial neural network. The ideal situation is achieved when the discriminative artificial neural network is no longer able to discriminate between the datasets of the original data distribution and those of the generated data distribution.

One example of generative adversarial nets includes conditional generative adversarial nets, which are specifically described in particular in Mirza (see reference [3]).

In conditional generative adversarial nets, a conditioning element (normally a vector, i.e. a “condition vector”) is introduced both with the generative artificial neural network and with the discriminative artificial neural network, as a result of which it is possible to generate (or discriminate between) datasets adapted to different situations or target regions (which are modeled by the conditions). In this context, the setting of such a condition is mainly designated “conditioning,” and the “conditionality” of the network means that it is able to observe such conditions and to generate its task under the condition(s) that may have been set.

A further known variant of generative adversarial nets is a least squares generative adversarial net, which is described, for example, in Mao (see reference [4]).

With this in mind, it is an object of the present disclosure to provide improved systems and methods for generating setting parameters for an MRI sequence to be performed by an MRI apparatus. This object is achieved by the embodiments of the present disclosure as further discussed herein.

