Patent Description:
In magnetic resonance imaging and spectroscopy, the homogeneity of the B<NUM> magnetic field is of crucial importance. However, as soon as an object, such as a part of a subject's body, is introduced into the main magnetic field, the field lines are distorted. This distortion, also referred to as demagnetization field, is determined by the magnetic susceptibility distribution within the object. Especially the interface between air and tissue, as is found for example in the human head because of the sinuses, leads to particularly high magnetic field inhomogeneities. Although every magnetic resonance (MR) scanner has an active shimming system using coils with adjustable currents, usually the demagnetization field can be compensated only partially by shimming.

If the magnetic susceptibility distribution Chi(r) in the body part to be examined is known, as well as its positional orientation within the static magnetic field B<NUM>, then the effect of the demagnetization field can be corrected for, as shown for example in reference <NUM> (all references are listed at the end of the description).

Also, shimming generally does not allow for any dynamic changes of the orientation of the susceptibility distribution Chi(r) because of any subject motion. These effects may not be negligible and may lead to significant image degradation. In particular, for MR imaging sequences sensitive to off resonances, for example, gradient-echo echo planar imaging (GRE EPI) (see reference <NUM>) or multi-TE GRE acquisitions as used for susceptibility weighted imaging (see reference <NUM>), the effect of the demagnetization field needs to be taken into account.

The following prior art documents can be seen as background for the invention:.

However, at present no fast and sufficiently detailed methods for obtaining a subject-specific magnetic susceptibility distribution map are known.

It is therefore an object of the invention to provide a method for generating a subject-specific map of a magnetic susceptibility of a region of interest within the subject.

These objects are fulfilled by the method according to claim <NUM>, a computer program according to claim <NUM> and a system according to claim <NUM>.

According to one aspect, the invention is directed to a computer implemented method for generating a subject-specific map of the magnetic susceptibility of a region of interest within the subject, the method comprising the steps of:.

The invention is based on the insight that the different tissue types in the human body have different susceptibility values, which however can be estimated in advance, as they are not subject-specific, or only to a small degree. Therefore, if an image is segmented into the various tissue types and typical susceptibility values for each tissue type are assigned to the segmented regions, a susceptibility map can be obtained. However, it has so far not been possible to automatically segment MR images in this way, because small and thin structures (such as the bones constituting the sinuses) as well as air and bone do not generate a large or any MR signal and therefore appear exactly the same in MR images, namely having very low pixel values within the noise range, which are generally depicted as black. Therefore, if an MR image is segmented into different tissue types, it is difficult to differentiate between small structures, bone and air, especially in the head. On the other hand, also attenuation correction maps (AC-maps) consist of only a limited number of tissue types, namely lungs, soft tissue, fat and bones. AC-maps are necessary for a correct reconstruction of PET volumes. Several methods have been proposed to estimate AC-maps from MR images, e.g., as described in reference <NUM>, where an ultrashort echo time (UTE) sequence is used to derive the AC-map. Such MR images can be acquired simultaneously to the PET experiments with a PET-MR, instead of AC-maps derived from a separately acquired CT volume. However, these techniques, which provide a distribution of air cavities, bone and tissue, can still wrongly assign spatial positions to tissue instead of bone, or to tissue instead of air cavities, especially in the head.

The invention therefore uses an MR input image of the region of interest and applies an algorithm thereto, wherein an output image having improved contrast between bone and air is generated. According to the invention, the algorithm comprises at least one trained artificial neural network (NN). The neural network may be a convolutional neural network, for example having <NUM> to <NUM> convolutional layers. The convolutional neural network may be based on an encoder-decoder structure with symmetric concatenations between corresponding states, for example of the type as described in reference <NUM>. For example, each layer in the neural network may comprise one or more 3x3x3 kernels, batch normalisation, an activation function, for example a rectified linear unit, and a dropout layer in the encoding part, and vice versa in the decoding part. The NN may have been trained by a machine learning algorithm, for example by a back-propagation method. The NN may have been trained using real images, for example if the output image is a pseudo CT image, the NN may have been trained using MR images of a body part as input training data, and real CT images of the same body part as output training data.

The MR input image is preferably a three-dimensional (3D) image, although a stack of two-dimensional (2D) images may also be used to estimate the magnetic susceptibility map. The MR input image may also comprise several images of the region of interest, in particular several images which have been acquired in one imaging sequence, for example in-phase and opposed-phase images obtained by chemical shift imaging. The method of the invention may also be applied to a time series of MR input images, for example a series of EPI images which are acquired during a fMRI (functional magnetic resonance imaging) experiment, in which a series of images, such as EPI images, is acquired in order to observe changes in the subject's brain activity over time, in particular the change in BOLD-contrast (Blood Oxygen Level Dependent). In this case, the method of the invention may be used to obtain a time series of susceptibility maps and thus enable correction of the shimming or imaging parameters during the fMRI experiment, to ensure the best image quality.

In an embodiment, the neural network is trained on the specific body part examined, for example the human head, prostrate, kidney, abdomen, lung, heart of specific organs or limbs. The algorithm comprising the at least one neural network may therefore be chosen according to the body part in which the region of interest is situated. By this smart use of artificial intelligence, the invention can make use of (preferably convolutional) neural networks to improve the contrast between bone and air, which otherwise does not allow suitable segmentation of the images.

The output image is then segmented into air and at least two types of tissue, namely at least bone and at least one type of soft tissue (or soft tissue in general), to obtain a segmented image. In the simplest form, the segmentation differentiates between bone, air and soft tissue, wherein soft tissue comprises all types of soft tissue, e.g. fat, muscle, white matter, grey matter, skin, liver, etc.. In more sophisticated embodiments, the soft tissue may also be differentiated into several different types, for example fat and other tissue. The segmentation step may use thresholding, 3D adaptive thresholding, model-based segmentation, atlas-based segmentation, histogram-based methods, edge detection, region-growing, or any other segmentation methods. The central assumption of model-based approaches is that the structures of interest have a tendency towards a particular shape. Therefore, one can seek a probabilistic model that characterizes the shape and its variation. When segmenting an image, constraints can be imposed using this model as a prior, as described in reference <NUM>.

In the next step, predetermined values for the magnetic susceptibility are assigned to each pixel or voxel in the segmented image, according to its type of material (bone, soft tissue or air), to obtain the subject-specific map. The values used for the magnetic susceptibility can be for example -<NUM> ppm for soft tissue, - <NUM> ppm for bone and <NUM> ppm for air, as disclosed in reference <NUM> or <NUM>. However, other predetermined values may also be used.

It has been shown that even this simplified map yields very good results in calculating the corresponding demagnetization field, since it is entirely subject-specific, without making use of any atlases or other preconceived ideas about the patient's anatomy. The only predetermined values are the susceptibility values of the different tissue types, which however are known and do not vary significantly from patient to patient.

The method of the invention is preferably performed by a computer, which may be any processing unit such as a CPU and/or a GPU of a server or PC, or preferably a processing unit within an MR scanner. It may be performed on any computing device including a cloud computer, a laptop, tablet computer or other mobile device. The method is preferably carried out during an MR examination of a subject, i.e., while the subject is in the sensitive region of the MR scanner. This is because the generated tissue property map may be used to improve the quality of further MR acquisitions. The MR scanner may also be a PET/MR scanner or other hybrid device. The subject may be a human or animal, in particular a patient during an MRI examination.

According to the invention, the output image of the neural network is a synthetic CT (Computed Tomography) image. Such a method for transforming a low-resolution MR input image into a high-resolution MR image using a CNN based network, and transforming the high-resolution MR image to a pseudo-CT image has been proposed in <CIT> (reference <NUM>). In this method, two convolutional neural networks are used one after the other, and the output image is again suitable for segmenting into the different tissue types, because CT images distinguish very well between bone and air, so a segmentation into soft tissue, bone and air does not provide any problems. Also, CT images have a high resolution, and a CNN may be trained to achieve such high resolution, even from lower resolution MR input images.

The MR input image of the region of interest may be acquired by any possible MR sequence, for example spin echo, fast spin echo, gradient echo, echo planar imaging (EPI) etc. The MR input image may be three-dimensional (3D) or two-dimensional (2D), wherein the method may also be applied to a stack of 2D images. The MR input image may also comprise several images, such as in-phase and opposed-phase, or a time series of images. According to an embodiment, the MR input image has been acquired using a fast or a standard MR imaging protocol, preferably Dixon, MPRAGE, MP2RAGE, MP3RAGE or sequences with ultrashort echo time (UTE). The MR input image can be an image that is acquired also for diagnostic purposes during a patient examination. It is advantageous if the MR input image is acquired within less than <NUM> minutes, preferably <NUM> seconds to <NUM> minutes, and the above-mentioned imaging methods allow an acquisition of a 3D image volume in about <NUM>-<NUM> seconds, preferably <NUM>-<NUM> seconds. A high-resolution 3D Dixon VIBE protocol may be used with CAIPIRINHA acceleration. As an example, such a protocol may take <NUM> seconds. The acquisition may be made even shorter if an acquisition with Compressed Sensing is used, and/or the resolution is reduced. A further advantage of UTE, MPRAGE and Dixon sequences is that they are available for MR scanners at all field strengths ranging from <NUM> T to <NUM> T, for example for <NUM> T, <NUM> T and <NUM> T MR scanners. A further advantage is that MR input images acquired using UTE and Dixon at <NUM> T have a good suitability to be converted for example into AC-maps using convolutional neural networks, as demonstrated in references <NUM> and <NUM>.

According to an embodiment, the region of interest is at least partially within the head of the subject. As mentioned above, the human head poses particular challenges to MR protocols which are sensitive to be B<NUM> field inhomogeneities, because of the many air cavities in the head. Therefore, the use of an accurate magnetic susceptibility map can yield significant improvements in image quality. In other embodiments, the region of interest may at least partially include the human prostate, the kidney, the heart, or indeed any other organ or body part.

According to an embodiment, the method comprises the further step of estimating a map of the demagnetization field caused by the subject when placed in a B<NUM> magnetic field, wherein the estimation is done based on the magnetic susceptibility map. The demagnetization field Bd is the magnetic field generated by an object with a given magnetisation and is related to the susceptibility distribution of the object and the static magnetic field strength. It can be estimated by the convolution of the magnetic susceptibility distribution/map with the magnetic dipole and the Lorentz sphere correction, as described in references <NUM> and <NUM>. Similar methods may be used to calculate a map of the demagnetization field from the susceptibility map and the B<NUM> magnetic field, as described for example in reference <NUM>. This corresponds to a calculation of the magnetic field inhomogeneity induced by the presence of the part of the subject in the B<NUM> field. The demagnetization field map depends also on the exact orientation of the susceptibility map within the field of view.

The proposed invention can make use of convolutional neural networks (CNN's) and the possibility to segment the tissue types of interest, as described in references <NUM> and <NUM>, and assigning them their corresponding susceptibility values. This approach is then much simpler and quicker than to perform the method proposed in reference <NUM>. It only requires moderate computational power in a reasonable amount of time, wherein a modern GPU is already sufficient. The proposed approach may avoid using an atlas, it only relies on MR measurements from one or several acquisitions.

In the following, novel uses of a thus obtained magnetic susceptibility map are described. The different uses described herein may be applied cumulatively in one MR imaging session.

Generally, according to a further aspect, the use of a magnetic susceptibility map generated by the method described above to improve the quality of further MR acquisition from the region of interest can be contemplated. Preferably, these further MR acquisitions take place in the same MR examination session, although the magnetic field map may also be used in later MR examination sessions, once it has been registered with the position of the respective body part during that later session.

The magnetic suitability map can also be used to improve the homogeneity of a B<NUM> magnetic field of an MR scanner in which the subject is placed for MR acquisition, by estimating a map of the demagnetization field caused by the subject when placed in the B<NUM> magnetic field from the magnetic susceptibility map, and adjusting the shim coefficients of the MR scanner to compensate for the demagnetization field. Thereby, the standard shimming using the shim coils is improved, because the demagnetising field map contains information also in regions where no MR signal is available and has potentially a higher resolution than the field maps which are usually acquired in the context of shimming. Thus, more voxels can be used for fitting the shim coefficients.

Furthermore, the quality of MR acquisition from the region of interest may be improved by tracking any motion of the region of interest during the acquisition of an MR input image, applying the tracked motion to the magnetic susceptibility map to obtain a dynamic magnetic susceptibility map, estimating a dynamic demagnetization field map from the dynamic magnetic susceptibility map, and adjusting the shim coefficients in real time to compensate for the dynamic demagnetization field.

Subject motion is a major problem for magnetic resonance imaging. Prospective motion correction methods use an external device to track the subject's position, for example a tracking target comprising reflective markers and which are being watched by tracking cameras, as described in reference <NUM>. Such a tracking system is capable of reporting positions and orientations of several tracking targets attached to the patient and thereby record or track patient motion in real time. This information may be used to update the position of the imaging volume prior to every excitation of the spin system. Other methods for tracking the motion of the region of interest include image-based tracking, such as described in reference <NUM>. According to this method, a rigid body estimation of head movements is obtained by image-based motion detection. Both of these methods allow to follow the position and orientation of the region of interest during the acquisition of an MR image. This motion, which may preferably be a rigid motion including three translational and three rotational degrees of freedom, but which may also have more degrees of freedom, may be applied to the magnetic susceptibility map during the MR acquisition to obtain a dynamic magnetic susceptibility map. In this context, "dynamic" means that the map is available for different time points, i.e. it is a time sequence of magnetic susceptibility maps which is generated for the time span of the MR input image acquisition, during which the motion is tracked. For example, the tracked translational and rotational movements are imposed on the susceptibility map at each time point during the MR acquisition, with a pre-determined temporal resolution. Preferably in real time, e.g. at the pre-determined temporal resolution, a dynamic demagnetization field map is estimated from the dynamic magnetic susceptibility map, by the methods described herein. This map of the demagnetization field therefore takes into account any patient motion and is preferably generated in real time during the MR input image acquisition. According to this aspect, it may be used to adjust the shim coefficients in real time to compensate for the respective dynamic demagnetization field. Thereby, an excellent image quality is obtained even for moving subjects.

Furthermore, the magnetic susceptibility map may be used for motion correction of an MR image acquired with an imaging sequence from the region of interest by tracking any motion of the region of interest during the acquisition of an MR image; applying the tracked motion to the magnetic susceptibility map to obtain a dynamic magnetic susceptibility map; estimating a dynamic demagnetization field map from the dynamic magnetic susceptibility map; and adjusting the imaging sequence in real time to compensate for the dynamic demagnetization field. Again, the motion tracking may be performed either using image-based tracking or using an external tracking device, for example an optical tracking device. The further steps including generating a dynamic magnetic susceptibility map and therefore a dynamic demagnetization field may be performed as described above. In the final step, the imaging sequence is adjusted in real time to compensate for the dynamic magnetisation field. In other words, the dynamic demagnetization field map can be used for dynamic geometric distortion correction, for example for EPI and other sequences sensitive to the inhomogeneous field and its changes, using rotations and translations for body regions which are moving during the MR exam, and which information may be made available by the herein described methods. The encountered motion during the acquisition can be applied to the susceptibility distribution. More complex nonrigid deformations for the body region of interest may also be tracked. Also in this case, the shim magnetic field may be taken into account.

Furthermore, a dynamic demagnetization field map may be obtained as described herein above, and may be used for retrospective motion correction of an acquired MR image, which may be the MR input image, but may also be an MR image acquired afterwards during the same patient examination from the same or overlapping field of view. A suitable method is for example described in reference <NUM>, <CIT>. This patent also uses a computed field inhomogeneity, which however is calculated from a susceptibility model which is less detailed than the susceptibility map proposed herein. The dynamic magnetisation field may be in particular used for correcting image distortions and/or intensity modulations. Thereby, the image quality of MR images of moving subjects may be improved. Using this method, even EPI imaging may be used, which is very sensitive to B<NUM> distortions. If a time series of MR images is acquired, the dynamic demagnetization field map may be used to correct the geometric image distortions at each time point during the acquisition, in particular for each MR image in the time series.

Furthermore, the magnetic susceptibility map may be used to improve the quality of dynamic chemical exchange saturation transfer (CEST) image acquisition of the region of interest by tracking any motion of the region of interest, applying the tracked motion to the magnetic susceptibility map, estimating a dynamic demagnetization field map and correcting the dynamic CEST images by using the dynamic demagnetization field map. Since CEST imaging is based on the continuous transfer of excited <NUM>H protons from one chemical species to the surrounding water, leading to a build-up of saturation in water, it requires frequency selective excitation of protons and therefore a high homogeneity of the B<NUM> magnetic field. A knowledge of the demagnetization field map can be used to shift back the z-spectrum. In other words, knowledge of the off-resonance frequency shift per voxel is required so that all the measurements can be shifted back to their designated spectral positions.

Furthermore, the magnetic susceptibility map can also be used to improve the quality of dynamic CEST image acquisition of the region of interest by tracking any motion of the region of interest during the acquisition; applying the tracked motion to the magnetic susceptibility map to obtain a dynamic magnetic susceptibility map; estimating a dynamic demagnetization field from the dynamic magnetic susceptibility map; and correcting the dynamic Chemical Exchange Saturation Transfer images by using the dynamic demagnetization field map.

In other words, the inventive susceptibility map also allows to improve the quality of dynamic CEST images. Z-spectra estimated voxel-wise from CEST acquisitions, typically gradient echo acquisitions, need to be corrected for B<NUM> inhomogeneity at the corresponding voxel. When dynamic CEST experiments are performed, as for example in glucose CEST described in references <NUM> and <NUM>, then it is necessary to take the time variations of the magnetic inhomogeneity field into account, which can be dependent of subject motion. Again, an accurate subject-specific susceptibility model which can be varied with the subject's position will improve the correction for the field inhomogeneity.

Furthermore, the use of a magnetic susceptibility map in the calculation of radio-frequency (RF) excitation pulses of a multi-transmit system of an MR scanner for a given excitation trajectory for parallel excitation can be contemplated. In parallel excitation, a transmit system having several RF antennas (i.e. a multi-transmit system), for example an array or phased array of RF coils, is used to produce a complex RF pulse. For the same RF pulses, gradient fields are applied during the RF excitation, allowing specific desired trajectories of the magnetisation in transmit k-space. To realize these trajectories, an accurate knowledge of the magnetic field is important, see reference <NUM>. Therefore, a subject-specific demagnetization field can be used to improve the calculation of RF excitation pulses.

Furthermore, the subject-specific demagnetization field map can be used instead of a standard field map in advanced image reconstruction methods. Thus, after the method for generating a subject-specific map of a tissue property has been carried out, a map of the demagnetization field is calculated from the magnetic susceptibility map. Optionally, a further MR image is then acquired while the subject is placed with the MR Scanner, and the demagnetization field is used in the reconstruction of the acquired image. Preferably, image reconstruction involves advanced image reconstruction methods, such as iterative image reconstruction using an MR forward model, as described for example in reference <NUM> or <NUM>. The image reconstruction method of ref. <NUM> includes a retrospective motion correction by including motion operations into the MR forward model, wherein the MR forward model is essentially a mathematical description (e.g. an encoding matrix) of the operation by which the MR image is generated from the proton distribution. In case of multi-channel k-space data acquired in a multi-shot imaging protocol, the MR forward model may be described by an encoding matrix, which includes the effects of rigid-body motion for each shot, Fourier encoding, and optionally subsampling and coil sensitivities of the multi-channel coil array. The solving of such an MR forward model usually requires several optimization iterations. The SENSE (SENSitivity Encoding) forward model was introduced by reference <NUM>. An MR forward model used in advanced, in particular iterative, image reconstruction may include the effect of the demagnetization field calculated from a susceptibility map, which has been generated according to the inventive method. This reduces the geometric distortions of the reconstructed image.

Furthermore, the demagnetization field may be used in the reconstruction of MR data acquired using compressed sensing (e.g. described in reference <NUM>) to improve the reconstructed images with regard to geometric distortion, as described in reference <NUM>. Compressed sensing typically involves randomized subsampling of k-space.

Furthermore, the subject-specific demagnetization field map can be used in quantitative susceptibility mapping, in particular in one or several steps of the post-processing pipeline for quantitative susceptibility mapping (QSM), which are summarized in references [<NUM>] and [<NUM>]. Typically (but not limited to), a 3D multi-TE GRE acquisition is used for the acquisition and no signal is available in regions consisting of bones. The QSM pipeline can be separated in several steps. One of the steps comprises removing the background field of the frequency map obtained from the acquired data over a masked region of the brain or the body part in question (the masked region is defined where MR signal is sufficient, e.g. above noise level) to obtain a background field corrected frequency map. The subject-specific demagnetization field map in this step can lead to an improved background field corrected frequency map, because magnetic field information outside of the brain or the body part is available. In a final step of the QSM post-processing pipeline, the susceptibility distribution is estimated from the masked background field corrected frequency map which can be achieved numerically by solving the ill-posed problem of deconvoluting the masked background field corrected frequency map with the unit magnetic dipole response. The additional knowledge of having a subject-specific susceptibility distribution helps to improve the condition of the ill-posed problem by, for example, generating a simulated demagnetization field in at least two additional different orientations of the susceptibility distribution to the acquired 3D multi-TE GRE image, similar as in reference [<NUM>]. Another possibility is to further constrain the inverse ill-posed problem by including the susceptibility distribution in the numerical algorithm.

The invention is also directed to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method for generating a subject-specific map of a magnetic susceptibility. The computer may be any processing unit, cloud computer or server, also a processing unit on a mobile device such as a laptop, smartphone or tablet.

The computer program can be stored on a non-transient digital storage medium. Such digital storage medium may be a hard disk, optical disk, magnetic storage medium, SD card, USB stick, CD-ROM or any possible portable or nonportable data storage medium.

The invention is also directed to a system for generating a subject-specific map of a magnetic susceptibility of a region of interest within a subject, the system comprising: a first interface, configured for receiving an MR image of the region of interest; a computational unit, configured for.

a second interface, configured for providing the map of the magnetic susceptibility.

The system may be part of an MR scanner, and particular part of the computer controlling the MR scanner and the image acquisition. However, the system may also be a remote computer to which the MR image is transferred.

The invention will now be illustrated by means of embodiments with reference to the attached drawings. In the drawings.

Similar features are designated with the same reference numbers in the following.

<FIG> illustrates a method outside the scope of the present invention for generating a subject-specific map of a tissue property, in this instant the magnetic susceptibility of a region of interest. As a starting point, an MR image <NUM> of the region of interest is fed into a trained neural network <NUM>, wherein the MR image <NUM> may for example be acquired using a localiser-like sequence using a fast imaging protocol. The neural network <NUM> converts this MR image <NUM> into a synthetic AC-map <NUM>. The AC-map <NUM> may have the same or higher resolution than the input MR image and generally has better contrast between bone and air. In a next step <NUM>, the AC-map <NUM> is segmented into at least two types of tissue, namely at least bone and a type of soft tissue, and air. For example, this step may result into a segmented image in which every voxel is assigned to one of the above tissue types or air. In the next step <NUM>, predetermined values for the tissue property, in particular the magnetic susceptibility, is assigned to each type of tissue and air in the segmented image. These predetermined values are for example known from reference <NUM> and may be stored in a computer or database <NUM>. This step already results in the subject-specific susceptibility map <NUM>. For the purposes of estimating the magnetic field inhomogeneity caused by the subject with this susceptibility map being placed in the B<NUM> field, the segmented susceptibility image <NUM> is more than sufficient. It is more accurate to calculate the field distortions from a magnetic susceptibility map, than to measure them directly, and has the advantage over other MR-based methods that values are available in areas from which no MR signal can be obtained, such as from regions of air inside and outside the body. The susceptibility map <NUM> will be the more accurate, the more tissue types are being segmented.

<FIG> shows a method according to the invention, in which the MR image <NUM> is fed into a different type of trained neural network <NUM>, which has as output image a synthetic CT image <NUM>. Again, the synthetic CT image <NUM> may have high resolution and particular good contrast between bone and air, better than the input MR image. In the next step <NUM>, the synthetic CT image <NUM> is segmented into different tissue types and air. In the next step <NUM>, again predetermined values for the magnetic susceptibility are assigned to each voxel and thereby the magnetic susceptibility map <NUM> is generated.

<FIG> illustrates the trained neural network according to an embodiment in more detail. In this embodiment, the neural network <NUM> actually comprises two neural networks, wherein the first neural network <NUM> gives as output an MR image <NUM> of higher resolution than the MR input image <NUM>. In the next step <NUM> performed by another neural network, a synthetic CT image <NUM> is generated.

<FIG> illustrates a trained convolutional neural network, which may be used for the conversions described herein, it may for example be the neural network <NUM>, but may also be an example for a neural network <NUM> or <NUM>. In detail, the neural network takes the MR input image <NUM> (which may include several MR images) as input layer and comprises a number of layers <NUM>, <NUM> between the input layer and the output layer to allow deep learning. According to a preferred embodiment, the convolutional neural network <NUM> comprises an encoder portion <NUM> and a decoder portion <NUM>. The encoder portion <NUM> comprises a number of layers <NUM>, for example two to six such layers. Each layer <NUM> comprises the operation of one or several convolutional kernels, for example 3x3x3 kernels for a 3D input image <NUM>, followed by an activation function, followed by a down-sampling/pooling operation, for example a MAX function. Thereby, the size of the layers is reduced going from one layer to the next in the encoder portion. Usually, several convolutions are applied, so that the layers have more and more channels from one layer <NUM> to the next. There may be one or several layers, which may also be fully connected layers, in the central portion between the encoder part <NUM> and the decoder part <NUM>. The decoder part <NUM> comprises also e.g., two to eight layers <NUM>, generally the same number of layers as the encoder portion. Each layer <NUM> also comprises a convolution kernel, an activation function and an up-sampling operation, so that the output layer may have the same dimensions as the input layer, although it may also have a higher or lower dimension. In addition, the neural network may comprise skip connections <NUM> by which the more detailed information from the encoder layers <NUM> is directly fed into the layers <NUM> of the decoder portion <NUM>. Thereby, the central layers of the CNN are skipped.

<FIG> illustrates a method outside the scope of the present invention, in which a morphological image <NUM> is used. The left side is the same as the embodiments of <FIG> and <FIG> and includes neural networks <NUM> or <NUM> generating synthetic AC-maps or CT images <NUM>, <NUM>, followed by segmenting these images in step <NUM> into at least bone, soft tissue and air. On the right side, in addition a morphological MR image <NUM> of the region of interest is obtained, either from a previous or the same session, and it is used to segment different types of soft tissue in step <NUM>, for example white matter, grey matter, skin and CFS for a head image. In step <NUM>, the soft tissue segmentation <NUM> is combined with the segmentation of bone, soft tissue and air in segmented image <NUM>, so that the improved segmented image comprises more soft tissue types. This improved segmented image is then assigned predetermined susceptibility values for each voxel in step <NUM> to yield the susceptibility map <NUM>.

<FIG> illustrate possible uses of a susceptibility map generated according to embodiments of the invention. According to <FIG>, for example, the susceptibility map <NUM> is used to estimate a map of the demagnetization field caused by the subject when placed in a B<NUM> magnetic field in step <NUM>. This demagnetization field map <NUM> is more accurate than any field map estimated by previous methods and can be used for example to adjust the shim coefficients of the MR scanner in step <NUM>. Alternatively, step <NUM> may include correcting the z-spectra obtained from CEST imaging for each voxel. Further alternatively, in step <NUM>, RF excitation pulses may be calculated with more accuracy. Last but not least, the demagnetization field map <NUM> can be used for iterative image reconstruction.

The susceptibility map <NUM> may also be used for dynamic corrections, as illustrated in <FIG>. In this embodiment, in step <NUM>, the motion of the subject is observed or tracked by known methods, for example by tracking a motion marker or tracking target which is attached to the subject close to the region of interest. This may be carried out during the execution of an MR sequence, e.g. an imaging sequence, for acquiring MR data. In the next step <NUM>, this tracked motion, which comprises for example changes in the translation and orientation of the object from one time point to the next, wherein the time resolution may correspond to the RF excitation pattern of the MR sequence, is applied to the susceptibility map <NUM> to obtain a dynamic susceptibility map <NUM>. In step <NUM>, a dynamic demagnetization field map <NUM> is estimated from the dynamic magnetic susceptibility map <NUM>. This dynamic demagnetization field <NUM> map may then be used for example to adjust the MR sequence, or to adjust the shim currents in step <NUM>. This is repeated in real time, as indicated by arrow <NUM>, until the MR image acquisition is completed and a motion corrected and/or distortion corrected MR image <NUM> is generated.

<FIG> illustrates a system <NUM> for carrying out the method according to the invention. The system <NUM> in this case is an MR scanner comprising a main magnet <NUM> and a patient bed <NUM>. A patient <NUM> is lying on the patient bed, while its head <NUM> is being imaged. The MR scanner <NUM> is connected to a computer <NUM> comprising at least a CPU <NUM> and a data storage <NUM>. The computer <NUM> may be the computer generally controlling the MR acquisition of the MR scanner <NUM> and is adapted to perform the method.

Claim 1:
A computer-implemented method for generating a subject-specific map (<NUM>) of a magnetic susceptibility of a region of interest within a subject (<NUM>), the method comprising the steps of:
- Receiving an MR image (<NUM>) of the region of interest;
- Feeding the MR image (<NUM>) into an algorithm (<NUM>, <NUM>),
wherein the algorithm (<NUM>, <NUM>) comprises at least one trained artificial neural network, wherein the output of the algorithm (<NUM>, <NUM>) is an output image having improved contrast between bone and air, wherein the output image of the algorithm (<NUM>, <NUM>) is a synthetic CT image (<NUM>), ;
- Segmenting (<NUM>) the output image into air and at least two types of tissue, namely at least bone and at least one type of soft tissue, to obtain a segmented image;
- Assigning (<NUM>) pre-determined values for the magnetic susceptibility to each category of tissue and air in the segmented image to obtain the subject-specific map of the magnetic susceptibility.