Method of visualizing MR images

In a method and a device MR images having various contrasts are scanned and then values for some or all of the parameters T1, T2 and PD related to the scanned MR images are determined. Based on the scanned MR images and the determined parameter values an initial conventional MR contrast image with some default scanner settings is generated, or alternatively, a stronger non-physical MR contrast image. The initial MR contrast image can then be manipulated by a user in response to movement of a user-controlled marker on a screen showing the contrast image such that a contrast optimized image can be obtained for a particular diagnosis in a very short time. Furthermore a quantitative image can be generated representing the amount of a single tissue type.

TECHNICAL FIELD

The present invention relates to a method and a device for visualizing magnetic resonance (MR) images.

BACKGROUND

Magnetic Resonance Imaging (MRI) can generate cross-sectional images in any plane (including oblique planes). Medical MRI most frequently relies on the relaxation properties of excited hydrogen nuclei in water and fat. When the object to be imaged is placed in a powerful, uniform magnetic field the spins of the atomic nuclei with non-integer spin numbers within the tissue all align either parallel to the magnetic field or anti-parallel. The output result of an MRI scan is an MRI contrast image or a series of MRI contrast images.

In order to understand MRI contrast, it is important to have some understanding of the time constants involved in relaxation processes that establish equilibrium following RF excitation. As the high-energy nuclei relax and realign, they emit energy at rates which are recorded to provide information about their environment. The realignment of nuclear spins with the magnetic field is termed longitudinal relaxation and the time (typically about 1 sec) required for a certain percentage of the tissue nuclei to realign is termed “Time 1” or T1. T2-weighted imaging relies upon local dephasing of spins following the application of the transverse energy pulse; the transverse relaxation time (typically <100 ms for tissue) is termed “Time 2” or T2. On the scanner console all available parameters, such as echo time TE, repetition time 0, flip angle α and the application of preparation pulses (and many more), are set to a certain value. Each specific set of parameters generates a particular signal intensity in the resulting images depending on the characteristics of the measured tissue.

Image contrast is then created by using a selection of image acquisition parameters that weights signal by T1, T2 or no relaxation time PD (“proton-density images”). Both T1-weighted and T2-weighted images as well as PD images are acquired for most medical

In contrast imaging the absolute signal intensity observed in the image has no direct meaning; it is rather the intensity difference, the contrast, between different tissues that lead to a diagnosis. The TE, TR, α and pre-pulses are chosen such that it provides the best contrast for a specific application. This implies that for each desired contrast a separate image has to be taken. This in turn will make a complete examination rather time consuming and demanding for the patient. Also, it will become costly since equipment and other resources can only be used for one patient at the time. If the known parameter settings do not provide the desired contrast, insufficient for diagnosis, it is far from straightforward to achieve an improvement.

SUMMARY

It is an object of the present invention to overcome or at least reduce some of the problems associated with existing method for visualizing data obtained in an MRI scan.

It is another object of the present invention to provide a method and a device that reduces the time required for an individual examination utilizing MRI, thereby freeing up resources.

It is another object of the present invention to visualize patient tissue in a much stronger manner than conventional imaging systems are capable of.

These object and others are obtained by a method and a device as set out in the appended claims and adapted to display synthesized Magnetic Resonance (MR) contrast images where MR contrast and/or image Signal to Noise Ratio (SNR) is automatically optimized by means of one or several specific user-interactive events.

Hence, by first scanning MR images having various contrast and then computing values for some or all of the parameters T1, T2 and PD related to the scanned MR images, an initial MR contrast image with some default scanner settings can be generated. The initial MR contrast image can then be manipulated by a user in response to movement of a user-controlled marker a screen showing the contrast image such that a contrast optimized image can be obtained for a particular diagnosis in a very short time.

In accordance with the present invention it is possible to show the user a synthesized MR image based on previously acquired scans where the user is enabled to choose optimal T1, T2or PD contrast and also is enabled to use particular pre-pulses.

In accordance with another aspect of the present invention a user is enabled to select a single Region of Interest (ROI) within a synthesized image for which the optimal contrast is calculated or to view a synthesized image where the optimal contrast difference between several Regions of Interest is calculated.

In accordance with the present invention any scanner setting can be chosen with the corresponding contrast once the tissue characteristics are measured. In practice this means that the patient will undergo a single quantification scan after which any desired contrast image can be reconstructed in post-processing at any time after the examination. Hence it will be possible to automatically synthesize the most optimal contrast images based on only a limited input of the user, which will save time and resources. The method of synthesizing contrast image can advantageously be implemented in computer software and stored on a computer program product.

In accordance with another aspect of the present invention the quantitative nature of the MR measurement is used to automatically highlight or suppress a single tissue type. Each tissue has its own unique combination of MR tissue parameters which is utilized to selectively visualize one or more tissues while suppressing all others. Hereby it is possible to single out a particular type of tissue and display the tissue type in a clear way. In accordance with one embodiment the visualization of a particular type of tissue is automated such that similar tissue as underneath a region of interest automatically lights up or vanishes from a displayed image.

In accordance with yet another aspect of the present invention a direct correspondence of the value of each tissue parameter to a basis color value is utilized. Since each tissue has its own unique set of three MR tissue parameters (T1, T2, PD) it will be possible to display as a unique color composition of three basis colors, or a subset thereof. The color transfer functions can be visualized and updated in another panel. It is not necessary that each tissue parameter value is linked to a visible color. For example it is possible to set ranges for the values to display. Outside these ranges the tissue will not be visible on the synthetic MR color image.

In addition to this a region of interest might be visualized on the image where only other pixels with similar tissue parameters are updated or shown. This will be helpful in segmentation of tissue. More ROIs may be displayed to highlight several tissues simultaneously. The MR color images are ideally displayed in combination with variable color transparency, volume rendering and/or 3D visualization.

DETAILED DESCRIPTION

InFIG. 1a general view of a setup of a MRI system100is depicted. The system100comprises a MR scanner101. The MR scanner is operative to generate MRI data by means of scanning a living object. The MR scanner is further connected to a computer103for processing data generated by the scanner101. The computer comprises a central processor unit coupled to a memory and a number of input and output ports for receiving and outputting data and information. The computer103receives input commands from one or several input devices generally represented by an input device105. The input device may be one or many of a computer mouse, a keyboard, a track ball or any other input device. The computer103is further connected to a screen107for visualizing the processed scanner data as a contrast image.

InFIG. 2, a flowchart illustrating steps performed when generating contrast images is shown. First in a step201a pre-scan is performed to acquire several MR images of a patient with various contrasts. Next, in a step203the images generated in step201are used to determine the patient specific T1relaxation time, the T2relaxation time and the Proton Density (PD), or for some applications a subset thereof. The method for determining the patient specific T1relaxation time, T2relaxation time and Proton Density (PD) can be any suitable method. However it is preferred to use a fast method to reduce the overall time of an examination. Examples of fast pre-scan methods are described in: Warntjes J B M, Dahlquist O, Lundberg P. Method for rapid, whole volume T1, T2* and Proton Density quantification. Magn Reson Med, accepted 2006 Nov. 10.; Neeb H, Zilles K, Shah N J. A new method for fast quantitative mapping of absolute water content in vivo. NeuroImage 2006; 31:1156-1168; and Deoni S C L, Rutt B K, Peters T M. High resolution T1and T2mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med 2005; 53:237-241.

Using the pre-scan information any MR contrast image can be synthesized. This is because the intensity in the synthesized contrast images is a function of the patient specific parameters as well as freely chosen MR scanner parameters such as echo time (TE), repetition time (TR), RF flip angle and inversion pulse delay time TI. Equations for the calculation of the expected intensity can be found in Haacke, E M, Brown R W, Thompson M R, Venkatesan R. Magnetic Resonance Imaging, physical principles and sequence design. ISBN 0-471-35128-8 J. Wiley & Sons. Practical examples of methods for contrast image synthesis are described in Gulani V, Schmitt P, Griswold M, Webb A G, Jakob P M. Towards a single-sequence neurologic magnetic resonance imaging examination: multiple-contrast images from an IR TrueFISP experiment. Invest Radiol 2004:39; 767-74. and Zhu X P, Hutchinson C E, Hawnaur J M, Cootes T F, Taylor C J, Isherwood I. Magnetic resonance image synthesis using a flexible model. Br J Radiol 1994:67; 976-82.

Next, in a step205, an arbitrary initial MR contrast image is synthesized using some default scanner parameter settings as a starting point for generating such an initial contrast image. The image generated in step205will be very similar to a conventional contrast image. Using the default initial contrast image as a starting point a user operating the MR visualizing system as described herein has a number of different options to manipulate the synthesized contrast image displayed on the screen as will be described hereinafter.

In accordance with a first option, when the computer is set to operate in a first mode207, the user may want to change the weight of T1weighted contrast in the displayed synthesized MR image. In response to such an indication for example the computer may be configured to interpret a horizontal mouse movement as such an indication the value of TR, TI and/or flip angle in the scanner settings are changed by the computer, leading to an apparent change of T1weighted contrast in the displayed synthesized MR image. In a corresponding manner the computer can be configured to interpret, also given as an example, a vertical mouse movement as a change of the value of TE, leading to an apparent change of T2weighted contrast in the displayed synthesized MR image. The change of contrast is performed in a step209.

In accordance with a second option, when the computer is set to operate in a second mode211, the computer can be configured to interpret a movement of an indication of a Region Of Interest (ROI) in the displayed synthesized MR image as a request for an automatic change of scanner settings for the displayed synthesized MR image, depending on the T1, T2and PD inside the ROI. In response to such a request the computer determines the requirement for the updated scanner settings satisfy some pre-determined condition in a step213. For example, the condition may advantageously by the optimal inversion delay time TI for zero intensity inside the ROI resulting in that the ROI area is visualized as black on the screen or the optimal setting for TR and flip angle for the highest SNR.

In accordance with the second option, the complete image changes contrast as soon as the user moves or changes his ROI in order to keep the average of the ROI optimal. A good example of when this option can be utilized is the so-called late-enhancement method where a T1contrast agent is administered to a patient with suspected myocardial infarction. After 10-20 minutes ischemic myocardium shows a higher contrast agent concentration and hence a lower T1value than healthy myocardium. An inversion recovery sequence is then performed in order to try to display the healthy tissue as black and the ischemic tissue as bright. The contrast in the image depends critically on the delay time between the inversion pulse and the actual measurement. Using conventional methods, finding the correct inversion delay time may take an experienced operator up to 10 minutes.

If, however, the value of T1is known and the image can be reconstructed based on quantification, the scanner parameters, in this case mostly the inversion delay time, are optimized such that the tissue in the ROI appears black. Automatically ischemic tissue turns bright.

As an example for the reconstruction of intensity images based on the quantification scans a simple spin echo sequence can be reconstructed as follows. The intensity I of each pixel in the image is a function of the scanner parameters echo time TE, repetition time TR, and the flip angle α and simultaneously a function of tissue characteristics such as T1relaxation, T2relaxation and proton density PD:

Knowing the tissue characteristics it is possible to reconstruct the images with any desired scanner settings. This can be extended to include preparation pulses (e.g. inversion or saturation), multi-shots (turbo factors) and various timings (as with multi slice sequences). If the tissue characteristics are known in 3D any slice thickness and orientation can be chosen. It might even improve the original image quality because no additional image artifacts will be introduced such as eddy currents, ghosting or flow artifacts.

Another example is the calculation of the expected intensity after an inversion pre-pulse of a gradient echo sequence as used in the late enhancement example: The saturated magnetization M0* during the measurement can then be calculated as:

M0*=M0⁢1-exp⁡(-TR/T1)⁢cos⁡(α)TFE+exp⁡((-TR+Tinv)/T1)·(cos⁡(α)TFE-1)1-exp⁡(-TR/T1)⁢cos⁡(θ)⁢cos⁡(α)TFE(2)
where the unsaturated magnetization M0is proportional to PD, TFE is the number of Turbo Field Echo shots and Tinvis the inversion delay time after the inversion pulse θ. Subsequently the intensity at a particular inversion delay time Tinvis calculated as:
I∝exp(−TE/T2*)sin(α)[M0−(M0+M0*)exp(−Tinv/T1)]  (3)
where T2is replaced by T2* relaxation since it is a gradient echo sequence instead of a spin echo sequence. Similar equations can be derived for any other contrast.

In accordance with a third option, when the computer is set to operate in a third mode215, the movement of two indications of Regions Of Interest in the displayed synthesized MR image can be set to result in an automatic change of scanner settings for the displayed synthesized image, depending on the T1, T2and PD inside the two ROIs as indicated in step217. The computer when operating in this third mode can advantageously be configured to set the scanner settings such that it matches the criteria of optimizing intensity difference between the two ROIs marked on the screen.

An example illustrating when the display of multiple ROIs in the reconstructed image to optimize the contrast between the ROIs is the imaging of the brain where the contrast between grey and white matter is optimized by placing one ROI on the white matter and one ROI on the grey matter. Possible choices are to optimize TI, TR and a for T1contrast or TE for T2contrast. Simultaneously the Signal to Noise ratio in the synthesized imaged can be optimized as well.

Using the equations as set out above in conjunction with the description of the operation of the computer when in the third mode will also allow for calculation of the largest intensity difference between various tissues with different tissue characteristics. The general T1, T2and PD become tissue specific T1a, T2aand PDafor tissue a, T1b, T2band PDbfor tissue b etc. For two ROIs the largest intensity difference for T1contrast can be found by varying TR and α, for T2contrast TE can be changed. An optimum can then be found using the general formula
(Ia−Ib)′=0
where the prime denotes the derivative to any variable.

In another embodiment of the present invention the synthesis of conventional contrast images can be enhanced by modifying the intensity equations (Eq. 1-3). For example, the normalization of the PD parameter can be set such that the measured proton density appears to be uniform over the image. A T1 weighted image will then be purely T1 weighted without the counteracting proton density contrast.

In accordance with another embodiment the influence of T1 on the image intensity can be inverted, e.g. by using
I∝PDexp(−TE/T2)sin(α)exp(−TE/T1)
for the synthesis. This will enhance T2 weighted imaging owing to additional T1 weighting.

It is not necessary to visualize the obtained T1, T2 and PD parameters as conventional contrast images. Instead, non-physical but much stronger contrasts can be used. The three parameters can be taken as coordinates in a three-dimensional space where T1, T2 and PD, or a function of T1, T2 and PD, serve as axes (x,y,z). In such a space similar tissue will form a cluster of coordinates. Since the MR measurement is quantitative, clusters will always appear at the same position inside this space. Vectors can be defined as
(x,y,z)=((WT1(T1−T1,0)),(WT2(T2−T2,0)),(WPD(PD−PD0)))
where T1,0, T2,0, PD0is a defined origin in T1-T2-PD space and WT1, WT2, WPDare the weights of the vector elements.

An exemplary function that can be used on T1 and T2 is the inverse, in order to obtain the relaxation rates R1=1/T1 and R2=1/T2. Using relaxation rates the weighted vector combination becomes:
(x,y,z)=((WR1(R1−R1,0)),(WR2(R2−R2,0)),(WPD(PD−PD0)))
where R1,0, R2,0, PD0is a defined origin in R1-R2-PD space and WR1, WR2, WPDare the weights of the parameters.

The origin of such a vector can be placed in the center of a first tissue cluster and the normalized distance can be calculated with respect to all other tissue clusters. Inside the R1 R2 PD space this value would correspond to a partial volume of the first tissue type in relation with the others. Images can therefore be synthesized to display the relative amount of a certain tissue type between 0 and 100%. Integration of such an image results in the absolute volume of that tissue inside the displayed slice. A reference table can be set up to predefine the origin locations of specific tissue types.

A clinical application for the brain of such synthetic vector imaging is display of a ‘white matter image’, a ‘grey matter image’ and a ‘CSF image’ on a scale between 0 and 100%. This can be used to quantify for example brain atrophy.

In accordance with another embodiment, the display of an image of the brain where all tissue is set to 1. Subsequently the aforementioned ‘white matter image’, ‘grey matter image’ and ‘CSF image’ are subtracted. The resulting non-WM/GM/CSF image can be used to show a ‘disease image’ containing the relative amount of tissue that is not recognized as WM, GM or CSF. This technique is sensitive for the detection of e.g. Multiple Sclerotic lesions or the excess amount of water in case of oedema.

Other clinical applications are the specific suppression of fat in the images or the specific enhancement of blood vessels (angio). In these cases images can be displayed where a single tissue cluster is suppressed or enhanced. The cluster can be selected by placing the origin at the center and defining a 3D width in space.

The synthetic vector images can be generated automatically by displaying a region of interest on the synthetic image where the origin of the vector is automatically located at the tissue cluster underneath the ROI. The intensity in the image will automatically be set to correspond to the absolute distance (vector length) from the origin.

Another example is the display of two regions of interest where one region (hence cluster) is set to black and the other region (hence cluster) set to white. All other coordinates in the space are automatically set to a grey value corresponding to e.g. the ratio of the absolute distance to one cluster and the absolute distance to the second cluster.

In accordance with another embodiment of the present invention each MR tissue parameter is associated with a color. For example in an RGB (Red, Green, Blue) coded image each MR tissue parameter is linked to one of the colors Red, Green or Blue. Since each tissue has its own unique set of three MR tissue parameters (T1, T2, PD) it will be possible to display as a unique color composition of three basis colors (or a subset thereof). The color transfer functions can be visualized and updated in another panel. It is not necessary that each tissue parameter value is linked to a visible color. Hence it is possible to set ranges for the values to display. Outside these ranges the tissue will not be visible on the synthetic MR color image.

In addition to this a region of interest might be visualized on the image where only other pixels with similar tissue parameters are updated or shown. This will be helpful in segmentation of tissue. More ROIs may be displayed to highlight several tissues simultaneously. The MR color images are ideally displayed in combination with variable color transparency, volume rendering and/or 3D visualization. The steps performed when generating a color coded image as set out above will be similar to the steps performed when generating a contrast image as described above in con junction withFIG. 2.

InFIG. 3a flow chart illustrating steps performed when generating a color coded image is shown. Hence, first in a step301a pre-scan is performed to acquire several MR images of a patient with various contrasts. Next, in a step303the images generated in step301are used to determine the patient specific T1relaxation time, the T2relaxation time and the Proton Density (PD), or for some applications a subset thereof.

Thereupon, in a step305, a color coded image is generated. The correspondence between an absolute MRI parameter the amount of color or opacity and color palette itself are free to choose. By way of example, the value of the absolute T1relaxation time can be set to determine the amount of red. For example values of 200-2000 ms correspond linearly with 0-255 of red. The value of the absolute T2relaxation time can similarly be set to determine the amount of green. For example values of 200-500 ms correspond linearly with 0-255 of green. Finally the value of the absolute proton density can be set to determine the amount of blue. For example values of 200-1000 ms correspond linearly with 0-255 of blue. Tissue with T1=2000, T2=500 and PD=1000 would then appear white (=255, 255, 255) in the color coded image.

The color coded image generated in step305can then be manipulated in a manner similar to the contrast image generated in step205above. Thus, in response to different input device movements, such a computer mouse movement, the color transfer function may be altered. Thus if in a first mode, a computer mouse is moved in one direction, in a step307, the generated and displayed color coded image is displayed using another color coding transfer function, step309. The change in color coding transfer function is then set to correspond to the computer mouse movement. For example by moving the computer mouse in one direction the midpoint of the color scale may be altered whereas moving the computer mouse in another direction may be set to correspond to a change in the range of the color scale. In the example given above where the T1relaxation time is set to determine the amount of red and where values of 200-2000 ms correspond linearly with 0-255 of red in the initial image, a movement in one direction may be set to alter the lover value in the time range and a movement in another direction may correspond to a change of the upper value in the range. Hence, if the user is interested in the range 1600-1800 ms such a range can easily be set to correspond linearly with 0-255 of red.

In a second mode, if in a step311an ROI is selected, the color coding transfer function is set to only be changed in response to user manipulation for those voxels having similar properties as the voxels within the selected ROI, step313. Also is in a third mode, if more than one ROI is selected, in a step315, the color coding transfer function is set to be changed in response to user manipulation only for voxels having properties similar to the voxels inside the selected ROIs, step317.

Furthermore it is also possible to combine visualization of the procedures described above in conjunction withFIG. 2andFIG. 3. Thus, in such a combined visualization scheme contrast and color can be manipulated side by side or simultaneously by a user. The transfer function will then be adapted to both perform contrast variations and color variation in response to user input.

The methods and systems described above quantify the absolute MR tissue parameters (T1, T2, and PD). Subsequently, using a direct correspondence of the values of these parameters in combination with chosen values of the MR scanner settings (TE, TR, α, pre-pulses) images can be visualized in a manner similar ordinary MR contrast images.

However, it is not necessary to display a physically possible contrast image, i.e. one that can be obtained using conventional methods for obtaining MR contrast images. An example of a non-physical contrast image that is of high diagnostic value is the proton density normalized image. This is a synthetic contrast image where the proton density of all tissues has been set to the same value. This way the ever-present proton contrast has been removed from the contrast image and a pure T1-weighted or a pure T2-weighted image can be visualized. This is not possible to display other than by using synthetic MRI

MR signal intensity can be split up into three components, each component depending on PD, T2 and T1, respectively. An example is the expected intensity of a normal spin echo sequence:

I∝PD⁢⁢exp⁡(-TE/T2)⁢sin⁡(α)⁢1-exp⁡(-TR/T1)1-exp⁡(-TR/T1)⁢cos⁡(α)
where TE is the echo time, TR is the repetition time and α is the RF flip angle.

There is no MR sequence that can vary the contribution of PD in this equation. Using absolute quantification PD can be set to any arbitrary value. Especially the pure T1-weighted image is important since PD gives a positive contribution to the intensity and T1 gives a negative contribution thus resulting in that the two components counteract each-other.

InFIG. 4, a flow chart illustrating different steps performed when generating a non-physical MR image. First in a step401a pre-scan is performed to acquire several MR images of a patient with various contrasts. Next, in a step403the images generated in step401are used to determine the patient specific T1relaxation time, the T2relaxation time and the Proton Density (PD), or for some applications a subset thereof. The method for determining the patient specific T1relaxation time, T2relaxation time and Proton Density (PD) can be any suitable method. Finally, in a step405, a synthesized image where the PD value is set to the same value for all tissues is generated and displayed. Hereby a PD normalized image is obtained which will provide an enhanced view of in particular T1 weighted images.

Using the method and device as described herein a patient undergoing a medical examination will only undergo a single quantification scan after which any desired contrast image can be reconstructed in post-processing at any time after the examination. Hence it will be possible to automatically synthesize the most optimal contrast images based on only a limited input of the user, which in turn will save time and resources. The method can suitably be implemented using computer software adapted to be executed on a computer.