阅读说明：本技术 使用多幅扩散加权mr图像来计算b0图像 (Computing B0 images using multiple diffusion weighted MR images ) 是由 J-P·F·A·M·埃尔梅斯 G·瓦尔瓦诺 于 2020-02-18 设计创作，主要内容包括：本发明提供了医学成像系统(100、300)。所述机器可执行指令(110)的执行使处理器(102)：接收(200)对象(318)的多幅扩散加权图像(112),其中,所述多幅扩散加权图像各自具有分配的b值,其中,所述多幅扩散加权图像各自具有分配的扩散加权方向,其中,针对感兴趣区域(309),在所述多幅扩散加权图像中的每幅扩散加权图像中存在至少一个对应体素(506)；构建(202)针对所述至少一个对应体素中的每个对应体素的方程组(114),其中,所述方程组是根据针对所述多幅扩散加权图像中的每幅扩散加权图像的所述分配的扩散加权方向的表观扩散方程来构建的；对针对每个体素的所述方程组求解(204)b-0值作为优化结果；并且使用针对每个体素的所述b-0值来构建(206)b-0图像。(The invention provides a medical imaging system (100, 300). Execution of the machine-executable instructions (110) causes the processor (102) to: receiving (200) a plurality of diffusion weighted images (112) of an object (318), wherein the plurality of diffusion weighted images each have an assigned b-value, wherein the plurality of diffusion weighted images each have an assigned diffusion weighting direction, wherein for a region of interest (309) at least one corresponding voxel (506) is present in each of the plurality of diffusion weighted images; constructing (202) for each of the at least one corresponding voxelWherein the system of equations is constructed from an apparent diffusion equation for the assigned diffusion weighting direction for each diffusion weighted image of the plurality of diffusion weighted images; solving (204) b for the system of equations for each voxel 0 Taking the value as an optimization result; and using said b for each voxel 0 Value to construct (206) b 0 And (4) an image.)

1. A medical imaging system (100, 300), comprising:

a memory (108) for storing machine executable instructions (110);

a processor (102) for controlling the medical imaging system, wherein execution of the machine executable instructions causes the processor to:

receiving (200) a plurality of diffusion weighted images (112) of an object (318), wherein the plurality of diffusion weighted images each have an assigned b-value representing a diffusion weighted intensity, wherein the plurality of diffusion weighted images each have an assigned diffusion weighted direction, wherein for a region of interest (309) there is at least one corresponding voxel (506) in each of the plurality of diffusion weighted images;

constructing (202) a system of equations (114) for each corresponding voxel of the at least one corresponding voxel, wherein the system of equations is constructed from apparent diffusion equations for the assigned diffusion-weighted directions for each diffusion-weighted image of the plurality of diffusion-weighted images;

solving (204) the system of equations for each voxel to represent zero diffusion weighting₀Taking the value as an optimization result; and is

Using the b for each voxel₀Value to construct (206) b₀Image, wherein b₀The values correspond to the image intensities where there is no diffusion weighting.

2. The medical imaging system of claim 1, wherein execution of the machine executable instructions further causes the processor to use the b₀Image to calculate (208) an image intensity correction for at least one diffusion weighted image of the plurality of diffusion weighted images to correct for gradient non-linearity.

3. The medical imaging system of claim 2, wherein the image intensity correction is computed in each assigned diffusion weighting direction.

4. The medical imaging system of claim 1, 2 or 3, wherein the medical imaging system further comprises a magnetic resonance imaging system (302), wherein the memory further contains pulse sequence commands (320) configured to control the magnetic resonance imaging system to acquire magnetic resonance imaging data (322) according to a diffusion weighted magnetic resonance imaging protocol, wherein execution of the machine executable instructions further causes the processor to:

controlling (400) the magnetic resonance imaging system to acquire the magnetic resonance imaging data with the pulse sequence commands; and is

Reconstructing (402) the plurality of diffusion weighted images using the magnetic resonance imaging data.

5. The medical imaging system of any one of the preceding claims, wherein execution of the machine executable instructions further causes the processor to perform motion correction between the plurality of diffusion weighted images prior to constructing the system of equations.

6. The medical imaging system of any one of the preceding claims, wherein the plurality of diffusion weighted images are EPI plurality of diffusion weighted images, wherein execution of the machine executable instructions further causes the processor to perform EPI distortion correction of the plurality of diffusion weighted images prior to constructing the system of equations.

7. The medical imaging system of any one of the preceding claims, wherein each equation of the system of equations is constructed by: setting the logarithm of the measured intensity of a voxel equal to b₀The value term is added to the product of the b-value term and the apparent diffusion coefficient term for each diffusion direction.

8. The medical imaging system of any one of the preceding claims, wherein the optimization result is solved as an exponential fit over the b-values in each equation of the system of equations.

9. The medical imaging system of any one of the preceding claims, wherein the b-value for one of the plurality of diffusion weighted images is 0.

10. The medical imaging system of any one of the preceding claims, wherein execution of the machine executable instructions further causes the processor to solve the b for the system of equations for each voxel₀During the values, the apparent diffusion coefficients for each voxel are calculated.

11. The medical imaging system of claim 10, wherein execution of the machine executable instructions further causes the processor to use the b₀The image to calculate a corrected apparent diffusion coefficient.

12. A method of medical imaging, wherein the method comprises:

receiving (200) a plurality of diffusion weighted images (112) of an object (318), wherein the plurality of diffusion weighted images each have an assigned b-value representing a diffusion weighted intensity, wherein the plurality of diffusion weighted images each have an assigned diffusion weighted direction, wherein for a region of interest (309) there is at least one corresponding voxel (506) in each of the plurality of diffusion weighted images;

constructing (202) a system of equations (114) for each corresponding voxel of the at least one corresponding voxel, wherein the system of equations is constructed from apparent diffusion equations for the assigned diffusion-weighted directions for each diffusion-weighted image of the plurality of diffusion-weighted images;

solving (204) the system of equations for each voxel to represent zero diffusion weighting₀Taking the value as an optimization result; and is

Using the b for each voxel₀Value to construct (206) b₀Image, wherein b₀The values correspond to the image intensities where there is no diffusion weighting.

13. The method of claim 12, wherein the method further comprises using the b₀An image to calculate an image intensity correction for at least one diffusion weighted image of the plurality of diffusion weighted images to correct for gradient non-linearity.

14. A computer program product comprising executable instructions for execution by a processor (102) controlling a medical imaging system (100, 300), wherein execution of the machine executable instructions causes the processor to:

receiving (200) a plurality of diffusion weighted images (112) of an object (318), wherein the plurality of diffusion weighted images each have an assigned b-value representing a diffusion weighted intensity, wherein the plurality of diffusion weighted images each have an assigned diffusion weighted direction, wherein for a region of interest at least one corresponding voxel is present in each of the plurality of diffusion weighted images;

constructing (202) a system of equations (114) for each corresponding voxel of the at least one corresponding voxel, wherein the system of equations is constructed from apparent diffusion equations for the assigned diffusion-weighted directions for each diffusion-weighted image of the plurality of diffusion-weighted images;

solving (204) the system of equations for each voxel to represent zero diffusion weighting₀Taking the value as an optimization result; and is

Using the b for each voxel₀Value to construct (206) b₀Image, wherein b₀The values correspond to the image intensities where there is no diffusion weighting.

15. The computer program product of claim 14, wherein the medical imaging system further comprises a magnetic resonance imaging system (302), wherein execution of the machine executable instructions further causes the processor to:

controlling (400) the magnetic resonance imaging system to acquire magnetic resonance imaging data (322) with pulse sequence commands (320), wherein the pulse sequence commands are configured to control the magnetic resonance imaging system to acquire magnetic resonance imaging data according to a diffusion weighted magnetic resonance imaging protocol;

reconstructing (402) the plurality of diffusion weighted images using the magnetic resonance imaging data.

Technical Field

The present invention relates to magnetic resonance imaging, and in particular to diffusion weighted imaging.

Background

Magnetic Resonance Imaging (MRI) scanners align the nuclear spins of atoms using a large static magnetic field as part of the procedure to generate images within a patient. The large static magnetic field is called B₀A field or main magnetic field. Gradient magnetic field (B) caused by Radio Frequency (RF) pulses₁A field) can be used to manipulate the orientation of the spins. If B is present₁The field rotates the spins such that the spins do not interact with B₀The field is aligned, the spins will begin processing and generate radio frequency signals.

Diffusion Weighted Imaging (DWI) after spin versus B₀After the field is rotated to a certain angle, a first diffusion weighted gradient is used to dephasise the spins within the volume. The same second diffusion weighted gradient then rephases the spins within the volume. After the spins are phased in a complex, the radio frequency signal of the spins can be measured. If the spins move into or out of the volume, the spins will not get the correct complex phasing. Therefore, the intensity of the diffusion weighted image is very sensitive to the diffusion of spins.

The dephasing and the rephasing of the diffusion-weighted gradients depend on the magnitude of the gradients, the duration of the gradients, and the degree of temporal separation between two diffusion-weighted gradients. A measure that takes these factors into account is the b value. If the b value is equal to zero, no diffusion weighting gradient is used. When the value of b is zero, it is called b₀。b₀The image is a diffusion weighted image in which the diffusion weighted gradient is turned off or not used. That is, b₀The image is a magnetic resonance image without diffusion encoding, i.e. the underlying magnetic resonance signal is not diffusion sensitive. When no diffusion weighting is applied, b₀The value corresponds toVoxel/pixel value. In other words, b₀The value is the image intensity where there is no diffusion weighting. A method for correcting Gradient non-linearity Bias in ADC measurement is disclosed in the journal article "Analysis and correction of Gradient nonlinear Bias in application Diffusion coherent Measurements", Malylarenko et al (Magnetic Resonance in Medicine, Vol. 71, p. 1312-. The disclosed method relies on having b₀And (4) an image.

In US patent publication US 9851226B 2, the same authors as the article by malyarnko et al disclose a technique for correcting gradient non-linear bias in average diffusivity measurements by an MRI system, and this technique includes a minimum number of spatial correction terms to achieve adequate error control using three orthogonal Diffusion Weighted Imaging (DWI) gradients. The correction is based on rotating the system gradient non-linear tensor into the DWI gradient frame, where the spatial deviation of the b matrix is described by its euclidean norm. This technique avoids time consuming multi-directional acquisition of the complete diffusion tensor and noise sensitive mathematical diagonalization for arbitrary anisotropic media.

Disclosure of Invention

The invention provides a medical imaging system, a computer program product and a method in the independent claims. Embodiments are given in the dependent claims.

In a journal article by Malyarnko et al, a method of correcting gradient non-linearity is disclosed. However, this method requires knowledge of b₀The image to make this correction. Of course b can be measured directly₀Images, but b₀Noise in the image reduces the accuracy of the gradient non-linearity correction. Examples can calculate b by using an optimization procedure₀The image further provides better correction of gradient non-linearity.

This may be done using a plurality or collection of diffusion weighted images acquired for different values of b and possibly with diffusion weighting gradients pointing in different directions. The apparent diffusion equation can then be used to construct a system of equations for each voxel. Once the system of equations is constructed, b for the voxel can be used₀Optimization of values from the system of equations. The more diffusion weighted images are used, b₀The more accurate the value. An insight of the present invention is that b is calculated by using an optimization procedure₀Pair of images b₀The sensitivity of noise in the image is less than that of directly measured b₀Pair of images b₀Susceptibility to noise in the image. It is also possible to use the measured b in such an optimization₀In images to improve calculated b₀And (4) an image.

In one aspect, the invention provides a medical imaging system comprising a memory for storing machine executable instructions. The medical imaging system further comprises a processor for controlling the medical imaging system. Execution of the machine-executable instructions causes the processor to receive a plurality of diffusion weighted images of an object. The plurality of diffusion weighted images each have an assigned b value. The plurality of diffusion weighted images each have an assigned diffusion weighting direction. For a region of interest, there is at least one corresponding voxel in each diffusion-weighted image of the plurality of diffusion-weighted images.

As described above, in diffusion weighted imaging, the spins are first rotated to an angle at which they are processed and then a diffusion weighting gradient is applied in the diffusion weighting direction. This operation dephasing the spins. After a period of time, the diffusion weighting gradient is again applied and this operation is used to phase the spins back. The spins leaving or entering the volume do not correctly undergo a phase inversion. Basically, the decrease in intensity can indicate the amount of diffusion out of the volume. The significance of the diffusion weighted gradient is the b value mentioned above. The amount of dephasing depends on the magnitude and time of the gradient.

A plurality of diffusion weighted images are acquired using a magnetic resonance imaging system, and the b-value is a b-value of a diffusion weighting gradient used during the acquisition. The direction in which the gradient is applied is the diffusion weighting direction. In general, when performing diffusion weighted imaging, there are three orthogonal diffusion weighting directions selected for the various b values. Although mathematically convenient, it is not necessary that the diffusion weighting direction beOrthogonal or even aligned with the coordinate system of the magnetic resonance imaging system. The value of b when no gradient is used is called b₀The value is obtained.

Execution of the machine-executable instructions further cause the processor to construct a system of equations for each of the at least one corresponding voxel. Corresponding voxels means that a voxel is present in each of the images and that voxel is identical to the other voxels. Ideally, the object would be stationary and there would be a series of images with a one-to-one correspondence to each of the voxels. The system of equations is constructed from an apparent diffusion equation for the assigned diffusion weighting direction for each of the plurality of weighted images. An apparent diffusion equation, as used herein, is an equation that can be used to calculate the apparent diffusion equation coefficients. (self-hinting: an example of an apparent diffusion equation inserted here) the apparent diffusion equation for a single voxel is:

wherein, S (b)^k) Is to follow a diffusion gradient b^kOf the image intensity of, and S₀Non-diffuse image intensity. ADC (analog to digital converter)^kIs at a diffusion gradient b^kThe apparent diffusion coefficient in the direction of (a). Value S₀Is b₀The value is obtained. For each diffusion weighted image of the plurality of diffusion weighted images, there is one equation (1) that can be added to the system of equations. S (b)^k) Is a measured value. By using optimization, b can be solved₀Values and ADC values. Equation 1 can be formulated in different ways. E.g. b^kValue sum ADC^kThe values can be vectors and the dot product of the two vectors is taken in equation (1).

Equations forming mathematically equivalent sets of equations can be formulated in different ways. For example, the above apparent diffusion equation is rewritten into a linear equation by taking a logarithm. This may, for example, make the calculation more straightforward and easy, even if equation (1) is in the form as written above, it can still be solved.

Execution of the machine-executable instructions further cause the processor to solve the system of equations for each voxel by b₀The value is used as the optimization result. In particular, noise may be present in the voxels due to intrinsic measurements or possibly small amounts of motion or other imperfections in the multiple diffusion weighted images. By solving said equation for b₀Values as optimization results, such b can be obtained₀Value, the noise ratio of which is measured directly b₀The values are less noisy. It should also be noted that one of the assigned b values can be zero, so that b is measured directly₀The value is obtained. The following effects can be achieved using optimization: making such measurements b by using other acquired multiple diffusion weighted images₀The image is further corrected.

In another embodiment execution of the machine executable instructions further cause the processor to use the b for each voxel₀Value to construct b₀And (4) an image. b₀Images can be useful for several different things. For example, it may be useful in correcting for gradient non-linearity. B can also be₀The image is compared to other pluralities of diffusion weighted images for various b-values for quantitative estimation of diffusion.

In another embodiment, execution of the machine-executable instructions further causes the processor to use the b₀An image to calculate an image intensity correction for at least one diffusion weighted image of the plurality of diffusion weighted images to correct for gradient non-linearity. Once b is calculated₀Images, the method described in the article by malylarenko et al can be used.

In another embodiment, the image intensity correction is calculated in each assigned diffusion weighting direction. This may be beneficial because it may provide better correction for diffusion weighted images.

In another aspect, the medical imaging system further comprises a magnetic resonance imaging system. The memory also contains pulse sequence commands configured to control the magnetic resonance imaging system to acquire magnetic resonance imaging data according to a diffusion weighted magnetic resonance imaging protocol.

Execution of the machine-executable instructions further causes the processor to control the magnetic resonance imaging system to acquire the magnetic resonance imaging data. Execution of the machine-executable instructions further cause the processor to reconstruct the plurality of diffusion weighted images using the magnetic resonance imaging data.

After reconstructing the plurality of diffusion weighted images, both the b-value and the diffusion weighting direction may be assigned to a particular diffusion weighted image.

In another embodiment execution of the machine-executable instructions further cause the processor to perform motion correction between the plurality of diffusion weighted images prior to constructing the system of equations. If the object moves between the acquisition of different multiple diffusion weighted images, the voxels may shift. Standard image correction methods can be used to correct for this shift. For example, there may be image registration between various multiple diffusion weighted images to correct for motion.

In another embodiment, the plurality of diffusion weighted images are EPI plurality of diffusion weighted images. Execution of the machine-executable instructions further cause the processor to perform EPI distortion correction of the plurality of diffusion weighted images prior to constructing the system of equations. EPI or echo planar imaging is used to acquire very common diffusion weighted images. B is₀The non-uniformity may cause spatially non-linear image distortion in the EPI image. For example, deformable registration may be used to correct EPI distortion.

In another embodiment, each equation in the system of equations is constructed by: setting the logarithm of the measured intensity of a voxel equal to b₀The value term is added to the product (or dot product) of the b-value term and the apparent diffusion coefficient term for each diffusion direction. This can be derived, for example, by taking the logarithm of equation 1:

the superscript k denotes the direction of the diffusion weighting gradient and i denotes the ith b value. In this equation, there is only one b-value term and one apparent diffusion coefficient term. If, for example, the coordinate system is not aligned with the direction of the diffusion weighting gradient, there will be more terms in the equation.

In another embodiment, the optimization result is solved as an exponential fit over the b-value in each equation in the system of equations.

In another embodiment, the b value for one of the plurality of diffusion weighted images is 0. As mentioned before, b can be measured₀And (4) an image. However, b₀The image may be noisy. If the b-value for one of the diffusion weighted images is zero, then the optimization may provide a way to improve quality, e.g., at the expense of signal-to-noise ratio.

In another embodiment, execution of the machine-executable instructions further causes the processor to solve the b for the system of equations for each voxel₀During the values, the apparent diffusion coefficients for each voxel are calculated. This embodiment may be beneficial because the optimization may not only be used to calculate b₀Values, but also to calculate the apparent diffusion coefficient. The optimization used to solve the system of equations formed using equation 1 can also be used to solve the ADC values simultaneously.

In another embodiment, execution of the machine-executable instructions further causes the processor to use the b₀The image to calculate a corrected apparent diffusion coefficient. The method described in any of the articles by malyarnko et al may be used (see equation 6 in the article by malyarnko).

In another aspect, the invention provides a method of medical imaging. The method includes receiving a plurality of diffusion weighted images of a subject. The plurality of diffusion weighted images each have an assigned b value. The plurality of diffusion weighted images each have an assigned defined diffusion weighting direction. For a region of interest, there is at least one corresponding voxel in each diffusion-weighted image of the plurality of diffusion-weighted images. The method further comprises constructing a voxel map for each of the at least one corresponding voxelA system of equations. The system of equations is constructed from an apparent diffusion equation for the assigned diffusion weighting direction for each diffusion weighted image of the plurality of diffusion weighted images. The method further comprises solving the system of equations for each voxel for b₀The value is used as the optimization end. The method further comprises using the b for each voxel₀Value to construct b₀And (4) an image.

In another embodiment, the method further comprises using said b₀An image to calculate an image intensity correction for at least one diffusion weighted image of the plurality of diffusion weighted images to correct for gradient non-linearity.

In another aspect, the invention provides a computer program product comprising executable instructions for execution by a processor controlling a medical imaging system. Execution of the machine-executable instructions causes the processor to receive a plurality of diffusion weighted images of an object. The plurality of diffusion weighted images each have an assigned b value. The plurality of diffusion weighted images each have an assigned diffusion weighting direction.

For a region of interest, there is at least one corresponding voxel in each diffusion-weighted image of the plurality of diffusion-weighted images. Execution of the machine-executable instructions further causes the processor to construct a system of equations for each of the at least one corresponding voxel. The system of equations is constructed from an apparent diffusion equation for the assigned diffusion weighting direction for each diffusion weighted image of the plurality of diffusion weighted images. Execution of the machine-executable instructions further cause the processor to solve the system of equations for each voxel to solve the b₀The value is used as the optimization result. Execution of the machine-executable instructions further cause the processor to use the b for each voxel₀Value to construct b₀And (4) an image.

In another embodiment, the medical imaging system further comprises a magnetic resonance imaging system. Execution of the machine-executable instructions further causes the processor to control the magnetic resonance imaging system to acquire magnetic resonance imaging data with pulse sequence commands. The pulse sequence commands are configured to control the magnetic resonance imaging system to acquire magnetic resonance imaging data according to a diffusion weighted magnetic resonance imaging protocol. Execution of the machine-executable instructions further cause the processor to reconstruct the plurality of diffusion weighted images using the magnetic resonance imaging data.

It should be understood that one or more of the foregoing embodiments of the invention may be combined, as long as the combined embodiments are not mutually exclusive.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as an apparatus, method or computer program product. Accordingly, aspects of the present invention may take the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," module "or" system. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable media having computer-executable code embodied thereon.

Any combination of one or more computer-readable media may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. "computer-readable storage medium" as used herein encompasses any tangible storage medium that can store instructions that are executable by a processor of a computing device. The computer-readable storage medium may be referred to as a computer-readable non-transitory storage medium. The computer readable storage medium may also be referred to as a tangible computer readable medium. In some embodiments, the computer-readable storage medium is also capable of storing data that is accessible by a processor of the computing device. Examples of computer-readable storage media include, but are not limited to: a floppy disk, a magnetic hard drive, a solid state disk, flash memory, a USB thumb drive, Random Access Memory (RAM), Read Only Memory (ROM), an optical disk, a magneto-optical disk, and a register file for a processor. Examples of optical disks include Compact Disks (CDs) and Digital Versatile Disks (DVDs), e.g., CD-ROMs, CD-RWs, CD-R, DVD-ROMs, DVD-RWs, or DVD-R disks. The term "computer-readable storage medium" also refers to various types of recording media that can be accessed by a computer device via a network or a communication link. For example, data may be retrieved over a modem, over the internet, or over a local area network. Computer executable code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with computer executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to: electromagnetic, optical, or any suitable combination thereof. The computer readable signal medium may be any computer readable medium that: the computer readable medium is not a computer readable storage medium and can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

"computer memory" or "memory" is an example of computer-readable storage media. Computer memory is any memory that can be directly accessed by a processor. A "computer storage device" or "storage device" is another example of a computer-readable storage medium. The computer storage device is any non-volatile computer-readable storage medium. In some embodiments, the computer storage device may also be computer memory, or vice versa.

"processor" as used herein encompasses an electronic component capable of executing a program or machine-executable instructions or computer-executable code. References to a computing device that includes a "processor" should be interpreted as potentially containing more than one processor or processing core. The processor may be, for example, a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed among multiple computer systems. The term "computing device" should also be read to possibly refer to a collection or network of multiple computing devices, each of which includes one or more processors. The computer executable code may be executed by multiple processors, which may be within the same computing device or even distributed across multiple computing devices.

The computer executable code may include machine executable instructions or programs that cause a processor to perform an aspect of the present invention. Computer executable code for performing operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language (e.g., Java, Smalltalk, C + +, etc.) and a conventional procedural programming language (e.g., the "C" programming language or similar programming languages), and compiled as machine executable instructions. In some instances, the computer executable code may be in a high level language form or in a pre-compiled form, and may be used in conjunction with an interpreter that generates the machine executable instructions on the fly.

The computer executable code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

Aspects of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block or portion of the blocks of the flowchart, illustrations and/or block diagrams can be implemented by computer program instructions in computer-executable code where appropriate. It will also be understood that blocks of the various flow diagrams, illustrations, and/or block diagrams, when not mutually exclusive, may be combined. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

As used herein, a "user interface" is an interface that allows a user or operator to interact with a computer or computer system. The "user interface" may also be referred to as a "human interface device". The user interface may provide information or data to and/or receive information or data from an operator. The user interface may enable input from an operator to be received by the computer and may provide output from the computer to a user. In other words, the user interface may allow an operator to control or manipulate the computer, and the interface may allow the computer to indicate the effect of the operator's control or manipulation. Displaying data or information on a display or graphical user interface is an example of providing information to an operator. Receiving data through a keyboard, mouse, trackball, trackpad, pointing stick, tablet, joystick, gamepad, webcam, head-mounted device, foot pedal, wired glove, remote control, and accelerometer are all examples of user interface components that enable receiving information or data from an operator.

As used herein, "hardware interface" encompasses an interface that enables a processor of a computer system to interact with and/or control an external computing device and/or apparatus. The hardware interface may allow the processor to send control signals or instructions to an external computing device and/or apparatus. The hardware interface may also enable the processor to exchange data with external computing devices and/or apparatus. Examples of hardware interfaces include, but are not limited to: a universal serial bus, an IEEE 1394 port, a parallel port, an IEEE 1284 port, a serial port, an RS-232 port, an IEEE-488 port, a Bluetooth connection, a wireless local area network connection, a TCP/IP connection, an Ethernet connection, a control voltage interface, a MIDI interface, an analog input interface, and a digital input interface.

"display" or "display device" as used herein encompasses an output device or user interface suitable for displaying images or data. The display may output visual, auditory, and/or tactile data. Examples of displays include, but are not limited to: computer monitors, television screens, touch screens, tactile electronic displays, braille screens, Cathode Ray Tubes (CRTs), memory tubes, bi-stable displays, electronic paper, vector displays, flat panel displays, vacuum fluorescent displays (VFs), Light Emitting Diode (LED) displays, electroluminescent displays (ELDs), Plasma Display Panels (PDPs), Liquid Crystal Displays (LCDs), organic light emitting diode displays (OLEDs), projectors, and head mounted displays.

Magnetic Resonance Imaging (MRI) data is defined herein as being the recorded measurements of radio frequency signals emitted by atomic spins using an antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan. Magnetic resonance data is an example of medical image data. A Magnetic Resonance Imaging (MRI) image or MR image is defined herein as being a two-dimensional visualization or a three-dimensional visualization reconstructed of anatomical data contained within the magnetic resonance imaging data. The visualization can be performed using a computer.

Drawings

Preferred embodiments of the present invention will be described hereinafter, by way of example only, and with reference to the accompanying drawings, in which:

fig. 1 illustrates an example of a medical imaging system;

FIG. 2 shows a flow chart illustrating a method of operating the medical imaging system of FIG. 1;

fig. 3 illustrates a further example of a medical imaging system;

FIG. 4 shows a flow chart illustrating a method of operating the medical imaging system of FIG. 3; and is

Fig. 5 shows several meshes representing a set of multiple diffusion weighted images.

List of reference numerals

100 medical imaging system

101 computer

102 processor

104 hardware interface

106 user interface

108 memory

110 machine-executable instructions

112 multiple diffusion weighted images

114 system of equations

116 b₀Image of a person

118 apparent diffusion coefficient

120 intensity corrected multiple diffusion weighted images

200 receiving a plurality of diffusion weighted images of an object

202 construct a system of equations for each of the at least one corresponding voxel

204 solving the system of equations for each voxel b₀Value as optimization result

206 uses b for each voxel₀Value to construct b₀Image of a person

208 use b₀Image to calculate an image intensity correction for at least one diffusion weighted image of a plurality of diffusion weighted images to correct for gradient non-linearity

300 medical imaging system

302 magnetic resonance imaging system

304 magnet

306 bore of magnet

308 imaging zone

309 region of interest

310 magnetic field gradient coil

312 magnetic field gradient coil power supply

314 radio frequency coil

316 transceiver

318 object

320 subject support

320 pulse sequence commands

322 magnetic resonance imaging data

400 control magnetic resonance imaging system to acquire magnetic resonance imaging data

Reconstructing 402 a plurality of diffusion weighted images using magnetic resonance imaging data

500 diffusion weighted image

502 diffusion weighted image

504 diffusion weighted image

Voxel 506

Detailed Description

In the drawings, like numerals refer to either equivalent elements or perform the same function. Elements previously discussed will not be discussed in later figures if they are functionally equivalent.

Fig. 1 illustrates an example of a medical imaging system 100. In the example in fig. 1, the medical imaging system is a workstation for image processing. The medical imaging system 100 is shown as including a computer 101. The computer 101 includes a processor 102. Processor 102 is intended to represent one or more processors and may also represent a processor distributed to multiple computer systems. The processor 102 is shown connected to an optional hardware interface 104. The hardware interface 104 may, for example, enable the processor 102 to control other components of the medical imaging system 100, and the hardware interface 104 may also be a network interface. The processor 102 is shown as also being connected to a user interface 106.

The user interface 106 may be used, for example, to display information and/or receive commands from an operator. The processor 102 is shown as also being connected to the memory 108. The memory 108 may be any combination of memory accessible to the processor 102. This may include memory such as main memory, cache memory, and may also include non-volatile memory, such as flash RAM, a hard drive, or other storage devices. In some examples, memory 108 may be considered a non-transitory computer-readable medium.

The memory 108 is shown as containing machine-executable instructions 110. The machine-executable instructions 110 enable the processor 102 to control various operations and functions of the medical imaging system 100 and perform various data and image manipulation tasks.

The memory 108 is shown as also containing a plurality of diffusion weighted images 112. They may have been received, for example, via a network or other storage medium. The memory 108 is also shown as containing a system of equations 114. A system of equations is constructed from the apparent diffusion equations by using a plurality of diffusion weighted images 112. Memory 108 is also shown as containing b constructed by solving equation set 114 using optimization₀An image 116. For example, the optimization may be programmed into the machine-executable instructions 110.

Memory 108 is shown as containing an optional apparent diffusion coefficient. The apparent diffusion coefficient may also be solved simultaneously when solving the system of equations 114. The memory 108 is also shown as containing a plurality of intensity-corrected diffusion-weighted images 120. These intensity-corrected multiple diffusion-weighted images 120 have been used b₀The images are corrected to correct for gradient non-linearities of the plurality of diffusion weighted images 112.

Fig. 2 shows a flow chart illustrating a method of operating the medical imaging system 100 of fig. 1. First, in step 200, a plurality of diffusion weighted images 112 are received. Each diffusion weighted image of the plurality of diffusion weighted images 112 has an assigned b value. The plurality of diffusion weighted images each have an assigned diffusion weighting direction. For a region of interest, there is at least one corresponding voxel in each diffusion-weighted image of the plurality of diffusion-weighted images. Next in step 202, an equation set 114 is constructed for each of the at least one corresponding voxel.

The system of equations is based on a plurality of expansion coefficientsAn apparent diffusion equation for the assigned diffusion-weighted direction for each diffusion-weighted image in the scatter-weighted image is constructed. Then, in step 204, the system of equations for each voxel is solved for b₀The value is used as the optimization result. Then, in step 206, b for each voxel is used₀Value to construct b₀And (4) an image. In step 208, optionally, there is an image intensity correction, using b₀The image 116 is used to calculate an image intensity correction for at least one diffusion-enhanced image of the plurality of diffusion-weighted images 112 to correct for gradient non-linearity. This results in a plurality of intensity-corrected diffusion-weighted images 120.

Fig. 3 illustrates a further example of a medical imaging system 300. The medical imaging system 300 in fig. 3 further comprises a magnetic resonance imaging system 302. The magnetic resonance imaging system 302 comprises a magnet 304. The magnet 304 is a superconducting cylindrical magnet having a bore 306, the bore 306 passing through the magnet 304. Different types of magnets may also be used; for example, split cylindrical magnets and so-called open magnets may also be used. The split cylindrical magnet is similar to a standard cylindrical magnet, except that the cryostat has been split into two parts to allow access to the iso-plane of the magnet, such a magnet may be used, for example, in conjunction with charged particle beam therapy.

An open magnet has two magnet portions, one above the other, with a space between them large enough to be able to receive an object: the regional arrangement of these two parts is similar to that of a helmholtz coil. Open magnets are popular because the subject is less restricted. Inside the cryostat of the cylindrical magnet there is a collection of superconducting coils. Within the bore 306 of the cylindrical magnet 104 there is an imaging zone 108 where the magnetic field is sufficiently strong and uniform to enable magnetic resonance imaging to be performed in the imaging zone 108. A region of interest 309 is shown within the imaging region 308. Typically the acquired magnetic resonance data is acquired for a region of interest. The object 318 is shown supported by an object support 320 such that at least a portion of the object 318 is within the imaging region 308 and the region of interest 309.

Also present within the bore 306 of the magnet is a set of magnetic field gradient coils 310, the set of magnetic field gradient coils 310 being used to acquire preliminary magnetic resonance data to spatially encode magnetic spins within the imaging zone 308 of the magnet 304. The magnetic field gradient coils 310 are connected to a magnetic field gradient coil power supply 312. The magnetic field gradient coils 310 are intended to be representative. Typically, the magnetic field gradient coils 310 comprise three sets of individual coils that are used for spatial encoding in three orthogonal spatial directions. A magnetic field gradient power supply supplies current to the magnetic field gradient coils. The current supplied to the magnetic field gradient coils 310 is controlled as a function of time and may be ramped or pulsed.

Adjacent to the imaging zone 308 is a radio frequency coil 314, the radio frequency coil 314 for manipulating the orientation of magnetic spins within the imaging zone 308 and also for receiving radio transmissions also from spins within the imaging zone 308. The radio frequency antenna may comprise a plurality of coil elements. The radio frequency antenna may also be referred to as a channel or antenna. The radio frequency coil 314 is connected to a radio frequency transceiver 316. The radio frequency coil 314 and the radio frequency transceiver 316 may be replaced by separate transmit and receive coils and separate transmitters and receivers. It should be understood that the radio frequency coil 314 and the radio frequency transceiver 316 are representative. The radio frequency coil 314 is intended to also represent a dedicated transmit antenna and a dedicated receive antenna. Likewise, the transceiver 316 may also represent a separate transmitter and receiver. The radio frequency coil 314 may also have multiple receive/transmit elements, and the radio frequency transceiver 316 may have multiple receive/transmit channels. For example, if a parallel imaging technique such as SENSE is performed, the radio frequency coil 314 will have multiple coil elements. The transceiver 316 and the gradient controller 312 are shown connected to the hardware interface 104 of the computer system 101.

The memory 108 is also shown as containing pulse sequence commands 320. The pulse sequence command 320 is a command or data that can be converted into a command as follows: the commands control the magnetic resonance imaging system 302 to acquire magnetic resonance imaging data according to a diffusion weighted imaging magnetic resonance imaging protocol. The memory 108 is shown as also containing magnetic resonance imaging data 322 acquired by controlling the magnetic resonance imaging system 302 with the pulse sequence commands 320.

Fig. 4 shows a flow chart illustrating a method of operating the medical imaging system 300 of fig. 3. First in step 400, the magnetic resonance imaging system 302 is controlled to acquire magnetic resonance imaging data 322 with pulse sequence commands 320. Next, in step 404, a plurality of diffusion weighted images 112 are reconstructed from the magnetic resonance imaging data 322. After step 402, the method proceeds to step 200-208 of the method shown in FIG. 2.

Fig. 5 shows several grids representing a collection of diffusion weighted images 112. In this example, three diffusion weighted images 500, 502, and 504 are shown. These images are represented by a grid. Each space in the grid represents a voxel. At the same location in each grid, there is a voxel labeled 506. Voxel 506 is one of the corresponding voxels for diffusion weighted images 500, 502, and 504. The system of equations is constructed using the intensity values for voxels 506. This gives that it can be solved for b₀A system of equations of (1). The process is then repeated for each of the other voxels in the diffusion weighted images 500, 502, 504.

Examples may provide improved Apparent Diffusion Coefficient (ADC) correction by correcting the image intensity of individual DWI images without requiring additional b₀And (4) an image. Even if extra b is collected₀Images, also the case including all DWI acquisitions (multiple diffusion weighted images) can be used to calculate the individual b₀Usually this will result in b being measured₀Better SNR and less artifacts for the image.

As mentioned above, the example skips the acquisition of non-diffuse b, as compared to Malylarenko's article₀Image requirements and still be able to correct for gradient non-linearities. This may allow for (slightly) shorter scan times to be spent and/or for (slightly) greater flexibility in the scanning protocol.

Another advantage is that it is compatible with the acquisition of b₀In contrast, calculated b₀The estimation results generally show less noise and fewer artifacts, which may improve the image quality of DWI scans applying gradient non-linearity correction.

Assuming that at the measured b valueWhere exponential decay occurs, b is estimated from the least squares solution₀Sample image 116, and as such b₀The sample image is used in further calculations for gradient non-linearity correction.

The Apparent Diffusion Coefficient (ADC) in the gradient direction k is defined for a single pixel as shown in equation 1 above.

Malylarenko discloses a correction map c dependent on the position r^k(r) to correct image intensity:

the S (0, r) value is b for a particular location r (voxel location)₀The value is obtained. S_c(b^kAnd r) is the corrected image intensity. S (b)^kAnd r) is the measured image intensity as in equation 1. Correction map c dependent on position r^k(r) is the gradient non-linearity correction.

Correction requirement b according to equation (3)₀Image, however, when measuring multiple b-values (which is a requirement for ADC map derivation), b can also be estimated₀. Even if b is collected₀Image, b can be calculated from a plurality of b values₀And estimating the result. In general, with measured b₀In contrast, b₀The estimation results show less noise and fewer artifacts. Since equation (3) shows that the corrected image is b₀Image and b_kCombining the images; thus, improve b₀The corrected image is improved.

Estimating b by performing a least squares single exponential fit on the b values₀(equation (1)). Since the measured tissue can be anisotropic and the gradient non-linearity may vary in the diffusion direction, the ADC value for each diffusion direction is estimated. For a linear equation for a single pixel, we will optimize:

y＝A·x (4)

where the vector y contains the natural logarithm of all measured b-values and b-directions, A is the model matrix containing b-values, and x is the model matrix containing unknown b-values₀A vector of values and ADC values.The above equation (2) can be used to establish equation 4.

For example, equation 4 can be rewritten as:

wherein, the superscript b^x,y,zRefers to orthogonal diffusion directions (not necessarily aligned with any axis, they can be tilted), and subscript b_1,2,...Refer to different values of b (or amplitude as in (1)).

If b in which observations can be easily discarded is not acquired₀If, the first observation in equation (5) comes from b₀And (5) collecting.

While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. Although some measures are recited in mutually different dependent claims, this does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. Any reference signs in the claims shall not be construed as limiting the scope.