System and method for non-uniformity correction of diffusion weighted magnetic resonance images

Abstract

A magnetic resonance (MR) imaging method of correcting inhomogeneity in diffusion weighted (DW) MR images of a subject is provided. The method includes applying a DW pulse sequence along a plurality of diffusion directions having one or more numbers of excitations (NEX); and acquiring a plurality of DW MR images of the subject along the plurality of diffusion directions having the one or more NEX. The method also includes deriving a reference image and a basis image based on the plurality of DW MR images; generating an inhomogeneity factor image based on the reference image and the basis image; and combining the plurality of DW MR images into a combined image. The method further includes correcting inhomogeneity of the combined image using the inhomogeneity factor image; and outputting a corrected image.

Description

Technical Field

[0001] The field of this disclosure relates generally to systems and methods for non-uniformity correction, and more specifically, to systems and methods for non-uniformity correction in diffusion-weighted magnetic resonance (MR) images. Background Technology

[0002] Magnetic resonance imaging (MRI) has proven useful for the diagnosis of many diseases. MRI provides detailed images of soft tissues, abnormal tissues (such as tumors), and other structures that cannot be easily imaged by other imaging modalities such as computed tomography (CT). Furthermore, MRI operates without exposing the patient to the ionizing radiation experienced in modalities such as CT and X-rays.

[0003] Diffusion-weighted MR imaging is used to study the diffusion of biological tissues and for the diagnosis of diseases. Known diffusion-weighted MR imaging has several drawbacks, and improvements are desired. Summary of the Invention

[0004] In one aspect, a magnetic resonance (MR) imaging method is provided for correcting non-uniformity in diffusion-weighted (DW) MR images of a subject. The method includes applying a DW pulse sequence along multiple diffusion directions having one or more excitation frequencies (NEX); and acquiring multiple DW MR images of the subject along the multiple diffusion directions having one or more NEX. The method also includes deriving a reference image and a base image based on the multiple DW MR images; generating a non-uniformity factor image based on the reference image and the base image; and combining the multiple DW MR images into a composite image. The method further includes correcting the non-uniformity of the composite image using the non-uniformity factor image; and outputting the corrected image.

[0005] On the other hand, a non-uniformity correction system is provided for correcting non-uniformity in DW MR images of a subject. The system includes a non-uniformity correction computing device comprising at least one processor communicating with at least one memory device. The at least one processor is programmed to receive multiple DW MR images of a subject along multiple diffusion directions having one or more excitation numbers (NEX); derive a reference image and a base image based on the multiple DW MR images; generate a non-uniformity factor image based on the reference image and the base image; and combine the multiple DW MR images into a combined image. The at least one processor is also programmed to correct the non-uniformity of the combined image using the non-uniformity factor image; and output the corrected image. Attached Figure Description

[0006] Figure 1A This is a schematic diagram of an exemplary magnetic resonance imaging (MRI) system.

[0007] Figure 1B This is a schematic diagram of a diffusion-weighted pulse sequence.

[0008] Figure 2 It is a diffusion-weighted image that does not use the systems and methods disclosed in this paper.

[0009] Figure 3A This is an exemplary non-uniformity correction system.

[0010] Figure 3B This is a flowchart of an exemplary method for non-uniformity correction.

[0011] Figure 4 An exemplary diffusion-weighted image and apparent diffusion coefficient (ADC) plot are shown.

[0012] Figure 5 More exemplary diffusion-weighted images and ADC plots are shown.

[0013] Figure 6 The ADC plot without using the systems and methods disclosed in this paper and Figure 4 and Figure 5 The comparison of the ADC graphs shown.

[0014] Figure 7 This is a block diagram of an exemplary computing device. Detailed Implementation

[0015] This disclosure includes systems and methods for reducing shading or correcting non-uniformity in diffusion-weighted magnetic resonance (MR) images of a subject. As used herein, the subject is a human, animal, or dummy animal. As used herein, reducing shading or correcting non-uniformity means reducing and / or removing shading or non-uniformity in diffusion-weighted MR images. Liver diffusion imaging is described herein only as an example. The systems and methods disclosed herein can be applied to correcting shading in diffusion images of other parts of the body, such as the pancreas. Methodological aspects will be apparent in part and discussed explicitly in part in the following description.

[0016] In magnetic resonance imaging (MRI), the subject is placed in a magnet. When the subject is in a magnetic field generated by the magnet, the magnetic moments of nuclei, such as protons, attempt to align with the magnetic field, but precess around the field in a random order at the Larmor frequency of the nucleus. The magnetic field of the magnet is called B0 and extends longitudinally, or in the z-direction. During the acquisition of MRI images, a magnetic field in the xy-plane and close to the Larmor frequency (called the excitation field B1) is generated by a radio frequency (RF) coil and can be used to rotate or “tilt” the net magnetic moment Mz of the nucleus from the z-direction toward the transverse or xy-plane. After the excitation signal B1 terminates, the nucleus emits a signal, which is called the MR signal. To generate an image of the subject using the MR signal, magnetic field gradient pulses (Gx, Gy, and Gz) are used. The gradient pulses are used to scan the reverse direction of space or distance through k-space, spatial frequency, and space. There is a Fourier relationship between the acquired MR signal and the image of the subject, so the image of the subject can be derived by reconstructing the MR signal.

[0017] Figure 1A A schematic diagram of an exemplary MRI system 10 is shown. In an exemplary embodiment, the MRI system 10 includes a workstation 12 having a display 14 and a keyboard 16. The workstation 12 includes a processor 18, such as a commercially available programmable machine running a commercially available operating system. The workstation 12 provides an operator interface that allows scanning protocols to be input into the MRI system 10. The workstation 12 is coupled to a pulse sequence server 20, a data acquisition server 22, a data processing server 24, and a data storage server 26. The workstation 12 and each of the servers 20, 22, 24, and 26 communicate with each other.

[0018] In an exemplary embodiment, the pulse sequence server 20 responds to instructions downloaded from workstation 12 to operate the gradient system 28 and the radio frequency (“RF”) system 30. The instructions are used to generate gradient waveforms and RF waveforms in the MR pulse sequence. The RF coil 38 and the gradient coil assembly 32 are used to execute the prescribed MR pulse sequence. The RF coil 38 is shown as a whole-body RF coil. The RF coil 38 may also be a local coil that can be placed near the anatomical structure to be imaged, or a coil array comprising multiple coils.

[0019] In an exemplary embodiment, a gradient waveform for performing a delimited scan is generated and applied to a gradient system 28, which excites the gradient coils in the gradient coil assembly 32 to generate magnetic field gradients for frequency encoding, phase encoding, and slice selection / encoding of the MR signal. , and The gradient coil assembly 32 forms part of the magnet assembly 34, which also includes a polarized magnet 36 and an RF coil 38.

[0020] In an exemplary embodiment, RF system 30 includes an RF transmitter for generating RF pulses used in an MR pulse sequence. The RF transmitter responds to a scan scheme and orientation from pulse sequence server 20 to generate RF pulses with desired frequency, phase, and pulse amplitude waveforms. The generated RF pulses can be applied by RF system 30 to RF coil 38. The responsive MR signal detected by RF coil 38 is received by RF system 30 and amplified, demodulated, filtered, and digitized under the direction of commands generated by pulse sequence server 20. RF coil 38 is described as both a transmitter and a receiver coil, such that RF coil 38 transmits RF pulses and detects MR signals. In one embodiment, MRI system 10 may include a transmitter RF coil for transmitting RF pulses and a separate receiver coil for detecting MR signals. A transmission channel of RF system 30 can be connected to the RF transmission coil, and a receiver channel can be connected to a separate RF receiver coil. Typically, the transmission channel is connected to the whole-body RF coil 38, and each receiver segment is connected to a separate local RF coil.

[0021] In an exemplary embodiment, the RF system 30 further includes one or more RF receiver channels. Each RF receiver channel includes an RF amplifier that amplifies the MR signal received by the RF coil 38 to which the channel is connected; and a detector that detects and digitizes the received MR signal. I orthogonal components and sum Q Orthogonal components. Then, the magnitude of the received MR signal can be determined as... I Components and Q The square root of the sum of the squares of the components is shown in equation (1) below:

[0022] (1);

[0023] Furthermore, the phase of the received MR signal can also be determined as shown in equation (2) below:

[0024] (2).

[0025] In an exemplary embodiment, digitized MR signal samples generated by RF system 30 are received by data acquisition server 22. Data acquisition server 22 can operate in response to instructions downloaded from workstation 12 to receive real-time MR data and provide buffer storage so that no data is lost due to data overflow. In some scans, data acquisition server 22 simply transmits the acquired MR data to data processing server 24. However, in scans where information derived from the acquired MR data is needed to control further execution of the scan, data acquisition server 22 is programmed to generate the required information and transmit it to pulse sequence server 20. For example, during a pre-scan, MR data is acquired and used to calibrate a pulse sequence performed by pulse sequence server 20. Additionally, navigator signals can be acquired during the scan and used to adjust operating parameters of RF system 30 or gradient system 28, or to control the view sequence for sampling k-space.

[0026] In an exemplary embodiment, the data processing server 24 receives MR data from the data acquisition server 22 and processes the MR data according to instructions downloaded from the workstation 12. Such processing may include, for example, performing a Fourier transform on the raw k-space MR data to produce a two-dimensional or three-dimensional image, applying filters to the reconstructed image, generating a functional MR image, and calculating a motion or flow image.

[0027] In an exemplary embodiment, the image reconstructed by the data processing server 24 is transmitted back to workstation 12 and stored there. In some embodiments, the real-time image is stored in a database storage cache. Figure 1A (Not shown in the image) Real-time images can be output from the database storage cache to the operator's display 14 or a display 46 located near the magnet assembly 34 for use by the attending physician. Batch-processed images or selected real-time images can be stored on disk storage 48 or a host database in the cloud. When such images have been reconstructed and transferred to the storage device, the data processing server 24 notifies the data storage server 26. The operator can use workstation 12 to archive images, generate films, or send images to other facilities via a network.

[0028] The systems and methods disclosed in this paper are used to reduce shading or correct non-uniformity in diffusion-weighted images. Diffusion refers to the random microscopic movement of water and other small molecules due to thermal agitation, also known as Brownian motion. In vivo, extracellular water generally diffuses more freely than intracellular water to some extent because the latter has more opportunities to collide with cell walls, organelles, macromolecules, etc. Diffusion can be isotropic, where water molecules diffuse equally around each other in all directions, which occurs in fluids and some homogeneous solid materials such as gels. On the other hand, biological tissues contain asymmetric structures, which produces diffusion anisotropy, i.e., water molecules preferentially diffuse in certain directions more than in others. Disease can increase or decrease water diffusion in tissues. For example, diffusion time is generally prolonged in many non-acute and chronic diseases, while reduced tissue diffusion can occur in diseases such as acute ischemia, infection, and high-cell tumors.

[0029] Diffusion is characterized by the diffusion coefficient D, which represents the flux of water or small particles across a surface via Brownian motion over a period of time, and has, for example, a value in mm. 2 / second is the unit area per second per unit time. For isotropic diffusion, diffusion can be characterized by a single diffusion coefficient D. For anisotropic diffusion, the diffusion coefficient D is a tensor, a 3x3 matrix containing elements such as Dxx, Dxy, and Dxz, which represent the diffusion rate along different directions.

[0030] MRI is unique compared to other imaging modalities because MRI signals are represented by complex numbers, rather than scalar or real numbers. Therefore, the image value of each image pixel includes both magnitude and phase. Composite MR images can be reconstructed based on I-orthogonal MR signals and Q-orthogonal MR signals using procedures such as Fourier transform. In MR, diffusion is studied and measured using diffusion-weighted or diffusion-based MR imaging with diffusion-weighted pulse sequences, where individual elements of the diffusion tensor are estimated by measuring phase dispersion and signal loss, due to the application of diffusion-sensitive, diffusion-weighted, or diffusion gradients in various directions. In MR, the pulse sequence is a sequence of RF pulses, gradient pulses, and data acquisition applied by the MRI system 10 during MR signal acquisition. Figure 1BThis is an exemplary diffusion-weighted (DW) sequence 150. The phase 152 of the spin or spin angular momentum is plotted along the same timeline as the RF pulse 154 and the diffusion gradient 156 to illustrate the evolution of the phase during the application of the diffusion gradient 156. The pulse sequence 150 also includes an image acquisition module 160, which is part of the pulse sequence 150, for acquiring diffusion-weighted images, such as fast spin echo sequences or echo-plane imaging sequences. The DW pulse sequence 150 may contain a diffusion-sensitive gradient (DG) 156 on either side of the 180° RF pulse 158. The phase of the stationary spin (line 157-s) is unaffected by the DG on 156-1, 156-2 because any phase accumulation from the first gradient lobe 156-1 is reversed by the second gradient lobe 156-2. However, the diffuse spin shifts to a different position between the first lobe 156-1 and the second lobe 156-2, losing phase and signal (see line 157-d). The signal S after applying the diffusion gradient is given by the following equation:

[0031] (3)

[0032] in This is the MR signal before the diffusion gradient is applied, and b, or the b value, is a factor reflecting the intensity and timing of the diffusion gradient used to generate the diffusion-weighted image. In pulse sequence 150, Where γ is the gyromagnetic ratio of the nucleus (such as a proton), G Let δ be the magnitude of the diffusion gradient 156, δ be the duration of the diffusion gradient 156, and Δ be the time interval between the two lobes 156-1 and 156-2. b also depends on the shape of the diffusion gradient pulses 156-1 and 156-2. The amount of diffusion weighting can be changed by changing the value of b. The direction of diffusion weighting can be changed by applying the diffusion gradient 156 in different directions. For example, the diffusion gradient 156 can be applied in the readout direction (x), the phase encoding direction (y), the slice selection or the second phase encoding direction (z), or any combination of x, y and / or z. Therefore, the rate and direction of the diffusion coefficient can be estimated based on the diffusion weighting image and equation (3). The apparent diffusion coefficient (ADC) is the commonly estimated diffusion coefficient, which is the average diffusion in the voxel, taken as the sum or average of the diagonal elements of the diffusion tensor. The ADC image reflects the diffusion rate in the voxel.

[0033] Because diffusion-weighted imaging is designed to be sensitive to motion, motion other than diffusion (such as tissue movement) affects diffusion-weighted images. For example, in abdominal diffusion-weighted imaging, liver tissue moves due to cardiac motion and / or respiration, and signal loss occurs in these areas due to motion. Signal loss also exists even during breath-hold diffusion-weighted imaging because cardiac motion is not affected by breath-holding. Therefore, a portion of a diffusion-weighted image may appear darker than other parts of the image, incorrectly representing a higher diffusion rate. Figure 2 Image 200 shows a diffusion-weighted image of a subject without using the systems and methods disclosed herein. Image 200 shows the liver 202 of the subject. The image was obtained using diffusion weighting at b=600 s / mm. 2 Image 200 was acquired. As shown in image 200, the intensity of image 200 is non-uniform, with the intensity at location 204 being much darker or having a much lower signal intensity than the intensity at location 206. The shadows may be caused by phase changes in cardiac motion. The shadows are more pronounced at higher b-values ​​because diffusion-weighted amplification of the motion effects.

[0034] The system and method described in this paper are used to reduce shading and correct non-uniformity in diffusion-weighted images without requiring a separate reference image for correction. The accuracy of the estimated diffusion coefficient is greatly increased.

[0035] Figure 3A This is a schematic diagram of an exemplary non-uniformity correction system 300. In an exemplary embodiment, system 300 includes a non-uniformity correction computing device 302 configured to correct non-uniformity in MR images. The non-uniformity correction computing device 302 may be included in a workstation 12 of the MRI system 10, or it may be included in a separate computing device that communicates with the workstation 12 via wired or wireless communication. In some embodiments, the non-uniformity correction computing device 302 is a computing device separate from the workstation 12 and receives MR images acquired by the workstation 12 via a portable storage device, such as a flash drive or thumb drive.

[0036] Figure 3B This is a flowchart of an exemplary method 350. Method 350 may be implemented on a non-uniformity correction computing device 302. In an exemplary embodiment, the method includes receiving 352 plurality of diffusion-weighted (DW) MR images along a plurality of diffusion-weighted (DW) directions having one or more excitation numbers (NEX). For example, the diffusion-weighting direction can be in the up-down (SI), left-right (LR), front-back (AP) directions, or any other direction not along the three orthogonal axes. NEX is the number of times the scan passes through k-space or the proportion of k-space scanned. NEX greater than 1 is used to increase the signal-to-noise ratio (SNR) of the image.

[0037] In an exemplary implementation, method 350 further includes deriving a reference image and a base image based on a plurality of DW MR images. The plurality of DW MR images can be represented as... When using multi-NEX and multi-directional DWI scanning, images are acquired from each NEX and orientation. Due to diffusion gradient and motion, each Their phases are different from each other. In some implementations, for each Remove phase to obtain For example, MR images are represented as magnitude and phase components:

[0038] (4)

[0039] in It is the absolute or quantitative value of the image at the pixel location, and the angle... It represents the phase of the image at a given pixel. The image can be removed by subtracting the phase. The phase is as follows:

[0040] (5)

[0041] Among the angles This refers to the image phase after applying a low-pass filter. The low-pass filter weights pixels around the center of k-space, making them heavier than pixels in the periphery. The region around the center of k-space (called the center region) corresponds to low spatial frequencies. The region in the periphery of k-space (called the periphery region) corresponds to high spatial frequencies. The signal-to-noise ratio (SNR) in the center region is typically higher than that in the periphery region. The low-pass filter smooths the image, reduces spikes in the periphery region, and increases the overall SNR of the image. Applying a low-pass filter can reduce the resolution of an image because the signal at high spatial frequencies is reduced by the filter. An exemplary low-pass filter is a Hanning window or a modified Hanning window that weights the region around the center of k-space more heavily than the standard Hanning window. Phase removal does not change the image magnitudes but only the image phase. After phase removal, the phase of the signal is reduced to almost zero, and the phase of the noise is... The distribution is a Gaussian random distribution up to π.

[0042] In an exemplary embodiment, the base image is a combined image of multiple DW images. The multiple DW images may or may not be phase-removed. Phase removal increases the accuracy of the DW images and the estimation of diffusion coefficients (such as ADCs) by keeping the phase between the diffusion direction and the NEX consistent with each other. Multiple DW images may be applied with a low-pass filter before being combined to derive the base image. An exemplary low-pass filter is a Hanning window or a modified Hanning window.

[0043] In one example, the base image could be a DW image. Arithmetic mean of the NEX count and DW image Geometric mean of the number of directions, such as:

[0044] and (6)

[0045] In some implementations, the base images are multiple DW images with or without phase removal. One of the DW images.

[0046] In another example, the base image is multiple DW images. The square root of the sum of the squares of the magnitudes, such as:

[0047] (7)

[0048] This combination is performed pixel-by-pixel. That is, Σ in equation (7) is the DW image. The sum of the values ​​of the same pixel at different diffusion directions and at individual NEX. This combination is referred to as the SOS combination in this paper. The SOS combination combines multiple DW images. The magnitudes are measured using orthogonal detectors that provide real and imaginary signals. It can be assumed that the noise in each signal has a Gaussian distribution. The MR image is reconstructed from the MR signal using a composite Fourier transform, which is a linear operation and preserves the Gaussian nature of the noise. The magnitude image is formed by calculating the magnitudes pixel-by-pixel from the real and imaginary images as the square root of the sum of the squares of the real and imaginary components. This operation is non-linear, and the noise distribution becomes a Ricean distribution. When the image's SNR is high, the noise distribution of the magnitude image can be approximated by a Gaussian distribution. However, when the image's SNR is low, and when there is no MR signal in the image region, the noise distribution of the magnitude image approximates a Rayleigh distribution. At high b values, the SNR of the DW image is low due to the loss of diffusion-weighted signal. The SOS combination is a magnitude combination that combines multiple DW images. The magnitude of the noise distribution can be altered, and changes in the noise distribution affect the SNR of the diffusion-weighted image and the accuracy of the estimated diffusion coefficient.

[0049] In other implementations, the base image is a plurality of images. Composite combinations, such as:

[0050] (8)

[0051] in This indicates pixel-wise complex multiplication when the multiplier or multiplicand is represented by a complex number. For example, if x + yi and u + vi are two complex numbers, then (x + yi) (u+vi) = xu-yv+(xv+yu)i. The DW image is filtered by a low-pass filter. An exemplary low-pass filter is a Hanning window or a modified Hanning window. It is the square root of the sum of squares of the magnitudes at pixels across the diffusion direction and NEX. This combination, expressed in equation (8), can be called a c3-class combination. The c3-class combination is also performed pixel-by-pixel. That is, Σ in equation (8) is the DW image The sum of the values ​​of the same pixel at different diffusion directions and individual NEX. In one example, a low-pass filter is not applied in the c3 class combination, where In equation 8, it is replaced by Class c3 combinations are composite combinations that do not alter the noise distribution. Compared to SOS combinations, images combined using class c3 combinations show better noise distribution than images combined using the same set of DW images. The combined images of the SOS combination have lower background noise levels. For reconstruction methods using deep learning, the c3 combination performs better than the SOS combination because the neural network models used in deep learning reconstruction are typically trained with noise that has a Gaussian distribution.

[0052] In an exemplary embodiment, the reference image is a plurality of DW images. The maximum intensity projection image in NEX. At each pixel, the maximum intensity projection image contains the diffusion direction and the maximum value in NEX for that pixel. For example, if multiple DW images Acquired along three diffusion directions and three NEXs, the maximum intensity projected image at each pixel is the maximum value of nine magnitudes at that pixel across different diffusion directions and individual NEXs. Maximum intensity projection is performed pixel-by-pixel because the entire slice or imaging volume does not move together. In some embodiments, a low-pass filter is applied to multiple DW images before generating the maximum intensity projected image. That is, the maximum intensity projected image contains the maximum value of pixel-by-pixel magnitudes from multiple filtered DW images. In other embodiments, the reference image is the maximum intensity projected image with a low-pass filter applied after the maximum intensity projected image has been generated. An exemplary low-pass filter is a Hanning window or a modified Hanning window. Motion affects signal strength. However, motion is not always present or present in all directions and / or does not have the same velocity in all directions. Therefore, the effects of motion may be present in some diffusion directions or at some NEXs or less severe than in other diffusion directions or NEXs. Therefore, the maximum intensity projected image is minimally affected by motion. The advantage of using the maximum intensity projected image as a reference image is that the reference image is self-generated and does not need to be provided separately. Providing a reference image of the diffusion image of a living subject alone (i.e., a diffusion-weighted image that does not contain motion effects or has greatly reduced motion effects) is impractical because some parts of the living subject (such as the heart and surrounding tissues) are constantly moving even during breath-holding.

[0053] In an exemplary implementation, a 356-dimensional non-uniformity factor image is generated based on a reference image and a base image. The non-uniformity factor image is the base image divided pixel-by-pixel by the reference image. If the reference image is a composite image, the division operation when deriving the non-uniformity factor image can be performed as either a composite division or a pixel-by-pixel division by the reference image. For example, for complex numbers x + yi and u + vi, (u + vi) / (x + yi) = (1 / (x + yi)) 2 +y 2Method 350 further includes combining multiple DW MR images 358 into a combined image. Before combining, a low-pass filter may be applied to the multiple DW images. The combination may be any of the combinations described above. The combined image and the base image may be the same or similar, wherein they are combined by the same combination mechanism described above. Alternatively, different combination schemes may be used to combine the combined image and the base image. For example, the base image may be combined by arithmetic mean and geometric mean according to equation (6), and the combined image for deriving the corrected image may be combined using a combination of type c3 according to equation (8). The non-uniformity of the combined image is corrected 360 by dividing the combined image by the non-uniformity factor image. If the non-uniformity image is a composite image, the division may be a composite division or a pixel-by-pixel division by the magnitude of the non-uniformity factor image. The corrected image is output 362. In some embodiments, combining 358 multiple DW MR images is skipped, and correcting the non-uniformity of the combined image 360 ​​becomes correcting the non-uniformity of one of the multiple DW MR images. In some implementations, system 300 is configured to output a non-uniformity factor image.

[0054] In some implementations, when the combined image and the base image are identical, the corrected image is mathematically identical to the reference image (such as a MIP image) when the corrected image is calculated as the combined image divided by a non-uniformity factor image, which is the quotient of the base image divided by the reference image. However, in practice, the corrected image has an increased SNR compared to the MIP image due to the combination and averaging of multiple NEX images. Furthermore, if motion occurs between the input images, the MIP image exhibits overlap or phantom artifacts at image edges due to motion, while such artifacts are reduced in the corrected image.

[0055] Figure 4 DW images 402 and 404, and ADC image 406, of the subject's liver region with b-values ​​of 50 and 700 using the SOS combination are shown. Even at the high b-value of 700, the shading in the liver 202 is significantly reduced compared to image 200, which does not use the non-uniformity correction system and method described herein.

[0056] Figure 5 This demonstrates the use of class c3 composition with Figure 4 The images 402 and 404 are DW images with b values ​​of 50 and 700 at the same slice location, and ADC image 506. Similarly, the shadow at liver 202 is greatly reduced.

[0057] Figure 6This is a comparison of ADC maps 602, 406, and 506 for the same slice location of the subject with and without non-homogeneity correction (ADC map 406, 506). Without non-homogeneity correction, the diffusion rate in some areas of liver 202 (indicated by arrow 604) is overestimated. This overestimation negatively impacts diagnostic accuracy.

[0058] The workstation 12 and the non-uniformity correction computing device 302 described herein can be any suitable computing device 800 and the software implemented therein. Figure 7 This is a block diagram of an exemplary computing device 800. In an exemplary embodiment, the computing device 800 includes a user interface 804 that receives at least one input from a user. The user interface 804 may include a keyboard 806 that enables the user to input relevant information. The user interface 804 may also include, for example, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad and a touchscreen), a gyroscope, an accelerometer, a position detector, and / or an audio input interface (e.g., including a microphone).

[0059] Furthermore, in an exemplary embodiment, computing device 800 includes a display interface 817 that presents information (such as input events and / or verification results) to a user. Display interface 817 may also include a display adapter 808 coupled to at least one display device 810. More specifically, in an exemplary embodiment, display device 810 may be a visual display device, such as a cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED) display, and / or "electronic ink" display. Alternatively, display interface 817 may include audio output devices (e.g., audio adapters and / or speakers) and / or a printer.

[0060] The computing device 800 also includes a processor 814 and a memory device 818. The processor 814 is connected to a user interface 804, a display interface 817, and the memory device 818 via a system bus 820. In an exemplary embodiment, the processor 814 communicates with a user, such as by prompting the user via the display interface 817 and / or by receiving user input via the user interface 804. The term "processor" generally refers to any programmable system, including systems and microcontrollers, reduced instruction set computers (RISCs), complex instruction set computers (CISCs), application-specific integrated circuits (ASICs), programmable logic circuits (PLCs), and any other circuitry or processor capable of performing the functions described herein. The examples above are merely exemplary and are therefore not intended to limit the definition and / or meaning of the term "processor" in any way.

[0061] In an exemplary embodiment, memory device 818 includes one or more devices that enable information (such as executable instructions and / or other data) to be stored and retrieved. Furthermore, memory device 818 includes one or more computer-readable media, such as, but not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), solid-state drive, and / or hard disk. In an exemplary embodiment, memory device 818 stores, but is not limited to, application source code, application object code, configuration data, additional input events, application state, assertion statements, verification results, and / or any other type of data. In an exemplary embodiment, computing device 800 may also include a communication interface 830 coupled to processor 814 via system bus 820. Furthermore, communication interface 830 is communicatively coupled to a data acquisition device.

[0062] In an exemplary embodiment, processor 814 can be programmed by encoding operations using one or more executable instructions and by providing executable instructions in memory device 818. In an exemplary embodiment, processor 814 is programmed to select multiple measurement results received from a data acquisition device.

[0063] In operation, a computer executes computer-executable instructions embodied in one or more computer-executable components stored on one or more computer-readable media to implement aspects of the invention described and / or illustrated herein. Unless otherwise specified, the order of execution or implementation of the operations in the embodiments of the invention shown and described herein is not required. That is, unless otherwise specified, these operations can be performed in any order, and embodiments of the invention may include more or fewer operations than those disclosed herein. For example, it is contemplated that a particular operation may be performed or implemented before, simultaneously with, or after another operation within the scope of various aspects of the invention.

[0064] At least one technical effect of the systems and methods described herein includes (a) reducing non-uniformity; (b) improving the accuracy of diffusion coefficient estimation; and (c) correcting non-uniformity in the absence of a separately provided reference image.

[0065] Exemplary embodiments of systems and methods for nonuniformity correction have been described in detail above. These systems and methods are not limited to the specific embodiments described herein, but rather the components of the systems and / or the operation of the methods can be used independently and separately from other components and / or operations described herein. Furthermore, the described components and / or operations may also be defined in other systems, methods, and / or devices, or used in combination with other systems, methods, and / or devices, and are not limited to practice using only the systems described herein.

[0066] Although certain features of various embodiments of the invention may be shown in some figures but not others, this is only for convenience. Any feature of the figures may be referenced and / or claimed in conjunction with any feature of any other figure according to the principles of the invention.

[0067] This written description uses examples to disclose the invention, including the best mode, and also enables those skilled in the art to practice the invention, including making and using any device or system and performing any included methods. The scope of the invention is defined by the claims and may include other examples that would occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that are not indistinguishable from the literal language of the claims, or if they include equivalent structural elements that have minor differences from the literal language of the claims.

Claims

1. A method for correcting non-uniformity in diffusion-weighted (DW) magnetic resonance (MR) images of a subject, comprising: Multiple DW MR images of the subject are acquired along the multiple diffusion directions having one or more excitation numbers NEX by applying a DW pulse sequence along the multiple diffusion directions having one or more excitation numbers NEX. A reference image is derived based on the plurality of DW MR images; The base image is derived based on the multiple DW MR images; A non-uniformity factor image is generated based on the reference image and the base image; The multiple DW MR images are combined into a composite image; The non-uniformity factor image is used to correct the non-uniformity of the combined image; as well as Output the corrected image.

2. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein generating the non-uniformity factor image further includes generating the non-uniformity factor image by dividing the base image by the reference image, and correcting the non-uniformity of the combined image further includes correcting the non-uniformity of the combined image by dividing the combined image by the non-uniformity factor image.

3. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein combining the plurality of DW MR images further includes: For each pixel in the combined image, the image value of the combined image at that pixel is set to the square root of the sum of the squares of the magnitude values ​​of the plurality of DW MR images at that pixel in the diffusion direction and on the NEX.

4. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein combining the plurality of DW MR images further includes: For each pixel in the combined image Calculate the sum of the complex multiplications of the image values ​​of the plurality of DW MR images at the pixel in the diffusion direction and the NEX with the image values ​​of the plurality of DW MR images at the pixel; as well as The image value of the combined image at the pixel is set to the sum divided by the square root of the square of the magnitude values ​​of the plurality of DW MR images at the pixel in the diffusion direction and the NEX.

5. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein combining the plurality of DW MR images further includes: A low-pass filter is applied to the plurality of DW MR images to generate a plurality of filtered images; as well as For each pixel in the combined image Calculate the sum of complex multiplications of the image values ​​of the plurality of DW MR images at the pixel in the diffusion direction and the NEX with the image values ​​of the plurality of filtered images at the pixel; as well as The image value of the combined image at the pixel is set to the sum divided by the square root of the sum of the squares of the magnitudes of the plurality of filtered images at the pixel in the diffusion direction and the NEX.

6. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein deriving the reference image and the base image further includes: The reference image is derived as the maximum intensity projection image based on the plurality of DW MR images.

7. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 6, wherein deriving the reference image further includes: For each pixel of the reference image, the image value of the reference image at that pixel is set to the maximum value among the magnitude values ​​of the plurality of DW MR images at that pixel in the diffusion direction and on the NEX.

8. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 6, wherein deriving the reference image further includes: A low-pass filter is applied to the plurality of DW MR images to derive a plurality of filtered images; as well as For each pixel of the reference image, the image value of the reference image at that pixel is set to the maximum value among the plurality of filtered image magnitude values ​​at that pixel in the diffusion direction and on the NEX.

9. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 6, wherein deriving the reference image further includes applying a low-pass filter to the reference image to derive a filtered reference image, and generating the non-uniformity factor image further includes generating the non-uniformity factor image based on the filtered reference image and the base image.

10. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein deriving the reference image and the base image further includes: A low-pass filter is applied to the plurality of DW MR images to derive a plurality of filtered images; as well as The multiple filtered images are combined to generate the base image.

11. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein deriving the reference image and the base image further includes: For each pixel in the base image, the image value of the base image at the pixel is set to the square root of the sum of the squares of the magnitude values ​​of the plurality of DW MR images at the pixel in the diffusion direction and on the NEX.

12. The MR imaging method for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 1, wherein deriving the reference image and the base image further includes: A low-pass filter is applied to the plurality of DW MR images to generate a plurality of filtered images; as well as For each pixel in the base image Calculate the sum of complex multiplications of the image values ​​of the plurality of DW MR images at the pixel in the diffusion direction and the NEX with the image values ​​of the plurality of filtered images at the pixel; as well as The image value of the base image at the pixel is set to the sum divided by the square root of the sum of the squares of the magnitudes of the plurality of filtered images at the pixel in the diffusion direction and the NEX.

13. A non-uniformity correction system for correcting non-uniformity in diffusion-weighted magnetic resonance (DW) MR images of a subject, comprising a non-uniformity correction computing device, the non-uniformity correction computing device including at least one processor communicating with at least one memory device, and the at least one processor being programmed to: Multiple DW MR images of the subject are received along the multiple diffusion directions having one or more excitation numbers NEX by applying a DW pulse sequence along the multiple diffusion directions having one or more excitation numbers NEX. A reference image is derived based on the plurality of DW MR images; The base image is derived based on the multiple DW MR images; A non-uniformity factor image is generated based on the reference image and the base image; The multiple DW MR images are combined into a composite image; The non-uniformity factor image is used to correct the non-uniformity of the combined image; as well as Output the corrected image.

14. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: The non-uniformity factor image is generated by dividing the base image by the reference image; and The non-uniformity of the combined image is corrected by dividing the combined image by the non-uniformity factor image.

15. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: For each pixel in the combined image, the image value of the combined image at that pixel is set to the square root of the sum of the squares of the magnitude values ​​of the plurality of DW MR images at that pixel in the diffusion direction and on the NEX.

16. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: A low-pass filter is applied to the plurality of DW MR images to generate a plurality of filtered images; as well as For each pixel in the combined image Calculate the sum of complex multiplications of the image values ​​of the plurality of DW MR images at the pixel in the diffusion direction and the NEX with the image values ​​of the plurality of filtered images at the pixel; as well as The image value of the combined image at the pixel is set to the sum divided by the square root of the sum of the squares of the magnitudes of the plurality of filtered images at the pixel in the diffusion direction and the NEX.

17. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: For each pixel of the reference image, the image value of the reference image at that pixel is set to the maximum value among the magnitude values ​​of the plurality of DW MR images at that pixel in the diffusion direction and on the NEX.

18. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: A low-pass filter is applied to the plurality of DW MR images to derive a plurality of filtered images; and For each pixel of the reference image, the image value of the reference image at that pixel is set to the maximum value among the plurality of filtered image magnitude values ​​at that pixel in the diffusion direction and on the NEX.

19. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: For each pixel in the base image, the image value of the base image at the pixel is set to the square root of the sum of the squares of the magnitude values ​​of the plurality of DW MR images at the pixel in the diffusion direction and on the NEX.

20. The non-uniformity correction system for correcting non-uniformity in diffusion-weighted DW magnetic resonance MR images of a subject according to claim 13, wherein the at least one processor is further programmed to: A low-pass filter is applied to the plurality of DW MR images to generate a plurality of filtered images; as well as For each pixel in the base image Calculate the sum of complex multiplications of the image values ​​of the plurality of DW MR images at the pixel in the diffusion direction and the NEX with the image values ​​of the plurality of filtered images at the pixel; as well as The image value of the base image at the pixel is set to the sum divided by the square root of the sum of the squares of the magnitudes of the plurality of filtered images at the pixel in the diffusion direction and the NEX.

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