Medical imaging method and medical imaging system

Abstract

The embodiment of the invention provides a medical imaging method and a medical imaging system, and the method comprises the steps: determining an image combination mode corresponding to a diffusion sensitive factor according to the diffusion sensitive factor corresponding to a diffusion weighted image, and the diffusion weighted image comprises to-be-combined images in at least two diffusion directions; and according to the determined image combination mode, combining the to-be-combined images to generate a combined image.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of medical equipment technology, and in particular, to a medical imaging method and a medical imaging system. Background Art

[0002] Magnetic resonance imaging (MRI) technology has been widely used in the field of medical diagnosis. Magnetic resonance imaging provides detailed images of soft tissue, abnormal tissue (such as tumors), and other structures that cannot be easily imaged by other imaging modalities such as computed tomography (CT). Magnetic resonance imaging operates without exposing the patient to ionizing radiation such as experienced in CT and X-ray.

[0003] Diffusion weighted imaging (DWI), as a type of MRI, has the advantages of fast imaging speed and is widely used in the diffusion of biological tissues and the diagnosis of diseases. However, the known diffusion weighted imaging is disadvantageous in some aspects, and improvements are desired. Summary of the invention

[0004] Embodiments of the present application provide a medical imaging method and a medical imaging system.

[0005] According to one aspect of an embodiment of the present application, a medical imaging method is provided, the method comprising: determining an image combination mode corresponding to a diffusion sensitivity factor corresponding to a diffusion weighted image, the diffusion weighted image comprising images to be combined in at least two diffusion directions; and combining the images to be combined to generate a combined image according to the determined image combination mode.

[0006] According to one aspect of an embodiment of the present application, a medical imaging system is provided, the system comprising: a scanning unit; and a controller configured to execute the above-mentioned medical imaging method.

[0007] One of the beneficial effects of the embodiment of the present application is that: by determining the image combination method corresponding to the diffusion sensitivity factor according to the diffusion weighted image, and combining the images to be combined according to the determined image combination method to generate a combined image, the uniformity of the combined image can be improved, and the artifacts of the combined image can be reduced, which helps to reduce misdiagnosis / confusion.

[0008] With reference to the following description and accompanying drawings, the specific implementation of the embodiment of the present application is disclosed in detail, indicating the way in which the principle of the embodiment of the present application can be adopted. It should be understood that the implementation of the present application is not limited in scope. Within the scope of the spirit and clauses of the attached claims, the implementation of the present application includes many changes, modifications and equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The included drawings are used to provide a further understanding of the embodiments of the present application, which constitute a part of the specification, are used to illustrate the implementation methods of the present application, and together with the text description, explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other implementation methods can be obtained based on these drawings without creative work. In the drawings:

[0010] Figure 1 is a schematic diagram of a magnetic resonance imaging system according to an embodiment of the present application;

[0011] Figure 2 is a schematic diagram of a diffusion weighted (DW) sequence according to an embodiment of the present application;

[0012] Figure 3 is a schematic diagram of a medical imaging method according to an embodiment of the present application;

[0013] Figure 4 is a schematic diagram of the process of step 302 of the embodiment of the present application;

[0014] Figure 5 is a schematic diagram of a diffusion weighted image according to an embodiment of the present application and a combined image generated according to different image combination methods;

[0015] Figure 6 is another schematic diagram of a diffusion weighted image according to an embodiment of the present application and a combined image generated according to different image combination methods;

[0016] Figure 7 is another schematic diagram of the medical imaging method according to an embodiment of the present application;

[0017] Figure 8 is a schematic diagram of a combined image and an ADC image obtained according to different image combination methods in an embodiment of the present application;

[0018] Fig. 9 It is another schematic diagram of a combined image and an ADC image obtained according to different image combination methods in an embodiment of the present application. DETAILED DESCRIPTION

[0019] With reference to the accompanying drawings, the foregoing and other features of the embodiments of the present application will become apparent through the following description. In the description and the accompanying drawings, specific embodiments of the present application are specifically disclosed, which show some embodiments in which the principles of the embodiments of the present application can be adopted. It should be understood that the present application is not limited to the described embodiments. On the contrary, the embodiments of the present application include all modifications, variations and equivalents that fall within the scope of the attached claims.

[0020] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements in terms of title, but do not indicate the spatial arrangement or temporal order of these elements, etc., and these elements should not be limited by these terms. The term "and / or" includes any one and all combinations of one or more of the associated listed terms. The terms "comprising", "including", "having", etc. refer to the presence of the stated features, elements, components or components, but do not exclude the presence or addition of one or more other features, elements, components or components.

[0021] In the embodiments of the present application, the singular forms "a", "the", etc. include plural forms and should be broadly understood as "a kind" or "a type" rather than being limited to the meaning of "one"; in addition, the term "said" should be understood to include both singular and plural forms, unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least in part according to...", and the term "based on" should be understood as "at least in part based on...", unless the context clearly indicates otherwise.

[0022] Features described and / or shown for one embodiment may be used in one or more other embodiments in the same or similar manner, combined with features in other embodiments, or replace features in other embodiments. The term "includes / comprising" when used herein refers to the presence of a feature, an entirety, a step, or a component, but does not exclude the presence or addition of one or more other features, entireties, steps, or components.

[0023] For ease of understanding, Figure 1 FIG. 1 is a schematic diagram of a magnetic resonance imaging (MRI) system 100 according to an embodiment of the present application.

[0024] The MRI system 100 includes a scanning unit 111. The scanning unit 111 is used to perform magnetic resonance scanning on an object (eg, a human body) 170 to generate image data of a region of interest of the object 170, where the region of interest may be a predetermined anatomical site or anatomical tissue.

[0025] The operation of the MRI system 100 is controlled by an operator workstation 110, which includes an input device 114, a control panel 116, and a display 118. The input device 114 can be a joystick, a keyboard, a mouse, a trackball, a touch-activated screen, voice control, or any similar or equivalent input device. The control panel 116 can include a keyboard, a touch-activated screen, voice control, buttons, a slider, or any similar or equivalent control device. The operator workstation 110 is coupled to and communicates with a computer system 120, which enables an operator to control the generation and viewing of images on the display 118. The computer system 120 includes multiple components that communicate with each other via electrical and / or data connection modules 122. The connection module 122 can be a direct wired connection, an optical fiber connection, a wireless communication link, etc. The computer system 120 may include a central processing unit (CPU) 124, a memory 126, and an image processor 128. In some embodiments, the image processor 128 can be replaced by a medical imaging function implemented in the CPU 124. The computer system 120 may be connected to an archival media device, permanent or backup storage, or a network. The computer system 120 may be coupled to and communicate with a separate MRI system controller 130 .

[0026] The MRI system controller 130 includes a set of components that communicate with each other via an electrical and / or data connection module 132. The connection module 132 can be a direct wired connection, an optical fiber connection, a wireless communication link, etc. The MRI system controller 130 can include a CPU 131, a sequence pulse generator (or pulse generator) 133 that communicates with the operator workstation 110, a transceiver (or RF transceiver) 135, a memory 137, and an array processor 139. In some embodiments, the sequence pulse generator 133 can be integrated into the resonance assembly 140 of the scanning unit 111 of the MRI system 100. The MRI system controller 130 can receive commands from the operator workstation 110, coupled to the scanning unit 111, to indicate the MRI scanning sequence to be performed during the MRI scan, so as to control the scanning unit 111 to perform the above-mentioned magnetic resonance scanning process. The MRI system controller 130 is also coupled to and communicates with a gradient driver system 150, which is coupled to the gradient coil assembly 142 to generate a magnetic field gradient during the MRI scan.

[0027] The sequence pulse generator 133 may also receive data from a physiological acquisition controller 155, which receives signals from a plurality of different sensors (such as electrocardiogram (ECG) signals from electrodes attached to the patient) connected to a subject or patient 170 undergoing an MRI scan. The sequence pulse generator 133 is coupled to and in communication with a scan room interface system 145, which receives signals from various sensors associated with the state of the resonance assembly 140. The scan room interface system 145 is also coupled to and in communication with a patient positioning system 147, which sends and receives signals to control the movement of the patient table to a desired position for an MRI scan.

[0028] The MRI system controller 130 provides gradient waveforms to the gradient driver system 150, which includes a G x (x direction), G y (y direction) and G z (z direction) amplifier, etc. Each G x , G y and G z The gradient amplifiers all excite corresponding gradient coils in the gradient coil assembly 142 to generate magnetic field gradients for spatially encoding MR signals during MRI scanning. The gradient coil assembly 142 is disposed within a resonance assembly 140, which also includes a superconducting magnet having superconducting coils 144, which in operation provide a static uniform longitudinal magnetic field B0 throughout a cylindrical imaging volume 146. The resonance assembly 140 also includes an RF body coil 148, which in operation provides a transverse magnetic field B1 that is substantially perpendicular to B0 throughout the cylindrical imaging volume 146. The resonance assembly 140 may also include an RF surface coil 149 for imaging different anatomical structures of a patient undergoing an MRI scan. The RF body coil 148 and the RF surface coil 149 may be configured to operate in a transmit and receive mode, a transmit mode, or a receive mode.

[0029] The x direction can also be called the frequency encoding direction or the k direction in K space. x The y direction can be called the phase encoding direction or the k direction in K space. y Direction, the z direction can be called the layer selection direction. x It can be used for frequency encoding or signal readout and is usually called frequency encoding gradient or readout gradient. y Can be used for phase encoding, usually called phase encoding gradient. G z It can be used for slice (layer) position selection to obtain K-space data. It should be noted that the slice selection direction, phase encoding direction and frequency encoding direction can be modified according to actual needs.

[0030] An MRI scanned subject or patient 170 may be positioned within a cylindrical imaging volume 146 of the resonance assembly 140. A transceiver 135 in the MRI system controller 130 generates RF excitation pulses that are amplified by an RF amplifier 162 and provided to an RF body coil 148 via a transmit / receive switch (T / R switch) 164.

[0031] As described above, the RF body coil 148 and the RF surface coil 149 can be used to transmit RF excitation pulses and / or receive the resulting MR signals from the patient undergoing the MRI scan. The MR signals emitted by the nuclei excited in the patient's body of the MRI scan can be sensed and received by the RF body coil 148 or the RF surface coil 149 and sent back to the preamplifier 166 through the T / R switch 164. The T / R switch 164 can be controlled by a signal from the sequence pulse generator 133 to electrically connect the RF amplifier 162 to the RF body coil 148 during the transmit mode, and to connect the preamplifier 166 to the RF body coil 148 during the receive mode. The T / R switch 164 can also enable the RF surface coil 149 to be used in the transmit mode or the receive mode.

[0032] In some embodiments, the MR signals sensed and received by the RF body coil 148 or the RF surface coil 149 and amplified by the preamplifier 166 are stored as a raw k-space data array in the memory 137 for post-processing. A reconstructed magnetic resonance image can be acquired by transforming / processing the stored raw k-space data.

[0033] In some embodiments, the MR signals sensed and received by the RF body coil 148 or the RF surface coil 149 and amplified by the preamplifier 166 are demodulated, filtered, and digitized in the receive portion of the transceiver 135 and transmitted to the memory 137 in the MRI system controller 130. For each image to be reconstructed, the data is rearranged into a separate k-space data array, and each of these separate k-space data arrays is input to an array processor 139, which is operated to Fourier transform the data into an array of image data.

[0034] The array processor 139 uses a transform method, most commonly a Fourier transform, to create images from the received MR signals. These images are transmitted to the computer system 120 and stored in the memory 126. In response to commands received from the operator workstation 110, the image data may be stored in long-term storage or may be further processed by the image processor 128 and transmitted to the operator workstation 110 for presentation on the display 118.

[0035] In various embodiments, components of computer system 120 and MRI system controller 130 may be implemented on the same computer system or multiple computer systems. Figure 1 The MRI system 100 shown is for illustration purposes. Suitable MRI systems may include more, fewer, and / or different components.

[0036] The MRI system controller 130 and the image processor 128 may include a computer processor and a storage medium, respectively or in common, on which a program for predetermined data processing to be executed by the computer processor is recorded, for example, the storage medium may store a program for implementing scanning processing (e.g., scanning process, imaging sequence), image reconstruction, medical imaging, etc. For example, a program for implementing the magnetic resonance imaging method of the embodiment of the present invention may be stored. The above-mentioned storage medium may include, for example, a ROM, a floppy disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, or a non-volatile memory card.

[0037] Embodiments of the present application are used to correct the non-uniformity of the combined image and reduce artifacts in the combined image. 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 is usually more freely diffused than intracellular water to a certain extent, because the latter has more opportunities to collide with cell walls, organelles, macromolecules, etc. Diffusion can be isotropic, in which water molecules diffuse equally around water molecules 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 produce diffusion anisotropy, i.e., water molecules diffuse preferentially in certain directions more than other directions. Diseases can increase or decrease the diffusion of water in tissues. For example, diffusion time is usually prolonged in many non-acute and chronic diseases, and a reduction in tissue diffusion can occur in diseases such as acute ischemia, infection, and high cell tumors.

[0038] 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 values, for example, in mm 2 / s is the unit area / time. For isotropic diffusion, the 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 rates along different directions.

[0039] MRI is unique compared to other imaging modalities because MRI signals are represented by complex numbers, rather than by scalar or real numbers. Therefore, the image value of each image pixel includes both magnitude and phase. A composite MR image can be reconstructed based on the I quadrature MR signal and the Q quadrature MR signal using processes such as Fourier transforms. In MR, diffusion is studied and measured by diffusion-weighted or diffusion MR imaging using diffusion-weighted pulse sequences, where the individual elements of the diffusion tensor are estimated by measuring phase dispersion and signal loss as diffusion-sensitive, diffusion-weighted, or diffusion gradients are applied in various directions. In MR, a pulse sequence is a sequence of RF pulses, gradient pulses, and data acquisitions applied by the MRI system 100 when acquiring MR signals.

[0040] Figure 2 1 is a schematic diagram of a diffusion weighted (DW) sequence of an embodiment of the present application. The phase 152 of the spin or spin angular momentum is plotted along the same time line as the RF pulse 154 and the diffusion gradient 156 to show the evolution of the phase during the application of the diffusion gradient 156. The pulse sequence 250 also includes an image acquisition module 160, which is a part of the pulse sequence 250, for acquiring diffusion weighted images, such as a fast spin echo sequence or an echo planar imaging sequence. The DW pulse sequence 250 may include a diffusion sensitive gradient (DG) 156 on either side of the 180° RF pulse 154-1. The phase of the stationary spin (line 157-s) is not affected by the DG pair 156-1, 156-2 because any phase accumulation from the first gradient lobe 156-1 is inverted by the second gradient lobe 156-2. However, the diffuse spin moves to a different position between the first lobe 156-1 and the second lobe 156-2, loses phase and loses signal (see line 157-d). The signal S after applying the diffusion gradient is given by the following equation:

[0041] S=S0e -bD (1)

[0042] Wherein, S0 is the MR signal before the diffusion gradient is applied, b or b value is a factor reflecting the intensity and timing of the diffusion gradient used to generate the diffusion weighted image (hereinafter referred to as the diffusion sensitivity factor or diffusion gradient or b value), and a higher b value indicates a higher sensitivity to the diffusion behavior of water molecules. In pulse sequence 250, Wherein, γ is the gyromagnetic ratio of a nucleus (such as a proton), G is the magnitude of the diffusion gradient 156, δ is the duration of the diffusion gradient 156, and Δ is the time interval between the two lobes 156-1, 156-2. b also depends on the shape of the diffusion gradient pulses 156-1, 156-2. The amount of diffusion weighting can be changed by changing the b value. 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 in at least one of the readout direction (x), the phase encoding direction (y), the slice selection or the second phase encoding direction (z). Therefore, the rate and direction of the diffusion coefficient D can be estimated based on the diffusion weighted image and equation (1). The apparent diffusion coefficient (ADC) is a diffusion coefficient that is usually estimated as the average diffusion in the voxel, taking the sum or average of the diagonal elements of the diffusion tensor. The ADC map reflects the diffusion rate in the voxel.

[0043] Since diffusion-weighted imaging is designed to be motion sensitive, motion other than diffusion, such as tissue movement, can also affect the diffusion-weighted images. For example, in abdominal diffusion-weighted imaging, liver tissue moves due to cardiac motion and / or respiration, and the signal in these areas is lost due to motion. There is signal loss even during breath-hold diffusion-weighted imaging, because cardiac motion is not affected by breath-holding. As a result, diffusion-weighted images have poor uniformity and cannot correctly show diffusion rates. For example, one part of a diffusion-weighted image appears darker than other parts of the image, incorrectly shown as having a higher diffusion rate.

[0044] In the diffusion-weighted imaging process, when a diffusion gradient of a certain intensity (expressed by the b value) is used, the signal obtained actually has two sources of contrast: one is T2WI contrast, which depends on the content of water molecules in the imaging tissue and the T2 relaxation properties; the other contrast comes from the diffusion-weighted contrast of the diffusion-weighted gradient (determined by the b value) and the diffusion behavior of the water molecules themselves. When a lesion is found to show a high signal change on the diffusion-weighted image, it is necessary to interpret the corresponding signal change from two dimensions: the T2 relaxation property and the diffusion property of the lesion itself. The contrast composition of the diffusion-weighted image can be understood as follows: the signal expressed by the diffusion-weighted image is the diffusion-weighted contrast brought about by the addition of the diffusion-weighted gradient to the background signal formed by the T2WI contrast, and the final signal expression is a comprehensive reflection of these two contrasts.

[0045] This makes the signal performance of diffusion-weighted images more complex to a certain extent, resulting in the following special situations in diffusion-weighted images, such as T2 shine-through, T2 washout, T2 blackout, etc. Among them, the T2 shine-through effect refers to the fact that the lesion has a high T2 contrast background signal due to its high water content and long T2 properties. Although the diffusion of water molecules in the lesion may be unrestricted or even freer, it appears as a high signal on the final diffusion-weighted image.

[0046] The inventors have found that in the existing diffusion-weighted image processing method, the inhomogeneity is particularly obvious in images with high b values; and in images with low b values, artifacts are likely to appear in the image due to the influence of the difference in T2 transmission effects in different diffusion directions, which can easily lead to misdiagnosis / confusion by doctors.

[0047] In response to at least one of the above problems, an embodiment of the present application provides a medical imaging method and a medical imaging system.

[0048] It should be noted that the subject mentioned in the embodiments of the present application may be a human or an animal, etc. The diffusion weighted image of the liver is described in the embodiments of the present application only as an example, and the method and system disclosed in the embodiments of the present application can also be used to process diffusion weighted images of other parts of the body (such as the pancreas, etc.).

[0049] The following is a description with reference to embodiments.

[0050] The present application embodiment provides a medical imaging method, Figure 3 is a schematic diagram of a medical imaging method according to an embodiment of the present application. Figure 3 As shown, the method includes:

[0051] Step 301: determining an image combination method corresponding to a diffusion sensitivity factor corresponding to a diffusion weighted image, wherein the diffusion weighted image includes images to be combined in at least two diffusion directions; and

[0052] Step 302: According to the determined image combination method, the images to be combined are combined to generate a combined image.

[0053] Through the above embodiment, by determining the image combination mode corresponding to the diffusion sensitivity factor according to the diffusion weighted image, and combining the images to be combined according to the determined image combination mode to generate a combined image, the uniformity of the combined image can be improved, and the artifacts of the combined image can be reduced, which helps to reduce misdiagnosis / confusion.

[0054] In some embodiments, the diffusion weighted image corresponds to a diffusion sensitivity factor, ie, a b value. As described above, the higher the b value of the diffusion weighted image, the higher the sensitivity of the diffusion weighted image to the diffusion behavior of water molecules.

[0055] In some embodiments, the diffusion weighted image includes images to be combined in at least two diffusion directions. The diffusion sensitivity factor corresponds to the corresponding image combination mode. In step 301, the image combination mode for combining the images to be combined in the diffusion weighted image can be determined according to the diffusion sensitivity factor of the diffusion weighted image. In step 302, the images to be combined can be combined according to the determined image combination mode to generate a combined image. Thus, it can be applied to subsequent ADC post-processing and other processes.

[0056] In some embodiments, in step 301, when the b value is less than the first threshold, the image combination mode is the first image combination mode, and when the b value is greater than the second threshold, the image combination mode is the second image combination mode different from the first image combination mode. In step 302, when the b value is less than the first threshold, the first image combination mode is used to combine the images to be combined; when the diffusion sensitivity factor is greater than the second threshold, the second image combination mode different from the first image combination mode is used to combine the images to be combined.

[0057] Therefore, for different b values, an image combination method suitable for the b value can be selected to perform image combination, which helps to improve the quality of the combined image.

[0058] In some embodiments, the first threshold is a value less than the second threshold, for example, the first threshold is less than or equal to 200 s / mm 2 (seconds / square millimeter); and / or, the second threshold is greater than or equal to 500s / mm 2 The present application is not limited thereto, and the first threshold and the second threshold may also be other values.

[0059] In some embodiments, for b values ​​between the first threshold and the second threshold, images may be combined according to a preset first image combination method or a second image combination method, or the first image combination method or the second image combination method may be randomly selected for image combination.

[0060] In some embodiments, the difference between the first image combination method and the second image combination method may be that: the weighting coefficient of the image to be combined determined according to the first image combination method is different from the weighting coefficient of the image to be combined determined according to the second image combination method. The present application is not limited to this, and the difference between the first image combination method and the second image combination method may also be in other aspects.

[0061] In some embodiments, the weighting coefficient may be an index of the image to be combined in each diffusion direction in the diffusion weighted image, and the combined image may include the product of the images to be combined after the exponential operation. The present application is not limited thereto, and the weighting coefficient may also be other coefficients of the image to be combined. For example, the weighting coefficient may also be a multiple of the image to be combined, and the combined image may include the sum of the product of the image to be combined and the weighting coefficient.

[0062] In some embodiments, in the first image combination mode, the weighting coefficient of the image to be combined can be determined according to the number of images to be combined included in the diffusion weighted image. For example, in the first image combination mode, the weighting coefficient of the image to be combined can be inversely proportional to the number of images to be combined.

[0063] For example, the weighting coefficients of the images to be combined may be the same, for example, the weighting coefficients of the images to be combined may be the inverse of the number of images to be combined. The combined image is given by the following equation:

[0064]

[0065] Among them, I cmb is the combined image, N is the number of images to be combined, I i is the image to be combined.

[0066] In some embodiments, in the second image combination mode, the weighting coefficients of the images to be combined may be determined according to the values ​​of the images to be combined.

[0067] In some embodiments, the value of the image to be combined may be a value determined according to the pixel value within a preset range in the image to be combined, and the weighting coefficient determined according to the value of the image to be combined is the weighting coefficient of the pixel within the preset range. The preset range may include a single pixel. Alternatively, the preset range may include multiple pixels, for example, the preset range may correspond to a row, column, block or the entire image to be combined in the image to be combined. That is, when the images to be combined are combined, the weighting coefficient may be calculated for each single pixel, and each pixel may be weighted using the corresponding weighting coefficient; alternatively, a weighting coefficient may be calculated for multiple pixels, and the multiple pixels may be weighted using the same weighting coefficient, thereby reducing the amount of calculation and improving the calculation efficiency.

[0068] In some embodiments, when the preset range includes a single pixel, the value of the image to be combined may be the pixel value of the pixel. When the preset range includes multiple pixels, the value of the image to be combined may be the average value of multiple pixel values, the average value of the absolute values ​​of multiple pixel values, the maximum value of multiple pixel values, the maximum value of the absolute values ​​of multiple pixel values, the minimum value of multiple pixel values, the minimum value of the absolute values ​​of multiple pixel values, etc.

[0069] In some embodiments, in the second image combination mode, the weighting coefficient of the image to be combined is proportional to the value of the image to be combined. For example, the weighting coefficient of the image to be combined is given by the following equation:

[0070]

[0071] or,

[0072] Among them, I j is the image to be combined, W j isI j The weighting coefficients are as follows, and N is the number of images to be combined.

[0073] The combined image is given by the following equation:

[0074]

[0075] Among them, I cmb is the combined image, N is the number of images to be combined, I j is the image to be combined.

[0076] The following takes the case where the image to be combined is a two-dimensional image of M*K dimensions as an example to exemplify the manner of determining weighting coefficients and combining images. It is understandable that the following is also applicable to images to be combined of other dimensions.

[0077] In some embodiments, the image to be combined I j For each pixel in , the weighting coefficient of each pixel is calculated respectively, and the weighting coefficient corresponding to the pixel is applied to each pixel.

[0078] For example, the image to be combined I j The weighting coefficient of the pixel at the mth row and kth column of is given by the following equation:

[0079]

[0080] or,

[0081] Among them, I jm,k is the image to be combined I j The pixel value of the mth row and kth column, W jm,k is the image to be combined I j is the weighting coefficient of the pixel in the mth row and kth column of , where N is the number of images to be combined.

[0082] Combined Image I cmb The pixel at the mth row and kth column of is given by the following equation:

[0083] Given by the following equation:

[0084]

[0085] In some embodiments, the same image to be combined I j In other words, in a combined image I j The weighting coefficients can be calculated according to a preset granularity (preset range). The granularity can be a single pixel as described above, or a row, column, block, or the entire image to be combined. j wait.

[0086] For example, we can use an image I to be combined j Calculate a weighting factor W j , and for the image to be combined I j All pixels in the same weighting factor W are applied j For example, I in Equations 3 and 4 j It can be a value calculated based on the pixels in the images to be combined, for example, the average value of the pixels in the images to be combined, the average value of the absolute values ​​of the pixels in the images to be combined, the maximum value of the pixels in the images to be combined, the maximum value of the absolute values ​​of the pixels in the images to be combined, the minimum value of the pixels in the images to be combined, the minimum value of the absolute values ​​of the pixels in the images to be combined, and the like.

[0087] In some embodiments, as described above, the images to be combined of the diffusion weighted images may be combined in a first image combination mode or a second image combination mode to generate a combined image according to the b value of the diffusion weighted image. The present application is not limited thereto, and the images to be combined of the diffusion weighted images may also be combined in a first image combination mode and a second image combination mode to generate a first image and a second image respectively, and the first image and the second image may be combined to generate a combined image according to the b value of the diffusion weighted image.

[0088] Figure 4 FIG. 3 is a schematic diagram of the process of step 302 of the embodiment of the present application. Figure 4 As shown, step 302 may include:

[0089] Step 3021: combining the images to be combined by using a first image combination method to obtain a first image, and combining the images to be combined by using a second image combination method different from the first image combination method to obtain a second image;

[0090] Step 3022: Determine a first weighting coefficient of the first image and a second weighting coefficient of the second image according to the b value; and

[0091] Step 3023: Combine the first image and the second image according to the first weighting coefficient and the second weighting coefficient to generate a combined image.

[0092] The above steps are described exemplarily below.

[0093] In step 3021, the first image combination method may be used to combine the images to be combined to obtain a first image I cmb1 , as shown in the following formula (9):

[0094]

[0095] Among them, I cmb1 is the first image, N is the number of images to be combined, I i is the i-th image to be combined.

[0096] In step 3021, the second image combination method may be used to combine the images to be combined to obtain a second image I cmb2 , as shown in the following formula (10):

[0097]

[0098] The weighting coefficient of the image to be combined is proportional to the value of the image to be combined, as shown in the following formula (11):

[0099]

[0100] Among them, W j is the weighting coefficient of the jth image to be combined, and N is the number of images to be combined.

[0101] In step 3023, the first image I determined in step 3022 may be used to cmb1 The first weighting coefficient W cmb1 and the second image I cmb2 The second weighting coefficient W cmb2 Generate combined image I cmb , as shown in the following formula (12):

[0102] I cmb =W cmb1 *I cmb1 +W cmb2 *I cmb2 (12)

[0103] In some embodiments, the first weighting coefficient of the first image and the second weighting coefficient of the second image can be determined according to the b value in various ways. For example, the larger the b value, the larger the first weighting coefficient W. cmb1 The smaller the second weighting coefficient W is, cmb2 and / or, the lower the b value, the larger the first weighting coefficient Wcmb1 The larger the second weighting coefficient W is, cmb2 The smaller, and so on.

[0104] Above, to combine the image I cmb is the first weighting coefficient W cmb1 and the second weighting coefficient W cmb2 With the first image I cmb1 and the second image I cmb2 The product of is taken as an example. The present application is not limited thereto. The first weighting coefficient W cmb1 and the second weighting coefficient W cmb2 It can also be the first image I cmb1 and the second image I cmb2 Index, etc.

[0105] The following uses the diffusion weighted image including the images to be combined in three diffusion directions as an example to exemplarily illustrate the medical imaging method of the embodiment of the present application. It can be understood that the following content is also applicable to the case where the diffusion weighted image includes the images to be combined in other numbers of diffusion directions.

[0106] Figure 5 It is a schematic diagram of a diffusion weighted image according to an embodiment of the present application and a combined image generated by different image combination methods. Figure 5 An example where the b value is less than the first threshold value (ie, a low b value) is shown. Figure 5 As shown, the diffusion weighted image includes three images to be combined 501, 502 and 503 in three diffusion directions, namely, direction 1, direction 2 and direction 3. There are artifacts in the images to be combined 501 and 502 (as shown by arrows).

[0107] If the images to be combined 501, 502 and 503 are combined in the first image combination mode, a combined image 504 can be obtained, wherein there are no artifacts in the combined image 504 and the image is uniform. If the images to be combined 501, 502 and 503 are combined in the second image combination mode, a combined image 505 can be obtained, wherein although the combined image 505 is uniform in brightness and darkness, an artifact appears in the combined image 505 (as shown by the arrow).

[0108] The characteristics of different image combinations at low b values ​​are shown below:

[0109] Table 1 Characteristics of combined images generated by different image combination methods for diffusion-weighted images with low b values

[0110] Uniformity Artifacts The first image combination method good good The second image combination method good Difference

[0111] Figure 6It is another schematic diagram of the diffusion weighted image and the combined image generated by different image combination methods according to the embodiment of the present application. Figure 6 An example is shown where the b value is greater than the second threshold (i.e., a high b value). Figure 6 As shown, the diffusion weighted image includes three images 601, 602 and 603 to be combined in three diffusion directions, namely, direction 1, direction 2 and direction 3.

[0112] If the combined images 601, 602 and 603 are combined in the first image combination mode, a combined image 604 can be obtained, wherein the uniformity of the combined image 604 is poor. If the combined images 601, 602 and 603 are combined in the second image combination mode, a combined image 605 can be obtained, wherein the combined image 5056 is uniform in brightness.

[0113] The characteristics of different image combinations in the case of high b values ​​are shown below:

[0114] Table 2 Characteristics of combined images generated by different image combination methods for diffusion-weighted images with high b values

[0115] Uniformity Artifacts The first image combination method Difference good The second image combination method good good

[0116] According to Tables 1 and 2 above, by using the first image combination method to combine images at low b values ​​and using the second image combination method to combine images at high b values, diffusion-weighted images of various b values ​​can generate combined images with good uniformity and no artifacts, thereby helping to avoid misdiagnosis / confusion.

[0117] Figure 7 FIG. 1 is another schematic diagram of the medical imaging method according to an embodiment of the present application. Figure 7 As shown, the medical imaging method of the embodiment of the present application may include, in addition to the step of generating a combined image as described above, the following steps: acquiring DWI Raw data, ramp sampling, row flipping, phase correction, multiband unaliasing, transform and combination, ADC post-processing, and a final corrected image. For the specific implementation of these steps, reference may be made to the relevant technology, which will not be described in detail here. Among them, the step of generating a combined image is located before the ADC post-processing.

[0118] Figure 8 and Fig. 9is a schematic diagram of a combined image and an ADC image obtained according to different image combination methods in an embodiment of the present application, wherein: Figure 8 and Fig. 9 Corresponding to different slice positions, Figure 8 Corresponding to slice9, Fig. 9 Corresponding to slice11.

[0119] like Figure 8 As shown, the three images in the first row are a combined image 811 generated by using the first image combination method when b=50, a combined image 812 generated by using the first image combination method when b=700, and an ADC image 813 generated based on the two combined images. Figure 8 As shown, there is a shadow (as shown by the snowflake icon) in the combined image 812. In this case, the accuracy of the ADC image 813 generated based on the combined images 811 and 812 is low.

[0120] like Figure 8 As shown, the three images in the second row are respectively a combined image 821 generated by using the second image combination method with b=50, a combined image 822 generated by using the second image combination method with b=700, and an ADC image 823 generated based on the two combined images. The three images in the third row are respectively a combined image 831 generated by using the first image combination method with b=50, a combined image 832 generated by using the second image combination method with b=700, and an ADC image 833 generated based on the two combined images. Figure 8 As shown, there is no artifact shadow in the combined images 821 and 831, and the combined images 822 and 832 are uniform in brightness and darkness. In this case, the accuracy of the ADC images 823 and 833 is high.

[0121] like Fig. 9 As shown, the three images in the first row are a combined image 911 generated by using the first image combination method when b=50, a combined image 912 generated by using the first image combination method when b=700, and an ADC image 913 generated based on the two combined images. Fig. 9 As shown, there is a shadow (as shown by the snowflake icon) in the combined image 912. In this case, the accuracy of the ADC image 913 generated based on the combined images 911 and 912 is low.

[0122] like Fig. 9 As shown, the three images in the second row are a combined image 921 generated by using the second image combination method when b=50, a combined image 922 generated by using the second image combination method when b=700, and an ADC image 923 generated based on the two combined images. Fig. 9As shown, there is an artifact shadow (as shown by the arrow) in the combined image 921. In this case, the accuracy of the ADC image 923 generated according to the combined images 921 and 922 is low.

[0123] like Fig. 9 As shown, the three images in the third row are a combined image 931 generated by using the first image combination method when b=50, a combined image 932 generated by using the second image combination method when b=700, and an ADC image 933 generated based on the two combined images. Fig. 9 As shown, there is no artifact shadow in the combined image 931, and the combined image 932 is uniform in brightness and darkness. In this case, the ADC image 933 generated according to the combined images 931 and 932 has a high accuracy.

[0124] Through the above embodiment, by determining the image combination mode corresponding to the diffusion sensitivity factor according to the diffusion weighted image, and combining the images to be combined according to the determined image combination mode to generate a combined image, the uniformity of the combined image can be improved, and the artifacts of the combined image can be reduced, which helps to reduce misdiagnosis / confusion.

[0125] The present application also provides a medical imaging system. The medical imaging system is composed of Figure 1 As shown, the repeated parts will not be repeated.

[0126] In some embodiments, Figure 1 The difference between the aforementioned medical imaging system and the aforementioned medical imaging system is that the controller 130 is configured to execute the aforementioned medical imaging method, that is, determining an image combination mode corresponding to a diffusion sensitivity factor corresponding to a diffusion weighted image, wherein the diffusion weighted image includes images to be combined in at least two diffusion directions; and combining the images to be combined to generate a combined image according to the determined image combination mode.

[0127] In some embodiments, the controller 130 (which may also be a processor) includes a computer processor and a storage medium, on which a program for predetermined data processing to be executed by the computer processor is recorded. For example, the storage medium may store a program for implementing scanning processing (for example, including waveform design / conversion, etc.), image reconstruction, medical imaging, etc. For example, a program for implementing the medical imaging method of an embodiment of the present invention may be stored, the medical imaging method including determining an image combination method corresponding to a diffusion sensitivity factor corresponding to a diffusion weighted image according to the diffusion sensitivity factor, the diffusion weighted image including images to be combined in at least two diffusion directions; and combining the images to be combined to generate a combined image according to the determined image combination method, the specific implementation of which is as described above and will not be repeated here. The above storage medium may include, for example, a ROM, a floppy disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, or a non-volatile memory card.

[0128] Through the above embodiment, by determining the image combination mode corresponding to the diffusion sensitivity factor according to the diffusion weighted image, and combining the images to be combined according to the determined image combination mode to generate a combined image, the uniformity of the combined image can be improved, and the artifacts of the combined image can be reduced, which helps to reduce misdiagnosis / confusion.

[0129] An embodiment of the present application also provides a computer-readable program, wherein when the program is executed in a medical imaging system, the program enables a computer to execute the medical imaging method described in the aforementioned embodiment in the medical imaging system.

[0130] An embodiment of the present application further provides a storage medium storing a computer-readable program, wherein the computer-readable program enables a computer to execute the medical imaging method described in the aforementioned embodiment in a medical imaging system.

[0131] The above devices and methods of the present application can be implemented by hardware, or by hardware combined with software. The present application relates to such a computer-readable program, which, when executed by a logic component, enables the logic component to implement the above-mentioned devices or components, or enables the logic component to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0132] The method / device described in conjunction with the embodiments of the present application may be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figure and / or one or more combinations of the functional block diagrams may correspond to various software modules of the computer program flow or to various hardware modules. These software modules may correspond to the various steps shown in the figure, respectively. These hardware modules may be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0133] The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The software module may be stored in a memory of a mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if a device (such as a mobile terminal) uses a large-capacity MEGA-SIM card or a large-capacity flash memory device, the software module may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0134] For one or more of the functional blocks described in the drawings and / or one or more combinations of functional blocks, it can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in the present application. For one or more of the functional blocks described in the drawings and / or one or more combinations of functional blocks, it can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0135] The present application is described above in conjunction with specific implementation methods, but it should be clear to those skilled in the art that these descriptions are exemplary and are not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on the principles of the present application, and these modifications and variations are also within the scope of the present application.

Claims

1. A medical imaging method, characterized in that: The method comprises: Determining an image combination method corresponding to a diffusion-weighted image according to a diffusion-sensitivity factor corresponding to the diffusion-weighted image, wherein the diffusion-weighted image includes images to be combined in at least two diffusion directions; and According to the determined image combination mode, the images to be combined are combined to generate a combined image.

2. The method according to claim 1, characterized in that Combining the images to be combined to generate a combined image includes: When the diffusion sensitivity factor is less than a first threshold, combining the images to be combined by using a first image combination method; When the diffusion sensitivity factor is greater than a second threshold, the images to be combined are combined in a second image combination manner different from the first image combination manner.

3. The method according to claim 2, characterized in that The weighting coefficient of the images to be combined determined according to the first image combination mode is different from the weighting coefficient of the images to be combined determined according to the second image combination mode.

4. The method according to claim 3, characterized in that: The combined image includes the product of the images to be combined after exponential operation, and the weighting coefficient includes the exponent of the images to be combined.

5. The method according to claim 3, characterized in that: The combined image includes the sum of products of the images to be combined and the weighting coefficients.

6. The method according to claim 2, characterized in that In the first image combination mode, the weighting coefficients of the images to be combined are determined according to the number of the images to be combined included in the diffusion weighted image.

7. The method according to claim 6, characterized in that In the first image combination mode, the weighting coefficient of the images to be combined is inversely proportional to the number of the images to be combined.

8. The method according to claim 2, characterized in that: In the second image combination mode, the weighting coefficients of the images to be combined are determined according to the values ​​of the images to be combined.

9. The method according to claim 8, characterized in that In the second image combination mode, the weighting coefficient of the image to be combined is proportional to the value of the image to be combined.

10. The method according to claim 2, characterized in that The first threshold is less than or equal to 200 s / mm 2 .

11. The method according to claim 2, characterized in that The second threshold is greater than or equal to 500s / mm 2 .

12. The method according to claim 1, characterized in that Combining the images to be combined to generate a combined image includes: Combining the images to be combined by a first image combination method to obtain a first image, and combining the images to be combined by a second image combination method different from the first image combination method to obtain a second image; determining a first weighting coefficient of the first image and a second weighting coefficient of the second image according to the diffusion sensitivity factor; and The first image and the second image are combined according to the first weighting coefficient and the second weighting coefficient to generate the combined image.

13. The method according to claim 12, characterized in that The larger the diffusion sensitivity factor is, the smaller the first weighting coefficient is, and the larger the second weighting coefficient is; and / or The smaller the diffusion sensitivity factor is, the larger the first weighting coefficient is, and the smaller the second weighting coefficient is.

14. A medical imaging system, characterized in that: The system comprises: a scanning unit; and A controller configured to perform the medical imaging method according to any one of claims 1 to 13.

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