Image processing device, image processing method, and image processing program

Abstract

To improve image quality. [Solution] The image processing apparatus according to this embodiment has a processing circuit. The processing circuit acquires a sensitivity map of a receiving coil, collects an MR image based on the receiving coil, and corrects the signal intensity of the MR image using the sensitivity map and an intensity correction function. The intensity correction function changes according to the signal intensity of the MR image.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to an image processing apparatus, an image processing method, and an image processing program. For example, the embodiments disclosed in this specification generally relate to a method, a system, a processing circuit, and a computer program product for providing image processing of medical images. Also in one embodiment, it relates to a method, a system, and a computer program product for reducing the appearance of noise in a low-intensity image region of a magnetic resonance imaging image.

Background Art

[0002] In a magnetic resonance imaging (MRI) system, an MR reception (Rx) element often includes a plurality of Rx coils. The local reception magnetic field of each Rx element is not uniform according to the Biot-Savart law given by the following equation (Equation 1).

[0003]

Number

[0004] There is unevenness in the signal intensity of the final image generated using data from an uncorrected and non-uniform Rx magnetic field. Non-uniform signal intensity is often undesirable for doctors. As shown in FIG. 1A, the intensity is usually greater in the part closer to the reception element (e.g., within the white box shape) than in the part farther from the reception element of the scanned object (e.g., within the circle). To address this non-uniformity, a correction coefficient map, also called a sensitivity map, may be created and applied to the original uncorrected image. In one embodiment, the sensitivity map is created by a two-step prescan process. In the first step, a first image is generated from a whole-body coil (WBC) assumed to have uniform reception intensity, and in the second step of the prescan, a second image is generated using a phased array coil (PAC). The sensitivity map (S(x)) is generated as follows (Equation 2).

[0005]

number

[0006] Alternatively, the PAC map can be normalized by the sum of squares of all channels, or uniformly (to a value of 1.0). Then, the intensity correction of the original image is performed by dividing the original image using the sensitivity map shown in equation (3) below.

[0007]

number

[0008] As a result, a smoother image is created, as shown in Figure 1B. However, as shown within the white ellipse in Figure 1B, applying the above correction amplifies noise in the low-sensitivity region.

[0009] As is evident from the comparison between the original image in Figure 1C and the corrected image in Figure 1D, the noise amplification problem worsens when dark signals are present within the image. This situation can occur due to tissue suppression using methods such as inversion recovery (e.g., FLAIR, STIR, or SPAIR), saturation (e.g., CHESS, spatial presaturation), background tissue signal suppression (e.g., time-of-flight angiography), or subtraction (e.g., arterial spin labeling). Images with amplified noise appear grainy and are visually undesirable. Furthermore, noise amplification is also noticeable in the air surrounding the body.

[0010] As shown by comparing the original image in Figure 1E with the corrected image in Figure 1F, the same problem can occur with FLAIR images of the brain. As can be seen in Figure 1F, the cerebrospinal fluid (CSF) in the corrected image appears milky white due to noise amplification. In addition, amplification of the air around the head can cause insufficient image quality. [Prior art documents] [Patent Documents]

[0011] [Patent Document 1] Patent No. 7179483 [Overview of the Initiative] [Problems that the invention aims to solve]

[0012] One of the problems that the embodiments disclosed herein and in the drawings aim to solve is to improve image quality. However, the problems that the embodiments disclosed herein and in the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0013] The image processing apparatus according to this embodiment includes a processing circuit. The processing circuit acquires a sensitivity map of a receiving coil, collects an MR image based on the receiving coil, and corrects the signal intensity of the MR image using the sensitivity map and an intensity correction function. The intensity correction function changes according to the signal intensity of the MR image. [Brief explanation of the drawing]

[0014] [Figure 1A] Figure 1A is an example of a first original image that shows image non-uniformity due to proximity to the receiving element, illustrating that the portion of the scanned object closer to the receiving element (e.g., within the white box) has a higher intensity than the portion further away from the receiving element (e.g., within the circle). [Figure 1B] Figure 1B shows the first corrected image generated by applying the sensitivity map from Figure 1A to the original image. [Figure 1C] Figure 1C shows a second example image containing dark signals within the image. [Figure 1D] Figure 1D is a second corrected image obtained by correcting the image in Figure 1C, and shows visually undesirable noise amplification in the areas corresponding to the dark signals within the image. [Figure 1E]FIG. 1E is a diagram of a third example image obtained using the fluid-attenuated inversion recovery (FLAIR) method. [Figure 1F] FIG. 1F is a diagram of a third corrected image obtained by correcting the image of FIG. 1E, and shows visually unfavorable noise amplification in a region corresponding to cerebrospinal fluid (CSF). [Figure 2] FIG. 2 is a schematic diagram of an MRI apparatus. [Figure 3] FIG. 3 is a flowchart showing an overview of the image correction process described in this specification. [Figure 4A] FIG. 4A is a graph of an example of an intensity correction function. [Figure 4B] FIG. 4B is a graph of an example of an intensity correction function. [Figure 4C] FIG. 4C is a graph of an example of an intensity correction function. [Figure 4D] FIG. 4D is a graph of an example of an intensity correction function. [Figure 5] FIG. 5 is a graph of an example of an intensity function used with complex or signed absolute value data. [Figure 6] FIG. 6 is a graph of an example of an intensity correction function including dynamically calculated change points. [Figure 7A] FIG. 7A is a diagram of an image generated using only a sensitivity map. [Figure 7B] FIG. 7B is a diagram of an image generated using a sensitivity map and an intensity correction function. [Figure 7C] FIG. 7C is a diagram of a color difference image showing the difference in signal intensity between FIGS. 7A and 7B. [Figure 8A] FIG. 8A is a diagram of another image generated using only a sensitivity map. [Figure 8B] FIG. 8B is a diagram of another image generated using a sensitivity map and an intensity correction function. [Figure 8C] FIG. 8C is a diagram of a color difference image showing the difference in signal intensity between FIGS. 8A and 8B. [Figure 9A]Figure 9A shows a graphical user interface for selecting an intensity correction function to be applied to the image to be corrected. [Figure 9B] Figure 9B shows a graphical user interface for selecting an intensity correction function to be applied to the image to be corrected. [Modes for carrying out the invention]

[0015] In this specification, “one” is defined as one or more; “multiple” is defined as two or more; and “other” is defined as at least the second and subsequent. The expressions “including” and / or “having” are defined as “equipped with” (i.e., non-restrictive terms). Throughout this specification, “one embodiment,” “multiple embodiments,” “embodiments,” “examples,” “examples,” or other similar expressions mean that certain features, structures, or characteristics described in relation to the applicable embodiment are included in at least one embodiment of this disclosure. That is, such expressions found in many places in this specification do not necessarily mean the same embodiment. Furthermore, certain features, structures, or characteristics can be combined in any way as appropriate in one or more embodiments, without limitation.

[0016] This disclosure relates to a method, system, and non-temporary computer-readable storage medium for storing computer-readable instructions for reducing the appearance of noise in imaging data (e.g., magnetic resonance imaging (MRI) data) based on (a) correction coefficients related to a sensitivity map and (b) an intensity correction function that varies depending on the signal intensity of the imaging data.

[0017] In one embodiment, this disclosure can be considered as a system. An MRI apparatus is used as an example of an embodiment, but other system configurations may use other medical imaging devices (e.g., CT systems and integrated MRI and CT systems).

[0018] Next, refer to the drawings. Figure 2 is a block diagram showing the overall configuration of MRI apparatus 1. MRI apparatus 1 comprises a gantry 100, a control cabinet 300, a console 400, a patient table 500, and a radio frequency (RF) coil 20. The gantry 100, control cabinet 300, and patient table 500 constitute the scanner, or imaging unit.

[0019] The gantry 100 comprises a static magnetic field magnet 10, a gradient magnetic field coil 11, and a whole body (WB) coil 12, and these components are housed in a cylindrical casing. The bed 500 includes a bed body 50 and a table 51.

[0020] The control cabinet 300 includes three gradient coil power supplies 31 (31x for the X-axis, 31y for the Y-axis, and 31z for the Z-axis), a coil selection circuit 36, an RF receiver 32, an RF transmitter 33, and a sequence controller 34.

[0021] The console 400 includes a processing circuit 40, memory 41, display 42, and input interface 43. The console 400 functions as a host computer.

[0022] The static magnetic field magnet 10 of the gantry 100 has a roughly cylindrical shape and generates a static magnetic field inside the bore through which objects such as patients are moved. The bore is the space within the cylindrical structure of the gantry 100. The static magnetic field magnet 10 has a superconducting coil inside. The superconducting coil is cooled to extremely low temperatures by liquid helium. In excitation mode, the static magnetic field magnet 10 generates a static magnetic field by supplying current to the superconducting coil from a static magnetic field power source (not shown). Subsequently, the static magnetic field magnet 10 transitions to persistent current mode, and the static magnetic field power source is disconnected. Once in persistent current mode, the static magnetic field magnet 10 continues to generate a strong static magnetic field for a long period of time, for example, more than one year.

[0023] Furthermore, the gradient magnetic field coil 11 also has a roughly cylindrical shape and is fixed inside the static magnetic field magnet 10. The gradient magnetic field coil 11 uses current supplied from the gradient magnetic field coil power supplies 31x, 31y, and 31z to apply gradient magnetic fields (e.g., gradient pulses) to an object in the X, Y, and Z axis directions, respectively.

[0024] The bed body 50 of the bed 500 has a table 51 that can move vertically and horizontally. Before imaging, the bed body 50 moves the table 51 on which the object is placed to a predetermined height. Then, when imaging the object, the bed body 50 moves the table 51 horizontally to move the object into the bore.

[0025] The WB coil 12 has a roughly cylindrical shape that surrounds the object and is fixed inside the gradient coil 11. The WB coil 12 applies RF pulses transmitted from the RF transmitter 33 to the object. The WB coil 12 also receives magnetic resonance (MR) signals emitted from the object due to the excitation of hydrogen nuclei.

[0026] As shown in Figure 2, the MRI apparatus 1 may include RF coils 20 in addition to the WB coil 12. Each RF coil 20 is a coil placed near the surface of the object. There are various types of RF coils 20. For example, as shown in Figure 2, types of RF coils 20 include body coils attached to the chest, abdomen, and legs of the object, and spinal coils attached to the back of the object. Another type of RF coil 20 is a head coil for imaging the head of the object. Most RF coils 20 are for receiving only, but some RF coils 20, such as head coils, perform both transmission and reception. The RF coils 20 can be attached to and detached from the table 51 via cables.

[0027] The RF transmitter 33 generates each RF pulse based on instructions from the sequence controller 34. The generated RF pulses are transmitted to the WB coil 12 and applied to the object. The application of one or more RF pulses causes the object to emit an MR signal. Each MR signal is received by the RF coil 20 or the WB coil 12.

[0028] The MR signal received by the RF coil 20 is transmitted to the coil selection circuit 36 ​​via a cable installed on the table 51 and the bed body 50. The MR signal received by the WB coil 12 is also transmitted to the coil selection circuit 36.

[0029] The coil selection circuit 36 ​​selects the MR signal output from each RF coil 20 or the MR signal output from the WB coil 12 according to the control signal output from the sequence controller 34 or console 400.

[0030] The selected MR signal is output to the RF receiver 32. The RF receiver 32 performs analog-to-digital (AD) conversion of the MR signal and outputs the converted signal to the sequence controller 34. The digital MR signal is sometimes called raw data. AD conversion is performed within each RF coil 20 or within the coil selection circuit 36.

[0031] The sequence controller 34 scans for an object by driving the inclined coil power supply 31, the RF transmitter 33, and the RF receiver 32 under the control of the console 400. The sequence controller 34 receives raw data from the RF receiver 32 as the scan is performed and transmits the received raw data to the console 400.

[0032] The sequence controller 34 includes a processing circuit (not shown). The processing circuit is configured as, for example, a processor that executes a predetermined program, or as hardware such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).

[0033] As described above, the console 400 includes a memory 41, a display 42, an input interface 43, and a processing circuit 40.

[0034] Memory 41 is a recording medium that includes ROM (read-only memory) and RAM (random access memory) in addition to external memory devices such as hard disk drives (HDD) and optical disc drives. Memory 41 stores various programs executed by the processor of the processing circuit 40, as well as various data and information.

[0035] The input interface 43 includes various devices for the operator to input various information and data, and consists of, for example, a mouse, keyboard, trackball, and / or touch panel.

[0036] The display 42 is a display device such as a liquid crystal display panel, a plasma display panel, or an organic EL panel.

[0037] The processing circuit 40 is, for example, a circuit equipped with a central processing unit (CPU) and / or a purpose-specific or general-purpose processor. The processor implements the various functions shown below by executing a program stored in memory 41. The processing circuit 40 can be configured as hardware such as an FPGA and an ASIC. The various functions shown below can also be implemented by such hardware. Furthermore, the processing circuit 40 can implement various functions by combining hardware processing and software processing based on its own processor and program.

[0038] Figure 3 is a flowchart outlining the process described herein. The process of Method 300 begins in step 310, in which a uniform function (e.g., a sensitivity map) of the receiving coils is obtained. In one embodiment, the sensitivity map is used as the uniform function and is generated by a two-stage prescan process. In the first stage, a first image is generated from a whole-body coil (WBC) which is assumed to have a uniform received intensity. In the second stage of the prescan, a second image is generated using a phased array coil (PAC). Next, a sensitivity map (S(x)) is generated according to equation (2) above. Alternatively, the PAC map may be normalized by the sum of squares of all channels, or uniformly (to all values ​​of 1.0). That is, the sensitivity map may be generated by normalizing the measurements obtained from the phased array coil with the measurements obtained from the whole-body coil.

[0039] In step 320, an MR image based on the receiving coil is acquired. In step 330, the signal intensity of the MR image is corrected using a sensitivity map and an intensity correction function that varies with signal intensity. By using an intensity correction function that varies with signal intensity, noise amplification in low-signal regions (e.g., background (air) or regions where tissue signals are intentionally suppressed (FLAIR or FatSat)) can be reduced. This intensity correction function does not change the tissue signal or tissue SNR. Rather, it improves the apparent image quality by reducing noise amplification in regions that are expected to be dark or black (i.e., zero-value regions). This method changes the correction coefficient based on the signal intensity of the image. Thus, low-signal intensity regions (corresponding to air and suppressed tissue) are multiplied by a lower coefficient than usual. Intermediate or high signals (corresponding to tissue) are multiplied by the normal intensity correction coefficient and are therefore unaffected.

[0040] In one embodiment, the intensity correction function (for unsigned data) receives the image itself as input. Therefore, the image correction is given by the following equation.

[0041]

number

[0042] When the image signal intensity (pixel value) is high (large), β becomes equal to 1, and therefore the tissue signal is not affected. If the value of β is not 1 within the tissue, the intensity correction function may cause a change in image contrast. Figures 4A to 4D illustrate intensity correction functions. As shown in the figures, the function is continuous (Figures 4A to 4C) or discontinuous (Figure 4D) (for each section). Also, the portion before the statically selected change point 400 is linear (ramp) (Figures 4A and 4B) or nonlinear (e.g., a smooth curve) (Figure 4C). A strong intensity correction function (e.g., the Heaviside function) leads to an unnatural image, so a continuous intensity correction function is preferred.

[0043] The point of change may also be called the critical transition point. The point of change corresponds, for example, to the point in the intensity correction function where the modulation value (β in equation (4)) with respect to the image signal intensity (pixel value) increases monotonically, and is defined by the magnitude of the image signal intensity (pixel value) when the modulation value β reaches 1.

[0044] Furthermore, the term "ramp" corresponds to, for example, a slope. For instance, if the intensity correction function changes linearly, as shown in Figures 4A and 4B, the intensity correction function may be referred to as a linear ramp, a linear ramp function, or a slope line. As shown in Figures 4A and 4B, the intensity correction function is a linear ramp (a slope line that changes linearly) extending from the minimum value of the intensity correction function (β=0) to the critical transition point, and after the critical transition point, the value of the intensity correction function becomes the maximum value (β=1).

[0045] Furthermore, as shown in Figure 4C, when the intensity correction function changes nonlinearly, the intensity correction function may be referred to as a nonlinear ramp, nonlinear ramp function, curved ramp, curved ramp function, etc. As shown in Figure 4C, the intensity correction function is a curved ramp (a curved slope line) that changes smoothly from the minimum value of the intensity correction function (β=0) to the critical transition point, and after the critical transition point, the value of the intensity correction function becomes the maximum value (β=1). The shape of the intensity correction function shown in Figures 4A to 4D, and Figure 5 described later, may be set by the operator via the input interface (input unit) 43.

[0046] Furthermore, as shown in Figures 4A and 4B, when the intensity correction function is represented by a linear ramp, the change point corresponds to the point where the rate of change (slope) of the modulation value (β in equation (4)) with respect to the image signal intensity (pixel value) changes (the point where it is not differentiable at β=1). Also, as shown in Figure 4C, when the intensity correction function is represented by a curved ramp, the change point corresponds to the point where the modulation value β is 1 (β=1) and it is not differentiable. In other words, the change point corresponds to the point where the linear ramp reaches β=1 as the image signal intensity increases.

[0047] Furthermore, if the intensity correction function is a continuous function such as a sigmoid function and is a curved ramp function, the point of change corresponds to the point where the curved ramp function reaches β=1. In this case, the intensity correction function has a maximum and a minimum value and is a continuous function of signal intensity (image signal intensity, pixel value). For example, the maximum value of the intensity correction function is 1, and the minimum value of the intensity correction function is 0.

[0048] For complex number and / or signed absolute value data, the modulation function (intensity correction function) takes on a "V-shaped notch" shape, as shown in Figure 5. The modulation value β for image signal intensities showing high positive or negative values ​​(corresponding to tissue) is 1 (β=1), while the modulation value β for signal values ​​close to zero (positive or negative image signal intensities) is less than 1 (β<1).

[0049] Alternatively, as shown in Figure 6, a data-dependent critical transition point 600 can be dynamically calculated to identify an intensity value such that the intensity correction function starts at a value of 1.0. In one such embodiment, the critical transition point is identified by a threshold calculation using the Otsu threshold method on the uncorrected (original) image. That is, the point of change in the intensity correction function is identified by a threshold calculation on the signal intensity of the MR image. For example, using C++ notation, the intensity value at the critical transition point P can be obtained by the following formula.

[0050] P=OtsuMethod::GetLevel(magImage,dataSize,0.15,512)

[0051] 0.15 is the maximum threshold to use, and 512 is the number of bins used in the calculation. Alternatively, the threshold may be set based on the estimated noise level using pre-scan measurements of the noise.

[0052] This method reduces noise generation in low signal intensity regions (e.g., air or suppressed tissue signals), but does not improve or change the tissue's SNR. However, due to visual noise suppression, the method can improve the perception of image quality and produce more visually pleasing images. For example, Figures 7A and 7B are images generated using (a) a sensitivity map only and (b) a sensitivity map and intensity correction function, respectively. The image in Figure 7B forms a more favorable image due to the presence of dark areas on the inside and outside of the body. Figure 7C is a color difference image showing the difference in signal intensity between Figures 7A and 7B. The greatest advantage lies within the circular region corresponding to the central tissue, where sensitivity is weakest and noise amplification should be greatest. Furthermore, this method improves the halo of noise in the air surrounding the body, but does not affect the tissue signal or tissue SNR (intra-tissue difference = 0).

[0053] Similarly, Figures 8A and 8B are images generated using (a) only the sensitivity map and (b) both the sensitivity map and the intensity correction function, respectively. The image in Figure 8B is more preferable due to the presence of dark areas on the inside and outside of the body. Figure 8C is a color difference image showing the difference in signal intensity between Figures 8A and 8B.

[0054] Figures 9A and 9B illustrate a graphical user interface for selecting an intensity correction function to be applied to an image to be corrected. In Figure 9A, the user selects from a set of graphs representing the types of intensity correction functions to apply (e.g., fast linear function, slow linear function, fast non-linear function, slow non-linear function, etc.). Here, "fast" corresponds to, for example, a steep slope of the intensity correction function, i.e., a large rate of change in the modulation value β relative to the image signal intensity in the intensity correction function. "Slow" corresponds to, for example, a small slope of the intensity correction function, i.e., a small rate of change in the modulation value β relative to the image signal intensity in the intensity correction function. The user interface may also include checkboxes and other control units for specifying the static or dynamic (e.g., using the Otsu threshold function) determination method of the critical transition point.

[0055] Alternatively, as shown in Figure 9B, the user selects from a set of character representations describing the type of image to which the intensity correction function is applied (e.g., brain, buttocks (including hip joint, pelvis, etc.), lungs, etc.). That is, the user's instructions via the input interface (input unit) 43 select the type of organ to be imaged, including the brain, buttocks (including hip joint, pelvis, etc.), lungs, etc. At this time, the processing circuit 40 selects (determines) the shape of the intensity correction function based on the type of organ to be imaged. The user interface may also include checkboxes and other control units for specifying a static or dynamic method for determining the critical transition point (e.g., using the Otsu threshold function). In another embodiment, the image type is automatically detected from at least one of the following: (1) the image itself (e.g., using a trained neural network trained with images and known image types), or (2) data stored with the collected image data (e.g., data type information stored in a DICOM file). Based on the determined image type, the system can automatically apply a corresponding intensity correction function known to be appropriate for that image type.

[0056] For example, the processing circuit 40 selects the shape of the intensity correction function based on the image acquisition type of the MR image. The image acquisition type is, for example, the image type of the acquired MR image, which is distinguished by contrast. The contrast is, for example, T1-weighted, T2-weighted, diffusion-weighted, etc. At this time, the processing circuit 40 identifies the image type of the acquired MR image based on the examination order for the subject P or image processing of the acquired MR image. Next, the processing circuit 40 selects an intensity correction function corresponding to the identified image type. The processing circuit 40 corrects the acquired MR image using the selected intensity correction function and sensitivity map. The image type and intensity correction function are pre-associated and stored in the memory 41.

[0057] The image acquisition type may include sequence types that cause differences in contrast. In this case, the processing circuit 40 identifies the sequence type related to the acquired MR image based on the examination order for the subject P. Next, the processing circuit 40 selects an intensity correction function corresponding to the identified sequence type. The sequence type and intensity correction function are pre-associated and stored in the memory 41.

[0058] The methods and systems described herein are implementable in many arts but generally relate to imaging devices and processing circuits that perform the processing described herein. In one embodiment, the processing circuit (e.g., an image processing circuit and a control circuit) is implemented as one or a combination of the following: an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a generic array of logic (GAL), a programmable array of logic (PAL), a circuit that allows a logic gate (e.g., using a fuse) or a reprogrammable logic gate to be programmed once. The processing circuit may also include a computer processor and embedded and / or external non-volatile computer-readable memory (e.g., RAM, SRAM, FRAM®, PROM, EPROM, and / or EEPROM). The memory stores computer instructions (binary execution instructions and / or interpreted computer instructions) that control the computer processor to perform the processing described herein. Computer processor circuits enable the creation of a single processor or a multiprocessor, each with one or more cores, each corresponding to one or more threads.

[0059] Furthermore, embodiments of this disclosure may be provided for in the following supplementary matters.

[0060] (1) An image processing method comprising, but not limited to, acquiring a sensitivity map of a receiving coil, collecting an MR image based on the receiving coil, and correcting the signal intensity of the MR image using the sensitivity map and an intensity correction function, wherein the intensity correction function changes according to the signal intensity of the MR image.

[0061] (2) In the method described in (1), the sensitivity map is generated by normalizing the measured values ​​obtained from the phased array coil with the measured values ​​obtained from the whole-body coil.

[0062] (3) In the method according to (1) or (2), the intensity correction function has a maximum value and a minimum value and is a continuous function of signal intensity.

[0063] (4) In the method described in (3), the intensity correction function is a linear ramp that increases linearly up to a critical transition point, after which the value of the function reaches its maximum value.

[0064] In the method described in (5)(3), the intensity correction function is a smoothed curved ramp that extends to a critical transition point, and thereafter the value of the function is at its maximum.

[0065] In the method described in (6)(4), the critical transition point of the intensity correction function is determined by a threshold calculation with respect to the signal intensity of the MR image.

[0066] In the method described in (7)(5), the critical transition point of the intensity correction function is determined by a threshold calculation with respect to the signal intensity of the MR image.

[0067] (8) In the method described in any one of (3)-(6), the maximum value is 1.

[0068] In any one of the methods described in (9)(3)-(6), the minimum value is zero.

[0069] (10) In the method described in any one of (1)-(3), the shape of the intensity correction function is set by the operator.

[0070] (11) In the method described in any one of (1)-(3), the shape of the intensity correction function is selected based on the type of tissue being imaged.

[0071] (12) In the method described in any one of (1)-(3), the shape of the intensity correction function is selected based on the image acquisition type of the MR image.

[0072] (13) An image processing apparatus comprising, in no particular way, a processing circuit that performs any one of the steps described in (1) to (12).

[0073] (14) A non-temporary computer-readable storage medium that stores computer-readable computer instructions that cause a computer to perform any one of the image processing methods described in (1) to (12).

[0074] According to the embodiments described above, image quality can be improved. For example, according to the MRI apparatus 1 of the embodiment, the visual quality of the image can be improved by reducing the amplification of noise in dark areas and black regions of the image.

[0075] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. The novel methods, apparatus, and systems can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0076] 1 MRI machine 10 Static magnetic field magnet 11. Gradient field coils 12 Whole Body (WB) Coils 20 Radio Frequency (RF) Coils 30 Control Cabinets 31. Power supply with three gradient magnetic field coils 32 RF receivers 33 RF transmitter 34 Sequence Controller 36 Coil Selection Circuit 40 Processing Circuits 41 memory 42 displays 43 Input Interfaces 50 Bed frame 51 Tables 100 Gantry 300 Control Cabinet 400 Console 500 berths

Claims

1. Obtain the sensitivity map of the receiving coil, A MR image based on the receiving coil is collected, The system includes a processing circuit that corrects the signal intensity of the MR image using the aforementioned sensitivity map and intensity correction function. The intensity correction function changes according to the signal intensity of the MR image. Image processing device.

2. The aforementioned sensitivity map is generated by normalizing the measurements obtained from the phased array coil with the measurements obtained from the whole-body coil. The image processing apparatus according to claim 1.

3. The aforementioned intensity correction function has a maximum and minimum value and is a continuous function of signal intensity. The image processing apparatus according to claim 1.

4. The intensity correction function is a linear ramp extending to a critical transition point, After the critical transition point, the value of the intensity correction function reaches its maximum value. The image processing apparatus according to claim 3.

5. The aforementioned intensity correction function is a curved ramp that changes smoothly up to the critical transition point. After the critical transition point, the value of the intensity correction function reaches its maximum value. The image processing apparatus according to claim 3.

6. The change point of the intensity correction function is identified by a threshold calculation on the signal intensity of the MR image. The image processing apparatus according to claim 4 or 5.

7. The shape of the aforementioned intensity correction function is set by the operator. The image processing apparatus according to claim 1.

8. The shape of the intensity correction function is selected based on the type of organ being imaged. The image processing apparatus according to claim 1.

9. The shape of the intensity correction function is selected based on the image acquisition type of the MR image. The image processing apparatus according to claim 1.

10. Obtain the sensitivity map of the receiving coil, A MR image based on the receiving coil is collected, The system includes correcting the signal intensity of the MR image using the aforementioned sensitivity map and intensity correction function. The intensity correction function changes according to the signal intensity of the MR image. Image processing methods.

11. On the computer, Obtain the sensitivity map of the receiving coil, A MR image based on the receiving coil is collected, The signal intensity of the MR image is corrected using the aforementioned sensitivity map and intensity correction function. The intensity correction function changes according to the signal intensity of the MR image. An image processing program that achieves this.

Images