JP2012071000A - Image processor and magnetic resonance imaging apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain a diffusion tensor tractographic image which permits an area to be diagnosed to be examined in detail.SOLUTION: An image processor and a magnetic resonance imaging apparatus in an embodied form have a computation start area setting means and an image generation means. The computation start area setting means sets a plurality of computation start areas continuing on a medical image in which a region of a subject is imaged. The image generation means generates the diffusion tensor tractographic image describing a nerve bundle for each of the computation start areas.

Description

  Embodiments described herein relate generally to an image processing apparatus and a magnetic resonance imaging apparatus.

  Conventionally, diffusion imaging is known as an imaging technique using a magnetic resonance imaging (MRI) apparatus. Diffusion imaging is a technique for capturing a diffusion-weighted image that emphasizes the diffusion effect in which particles such as water molecules are scattered by Brownian motion due to heat. Such diffusion imaging is mainly used in brain diagnosis.

  Diffusion tensor imaging is also known in which nerve fiber running analysis is performed using a diffusion weighted image obtained by diffusion imaging. Diffusion tensor imaging is a diffusion tensor image that represents the direction of anisotropic diffusion (anisotropy) of water molecules using tensor analysis based on multiple diffusion-weighted images with different MPG (Motion Probing Gradient) directions. Is a method for generating

  In addition, a diffusion tensor tract graffiti that draws a nerve bundle using a diffusion tensor image obtained by diffusion tensor imaging is also known. Diffusion tensor tractography is a technique for generating a diffusion tensor tractography image in which the maximum diffusion direction at an arbitrary pixel included in a diffusion tensor image is tracked and the locus is depicted as a nerve bundle. In this diffusion tensor tractography, a calculation start region is normally set on a diffusion tensor tractography image by an operator such as a doctor or an engineer. Here, the calculation start area is an area set for designating the position of the pixel from which tracking in the maximum diffusion direction is started.

Susumu Mori and Jiangyang Zhang, "Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research", Neuron 51, p.527-539, September 7, 2006

  However, in the conventional diffusion tensor tractography described above, there is a case where the region to be diagnosed cannot be inspected in detail. For example, when examining the corpus callosum in detail, the corpus callosum is divided into a plurality of regions based on anatomical knowledge, and a nerve bundle passing through each region is observed for each region. However, in the conventional diffusion tensorgraphy, since the calculation start area is manually set, it is difficult to accurately set the calculation start area in each of the plurality of areas. For this reason, the corpus callosum could not be examined in detail.

  The image processing apparatus and the magnetic resonance imaging apparatus of the embodiment include a calculation start area setting unit and an image generation unit. The calculation start area setting means sets a plurality of continuous calculation start areas on a medical image in which a part of the subject is imaged. The image generation means generates a diffusion tensor tractography image in which a nerve bundle is depicted for each of the plurality of calculation start areas.

FIG. 1 is a diagram showing an overall configuration of the MRI system according to the first embodiment. FIG. 2 is a functional block diagram illustrating configurations of a storage unit, a data processing unit, an image processing unit, and a control unit according to the first embodiment. FIG. 3 is a diagram for explaining generation of a DTT image by the tractography generation unit according to the first embodiment. FIG. 4 is a diagram (1) illustrating an example of a DTT image displayed by the display control unit according to the first embodiment. FIG. 5 is a diagram (2) illustrating an example of the DTT image displayed by the display control unit according to the first embodiment. FIG. 6 is a flowchart showing a flow of creating a DTT image by the MRI apparatus 100 according to the first embodiment. FIG. 7 is a diagram illustrating an example of the setting of the region of interest by the region-of-interest setting unit according to the first embodiment. FIG. 8 is a diagram illustrating an example of setting a calculation start area by the calculation start area setting unit according to the first embodiment. FIG. 9 is a diagram (1) illustrating an example of the algorithm used by the calculation start area setting unit according to the present embodiment. FIG. 10 is a diagram (2) illustrating an example of the algorithm used by the calculation start area setting unit according to the present embodiment. FIG. 11 is a diagram (1) illustrating an example of display of a DTT image by the display control unit 77a. FIG. 12 is a diagram (2) illustrating an example of display of a DTT image by the display control unit 77a. FIG. 13 is a diagram (1) illustrating an example of a GUI displayed by the calculation start area setting unit according to the second embodiment. FIG. 14 is a diagram (2) illustrating an example of a GUI displayed by the calculation start area setting unit according to the second embodiment. FIG. 15 is a diagram illustrating another example of the GUI displayed by the calculation start area setting unit according to the second embodiment.

  Hereinafter, embodiments of an image processing apparatus and an MRI apparatus will be described in detail with reference to the drawings. In the embodiment described below, diffusion imaging is referred to as DWI (Diffusion Weighted Imaging), and an image generated by DWI is referred to as a DWI image. Also, diffusion tensor imaging is called DTI (Diffusion Tensor Imaging), and an image generated by DTI is called a DTI image. Also, diffusion tensor tractography is called DTT (Diffusion Tensor Tractography), and an image generated by DTT is called a DTT image.

(First embodiment)
First, the first embodiment will be described. FIG. 1 is a diagram showing an overall configuration of the MRI system according to the first embodiment. As shown in FIG. 1, the MRI apparatus 100 includes a gantry unit 10, a gradient magnetic field power supply 20, an RF transmission unit 30, an RF reception unit 40, a sequence control unit 50, a bed unit 60, and a computer system 70. Have

  The gantry 10 irradiates a subject P placed in a static magnetic field with a high-frequency magnetic field, thereby detecting an MR signal emitted from the subject P. The gantry 10 includes a static magnetic field magnet 11, a gradient magnetic field coil 12, a transmission RF (Radio Frequency) coil 13, and a reception RF coil 14.

  The static magnetic field magnet 11 is formed in a hollow cylindrical shape, and generates a uniform static magnetic field in a space in the cylinder. For example, a permanent magnet or a superconducting magnet is used as the static magnetic field magnet 11.

  The gradient coil 12 is formed in a hollow cylindrical shape and is disposed inside the static magnetic field magnet 11. The gradient coil 12 has three coils corresponding to the X, Y, and Z axes orthogonal to each other. Each coil receives a current supply from a gradient magnetic field power source 20 described later, and generates a gradient magnetic field whose magnetic field intensity changes along each of the X, Y, and Z axes. The Z-axis direction is the same as the static magnetic field.

  The gradient magnetic fields of the X, Y, and Z axes generated by the gradient coil 12 correspond to, for example, the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr, respectively. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section. The phase encoding gradient magnetic field Ge is used to change the phase of a magnetic resonance (MR) signal in accordance with a spatial position. The readout gradient magnetic field Gr is used to change the frequency of the MR signal in accordance with the spatial position.

  The transmission RF coil 13 is arranged inside the gradient magnetic field coil 12 and receives a high frequency pulse from the RF transmission unit 30 to generate a high frequency magnetic field.

  The reception RF coil 14 is disposed inside the gradient magnetic field coil 12 and receives an MR signal emitted from the subject P due to the influence of the high-frequency magnetic field generated by the transmission RF coil 13. Then, the receiving RF coil 14 outputs the received MR signal to the RF receiving unit 40.

  The gradient magnetic field power supply 20 supplies a current to the gradient magnetic field coil 12. The RF transmitter 30 transmits a high-frequency pulse corresponding to the Larmor frequency to the transmission RF coil 13. The RF receiving unit 40 generates raw data by digitizing the MR signal output from the receiving RF coil 14, and transmits the generated raw data to the sequence control unit 50.

  The sequence control unit 50 scans the subject P by driving the gradient magnetic field power source 20, the RF transmission unit 30, and the RF reception unit 40 based on the sequence information transmitted from the computer system 70. Further, when the raw data is transmitted from the RF receiving unit 40 as a result of scanning the subject P, the sequence control unit 50 transfers the raw data to the computer system 70.

  The sequence information here refers to the strength of power supplied to the gradient magnetic field coil 12 by the sequence controller 50 and the timing of supplying power, the strength of the RF signal transmitted from the RF transmitter 30 to the RF coil 13 for transmission, This is information defining a procedure for performing scanning, such as a timing at which an RF signal is transmitted and a timing at which the RF receiver 40 detects an MR signal.

  The bed unit 60 includes a top plate on which the subject P is placed, and the subject P is inserted into the opening of the gantry unit 10 during imaging by moving the top plate.

  The computer system 70 is a device that performs overall control of the MRI apparatus 100, data collection, image reconstruction, and the like, and includes an interface unit 71, an input unit 72, a display unit 73, a storage unit 74, and a data processing unit 75. And an image processing unit 76 and a control unit 77.

  The interface unit 71 controls input / output of various signals exchanged with the sequence control unit 50. For example, the interface unit 71 transmits sequence information to the sequence control unit 50 and receives raw data from the sequence control unit 50. Further, when the raw data is received, the interface unit 71 stores the received raw data in the storage unit 74 for each subject P.

  The input unit 72 receives various instructions and information input from the operator. As the input unit 72, for example, a pointing device such as a mouse or a trackball, a selection device such as a mode change switch, or an input device such as a keyboard is used.

  The display unit 73 displays various images referred to by the operator and a GUI (Graphical User Interface) for receiving various operations from the operator. For example, a display device such as a liquid crystal monitor or a CRT monitor is used as the display unit 73.

  The storage unit 74 stores the raw data transmitted from the sequence control unit 50 and various image data generated by the data processing unit 75 and the image processing unit 76 described later for each subject P.

  The data processing unit 75 reconstructs an image from the raw data stored in the storage unit 74. In addition, the data processing unit 75 generates a magnetic resonance image, a DWI image, or the like that shows morphological information in the body of the subject P.

  The image processing unit 76 generates various medical images by performing various types of image processing on the image data stored in the storage unit 74.

  The control unit 77 performs overall control of the MRI apparatus 100 by performing control movement between the above-described functional units and data transfer between the functional unit and the storage unit. Specifically, the control unit 77 includes a CPU (Central Processing Unit) and a memory, and controls each unit of the MRI apparatus 100 by executing various programs using them. For example, the control unit 77 generates sequence information based on the imaging conditions set by the operator, and transmits the generated sequence information to the sequence control unit 50 to execute various imaging sequences.

  Next, the configuration of the storage unit 74, the data processing unit 75, the image processing unit 76, and the control unit 77 according to the first embodiment will be described in detail. FIG. 2 is a functional block diagram illustrating configurations of the storage unit 74, the data processing unit 75, the image processing unit 76, and the control unit 77 according to the first embodiment.

  As illustrated in FIG. 2, the storage unit 74 includes a raw data storage unit 74a, a reconstructed image storage unit 74b, and an analysis image storage unit 74c.

  The raw data storage unit 74a stores the raw data transmitted from the sequence control unit 50 for each subject. The reconstructed image storage unit 74b stores the reconstructed image generated by the image reconstructing unit 75a described later for each subject. The analysis image storage unit 74c stores various analysis images generated by an analysis image generation unit 75b described later for each subject.

  As illustrated in FIG. 2, the data processing unit 75 includes an image reconstruction unit 75a and an analysis image generation unit 75b.

  The image reconstruction unit 75a generates a reconstructed image by performing post-processing, that is, reconstruction processing such as Fourier transform processing, on the raw data stored in the raw data storage unit 74a. Then, the image reconstruction unit 75a stores the generated reconstructed image in the reconstructed image storage unit 74b. For example, the image reconstruction unit 75a also generates morphological images such as a T1 weighted image and a T2 weighted image. The image reconstruction unit 75a generates a DWI image.

  Here, DWI will be described. In DWI, a pulse sequence with application of an MPG (Motion Probing Gradient) pulse that emphasizes attenuation of MR signals due to diffusion is used. Note that the signal intensity S due to diffusion is most simply expressed as the following equation (1).

S = PD (1-exp (-TR / T1) * exp (-TE / T2))
* Exp (-bD) (1)

In this formula (1), TE is echo time, TR is repetition time, and PD is proton density. T1 and T2 are signal relaxation times, and D is a diffusion coefficient (a diffusion coefficient representing the degree of diffusion). In addition, b [s / mm ² ] is a gradient magnetic field factor representing the degree of signal attenuation due to diffusion, and S ₀ is a signal intensity when the gradient magnetic field factor b is zero.

And
S ₀ = PD (1−exp (−TR / T1) * exp (−TE / T2))
age,
Experiment 1: S ₁ = S ₀ * exp (−b ₁ D)
Experiment 2: S ₂ = S ₀ * exp (−b ₂ D)
Then, the following equation (2) is established.

S ₂ / S ₁ = exp (− (b ₂ −b ₁ ) D) (2)

  Moreover, from the formula (2), the following formula (3) is obtained.

D = −ln (S ₂ / S ₁ ) / (b ₂ −b ₁ ) (3)

  As can be seen from this equation (3), the diffusion coefficient D can be calculated by using two different b values. For this reason, in a general clinical application of DWI, as a simple method, a unidirectional MPG pulse is applied, and diagnosis is performed using a DWI imaged at about b = 1000 and an image imaged at b = 0. Often. In general, since the imaging condition is set so that TE> 60 [ms], the image of b = 0 is a T2-weighted image having a contrast that emphasizes the difference of T2.

Here, the b value will be described in detail. In order to understand the b value, first, the gradient magnetic field pulse will be described. In the MRI apparatus, a strong gradient magnetic field that changes linearly along the bore is applied. This gradient magnetic field is called B ₀ field. An MRI apparatus generally includes gradient magnetic field units in the X, Y, and Z directions, and a magnetic field along an arbitrary direction can be applied by combining the gradient magnetic field units. The relationship between the frequency ω of the MR signal and the magnetic field strength B ₀ is expressed by a simple relational expression as shown in Expression (4) below.

ω = γB ₀ (4)

  Here, γ is a proportional constant specific to the nucleus.

  In DWI, spread data is determined by the magnitude and direction of an MPG pulse used for spread encoding. The magnitude of this MPG pulse is the b value. For example, in diffusion encoding, two MPG pulses are used that are arranged in contrast to before and after a high-frequency 180 ° refocus pulse. The first MPG pulse placed before the 180 ° refocus pulse causes a phase shift for all spins.

  Also, the second MPG pulse arranged after the 180 ° refocusing pulse inverts the phase shift caused by the first MPG pulse. This cancels out the phase shift for fixed molecules (protons in medical imaging). However, for a molecule that has moved to a position different from that at the time of the action of the first MPG pulse due to Brownian motion, the phase shift is not completely canceled. Therefore, for such molecules, there will remain a residual phase shift that results in signal attenuation. Thus, in diffusion imaging, diffusion encoding is controlled by the b value indicating the magnitude of the MPG pulse and the direction of the gradient magnetic field pulse.

  For example, the image reconstruction unit 75a performs the above-described DWI to generate a DWI image that emphasizes the diffusion effect in which particles such as water molecules are scattered by Brownian motion due to heat.

  The analysis image generation unit 75b generates various analysis images using the reconstructed image generated by the image reconstruction unit 75a. Then, the analysis image generation unit 75b stores the generated analysis image in the analysis image storage unit 74c. For example, the analysis image generation unit 75b reads the DWI image generated by the image reconstruction unit 75a from the reconstruction image storage unit 74b, and represents the anisotropy of molecular diffusion in the subject using the read DWI image. A DTI image is generated.

  Here, DTI will be described in detail. In DTI, six tensor coefficients or tensor parameters, that is, independent elements or components of a 3 × 3 symmetric tensor matrix are calculated for each voxel based on diffusion data. The symmetric tensor matrix is expressed by the following equation (5).

In this equation (5), the first subscript (x, y, z) in D _{xx to} D _zz represents the original direction of the cell or tissue. The second subscript (x, y, z) represents the direction of the gradient magnetic field. Further, D _xx , D _yy , and D _zz that are so-called diagonal components are components of diffusion coefficients that are measured in a three-axis apparatus coordinate system that is usually included in an MRI apparatus used for clinical use.

  Typically, tensor parameters are used to calculate a parameter map that is important for diagnosis. For example, the isotropic component of the diffusion tensor or the anisotropic component of the diffusion tensor is shown in the corresponding parameter map. The parameter map here is, for example, an average apparent diffusion coefficient map or an ADC (Apparent Diffusion Coefficient) map, a partial anisotropy map, or an FA (Fractional Anisotropy) map. The ADC map is a map of diffusion coefficients calculated by solving equation (2) for each pixel.

  Here, since there are a large number of diffusion data used in DTI, when a diffusion tensor is calculated for each voxel, an unknown parameter is determined by averaging. As a method for determining the unknown parameter, a method known by multivariate linear regression, for example, a method of forming a pseudo inverse matrix or a method of performing singular value analysis is used.

For example, according to the method described in Basser, PJ, J. Mattiello, and D. LeBihan. “MR diffusion tensor spectroscopy and imaging” Biophys. J., 66, p. 259-267, 1994, Each molecular diffusion anisotropy is expressed by, for example, an ellipsoidal model. In this method, six parameters of diffusion parameters are defined by setting the eigenvalues of the three axes of the ellipsoid to be λ ₁ , λ ₂ , and λ ₃ and the eigenvectors to be v ₁ , v ₂ , and v ₃ .

  For example, the analysis image generation unit 75b defines the anisotropy of molecular diffusion for each voxel included in the DWI image using the ellipsoid model. Then, the analysis image generation unit 75b determines the color of the voxel in the DTI image according to the direction of the longest axis in the defined ellipsoid model. For example, the analysis image generation unit 75b sets the X-axis direction in the apparatus coordinate system to red, the Y-axis direction to yellow, and the Z-axis direction to blue, and the X, Y, Z-axis and the longest axis in the ellipsoid model The voxel color is determined according to the positional relationship with the direction. The analysis image generation unit 75b generates a DTI image representing the anisotropy of molecular diffusion in the subject by performing such processing for each voxel included in the DWI image.

  As illustrated in FIG. 2, the image processing unit 76 includes a region of interest setting unit 76a, a calculation start region setting unit 76b, and a tractography generating unit 76c.

  The region-of-interest setting unit 76a sets a region of interest on the analysis image generated by the analysis image generation unit 75b. For example, the region-of-interest setting unit 76a reads out the DTI image generated by the analysis image generation unit 75b from the analysis image storage unit 74c, and sets the region of interest on the read DTI image.

  The calculation start area setting unit 76b sets a calculation start area on the analysis image generated by the analysis image generation unit 75b. The calculation start area referred to here is an area that is set in order to specify the position of the pixel from which tracking in the maximum diffusion direction starts in the DTT performed by the tractography generation unit 76c described later.

  The tractography generation unit 76c generates a DTT image depicting a nerve bundle based on the calculation start region set by the calculation start region setting unit 76b. FIG. 3 is a diagram for explaining generation of a DTT image by the tractography generation unit 76c according to the first embodiment. FIG. 3 shows a part of the DTI image generated by the analysis image generation unit 75b. Here, for convenience of explanation, a case where a two-dimensional DTI image is used will be described.

  In FIG. 3, a plurality of square areas divided in a lattice form indicate pixels included in the DTI image. Further, the ellipse in each pixel represents an ellipsoidal model representing the molecular diffusion anisotropy described above. Further, the color assigned to each pixel is assigned according to the magnitude of anisotropy, and the darker the color, the lower the anisotropy.

  As shown in FIG. 3, the tractography generation unit 76c sets the calculation start region set by the calculation start region setting unit 76b as the calculation start region C on the DTI image. Further, the tractography generation unit 76c calculates an average direction for pixels having high anisotropy (white pixels shown in FIG. 3) among pixels included in the DTI image.

Thereafter, the tractography generation unit 76c generates curves F ₁ and F ₂ along the calculated average direction with the pixels P ₁ and P ₂ included in the calculation start region C as base points. The curves F ₁ and F ₂ generated here are trajectories in the maximum diffusion direction in each pixel, and represent the flow of the nerve bundle. In this way, the tractography generation unit 76c generates an image showing the flow of the nerve bundle as a DTT image.

  Returning to the description of FIG. 2, the control unit 77 includes a display control unit 77a. The display control unit 77 a controls display of various types of information on the display unit 73. For example, the display control unit 77a causes the display unit 73 to display the DTT image generated by the tractography generation unit 76c.

4 and 5 are diagrams illustrating examples of DTT images displayed by the display control unit 77a according to the first embodiment. For example, as illustrated in FIG. 4, the display control unit 77 a displays a two-dimensional DTT image superimposed on the two-dimensional DWI image. 4, curve F _o, based on the calculation start region C _o has been generated by DTT, curve F _m, based on the symmetrical calculation start region C _m to the left and right and the calculation start region C _o DTT Is generated. By displaying the DTT curves F _o and F _m in this way, the state of the nerve bundle can be observed in a plane.

Alternatively, for example, as illustrated in FIG. 5, the display control unit 77 a combines and displays a three-dimensional DTT image with a two-dimensional DWI image. 5, a curve F _o, based on the calculation start region C _o has been generated by DTT, curve F _m, based on the symmetrical calculation start region C _m to the left and right and the calculation start region C _o DTT Is generated. By displaying the DTT curves F _o and F _m in this way, the state of the nerve bundle can be observed in three dimensions.

  The configuration of the MRI apparatus 100 according to the first embodiment has been described above. Under such a configuration, in the first embodiment, the MRI apparatus 100 sets a plurality of continuous calculation start areas on a medical image in which a region of the subject is imaged, and for each of the calculation start areas. A DTT image depicting a nerve bundle is generated. Thereby, the region to be diagnosed can be divided into a plurality of regions, and the nerve bundle can be observed for each region, so that the region to be diagnosed can be examined in detail.

  Next, a flow of creating a DTT image by the MRI apparatus 100 according to the first embodiment will be described. FIG. 6 is a flowchart showing a flow of creating a DTT image by the MRI apparatus 100 according to the first embodiment.

  As shown in FIG. 6, in the MRI apparatus 100 according to the first embodiment, first, the control unit 77 controls each unit such as the sequence control unit 50 based on the imaging conditions set by the operator. A DWI sequence is executed (step S101). Thereafter, the image reconstruction unit 75a generates a DWI image from the raw data collected by the DWI sequence (step S102).

  Subsequently, the analysis image generation unit 75b generates a DTI image using the DWI image generated by the image reconstruction unit 75a (step S103). The region-of-interest setting unit 76a sets a region of interest on the DTI image generated by the analysis image generation unit 75b (step S104).

  FIG. 7 is a diagram illustrating an example of setting the region of interest by the region-of-interest setting unit 76a according to the first embodiment. Here, a case where the region of interest is set in the range occupied by the corpus callosum in the DTI image of the head will be described as an example. In this case, for example, the region-of-interest setting unit 76a receives an operation for setting a seed point at an arbitrary position in the corpus callosum from the operator via the input unit 72. Thereafter, the region-of-interest setting unit 76a extracts a range occupied by the corpus callosum by performing region growing with reference to the seed point set by the operator. Then, the region-of-interest setting unit 76a sets the extracted corpus callosum range as the region of interest 2 on the DTI image 1, as shown in FIG.

  Note that the method of setting the region of interest is not limited to the region growing described here, and various methods can be used. For example, an operation for designating a range of a desired shape and size on the DTI image is accepted from the operator via the input unit 72, and the accepted range is set as a region of interest. Alternatively, for example, the region of interest may be automatically extracted using image processing such as threshold processing.

  Returning to the description of FIG. 6, the calculation start area setting unit 76 b then sets a plurality of continuous calculation start areas on the analysis image generated by the analysis image generation unit 75 b. Specifically, the calculation start region setting unit 76b divides the region of interest set by the region of interest setting unit 76a into a plurality of regions, and sets each of the plurality of regions as a calculation start region (step S105).

  FIG. 8 is a diagram illustrating an example of setting a calculation start area by the calculation start area setting unit 76b according to the first embodiment. As shown in FIG. 8, for example, the calculation start region setting unit 76b divides the region of interest 2 set by the analysis image generation unit 75b into six regions according to a predetermined algorithm, and each of them is calculated as a calculation start region. Set as 2a-2f.

  Here, various algorithms can be used as an algorithm for dividing the region of interest. For example, the calculation start region setting unit 76b divides the region of interest based on the anatomical characteristics of the part to be diagnosed. For example, when the diagnosis target is the corpus callosum, Witelson Scheme may be used. Witelson Scheme is a template for dividing the corpus callosum into functions (for example, WitelsonSF, Kigar DL. Anatomical development of corpus callosum in humans: a review with reference to sex and cognition. In: Molefese DL, Segalowitz SJ, eds. Brain lateralization in children: development implications. See New York, NY: Guilford, 1988; 35-37.

  9 and 10 are diagrams illustrating an example of an algorithm used by the calculation start region setting unit 76b according to the present embodiment. 9 and 10 show the Witelson Scheme template. As shown in FIGS. 9 and 10, the Witelson Scheme is a template that divides the corpus callosum into seven for each function with reference to a straight line passing through both ends (ACC and PCC) of the corpus callosum. For example, the calculation start area setting unit 76b divides the area of the corpus callosum into seven based on such a template of the Witelson Scheme.

  Returning to the description of FIG. 6, the tractography generation unit 76c then generates a DTT image depicting a nerve bundle for each of the plurality of calculation start regions set by the calculation start region setting unit 76b (step S106).

  Then, the display control unit 77a displays the DTT image generated by the tractography generation unit 76c on the display unit 73 (step S107). At this time, for example, the calculation start area setting unit 76b changes the display color of the nerve bundle drawn in the adjacent calculation start area and causes the display unit 73 to display the DTT image.

  11 and 12 are diagrams illustrating an example of display of a DTT image by the display control unit 77a. As illustrated in FIG. 11, for example, when the six calculation start areas A to F are set in the corpus callosum area on the DTI image 1, the calculation start area setting unit 76 b passes through each area. The DTT image 3 in which the display color is changed for each region is displayed on the display unit 73. At this time, for example, the calculation start area setting unit 76b displays all the nerve bundles passing through each area in different colors. Alternatively, as shown in FIG. 12, the calculation start region setting unit 76b causes the display unit 73 to display the DTT image 3 in which the color of the nerve bundle is alternately changed for each region using at least two display colors. Also good. Or the calculation start area | region setting part 76b may change whether a nerve bundle is displayed for every divided | segmented area | region.

  As described above, in the first embodiment, the calculation start area setting unit 76b sets a plurality of continuous calculation start areas on a medical image obtained by imaging a region of the subject. In addition, the tractography generation unit 76c generates a DTT image depicting a nerve bundle for each of a plurality of calculation start regions set by the calculation start region setting unit 76b. As a result, the region to be diagnosed can be divided into a plurality of regions and the nerve bundle can be observed for each region, so that the region to be diagnosed can be examined in detail. That is, according to the present embodiment, it is possible to obtain a DTT image that can inspect a region to be diagnosed in detail.

  In the first embodiment, the region-of-interest setting unit 76a sets the region of interest on the medical image obtained by imaging the region of the subject. The calculation start region setting unit 76b divides the region of interest set by the region of interest setting unit 76a into a plurality of regions, and sets each of the plurality of regions as a calculation start region. Therefore, according to the present embodiment, the region to be diagnosed can be inspected in detail by setting the region to be diagnosed as a region of interest.

  In the first embodiment, the calculation start region setting unit 76b divides the region of interest based on the anatomical characteristics of the part of the subject. Therefore, according to the present embodiment, it is possible to observe a nerve bundle for each of a plurality of regions divided based on anatomical knowledge.

  In the first embodiment, the tractography generation unit 76c changes the display color of the nerve bundle drawn in the adjacent calculation start area and causes the display unit 73 to display the DTT image. Therefore, according to the present embodiment, it is possible to more easily identify the nerve bundle for each region.

(Second Embodiment)
Next, a second embodiment will be described. In the present embodiment, an example will be described in which the area size can be changed for each of a plurality of calculation start areas in the MRI apparatus 100 described in the first embodiment.

  Specifically, in this embodiment, the calculation start area setting unit 76b sets a plurality of continuous calculation start areas on the medical image, and then sets the size of each calculation start area based on an instruction from the operator. To change. For example, the calculation start area setting unit 76b displays on the display unit 73 a GUI (Graphical User Interface) for accepting an operation for changing the size of each calculation start area from the operator after setting a plurality of calculation start areas. Let

  13 and 14 are diagrams illustrating an example of a GUI displayed by the calculation start area setting unit 76b according to the second embodiment. As illustrated in FIG. 13, for example, the calculation start area setting unit 76 b indicates a GUI having a first adjustment knob 4, a plurality of second adjustment knobs 5 a to 5 f, and a plurality of dividing lines 6 a to 6 f. Used as Here, the dividing lines 6a to 6f are straight lines connecting the first adjustment knob 4 and the second adjustment knobs 5a to 5f, respectively. In this case, the calculation start area setting unit 76b receives an operation for moving each adjustment knob to a desired position from the operator, and determines the position of each dividing line as the adjustment knob moves. And as shown in FIG. 14, the calculation start area | region setting part 76b is based on each positioned division line, and the position, the shape, and magnitude | size of several calculation start area | regions 2a-2f set to the region of interest 2 Set. Thus, the operator can easily adjust the size and shape of each calculation start area by moving each adjustment knob.

  Note that the GUI for accepting an operation for changing the size of each calculation start area from the operator is not limited to that shown in FIGS. FIG. 15 is a diagram illustrating another example of the GUI displayed by the calculation start area setting unit 76b according to the second embodiment. As illustrated in FIG. 15, for example, the calculation start area setting unit 76 b may use a color bar-shaped GUI configured with a plurality of display colors as an indicator. In this indicator, the positions of both ends of the color bar 7 correspond to the positions of both ends of the region of interest in the DTT image 3. Further, the color bar 7 is partitioned for each display color, and adjustment knobs 7a to 7e are provided at the partition portions. The position of each adjustment knob corresponds to the position of the boundary of each calculation start region set in the region of interest. In this case, the calculation start area setting unit 76b accepts an operation of moving each adjustment knob to a desired position along the color bar 7 from the operator, and the boundary of the calculation start area is accompanied by the movement of each adjustment knob. Move. Also in this example, the operator can easily adjust the size and shape of each calculation start area by moving each adjustment knob.

  In the present embodiment, when the size of the calculation start area is changed by the calculation start area setting unit 76b, the tractography generation unit 76c redraws the nerve bundle of the changed area. Thereby, the operator can change the size and shape of each calculation start area more appropriately while confirming the nerve bundle.

  As described above, in the second embodiment, the calculation start region setting unit 76b changes the size of the calculation start region based on an instruction from the operator after setting a plurality of calculation start regions. Further, when the size of the calculation start area is changed by the calculation start area setting unit 76b, the tractography generation unit 76c redraws the nerve bundle of the changed area. Therefore, according to the present embodiment, since the operator can easily and appropriately adjust the size and shape of each calculation region, it is possible to efficiently observe the region to be diagnosed.

  As mentioned above, although several embodiment of this invention was described, these embodiment is shown as an example and is not intending limiting the range of invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

100 MRI system (magnetic resonance imaging system)
70 Computer System 76 Image Processing Unit 76b Calculation Start Area Setting Unit 76c Tractography Generation Unit

Claims (6)

Calculation start area setting means for setting a plurality of continuous calculation start areas on a medical image in which a part of a subject is imaged;
Image generating means for generating a diffusion tensor tractography image depicting a nerve bundle for each of the plurality of calculation start regions;
An image processing apparatus comprising: A region of interest setting means for setting a region of interest on the medical image;
The image processing apparatus according to claim 1, wherein the calculation start area setting unit divides the region of interest into a plurality of areas, and sets each of the plurality of areas as the calculation start area.   The image processing apparatus according to claim 2, wherein the calculation start region setting unit divides the region of interest based on an anatomical feature of the part. The calculation start area setting means, after setting the plurality of calculation start areas, change the size of each calculation start area based on an instruction from the operator,
The image processing apparatus according to claim 1, wherein the image generation unit redraws the nerve bundle of the changed area when the size of the calculation start area is changed.   The display control part which changes the display color of the nerve bundle drawn by the adjacent calculation start area | region, and displays the said tensor tractography image on a display part, It further provided with any one of Claims 1-4 characterized by the above-mentioned. The image processing apparatus described in one. First image generation means for generating a diffusion tensor image representing anisotropy of molecular diffusion in a subject from data collected using a magnetic resonance phenomenon;
Calculation start area setting means for setting a plurality of continuous calculation start areas on the diffusion tensor image;
Image generating means for generating a diffusion tensor tractography image depicting a nerve bundle for each of the plurality of calculation start regions;
A magnetic resonance imaging apparatus comprising: