JP2013048800A - Image data processor, magnetic resonator, image data processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve the detection accuracy of the feature point of the site that is required to be photographed.SOLUTION: The correlation Rg of the data on each of lines L-Lof a subject 12 and template data DT is calculated, and a position in which the correlation Rg becomes more than a predetermined value is detected. When the position is detected, the position in which the coordinate value of an SI direction becomes a mean value is selected from among the positions, and this position is assumed to be a candidate point to detect the position of the SI direction of the upper end of a liver. In addition, the feature point of a shape model M is positioned in the candidate point E3, and the position of the SI direction of the upper end of the liver is detected.

Description

  The present invention relates to an image data processing apparatus, a magnetic resonance apparatus, an image data processing method, and a program for detecting feature points of an imaging region.

  When imaging a subject with a magnetic resonance apparatus, generally, an operator manually sets a slice position at an imaging site. However, manually setting the slice position has a problem such as a burden on the operator. Therefore, a technique for automatically setting the slice position is disclosed. For example, a technique for automatically setting a slice position of an intervertebral disc has been disclosed (see Patent Document 1).

JP 07-05248 A

  In recent years, a technique for automatically setting a slice position for a part such as a brain or a liver has been studied. For example, as one of the methods for automatically setting the slice position of the liver, image data for slice positioning is collected, and the feature points of the liver (for example, the upper and lower ends of the liver) are detected from the collected image data. There is a method of setting the slice position of the liver based on the characteristic points of the liver. However, when the patient's liver is deformed or when fat is not sufficiently suppressed, there is a problem that the detection accuracy of the feature points of the liver is deteriorated. Therefore, it is desired to improve the detection accuracy of the feature point of the part to be photographed.

According to a first aspect of the present invention, there is provided image data creating means for creating image data of an imaging part including a predetermined part of a subject;
Projection data creating means for creating first projection data of the image data;
From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the data on each line and the position of the feature point of the imaging part in a predetermined direction are detected. Calculating means for calculating the correlation with the template data used to
An image data processing apparatus comprising: detecting means for detecting a position of the feature point of the imaging region in the predetermined direction based on the correlation.

  A second aspect of the present invention is a magnetic resonance apparatus having the above image data processing apparatus.

According to a third aspect of the present invention, there is provided an image data creation step for creating image data of an imaging part including a predetermined part of the subject;
A projection data creation step of creating first projection data of the image data;
From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the data on each line and the position of the feature point of the imaging part in a predetermined direction are detected. A calculation step of calculating a correlation with the template data used to
A detection step of detecting a position of the feature point of the imaging region in the predetermined direction based on the correlation.

According to a fourth aspect of the present invention, there is provided image data creation processing for creating image data of an imaging region including a predetermined region of a subject;
Projection data creation processing for creating first projection data of the image data;
From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the data on each line and the position of the feature point of the imaging part in a predetermined direction are detected. Calculation processing for calculating the correlation with the template data used to
This is a program for causing a computer to execute detection processing for detecting the position of the feature point of the imaging region in the predetermined direction based on the correlation.

  From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the correlation between the data on each line and the template data is calculated, and based on the correlation Thus, the position of the feature point of the imaging region in the predetermined direction is detected. Therefore, the detection accuracy of the position of the feature point in the predetermined direction can be improved.

1 is a schematic view of a magnetic resonance imaging apparatus according to an embodiment of the present invention. It is a figure which shows the scan performed with this form. It is a figure which shows an imaging | photography site | part schematically. It is a figure which shows the flow when imaging a subject in this form. It is explanatory drawing of steps ST1-ST3. It is explanatory drawing of step ST4 and ST5. It is explanatory drawing of step ST61. It is explanatory drawing of an example of the preparation method of template data DT. It is explanatory drawing of step ST61 and ST62. It is explanatory drawing of step ST63. It is explanatory drawing of an example of the production method of the shape model M. FIG. It is explanatory drawing of step ST63. It is explanatory drawing of step ST63. It is explanatory drawing of step ST7.

  Hereinafter, although the form for inventing is demonstrated, this invention is not limited to the following forms.

FIG. 1 is a schematic view of a magnetic resonance apparatus according to one embodiment of the present invention.
A magnetic resonance apparatus (hereinafter referred to as “MR apparatus”, MR: Magnetic Resonance) 100 includes a magnet 2, a table 3, a receiving coil 4, and the like.

  The magnet 2 has a bore 21 in which the subject 12 is accommodated, a superconducting coil 22, a gradient coil 23, and a transmission coil 24. The superconducting coil 22 applies a static magnetic field, the gradient coil 23 applies a gradient pulse, and the transmission coil 24 transmits an RF pulse. In place of the superconducting coil 22, a permanent magnet may be used.

  The table 3 has a cradle 3a. The cradle 3a is configured to be able to move into the bore 21. The subject 12 is transported to the bore 21 by the cradle 3a.

  The reception coil 4 is attached to the abdomen of the subject 12. The receiving coil 4 receives a magnetic resonance signal from the subject 12.

  The MR apparatus 100 further includes a sequencer 5, a transmitter 6, a gradient magnetic field power source 7, a receiver 8, a central processing unit 9, an operation unit 10, a display unit 11, and the like.

  Under the control of the central processing unit 9, the sequencer 5 sends pulse sequence information to the transmitter 6 and the gradient magnetic field power supply 7.

  The transmitter 6 outputs a drive signal for driving the RF coil 24 based on the information sent from the sequencer 5.

  The gradient magnetic field power supply 7 outputs a drive signal for driving the gradient coil 23 based on the information sent from the sequencer 5.

  The receiver 8 processes the magnetic resonance signal received by the receiving coil 4 and outputs it to the central processing unit 9.

  The central processing unit 9 implements various operations of the MR apparatus 100 such as transmitting necessary information to the sequencer 5 and the display unit 11 and reconstructing an image based on data received from the receiver 8. The operation of each part of the MR apparatus 100 is controlled. The central processing unit 9 is constituted by a computer, for example. The central processing unit 9 includes image data creation means 91 to detection means 96 and the like.

The image data creation unit 91 creates image data of the imaging region including the liver of the subject.
The projection data creation unit 92 creates projection data of the image data created by the image data creation unit 91.
The projection profile creation unit 93 creates a projection profile of the projection data created by the projection data creation unit 92.
The range determination unit 94 determines a range inside the subject based on the projection profile created by the projection profile creation unit 93.
The calculating means 95 calculates the correlation between the data on the lines L _{1 to} L _n and the template data DT (see FIG. 7).

  The detection means 96 detects the position of the feature point (upper end and lower end) of the liver in the SI direction. The detection means 96 includes candidate point determination means 96a and position determination means 96b.

  Candidate point determination means 96a determines candidate points for detecting the position of the feature point of the liver in the SI direction based on the correlation calculated by calculation means 95.

  The position determining unit 96b determines the position of the feature point of the liver in a predetermined direction based on the candidate point determined by the candidate point determining unit 96a.

  The central processing unit 9 is an example of the image data creation unit 91 to the detection unit 96, and functions as these units by executing a predetermined program. The central processing unit 9 corresponds to an image data processing device.

The operation unit 10 is operated by the operator 13 and inputs various information to the central processing unit 9. The display unit 11 displays various information.
The MR apparatus 100 is configured as described above.

FIG. 2 is a diagram showing a scan executed in this embodiment, and FIG. 3 is a diagram schematically showing an imaging region.
In this embodiment, a scout scan and a main scan are executed.

The scout scan is a scan for acquiring image data of an imaging region including the liver. The image data acquired by the scout scan is used to detect a position z _{w in} the SI direction of the upper end of the liver and a position z _{z in} the SI direction of the lower end of the liver. The scout scan may be a 3D scan or a 2D scan.

The main scan is a scan for acquiring image data of the liver based on the positions z _w and z _z of the upper and lower ends of the liver in the SI direction.

  FIG. 4 is a diagram illustrating a flow when the subject is imaged in the present embodiment, and FIGS. 5 to 14 are diagrams used to describe each step of the flow illustrated in FIG. 4.

  In step ST1, a scout scan (see FIG. 2) is executed. The image data creation unit 91 (see FIG. 1) creates image data VD of the imaging region based on the magnetic resonance signals collected by the scout scan. FIG. 5A schematically shows image data VD acquired by scout scanning. After executing the scout scan, the process proceeds to step ST2.

  In step ST2, the projection data creating means 92 (see FIG. 1) creates sagittal projection data DS of the right half data VDr of the image data VD acquired in step ST1, as shown in FIG. 5B. After the sagittal projection data DS is created, the process proceeds to step ST3.

  In step ST3, the projection profile creating means 93 (see FIG. 1) projects the sagittal projection data DS created in step ST2 in the SI direction as shown in FIG. 5C to create a projection profile PP. After creating the projection profile PP, the process proceeds to step ST4.

  In step ST4, the range determining means 94 (see FIG. 1) determines the range inside the subject based on the projection profile PP. Since the signal value inside the subject is higher than the signal value outside the subject (external region), the range in which the value of the projection profile PP is large is considered as the range R inside the subject. Therefore, as shown in FIG. 6D, a reference line F for determining the range R inside the subject is calculated with respect to the projection profile PP, and an intersection of the reference line F and the projection profile PP is detected. By doing so, the positions Th1 and Th2 at both ends of the range R inside the subject can be obtained. As a calculation method of the reference line F, for example, the following formula can be used.

F = Noise + k * σ (1)
Here, Noise: average value of data extracted from the extracorporeal region of the projection profile PP
σ: Standard deviation of data extracted from extracorporeal area
k: The coefficient k can be set to k = 3, for example.

The range determination means 94 extracts data from the range R _out at the end of the projection profile PP in order to obtain the Noise value. Since this range R _out is sufficiently far from the center of the projection profile PP, by extracting data from this range R _out, only the data of the extracorporeal region is extracted. Therefore, the data to be extracted can be prevented from being included in the body region data. The range determining means 94 extracts data from the extracorporeal region, calculates Noise and σ, calculates a reference line F, and determines positions Th1 and Th2 at both ends of the range R inside the subject. Accordingly, the range R inside the subject can be determined. When the range R inside the subject is determined, the process proceeds to step ST5.

  In step ST5, as shown in FIG. 6E, the projection data creating means 92 extracts data VDm between the positions Th1 and Th2 obtained in step ST4 from the image data VD, and this data VDm The coronal projection data DC is created. After creating the coronal projection data DC, the process proceeds to step ST6.

  In step ST6, the position of the upper end of the liver in the SI direction is obtained. Hereinafter, steps ST61 to ST63 included in step ST6 will be described in order.

FIG. 7F is an explanatory diagram of step ST61.
In step ST61, calculation means 95 (see FIG. 1), retrieves the data on a plurality of lines _L 1 ~L _n across the area where the liver is projected. The data D _{i + 2} on the line L _{i + 2} is representatively shown on the right side of FIG. The calculation means 95 calculates the data on each line _L 1 ~L _n, the correlation between the template data DT. The template data DT is data used for detecting the position of the upper end of the liver in the SI direction, and is created in advance before imaging the subject. Hereinafter, an example of a method for creating the template data DT will be described.

FIG. 8 is an explanatory diagram showing an example of a method for creating the template data DT.
First, coronal projection data DC _{1 to} DC _n of each of the plurality of subjects SUB _{1 to} SUB _n are created in the same procedure as steps ST _{1 to} ST 5 (FIG. 8 (a 1) to (an)).

After creating a coronal projection data _DC 1 to DC _n, based on the coronal projection data _DC 1 to DC _n, specifying the position _PU 1 to PU _n in the coronal plane of the upper end of the liver of the subject _SUB 1 _{~SUB n.} This specification is performed manually by a person who creates template data with reference to the coronal projection data DC _{1 to} DC _n . When the positions PU _{1 to} PU _n of the upper end of the liver in the SI direction are specified, data on lines M _{1 to} M _n crossing these positions PU _{1 to} PU _n are extracted (FIG. 8 (b1) to (bn)). . Then, template data DT is created based on the data on the lines M _{1 to} M _n (see FIG. 8C). In the present embodiment, data obtained by averaging the data on the lines M _{1 to} M _n is used as template data DT. The template data DT is associated with position information of the feature point PU representing the position of the upper end of the liver in the SI direction. Incidentally, before averaging the data on line M ₁ ~M _n, such as running weighting process on the data on line M ₁ ~M _n, to create the template data TD in a different manner than FIG. 8 Also good. Further, the template data DT may be created using only data of one subject.

The calculating means 95 calculates a correlation R _g between the data on the lines L _{1 to} L _n of the subject 12 and the template data DT. For example, when calculating the correlation R _g between the data and the template data DT on the line L _{i + 2,} the template data DT is moved by one pixel in the SI direction, each time moving the template data DT, on the line L _{i + 2} The correlation between the data and the template data DT is obtained. FIG. 7 shows the correlation R _g = R _gf when the feature point PU of the template data DT moves to the position z _f in the SI direction on the line L _{i + 2} . For the correlation R _g between the data and the template data DT on other lines, while moving the template data DT in the SI direction, each time moving the template data DT, to calculate the correlation.

When the correlation is calculated, the candidate point determining unit 96a (see FIG. 1), for each line _L 1 ~L _n, the correlation _{R g} is a predetermined value _{R th (e.g.,} R th = 0.85) becomes more The position of the feature point PU of the template data DT is detected. FIG. 9G shows an example of the detected position. Here, when the feature point PU template data DT is moved to the position _E 1 to E _7, and correlation _{R g} is equal to or greater than the predetermined value _{R th.} These positions E _{1 to} E ₇ are determined as candidate points E _{1 to} E ₇ used for detecting the position of the upper end of the liver of the subject in the SI direction. Once you have decided on a candidate point _E 1 _{~E 7,} the process proceeds to step ST62.

In step ST62, the position determining unit 96b (see FIG. 1) selects a candidate point whose coordinate value in the SI direction is the median (median) from the candidate points E _{1 to} E ₇ determined in step ST61. . Here, the coordinate values of the SI direction candidate points E ₃ becomes median (median). Therefore, candidate point E ₃ is selected from candidate points E _{1 to} E ₇ . In FIG. 9 (h), it shows the candidate point _{E 3} selected. Once you have selected the candidate point _{E 3,} the process proceeds to step ST63.

In step ST63, the position determination unit 96b, based on the SI direction position _{z 3} candidate points _{E 3,} determining the position of the SI direction of the upper end of the subject liver. Hereinafter, this determination method will be described with reference to FIGS.

The position determining unit 96b uses the sagittal projection data DS (see FIG. 5B) created in step ST2 to determine the position of the upper end of the liver in the SI direction. FIG. 10 (i) shows the sagittal projection data DS created in step ST2. Incidentally, in the sagittal projection data DS in FIG. 10 (i), an AP-direction coordinate values Th1 and Th2 (see FIG. 6 (d)) calculated in step ST4, SI direction of candidate points _{E 3} determined in step ST62 The position z ₃ (see FIG. 9H) is also shown.

  First, as shown in FIG. 10 (j), the position determining means 96b positions a shape model M representing the contour of the upper end side of the liver in the sagittal plane with respect to the sagittal projection data DS. The shape model M is created in advance before imaging the subject. Hereinafter, an example of a method for creating the shape model M will be described.

FIG. 11 is an explanatory diagram illustrating an example of a method for creating the shape model M.
First, sagittal projection data DS _{1 to} DS _n of a plurality of subjects SUB _{1 to} SUB _n are created in the same procedure as in steps ST 1 and ST 2 (FIG. 11 (a 1) to (an)).

After creating a sagittal projection data _DS 1 to DS _n, based on the sagittal projection data _DS 1 to DS _n, specifying the outline _OL 1 _{~OL n} the upper side of the liver in the sagittal plane of the subject _SUB 1 _{~SUB n} (FIG. 11 (b1)-(bn)). The contours OL _{1 to} OL _n are specified manually by the person who creates the shape model M with reference to the sagittal projection data DS _{1 to} DS _n . When the contours OL _{1 to} OL _n are specified, the data of the contours OL _{1 to} OL _n are extracted. Then, a shape model M is created based on the data of the contours OL _{1 to} OL _n obtained for each of the subjects SUB _{1 to} SUB _n (see FIG. 11C). In the present embodiment, a model obtained by averaging the data of the contours OL _{1 to} OL _n is used as the shape model M. Incidentally, before averaging the data of the contour _OL 1 _{~OL n,} such as running weighting process on the data of the contour _OL 1 _{~OL n,} may create the shape model M in a different manner than Figure 11 . The shape model M has feature points c1 to c6 of the edge of the liver.

Returning to FIG. 10J, the description will be continued.
The position determining unit 96b obtains an intermediate position Thm between the positions Th1 and Th2 in the AP direction. The shape model M of the feature points c1~c6 of, as feature point c3 coordinate values of the SI direction becomes maximum overlap at the intersection of the SI direction position _{z 3} of the AP direction position Thm, geometric model Position M.

  After positioning the shape model M, the position determining unit 96b sets lines H1 to H6 that cross the feature points c1 to c6 of the shape model M as shown in FIG. In the present embodiment, the line H1 is set as a line extending in the direction perpendicular to the tangent at the feature point c1 of the shape model M. The other lines H2 to H6 are also set as lines extending in the direction perpendicular to the tangent line at the feature points c2 to c6 of the shape model M, respectively. However, the lines H1 to H6 may be set by other methods. When the lines H1 to H6 are set, the edges K1 to K6 on the upper end side of the subject's liver are detected for each of the lines H1 to H6. Since the liver has a high signal, but the tissue around the liver has a low signal, when the data on each line H1 to H6 is examined, a position where the data value rapidly decreases appears. The positions where the data values rapidly decrease are the edges K1 to K6 on the upper end side of the subject's liver. The position determining unit 96b deforms the shape model M so that the feature points c1 to c6 of the shape model M are positioned at the detected edges K1 to K6. FIG. 12 (l) shows the shape model M after deformation.

The position determining unit 96b detects a point W at which the coordinate value in the SI direction is maximum based on the position of the deformed shape model M. FIG. 13 (m) shows the detected point W. FIG. By detecting this point W, the position z _w of SI direction of the upper end of the subject liver is determined. FIG 13 (n), the position _{z w} of SI direction of the upper end of the liver are shown in coronal projection data DC. After determining the position z _w of SI direction of the upper end of the subject liver, the process proceeds to step ST7.

In step ST7, the detecting means 96 (see FIG. 1) detects the position of the lower end of the subject's liver in the SI direction. When detecting the position of the lower end of the liver in the SI direction, the same method as the method of detecting the position z _w of the upper end of the liver in the SI direction may be used, or another method may be used. As an example of another method, a method of creating a projection profile by projecting coronal projection data DC in the RL direction and detecting the lower end of the liver based on a change in the signal of the projection profile may be considered. FIG. 14 (o) shows the detected position z _z of the lower end of the liver in the SI direction. If the position of a lower end is detected, it will progress to step ST8.

In step ST8, based on the position z _w and z _z of detected upper and lower ends of the SI direction liver, it sets the scan area to include the entire liver. Then, the main scan for acquiring the image data of the set scan area is executed, and the flow ends.

In this embodiment, data on a plurality of lines L _{1 to} L _n crossing the region on which the liver is projected is extracted from the coronal projection data DC, and data on each of the lines L _{1 to} L _n , template data DT, The correlation _Rg is calculated. Then, to determine the candidate point _E 1 to E ₇ when correlation _{R g} is high, on the basis of these candidate points _E 1 to E _7, and determine the position of an SI direction of the upper end of the liver. Therefore, the detection accuracy of the position of the upper end of the liver in the SI direction can be improved.

Further, in this embodiment, at step ST62, from the candidate points _E 1 to E _7, selects the candidate points _{E 3} the coordinate values of the SI direction is median (median). By selecting a candidate point whose coordinate value in the SI direction is the median (median), candidate points having a large positional deviation from the upper end of the liver such as candidate points E ₆ and E ₇ are not selected. be able to. Therefore, the accuracy when determining the position of the upper end of the liver in the SI direction in step ST63 can be improved.

  In this embodiment, the position of the upper end of the liver in the SI direction is detected. However, the position in a direction different from the SI direction (for example, the AP direction, the RL direction, or the direction obliquely intersecting the SI direction) is detected. It may be detected. In this embodiment, the detection target is the upper end of the liver. However, a feature point different from the upper end of the liver (for example, the left end of the liver) may be set as the detection target, or a feature point of a part different from the liver is selected. It may be a detection target.

2 Magnet 3 Table 3a Cradle 4 Receiving coil 5 Sequencer 6 Transmitter 7 Gradient magnetic field power supply 8 Receiver 9 Central processing unit 10 Operation unit 11 Display unit 12 Subject 21 Bore 22 Superconducting coil 23 Gradient coil 24 Transmitting coil 100 MRI apparatus

Claims (15)

Image data creating means for creating image data of an imaging region including a predetermined region of the subject;
Projection data creating means for creating first projection data of the image data;
From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the data on each line and the position of the feature point of the imaging part in a predetermined direction are detected. Calculating means for calculating the correlation with the template data used to
An image data processing apparatus comprising: detecting means for detecting a position of the feature point of the imaging region in the predetermined direction based on the correlation. The detection means includes
Candidate point determination means for determining a candidate point for detecting the position of the feature point in a predetermined direction based on the correlation;
Position determining means for determining a position of the feature point in the predetermined direction based on the candidate point;
The image data processing apparatus according to claim 1, comprising:   The image data processing apparatus according to claim 2, wherein, when the candidate point determination unit determines a plurality of candidate points, the position determination unit selects one candidate point from the plurality of candidate points. The position determining means includes
The image data processing apparatus according to claim 3, wherein a candidate point whose coordinate value in the predetermined direction is a median value is selected from the plurality of candidate points. The projection data creating means includes
Creating second projection data of the image data;
The position determining means includes
Based on the position of the candidate point in the predetermined direction in the second projection data, the shape model used for detecting the position of the feature point of the imaging region in the predetermined direction is positioned, and using the shape model, The image data processing apparatus according to claim 2, wherein the position of the feature point in the predetermined direction is determined.   The image data processing according to claim 5, further comprising range determination means for determining a range inside the subject based on a projection profile created by projecting the second projection data in the predetermined direction. apparatus. The range determining means includes
The image data processing apparatus according to claim 6, wherein a reference line for determining a region inside the subject is obtained, and a range inside the subject is determined based on the projection profile and the reference line. .   The image data processing apparatus according to claim 7, wherein the reference line is created based on data extracted from an extracorporeal region of the projection profile.   The image data processing apparatus according to any one of claims 1 to 8, wherein the first projection data is coronal projection data.   The image data processing apparatus according to claim 5, wherein the second projection data is sagittal projection data.   The image data processing apparatus according to claim 1, wherein the predetermined part is a liver, and the feature point is an upper end of the liver.   The image data processing apparatus according to claim 1, wherein the predetermined direction is an SI direction.   A magnetic resonance apparatus comprising the image data processing apparatus according to claim 1. An image data creation step for creating image data of an imaging region including a predetermined region of the subject;
A projection data creation step of creating first projection data of the image data;
From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the data on each line and the position of the feature point of the imaging part in a predetermined direction are detected. A calculation step of calculating a correlation with the template data used to
A detection step of detecting a position of the feature point of the imaging region in the predetermined direction based on the correlation. Image data creation processing for creating image data of an imaging region including a predetermined region of the subject;
Projection data creation processing for creating first projection data of the image data;
From the first projection data, data on a plurality of lines crossing the region where the predetermined part is projected is extracted, and the data on each line and the position of the feature point of the imaging part in a predetermined direction are detected. Calculation processing for calculating the correlation with the template data used to
A program for causing a computer to execute, based on the correlation, detection processing for detecting the position of the feature point of the imaging region in the predetermined direction.