JP5922465B2 - Magnetic resonance apparatus, navigator area setting method, and program - Google Patents

Description

  The present invention relates to a magnetic resonance apparatus that sets a navigator area and images a subject, a navigator area setting method for setting the navigator area, and a program for setting the navigator area.

  A method using a navigator sequence for acquiring a respiratory signal of a subject is known as a method for imaging a region that moves due to breathing of the subject (see Patent Document 1).

JP 2011-193484 A

  When acquiring a respiratory signal of a subject using a navigator sequence, it is necessary to set a navigator area for collecting navigator echoes. The navigator area is set at the boundary between the lung and the liver, for example. Since the liver moves due to the breathing of the subject, the respiratory signal of the subject can be collected by setting a navigator region at the boundary between the lung and the liver. As an example of a method for setting the navigator area, there is a method in which image data is acquired in advance, an operator searches for the boundary between the lung and the liver while looking at the image data, and sets the navigator area. However, this method requires the operator to manually set the navigator area, which is complicated for the operator. In addition, it may be difficult for an inexperienced operator to set the navigator region at an optimal position, and in this case, there is a problem that the respiratory signal is deteriorated.

  Therefore, it is required to set the navigator area so that the work burden on the operator is reduced and a good breathing signal is collected.

A first aspect of the present invention is a magnetic resonance apparatus that sets a navigator region for collecting respiratory signals of the subject at the boundary between the first part and the second part of the subject,
Pixel extraction means for extracting a plurality of candidate pixels that are candidates for pixels located at the boundary based on image data of a first cross section crossing the first part and the second part;
Pixel specifying means for specifying a pixel located at the boundary from among the plurality of candidate pixels based on pixel values of pixels located around each candidate pixel extracted by the pixel extracting means;
And a navigator area determining means for determining a position of the navigator area based on the identified pixel.

A second aspect of the present invention is a navigator region setting method for setting a navigator region for collecting respiratory signals of the subject at the boundary between the first portion and the second portion of the subject,
A pixel extraction step of extracting a plurality of candidate pixels that are candidates for pixels located at the boundary based on image data of a first cross section crossing the first part and the second part;
A pixel specifying step of specifying a pixel located at the boundary from the plurality of candidate pixels based on a pixel value of a pixel located around each candidate pixel extracted by the pixel extracting step;
And a navigator area determining step for determining a position of the navigator area based on the identified pixel.

According to a third aspect of the present invention, there is provided a magnetic resonance apparatus program for setting a navigator region for collecting respiratory signals of the subject at a boundary between the first part and the second part of the subject. ,
A pixel extraction process that extracts a plurality of candidate pixels that are candidates for pixels located at the boundary based on image data of a first cross section that crosses the first part and the second part;
A pixel specifying process for specifying a pixel located at the boundary from the plurality of candidate pixels based on a pixel value of a pixel located around each candidate pixel extracted by the pixel extracting process;
A program for causing a computer to execute navigator area determination processing for determining a position of the navigator area based on the specified pixel.

  Pixel candidates located at the boundary between the first part and the second part are extracted, and the pixel located at the boundary is identified from the extracted candidate pixels. Then, the position of the navigator area is determined based on the specified pixel. Therefore, since the navigator area can be set at the boundary position, it is possible to acquire a good respiration signal. Further, since the operator does not have to search for the position of the navigator area, the operator's work load can be reduced.

1 is a schematic view of a magnetic resonance apparatus according to a first embodiment of the present invention. It is a figure which shows the scan performed with a 1st 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 a 1st form. It is explanatory drawing of the localizer scan LS. The projection profile _F 1 to F _n is a diagram schematically showing. It is an explanatory view of a method for detecting a position of a step appearing in the projection profile F _i. With respect to the projection profile F _i, it schematically shows the position of the binary template BT when the correlation is maximized. The position _b 1 ~b _n stepped projection profile _F 1 to F _n, which is detected by the binary template BT is a diagram schematically showing. It is a figure which shows a boundary region roughly. The differentiated image data _DI 1 -DI _n is a diagram schematically showing. From the coronal plane CO _i, is an explanatory diagram of a method of extracting a candidate pixel located at the border between the lung and the liver. It is explanatory drawing of an example of the method of specifying the pixel located in the boundary of a lung and a liver. Is a diagram illustrating an average value M1 and M2 obtained when setting the regions V and W for each candidate pixel x _a and x _b. Schematically shows the identified pixels from the boundary area R _i of the coronal plane CO _i. Schematically illustrates a set Set1~Setn of the identified pixels per coronal plane _CO 1 to CO _n. It is explanatory drawing when selecting the pixel set used when determining the position of a navigator area from the pixel sets Set1-Setn. It is a figure which shows roughly an example of the set of pixels Seti. It is a figure which shows schematically pixel set Seti before connecting a pixel break, and pixel set Seti 'after connecting a pixel break. It is explanatory drawing of a fitting process. It is explanatory drawing when the pixel located in the S direction side most is detected. And pixel _{x a,} schematically shows a navigator region _{R nav.} It is the schematic of MR apparatus in a 2nd form. It is a figure which shows the operation | movement flow of MR apparatus 200 in a 2nd form. It is explanatory drawing when scanning an axial surface. It is explanatory drawing when specifying the range of the RL direction of the body region of a subject. It is a diagram showing a state of extracting the candidate pixel from the boundary region R _i.

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

(1) First Embodiment FIG. 1 is a schematic view of a magnetic resonance apparatus according to a first 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 magnetic field, 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 supply 7, a receiver 8, a control unit 9, an operation unit 10, a display unit 11, and the like.

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

  The transmitter 6 supplies a current to the RF coil 24 based on the information sent from the sequencer 5.

The gradient magnetic field power supply 7 supplies a current to the gradient coil 23 based on the information sent from the sequencer 5.
The receiver 8 performs signal processing such as detection on the signal received from the receiving coil 4.

  The control unit 9 transmits necessary information to the sequencer 5 and the display unit 11 and reconstructs an image based on data received from the receiver 8 so as to realize various operations of the MR apparatus 100. The operation of each part of the MR apparatus 100 is controlled. The control unit 9 is configured by, for example, a computer. The control unit 9 includes image data creation means 91 to navigator area determination means 96 and the like.

The image data creation unit 91 creates image data of the imaging region of the subject.
The fat removing unit 92 removes fat from the image data created by the image data creating unit 91.
The boundary area determining means 93 determines an area including the boundary between the lung and the liver.
The pixel extraction unit 94 extracts candidate pixels that are candidates for pixels located at the boundary between the lung and the liver.
The pixel specifying means 95 specifies a pixel located at the boundary between the lung and the liver from the extracted candidate pixels.
The navigator area determination unit 96 determines the position of the navigator area based on the specified pixel.

  The control unit 9 is an example that constitutes the image data creation unit 91 to the navigator region determination unit 96, and functions as these units by executing a predetermined program.

The operation unit 10 is operated by an operator and inputs various information to the control 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 the first form, and FIG. 3 is a diagram schematically showing an imaging region.
In this embodiment, a localizer scan LS and a main scan MS are executed.

The localizer scan LS is a scan executed to set the navigator region R _nav (see FIG. 3). The navigator area R _nav is an area that is set for collecting respiratory signals of the subject. The main scan MS collects respiratory signals from the navigator region R _nav and also collects image data of a part including the liver. The flow when executing the localizer scan LS and the main scan MS will be described below.

FIG. 4 is a diagram showing a flow when the subject is imaged in the first embodiment.
In step ST1, a localizer scan LS (see FIG. 2) is executed.

FIG. 5 is an explanatory diagram of the localizer scan LS.
In the localizer scan LS, a plurality of coronal planes CO _{1 to} CO _n that traverse the imaging region including the liver are scanned. Image data generating means 91 (see FIG. 1), based on the data collected by the localizer scan LS, creates image data _DC 1 to DC _n of coronal plane _CO 1 to CO _n. Hereinafter, the image data of the coronal plane is referred to as “coronal image data”. After creating a coronal image data _DC 1 to DC _n, the process proceeds to step ST2.

In step ST2, the fat removing unit 92 (see FIG. 1) removes fat from the coronal image data DC _{1 to} DC _n . Since fat is a high signal, it is possible to remove fat by setting a threshold value for removing fat in advance and detecting pixels having pixel values larger than the threshold value. After removing the fat, the process proceeds to step ST3.

In step ST3, the boundary region determining means 93 (see FIG. 1) determines the boundary region between the lung and the liver based on the image data DC _{1 to} DC _n from which fat has been removed. This boundary region is determined as follows.

The boundary region determination unit 93 first creates a projection profile obtained by adding pixel values in the RL direction for the coronal image data DC _{1 to} DC _n from which fat has been removed. FIG. 6 schematically shows projection profiles F _{1 to} F _n created for the respective image data DC _{1 to} DC _n from which fat has been removed. Since the liver has a high signal but the lung has a low signal, when a projection profile of coronal image data that crosses the lung and the liver is created, the addition value decreases on the lung side and the addition value increases on the liver side. Due to such a difference in the added value, a step of the added value appears at the boundary between the liver and the lung. Therefore, it is possible to specify the boundary region between the liver and the lung by detecting the position of the step appearing in the projection profiles F _{1 to} F _n . Next, a method for detecting the position of the step appearing in the projection profiles F _{1 to} F _n will be described. In any projection profile, the method of detecting the position of the step is the same, so in the following, the projection profile F _i out of the projection profiles F _{1 to} F _n will be taken up and the step appearing in the projection profile F _i will be described. A method for detecting the position will be described.

FIG. 7 is an explanatory diagram of a method for detecting the position of the step appearing in the projection profile F _i .
In the present embodiment, the step of the projection profile F _i is detected using the binary template BT. The binary template BT is a template having a signal value step Δs with a value “1” and a value “0”. When detecting the stepped projection profile F _i, the binary template BT, moved gradually toward the liver side from the lungs side of the projection profiles F _i, each time moving the binary template BT, binary template BT and projection profile F Calculate the correlation with _i . Since the binary template BT has a step Δs, the position of the step appearing in the projection profile F _i can be detected by specifying the position of the binary template BT when the correlation is maximum. FIG. 8 schematically shows the position of the binary template BT when the correlation is maximum with respect to the projection profile F _i . In FIG. 8, when the binary template BT reaches the position b _i , the correlation between the projection profile F _i and the binary template BT is maximized. Therefore, it can be seen that the boundary between the lung and the liver in the coronal image data DC _i exists around the position b _{i in} the SI direction.

7 and 8, the method for detecting the position of the step of the projection profile F _i has been described. For other projection profiles, the correlation with the binary template BT is calculated, and the correlation is maximized. The position of the step can be detected by specifying the position of the binary template BT. Figure 9 shows the position _b 1 ~b _n stepped projection profile _F 1 to F _n, which is detected by the binary template BT schematically. After detecting the position b ₁ ~b _n stepped each projection profile F ₁ to F _n, based on the position b ₁ ~b _n of the detected level difference to determine the boundary region between the lung and liver (Fig. 10).

FIG. 10 is a diagram schematically showing the boundary region.
The boundary region determination unit 93 defines the range of the width w1 on the lung side and the range of the width w2 on the liver side with reference to the detected step positions b _{1 to} b _n . The regions thus defined are determined as the boundary regions R _{1 to} R _n between the lung and the liver in the coronal image data DC _{1 to} DC _n . The widths w1 and w2 are values set in advance before imaging the subject, and can be set to several centimeters, for example. After determining the boundary areas R _{1 to} R _n between the lung and the liver for each of the coronal planes CO _{1 to} CO _n , the process proceeds to step ST4.

In step ST4, differentiating the coronal image data _DC 1 to DC _n, to create the differentiated image data. FIG. 11 schematically shows the differential image data DI _{1 to} DI _n .

In coronal image data DC ₁ to DC _n, the pixel values of the liver of a pixel, a large difference between the pixel values of the lungs of a pixel. Therefore, if the coronal image data DC _{1 to} DC _n are differentiated, the differential value of the pixel located at the boundary between the lung and the liver becomes large. On the other hand, the differential value of the pixel inside the liver and the differential value of the pixel inside the lung are small. Therefore, by creating the differential image data DI _{1 to} DI _n , the pixels located at the boundary between the lung and the liver can be emphasized and drawn. In the differential image data DI _{1 to} DI _n in FIG. 11, the white portion indicates that the differential value is large, and the black portion indicates that the differential value is small. After the differential image data DI _{1 to} DI _n of the coronal planes CO _{1 to} CO _n are created, the process proceeds to step ST5.

In step ST5, the pixel extraction unit 94 (see FIG. 1), based on the differentiated image data _DI 1 -DI _n, each coronal plane _CO 1 to CO _n, the pixel located on the boundary between the lung and liver candidate To extract. Hereinafter, a method for extracting candidate pixels located at the boundary between the lung and the liver will be described. Since this extraction method is the same for all coronal planes, in the following, the coronal plane CO _i of the coronal planes CO _{1 to} CO _n will be picked up and positioned at the boundary between the lung and liver from the coronal plane CO _i. A method for extracting candidate pixels to be described will be described.

FIG. 12 is an explanatory diagram of a method of extracting candidate pixels located at the boundary between the lung and the liver from the coronal plane CO _i .

First, a line L extending in the SI direction is considered on the differential image data DI _i and a profile of differential values of pixels on the line L is obtained. FIG. 12 shows a profile on the line L with the coordinate value P = P _{i in} the RL direction.

As described above, the differential value of the pixel located at the boundary between the lung and the liver increases. Accordingly, by detecting a peak appearing in the boundary region R _{i of the} profile, it is possible to extract candidate pixels located at the boundary between the lung and the liver. In FIG. 12, two peaks a and b appear in the profile of the differential value. Thus, the pixel x _a and x _b corresponding to the peaks a and b are the pixel located at the border between the lung and liver candidate becomes (hereinafter sometimes referred to as a "candidate pixel").

In the above description, the method for extracting candidate pixels on the line L having the coordinate value P = P _i has been described. However, even if the line L has a coordinate value other than P _i , the candidate pixel is extracted by the same method. be able to. Accordingly, the coordinate value P of RL direction of the line L is changed between P ₀ to P _z, for each coordinate value of the RL direction, considering the profile of the differential value, by detecting the peak for each profile, Pixel candidates located at the boundary between the lung and the liver can be extracted from the coronal plane CO _i .

Further, FIG. 12 illustrates the case where candidate pixels are extracted from the coronal plane CO _i, but the same method can be used to extract candidate pixels from other coronal planes. Therefore, for each coronal plane CO _{1 to} CO _n , pixel candidates located at the boundary between the lung and the liver can be extracted. After extracting the candidate pixels, the process proceeds to step ST6.

In step ST6, a pixel located at the boundary between the lung and the liver is identified from the extracted candidate pixels. For example, referring to FIG. 12, on the line of the coordinate value P = _{P i,} 2 one candidate pixel _{x a} and _{x b} are extracted. Among the candidate pixel x _a and x _b, the pixels located at the boundary between the lungs and the liver is a candidate pixels x _a, candidate pixels x _b is the boundary between the lungs and the liver are not located. Therefore, it can be seen that pixels that are not located at the boundary between the lung and the liver are also extracted as candidate pixels. Therefore, among the candidate pixel x _a and x _b extracted, which pixels need to identify whether located on the boundary between the lungs and the liver. Below, this specific method is demonstrated.

FIG. 13 is an explanatory diagram of an example of a method for specifying a pixel located at the boundary between the lung and the liver.
First, a pixel located at the boundary between the lung and the liver is considered in the coronal image data DC crossing the lung and the liver. Here, a pixel located at the boundary between the lung and the liver is indicated by a symbol “x”.

  Next, for the pixel x, the region V is set on the lung side, and the region W is set on the liver side. The sizes of the regions V and W are n × m pixel sizes. In FIG. 13, an example of a pixel size of 5 × 5 is shown. Then, an average value M1 of pixel values of pixels included in the region V and an average value M2 of pixel values of pixels included in the region W are obtained.

In general, pixels in the lung region tend to have lower pixel values, while pixels in the liver region tend to have higher pixel values. Therefore, when the average value M1 of the pixel values in the region V is compared with the average value M2 of the pixel values in the region W, it is considered that the following relationship is established.
M1 <M2 (1)

The region V is located on the lung side. Since the pixel values of the pixels included in the lung tend to be small, the possible values of the average value M1 of the pixel values in the region V can be narrowed down to some extent. Specifically, it is considered that the average value M1 of the pixel values is likely to be included in the range represented by the following expression.
p <M1 <q (2)
Here, p: the lower limit value that can be allowed as the average value M1
q: Upper limit value that can be allowed as the average value M1

  The lower limit value p and the upper limit value q are values determined with reference to, for example, lung pixel values of image data obtained by actually scanning a plurality of persons.

Furthermore, the region W is located on the liver side. Since the pixel values of the pixels included in the liver tend to increase, the values that the average value M2 of the pixel values in the region W can take can be narrowed down to some extent. Specifically, it is considered that the average value M2 of pixel values is likely to be included in the range represented by the following expression.
r <M2 <s (3)
Here, r: lower limit value that can be allowed as average value M2
s: Upper limit value that can be allowed as average value M2

  The lower limit value r and the upper limit value s are values determined with reference to, for example, the liver pixel values of image data obtained by actually scanning a plurality of persons.

That is, when the pixel x is located at the boundary between the lung and the liver, the average value M1 of the pixel values in the region V and the average value M2 of the pixel values in the region W are considered to satisfy the following conditions 1-3. It is done.
(Condition 1) M1 <M2
(Condition 2) p <M1 <q
(Condition 3) r <M2 <s

Therefore, if a pixel satisfying all the three conditions 1 to 3 can be found, a pixel located at the boundary between the lung and the liver can be specified. Therefore, in the present embodiment, from among the candidate pixel x _a and x _b extracted in step ST5, when specifying the pixel located at the boundary between the lungs and the liver, and the region V for each candidate pixel x _a and x _b W is set, and average values M1 and M2 of the pixel values are calculated (see FIG. 14).

Figure 14 is a diagram showing an average value M1 and M2 obtained when setting the candidate pixels x _a and x _b regions V and W each.

Pixel specifying means 95 (see FIG. 1) first detects the position of the candidate pixel _{x a} and _{x b} on coronal image data DC _i. Then, set the region V and W for each candidate pixel _{x a} and _{x b,} calculates the average value M1 and M2 of the pixel values.

If you set the region V and W to the candidate pixel x _a (see enlarged view (a)), region V is located in the lung side, region W is located in the liver side. Thus, if the candidate pixels _{x a,} the average value M1 and M2 of the pixel values is considered to satisfy all the conditions 1 to 3.

However, when the regions V and W are set for the candidate pixel _xb (see the enlarged view (b)), not only the region V but also the region W is located on the lung side, so it is considered that the condition 3 is not satisfied. It is done.

Therefore, among the candidate pixel _{x a} and _{x b,} it can be seen that meet the three conditions 1 to 3 are candidate pixels _{x a.} In this way, it is possible to identify the pixel x _a at the boundary between the lungs and the liver.

In the above description, the method of specifying the pixel located at the boundary between the lung and the liver with respect to the candidate pixels x _a and x _b extracted on the line L with the coordinate value P = P _i has been described. _{Even when} two or more candidate pixels are extracted on the line L of coordinate values other than _i , a pixel located at the boundary between the lung and the liver can be specified by the same method. Therefore, when the coordinate value P in the RL direction is changed within the range of P _{0 to} P _Z and two or more candidate pixels are extracted with a certain coordinate value, two regions V are assigned to each candidate pixel. And W are set, and by determining whether or not the average values M1 and M2 of the pixel values satisfy the conditions 1 to 3, it is possible to specify the pixel located at the boundary between the lung and the liver. In this manner, pixels located at the boundary between the lung and the liver can be identified from the boundary region R _i of the coronal plane CO _i . FIG. 15 schematically shows pixels identified from the boundary region R _i of the coronal plane CO _i . Since the boundary between the lung and the liver is curved, the specified pixels are arranged in a curved line. In FIG. 15, the specified set of pixels is indicated by a symbol “Seti”.

In the above description, the case where the pixel located at the boundary between the lung and the liver is specified in the coronal plane CO _i is described. However, the case where the pixel located at the boundary between the lung and the liver is specified in another coronal plane. Can also be identified in the same way. Therefore, it is possible to specify a pixel located at the boundary between the lung and the liver for each coronal plane CO _{1 to} CO _n . Figure 16 schematically illustrates a set Set1~Setn of pixels that have been identified for each coronal plane _CO 1 to CO _n. After identifying the pixel located at the boundary between the lung and liver, the process proceeds to step ST7.

  In step ST7, the navigator area determining means 96 (see FIG. 1) determines the position of the navigator area. Hereinafter, steps ST71 to ST75 of step ST7 will be described.

In step ST71, the navigator area determining means 96, from the set of pixels Set1～Setn (see FIG. 16) in the coronal plane _CO 1 to CO _n, a set of pixels used when determining the position of the navigator area Select (see FIG. 17).

  FIG. 17 is an explanatory diagram when selecting a set of pixels to be used when determining the position of the navigator area from the set of pixels Set1 to Setn. In FIG. 17, for convenience of explanation, only the pixel sets Set1, Seti, Setj, and Setn are shown as representatives among the pixel sets Set1 to Setn.

In the present embodiment, the set of pixels located closest to the S side among the set of pixels Set1 to Setn is selected as the set of pixels used when determining the position of the navigator area. Referring to FIG. 17, among the pixel sets Set1 to Setn, the set of pixels located closest to the S side is Seti. Therefore, the navigator area determination unit 96 selects the pixel set Seti on the coronal plane CO _i from the pixel sets Set1 to Setn. After selecting the pixel set Seti, the process proceeds to step ST72.

  In step ST72, the navigator area determination means 96 determines whether or not there is a pixel break in the selected pixel set Seti (see FIG. 18).

FIG. 18 is a diagram schematically illustrating an example of a set of pixels Seti.
Since the boundary between the lung and the liver is continuously connected, ideally, the pixels located at the boundary between the lung and the liver should be continuously connected as shown in FIG. is there. However, depending on the case, there may be no pixel that satisfies the conditions 1 to 3 at a certain coordinate value in the RL direction. An example of this is shown in FIG. FIG. 18B shows a case where there are no pixels that satisfy the conditions 1 to 3 in the coordinate values P = P _t , P _u , and P _v . In this case, there is a break in the pixel set Seti.

  Therefore, in this embodiment, it is determined whether or not there is a break in the pixel. If there is no break, the process proceeds to step ST74, but if there is a break, the process proceeds to step ST73. In the case of FIG. 18A, since there is no break, the process proceeds to step ST74, and in the case of FIG. 18B, there is a break, the process proceeds to step ST73.

In step ST73, the navigator area determining means 96 connects pixel breaks. As a method of connecting pixel breaks, for example, dynamic programming can be used. When connecting cuts using dynamic programming, first, a plurality of paths connecting the start point and the end point are set with the pixel x ₁ positioned closest to the R side as the start point and the pixel x _z positioned closest to the L side as the end point. Think. Then, for each path, an addition value of the reciprocal of the differential value of the pixel is calculated, a path when the addition value is minimized is specified, and a pixel on the specified path is used as a pixel connecting the breaks. FIG. 19 schematically shows a pixel set Seti before connecting pixel breaks and a pixel set Seti ′ after connecting pixel breaks. After connecting the pixel breaks, the process proceeds to step ST74.

  In step ST74, the navigator area determination means 96 performs a fitting process on the pixel set Seti ′ (see FIG. 20).

FIG. 20 is an explanatory diagram of the fitting process.
FIG. 20A shows a set of pixels Seti ′ before the fitting process, and FIG. 20B shows a set of pixels Seti ″ after the fitting process.

  By performing the fitting process, it is possible to correct even if the pixel set Seti ′ has an unnatural bent portion c that is not originally seen at the boundary between the lung and the liver. As the fitting, for example, polynomial fitting (for example, quadratic fitting) can be used.

  If it is determined in step ST72 that there is no break (in the case of FIG. 18A), step ST73 is skipped and the process proceeds to step ST74. In this case, the fitting process is performed on the pixel set Seti. After performing the fitting process, the process proceeds to step ST75.

  In step ST75, the pixel located closest to the S direction is detected from the pixel set Seti ″ after the fitting process (see FIG. 21).

FIG. 21 is an explanatory diagram when detecting the pixel located closest to the S direction.
In Figure 21, the coordinate value Q for the pixel located at most S direction, a Q = _{q 1.} Incidentally, the pixel coordinate values q ₁ are a plurality exist. In this case, any one pixel is detected from the plurality of pixels. In this embodiment, to detect a pixel x _a located closest to the R side from the pixel coordinate values q _1. In this way, the position of the detected pixel x _a is determined as the position of the navigator region R _nav. FIG. 22 schematically shows the position of the navigator region R _nav . Coordinate value P in the RL direction pixel _{x a} is P = _{P i,} the coordinate values Q of the SI direction is Q = _{q 1.} Further, the pixel _{x a} so contained in the coronal plane CO _i, the coordinate values of the AP direction of coronal plane CO _i becomes the AP direction of the coordinate value of the pixel _{x a.} Therefore, three directions of a pixel _{x a} (RL direction, SI direction, and AP direction) since the coordinate values of are required, it is possible to set the navigator region _{R nav} to the position of the pixel _{x a.} Moreover, most positions of pixels x _a which is located in the R-side, by the position of the navigator region R _nav, since the navigator region R _nav can be separated from the heart, reducing the degradation of the respiration signal by heartbeat You can also. After the position of the navigator area R _nav is determined, the process proceeds to step ST8.

In step ST8, a main scan is executed. In the main scan, a navigator sequence for collecting a respiratory signal from the navigator region R _nav and an imaging sequence for collecting image data of a region including the liver are executed. When the main scan ends, the flow ends.

In this embodiment, candidate pixels that are candidates for pixels located at the boundary between the lung and the liver are extracted based on image data (coronal image data) on a coronal plane that crosses the lung and the liver. Then, based on the pixel values of pixels around the candidate pixel, a pixel located at the boundary between the lung and the liver is identified from among the extracted candidate pixels. Therefore, it is possible to specify a pixel located at the boundary between the lung and the liver for each of the coronal planes CO _{1 to} CO _n . Further, since determining the position of the navigator area R _nav based on a set Set1~Setn of pixels identified for each coronal plane CO ₁ to CO _n, it is possible to set the navigator area at the boundary between the lungs and the liver It is possible to obtain a good respiratory signal. Furthermore, since the operator does not have to search for the position of the navigator area, the operator's workload can be reduced.

  In this embodiment, when a pixel located at the boundary between the lung and the liver is specified, an average value M1 of pixel values of the pixels in the region V and an average value M2 of pixel values of the pixels in the region W are used (see FIG. 13 and FIG. 14). However, if a pixel located at the boundary between the lung and the liver can be specified, a feature amount different from the average value of the pixel values may be used. For example, the median value (median) of the pixel values of the pixels in the region V and the median value (median) of the pixel values of the pixels in the region W may be used.

  In this embodiment, an example in which a navigator area is set at the boundary between the lung and the liver is described. However, the present invention is not limited to the case where the navigator area is set at the boundary between the lung and the liver. The present invention can also be applied to the case where a navigator area is set at the boundary.

In this embodiment, the image data of _n coronal planes CO _{1 to} CO _n is acquired in the localizer scan LS, but only the image data of one coronal plane may be acquired. In this case, since step ST71 can be omitted, the flow when setting the position of the navigator region R _nav can be simplified. However, in order to position the navigator region R _nav at a more optimal position, it is desirable to acquire image data of a plurality of coronal surfaces in the localizer scan LS.

In this embodiment, the position of the navigator region R _nav is determined based on the coronal image data. However, the image data of a surface different from the coronal surface (for example, an oblique surface that obliquely intersects the coronal surface) is used. Based on this, the position of the navigator region R _nav may be determined.

  In this embodiment, after removing the fat in step ST2, the process proceeds to step ST3. However, the process may proceed to step ST3 without performing the fat removal in step ST2.

(2) Second Embodiment FIG. 23 is a schematic diagram of an MR apparatus in the second embodiment.
In the MR apparatus 200 of the second embodiment, the control unit 9 is provided with range specifying means 97 for specifying the range in the RL direction of the in-vivo region of the subject. Other configurations are the same as those of the first embodiment.

FIG. 24 is a diagram showing an operation flow of the MR apparatus 200 in the second embodiment.
In step ST1, a localizer scan LS is executed.
In the first embodiment, when the localizer scan LS is performed, only the scan of the coronal planes CO _{1 to} CO _n is performed (see FIG. 5). In the second embodiment, only the scan of the coronal planes CO _{1 to} CO _n is performed. In addition, scanning of the axial plane is also executed (see FIG. 25).

FIG. 25 is an explanatory diagram when scanning the axial plane.
Image data generating means 91 (see FIG. 23), based on the data collected by the scan axial planes _AX 1 _{~AX m,} creates image data _DA 1 to DA _m axial planes _AX 1 _{~AX m.} Hereinafter, the image data on the axial plane is referred to as “axial image data”.

In the second embodiment, not only the coronal image data DC _{1 to} DC _n but also the axial image data DA _{1 to} DA _m are obtained by the localizer scan LS. After performing the localizer scan, the process proceeds to step ST2. Steps ST2 to ST4 are the same as those in the first embodiment, and a description thereof will be omitted.

  In the second embodiment, not only steps ST2 to ST4 but also step ST20 is executed.

In step ST20, the range specifying unit 97 (see FIG. 23) specifies the range in the RL direction of the in-vivo region of the subject based on the axial image data DA _{1 to} DA _m .

  FIG. 26 is an explanatory diagram when the range in the RL direction of the in-vivo region of the subject is specified.

Range specifying means 97, first, determine the range _W 1 to _W-m of RL direction in the body area of the subject at each axial face _AX 1 _{~AX m.} The extracorporeal region of the subject has a low signal, but the in vivo region of the subject has a high signal. Therefore, due to the difference in signal value, the RL direction of the in vivo region of the subject is determined for each axial plane AX _{1 to} AX _m . it can be determined from the scope _W 1 to _W-m. The range specifying unit 98 calculates the average value of the lengths in the RL direction of these ranges W _{1 to} W _{m and} sets the range determined by this average value as the range W _{RL in} the RL direction of the in-vivo region of the subject. In this way, the range W _RL and RL direction in the body region of the subject is identified.

When Steps ST2 to ST4 and Step ST20 are executed, the process proceeds to Step ST5.
In step ST5, as in the first embodiment, candidate pixels that are candidates for pixels located at the boundary between the lung and the liver are extracted. FIG. 27 shows a state where candidate pixels are extracted from the boundary region R _i of the coronal plane CO _i . The method for extracting candidate pixels is the same as in the first embodiment. However, in the second mode, the range when extracting candidate pixels from the boundary region R _i is limited to the range W _RL in the RL direction of the in-vivo region of the subject specified in step ST20. Thus, by limiting the range in which candidate pixels are extracted, it is possible to prevent candidate pixels from being extracted from the extracorporeal region of the subject. In addition, since it is not necessary to perform a process for extracting candidate pixels in the extracorporeal region, the extraction process can be performed in a short time. After extracting the candidate pixels, the process proceeds to step ST6.

In step ST6, a pixel located at the boundary between the lung and the liver is identified from the extracted candidate pixels. Candidate pixels, because it is extracted from a range W _RL and RL direction in the body region, also a process of specifying the pixel may be carried out only within the range W _RL. Therefore, the process of specifying pixels can be performed in a short time. After specifying the pixel, the process proceeds to step ST7.

Since steps ST7 to ST8 are the same as those in the first embodiment, the description thereof is omitted.
In the second embodiment, as in the first embodiment, a navigator area can be set at the boundary between the lung and the liver, so that a good breathing signal can be obtained, and the operator can change the position of the navigator area. Since there is no need to find out, the operator's work load can be reduced. Furthermore, in the second embodiment, it is possible to prevent candidate pixels from being extracted from the extracorporeal region of the subject, and it is possible to prevent the candidate pixels from being extracted, or to locate the boundary between the lung and the liver. It is also possible to shorten the time required for the process of specifying the pixel to be performed.

In the second embodiment, the image data of _m axial surfaces AX _{1 to} AX _m are acquired in the localizer scan LS, but only the image data of one axial surface is acquired, and the RL in the body region is acquired. The direction range _WRL may be obtained.

2 Magnet 3 Table 3a Cradle 4 Receiving coil 5 Sequencer 6 Transmitter 7 Gradient magnetic field power supply 8 Receiver 9 Control unit 10 Operation unit 11 Display unit 12 Subject 21 Bore 22 Superconducting coil 23 Gradient coil 24 Transmitting coil 91 Image data creation means 92 Fat removal means 93 Boundary area determining means 94 Pixel extracting means 95 Pixel specifying means 96 Navigator area determining means

Claims (21)

A magnetic resonance apparatus for setting a navigator region for collecting respiratory signals of the subject at a boundary between the first part and the second part of the subject,
Based on image data of a plurality of first cross sections crossing the first part and the second part, a plurality of candidate pixels that are candidates for pixels located at the boundary are determined for each of the first cross sections. Pixel extracting means for extracting;
Based on the pixel values of the pixels located around each candidate pixel extracted by the pixel extracting means, a set of pixels located at the boundary is selected from the plurality of candidate pixels for each of the first cross sections. Pixel identification means to identify;
From the set of pixels identified for each of the first cross-sections, select a set of pixels used to determine the position of the navigator region, and based on the selected set of pixels , position of the navigator region Navigator area determining means for determining
A magnetic resonance apparatus. The navigator area determining means includes
Determine whether there is a pixel break in the selected set of pixels, and if there is a break in the pixel, connect the pixel break and based on the set of pixels after the pixel break is connected Te, it determines the position of the navigator area, the magnetic resonance apparatus according to claim 1. The navigator area determining means includes
The magnetic field according to claim 2 , wherein a fitting process is performed on a set of pixels after the pixel breaks are connected, and a position of the navigator region is determined based on the set of pixels after the fitting process. Resonator. The navigator area determining means includes
The magnetic resonance apparatus according to claim 3 , wherein a topmost pixel in the set of pixels after the fitting process is determined as a position of the navigator region. The navigator area determining means includes
5. The magnetic resonance apparatus according to claim 4 , wherein when there are a plurality of the uppermost pixels, one of the plurality of pixels is determined as a position of the navigator region. The pixel extracting means includes
The first creates a differential image data of the image data of the cross section, on the basis of the differential image data, for extracting the candidate pixel, a magnetic resonance device according to any one of claims 1-5. The pixel extracting means includes
The magnetic resonance apparatus according to claim 6 , wherein a profile of a differential value of a pixel on a line crossing the boundary is obtained using the differential image data, and the candidate pixel is extracted based on the profile. The pixel specifying means includes
When two or more candidate pixels are extracted on the line, the boundary is selected from the two or more candidate pixels based on the pixel values of pixels located around each candidate pixel on the line. The magnetic resonance apparatus according to claim 7 , wherein the pixel is located. The pixel specifying means includes
A first area and a second area are set for the candidate pixel, and based on a pixel value of a pixel included in the first area and a pixel value of a pixel included in the second area The magnetic resonance apparatus according to claim 8 , wherein a pixel located at the boundary is specified. The pixel specifying means includes
The average value of the pixel values of pixels included in the first region, based on the average value of pixel values of pixels included in the second region, identifying a pixel located at the boundary, according to claim 9 The magnetic resonance apparatus described in 1. The pixel specifying means includes
And median of the pixel values of pixels included in the first region, based on the median of the pixel values of pixels included in the second region, identifying a pixel located at the boundary, according to claim 9 The magnetic resonance apparatus described in 1. A boundary region determining means for determining a boundary region including the boundary based on the image data of the first cross section;
The pixel extracting means includes
Extracting the candidate pixel from the boundary area, the magnetic resonance apparatus according to any one of claims 1 to 11. The boundary region determining means includes
The magnetic resonance apparatus according to claim 12 , wherein a projection profile of the image data of the first cross section is created, and the boundary region is determined based on a position of a step appearing in the projection profile. Based on image data of a second cross section that intersects the first cross section, a range specifying unit that specifies a range in a predetermined direction of the in-vivo region of the subject,
The pixel extracting means includes
The magnetic resonance apparatus according to claim 12 or 13 , wherein a range when extracting candidate pixels from the boundary region is limited to a range in a predetermined direction of the in-vivo region. The magnetic resonance apparatus according to claim 14 , wherein the predetermined direction is an RL direction. The first section is a coronal plane, the magnetic resonance apparatus according to any one of claims 1 to 15. The first section is a coronal plane, said second cross section is axial plane, the magnetic resonance apparatus according to claim 14 or 15. The image data is, fat is image data that has been removed, a magnetic resonance device according to any one of claims 1 to 17. Wherein the first portion comprises a lung, the second site including the liver, magnetic resonance apparatus as claimed in any one of claims 1 to 18. A navigator area setting method for setting a navigator area for collecting respiratory signals of the subject at a boundary between a first part and a second part of the subject,
Based on image data of a plurality of first cross sections crossing the first part and the second part, a plurality of candidate pixels that are candidates for pixels located at the boundary are determined for each of the first cross sections. A pixel extraction step to extract;
Based on the pixel values of the pixels located around each candidate pixel extracted by the pixel extraction step, a set of pixels located at the boundary is selected from the plurality of candidate pixels for each first cross section. A pixel identification step to identify;
From the set of pixels identified for each of the first cross-sections, select a set of pixels used to determine the position of the navigator region, and based on the selected set of pixels , position of the navigator region Navigator area determination step for determining
A navigator area setting method. A program for a magnetic resonance apparatus that sets a navigator region for collecting respiratory signals of a subject at a boundary between a first part and a second part of the subject,
Based on image data of a plurality of first cross sections crossing the first part and the second part, a plurality of candidate pixels that are candidates for pixels located at the boundary are determined for each of the first cross sections. Pixel extraction processing to extract,
Based on pixel values of pixels located around each candidate pixel extracted by the pixel extraction process, a set of pixels located at the boundary is selected from the plurality of candidate pixels for each of the first cross sections. Pixel identification processing to identify,
From the set of pixels identified for each of the first cross-sections, select a set of pixels used to determine the position of the navigator region, and based on the selected set of pixels , position of the navigator region Navigator area determination processing for determining
A program to make a computer execute.