JP2012115434A - Magnetic resonance imaging apparatus, slice position setting method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a magnetic resonance imaging apparatus that determines a feature point of a predetermined part on the basis of a plurality of candidate points to allow a slice position suitable for a predetermined part to be set, thereby improving detection accuracy of the lower end of the liver.SOLUTION: Coronal scanning VS is executed to acquire coronal image data CI-CIof a part including the liver LV. From among the coronal image data CI-CI, the coronal image data CI-CIcrossing the liver are specified. After specifying the coronal image data CI-CI, a segmentation method or the like is applied, and the candidate points PL-PLof the lower end of the liver are determined for each of the coronal image data CI-CI. By searching a candidate point PLpositioned at the lowest position from the determined candidate points PL-PL, the lower end of the liver is determined.

Description

  The present invention relates to a magnetic resonance imaging apparatus, a slice position setting method, and a program for imaging a predetermined part of a subject.

  When imaging a subject with a magnetic resonance imaging 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).

Japanese Unexamined Patent Publication No. 07-051248

  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 upper and lower ends of the liver are detected as the characteristic points of the liver, the upper end of the liver can be detected with high accuracy, but the lower end of the liver tends to have a large detection error. There is a problem that it is difficult to set the slice position so as to match. Therefore, it is desired to automatically set a slice position suitable for a region to be photographed.

A first aspect of the present invention is a magnetic resonance imaging apparatus for imaging a predetermined part of a subject,
Cross-sectional image data creating means for creating a plurality of cross-sectional image data representing a cross-sectional image of an area including the feature point of the predetermined part;
Candidate point determination means for determining a plurality of candidate points that are candidate feature points of the predetermined part based on the plurality of cross-sectional image data;
Feature point determining means for determining a feature point of the predetermined part based on the plurality of candidate points;
Slice position setting means for setting a slice position of the predetermined part based on the position of the feature point of the predetermined part;
A magnetic resonance imaging apparatus.

A second aspect of the present invention is a slice position setting method for setting a slice position of a predetermined part of a subject,
A cross-sectional image data creating step for creating a plurality of cross-sectional image data representing a cross-sectional image of an area including the feature point of the predetermined part;
Candidate point determination step for determining a plurality of candidate points that are candidate feature points of the predetermined part based on the plurality of cross-sectional image data;
A feature point determining step for determining a feature point of the predetermined part based on the plurality of candidate points;
A slice position setting step for setting a slice position of the predetermined part based on the position of the feature point of the predetermined part;
Is a slice position setting method.

A third aspect of the present invention is a program for a magnetic resonance imaging apparatus for imaging a predetermined part of a subject,
Cross-sectional image data creation processing for creating a plurality of cross-sectional image data representing a cross-sectional image of an area including the feature point of the predetermined part;
Candidate point determination processing for determining a plurality of candidate points that are candidate feature points of the predetermined part based on the plurality of cross-sectional image data;
A feature point determination process for determining a feature point of the predetermined part based on the plurality of candidate points;
A slice position setting process for setting a slice position of the predetermined part based on the position of the feature point of the predetermined part;
Is a program for causing a computer to execute.

  Since feature points of a predetermined part are determined based on a plurality of candidate points, a slice position suitable for the predetermined part can be set.

1 is a schematic diagram showing a magnetic resonance imaging apparatus according to a first embodiment of the present invention. It is explanatory drawing of the scan performed when the test object 12 is image | photographed. 2 is a diagram showing a processing flow of the MRI apparatus 100. FIG. It is a figure which shows roughly the image data acquired by the coronal scan VS. It is explanatory drawing when specifying coronal image data. It is explanatory drawing of an example of how to determine the predetermined signal strength range R. Is a diagram showing a coronal image data _CI h _{~CI m} identified. Is a diagram showing a candidate point of the lower end of the liver was determined for each coronal image data CI _h ~CI _m. It is explanatory drawing of an example of the method of determining the lower end of a liver. It is a figure which shows an example of the method of determining the upper end of a liver. It is a figure which shows the set slice position schematically. It is the schematic of the MRI apparatus 200 of a 2nd form. It is explanatory drawing of the scan performed when image | photographing the test subject 12 in a 2nd form. It is a figure which shows the processing flow of the MRI apparatus. It is explanatory drawing of the slice set by scout scan SC. Schematically shows the obtained scout image data DI _s the scout scan SC. It is a diagram illustrating a 1D projection data PR ₁ created. It is a figure which shows roughly the image data acquired by the coronal scan VS. Is a diagram showing a candidate point of the lower end of the liver was determined for each coronal image data CI _h ~CI _m. It is a figure which shows scan range RS 'from which the part of the abdomen side of the liver was excluded.

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

(1) First Embodiment FIG. 1 is a schematic view showing a magnetic resonance imaging apparatus according to a first embodiment of the present invention.

  A magnetic resonance imaging (MRI) apparatus 100 includes a magnetic field generator 2, a table 3, a receiving coil 4, and the like.

  The magnetic field generator 2 includes 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 B0, 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 31. The cradle 31 is configured to be movable to the bore 21. The subject 12 is transported to the bore 21 by the cradle 31.

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

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

  Under the control of the central processing unit 9, the sequencer 5 sends information for executing a later-described scan (see FIG. 2) 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 performs signal processing on the magnetic resonance signal received by the receiving coil 4 and transmits data obtained by the signal processing to the central processing unit 9.

  The central processing unit 9 implements various operations of the MRI apparatus 100 such as transmitting necessary information to the sequencer 5 and the display unit 11 and reconstructing an image based on a signal received from the receiver 8. The operation of each unit of the MRI apparatus 100 is controlled. The central processing unit 9 is constituted by a computer, for example. The central processing unit 9 includes a slice image data creation unit 91, a candidate point determination unit 92, a feature point determination unit 93, a slice position setting unit 94, and the like.

The cross-sectional image data creation unit 91 generates coronal image data CI _{1 to} CI _n (see FIG. 4) representing the coronal cross-sectional images of the region including the liver LV based on the data acquired by the coronal scan VS (see FIG. 2). create.

Based on the coronal image data CI _{1 to} CI _n , the candidate point determination unit 92 includes a plurality of candidate points PL _{h to} PL _m (see FIG. 8) that are candidates for the lower end of the liver LV, and candidates for the upper end of the liver LV. A plurality of candidate points PU _{h to} PU _m (see FIG. 10) are determined.

The feature point determination means 93 determines the lower end of the liver based on the candidate points PL _{h to} PL _m and determines the upper end of the liver based on the candidate points PU _{h to} PU _m .

  The slice position setting means 94 sets the slice position of the liver based on the positions of the lower end and the upper end of the liver determined by the feature point determination means 93.

  The central processing unit 9 is an example of the slice image data creation unit 91, the candidate point determination unit 92, the feature point determination unit 93, and the slice position setting unit 94, and functions as these units by executing a predetermined program. To do. The central processing unit 9 is an example of a computer described in a means for solving the problem.

  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 magnetic resonance imaging apparatus 100 is configured as described above.
Next, a scan executed when imaging the subject 12 will be described.

FIG. 2 is an explanatory diagram of a scan executed when the subject 12 is imaged.
In the first embodiment, the coronal scan VS and the main scan MC are executed.

  The coronal scan VS is a scan for acquiring coronal image data of a region including the liver, and a scan is performed so that the liver is rendered with a high signal. Coronal image data is image data used to specify the upper and lower ends of the liver. In the first embodiment, the coronal scan VS is a 3D scan in which the subject 12 is scanned using a pulse sequence for 3D (Dimension) imaging. After the coronal scan VS, the main scan MC is executed. The main scan MC is an imaging scan for collecting liver image data.

  Next, a processing flow of the MRI apparatus 100 when imaging the subject 12 will be described.

FIG. 3 is a diagram showing a processing flow of the MRI apparatus 100.
In the following, a processing flow when imaging the liver will be described, but the present invention can also be applied to imaging other parts other than the liver.

In step ST11, a coronal scan VS (see FIG. 2) is executed.
FIG. 4 is a diagram schematically showing image data acquired by coronal scan VS.

By executing the coronal scan VS, 3D image data TD of a region including the liver LV can be acquired. Symbols RL, SI, and AP shown in FIG. 4 represent the RL direction (left-right direction), the SI direction (head-to-tail direction), and the AP direction (front-back direction), respectively. The cross-sectional image data creating unit 91 (see FIG. 1) represents image data representing a coronal cross-sectional image of a region including the liver LV from the 3D image data TD acquired by the coronal scan VS (hereinafter referred to as “coronal image data”). ) to create a _CI 1 _{~CI n.} In the lower side of FIG. 4, coronal image data CI _{1 to} CI _n are shown in a line.
After executing the coronal scan VS, the process proceeds to step ST12.

In step ST12, candidate point determining unit 92 (see FIG. 1), based on the coronal image data _CI 1 _{~CI n,} determining a plurality of candidate points that are candidates for the lower end of the liver LV. Hereinafter, an example of a procedure for determining candidate points will be described.

First, the candidate point determination unit 92 specifies coronal image data that crosses the liver LV from the coronal image data CI _{1 to} CI _n . A procedure for specifying coronal image data will be described with reference to FIG.

FIG. 5 is an explanatory diagram for specifying coronal image data.
Candidate point determination means 92 creates histograms H _{1 to} H _n for each coronal image data CI _{1 to} CI _n . The horizontal axis of the histogram represents the signal intensity of each pixel of the coronal image data, and the vertical axis represents the number of pixels having each signal intensity.

In general, when the liver LV is imaged by a method in which the liver LV is rendered with a high signal, a histogram of coronal image data crossing the liver LV has a predetermined signal intensity range R on the horizontal axis (pixel signal intensity) of the liver LV. The peak P due to the high signal appears. Therefore, when the peak P of the histogram of the coronal image data appears in the predetermined signal intensity range R, it is considered that the coronal image data is likely to cross the liver LV. On the other hand, when the peak P of the histogram of the coronal image data does not appear in the predetermined signal intensity range R, it is considered that the possibility that the coronal image data crosses the liver LV is low. Therefore, in the first embodiment, when the peak P appears in the predetermined signal intensity range R, it is determined that the coronal image data crosses the liver LV, while the peak P does not appear in the predetermined signal intensity range R. It is determined that the coronal image data does not cross the liver LV. Therefore, by determining whether or not the peak P appears in the predetermined signal intensity range R, the coronal image data crossing the liver LV can be specified from the coronal image data CI _{1 to} CI _n . As a method of determining whether or not the peak P appears, for example, there is a method of determining based on the information entropy value of the histogram.

  The predetermined signal intensity range R is determined in advance before the subject 12 is scanned. An example of how to determine the predetermined signal intensity range R will be described below (see FIG. 6).

FIG. 6 is an explanatory diagram of an example of how to determine the predetermined signal strength range R.
First, the livers LV _{1 to} LV _n of a plurality of subjects (about several tens of subjects) SU _{1 to} SU _n are scanned to acquire data of the livers LV _{1 to} LV _n . After obtaining the data, based on the obtained data, for each subject _SU 1 to SU _n, the average value _M 1 ~M _n signal intensity of the liver _LV 1 _{~LV n.} Then, a histogram of the average value _M 1 ~M _n signal strength.

The horizontal axis of the histogram is the average signal intensity values M _{1 to} M _n , and the vertical axis is the frequency at which the average signal intensity values M _{1 to} M _n appear. Such a histogram is created and a standard deviation σ of the histogram is obtained. The range of the standard deviation σ can be adopted as the predetermined signal intensity range R.

In the first embodiment, it is assumed that the peak P appears in the predetermined signal intensity range R of the coronal image data CI _{h to} CI _m among the coronal image data CI _{1 to} CI _n . Therefore, the coronal image data CI _{h to} CI _m are specified as coronal image data crossing the liver. FIG. 7 shows the identified coronal image data CI _{h to} CI _m .

After specifying the coronal image data CI _{h to} CI _m , the candidate point determining unit 92 determines candidate points that are candidates for the lower end of the liver for each of the specified coronal image data CI _{h to} CI _m (see FIG. 8). ).

FIG. 8 is a diagram illustrating candidate points at the lower end of the liver determined for each coronal image data CI _{h to} CI _m .

In the first embodiment, by applying a segmentation method to the coronal image data _CI h _{~CI m,} each coronal image data _CI h _{~CI m,} determines the candidate point _PL h through PL _m that are candidates for the lower end of the liver To do. As a segmentation method, for example, Active
Shape Model can be used. After the candidate points PL _{h to} PL _m are determined, the process proceeds to step ST13.

In step ST13, the feature point determination means 93 (see FIG. 1) determines the lower end of the liver based on the candidate points PL _{h to} PL _m (see FIG. 9).

FIG. 9 is an explanatory diagram of an example of a method for determining the lower end of the liver.
The feature point determination means 93 determines the candidate point PL _k having the lowest position in the SI direction among the candidate points PL _{h to} PL _m as the lower end of the liver LV. However, another method may be used for determining the lower end of the liver LV. For example, the SI position of the candidate point PL _k, the candidate point candidate points adjacent to the PL _k (e.g., the candidate point P _{k + 1)} is corrected based on the position of the SI direction, after the position of the SI direction is corrected the candidate points PL _k, may be determined with the lower end of the liver LV. After determining the lower end PL _k of the liver, the process proceeds to step ST14.

In step ST14, the upper end of the liver is determined (see FIG. 10).
FIG. 10 is a diagram illustrating an example of a method for determining the upper end of the liver.

As a method for determining the upper end of the liver, a method using a segmentation method can be considered as in the method for determining the lower end of the liver. Each coronal by applying a segmentation method to the image data _CI h _{~CI m,} determines the candidate point _PU h to PU _m that are candidates for the upper end of the liver, from the plurality of candidate points _PU h to PU _m, Candidate point PU _j located at the highest point can be determined as the upper end of the liver.

  Instead of the method shown in FIG. 10, 3D image data TD may be projected in the AP direction, and the upper end of the liver may be determined based on the projection data. Although the liver is rendered with a high signal, the lung adjacent to the liver is rendered with a low signal. Therefore, when the projection data is created, a position where the signal intensity rapidly changes appears in the projection data. Therefore, the position of the upper end of the liver can be detected by detecting the position where the signal intensity changes rapidly in the projection data.

After detecting the position of the upper end of the liver, the process proceeds to step ST15.
In step ST15, the slice position setting means 94 (see FIG. 1) sets the slice position of the liver based on the lower and upper ends of the liver determined in steps ST13 and ST14 (see FIG. 11).

FIG. 11 is a schematic diagram showing the set slice positions.
The slice position setting unit 94 sets the slice positions SL _{1 to} SL _n so as to include between the upper end PU _j and the lower end PL _k of the liver. When the slice positions SL _{1 to} SL _n are set, the process proceeds to step ST16, the main scan is performed, and the flow ends.

In the first embodiment, candidate points PL _{h to} PL _m at the lower end of the liver are determined for each of the coronal image data CI _{h to} CI _m , and the lower end of the liver is determined from the determined candidate points PL _{h to} PL _m. Has been decided. Since the lower end of the liver is located at the lowest position of the liver, the lower end of the liver is determined by searching for the candidate point PL _k located at the lowest position among the determined candidate points PL _{h to} PL _m. be able to. Therefore, a slice position suitable for the position and size of the subject's liver can be set.

In the first embodiment, the coronal scan VS is a 3D scan, but instead of the 3D scan, the coronal image data CI _{1 to} CI _n may be acquired by executing a 2D multi-slice scan.

  In the first embodiment, the coronal scan VS is executed. However, if the lower end of the liver can be determined, instead of the coronal scan VS, a cross section different from the coronal section (for example, an oblique section) is used. You may perform the scan for acquiring image data.

(2) Second Embodiment FIG. 12 is a schematic diagram of an MRI apparatus 200 of the second embodiment.

  In the second embodiment, the central processing unit 9 includes a scan range determining unit 90, a cross-sectional image data creating unit 91, a candidate point determining unit 92, a feature point determining unit 93, a slice position setting unit 94, and the like.

  The scan range determining unit 90 determines a scan range RS (see FIG. 17) of the coronal scan VS based on image data acquired by a scout scan SC (see FIG. 13) described later.

The cross-sectional image data creating unit 91 creates coronal image data CI _{h to} CI _m (see FIG. 18) representing the coronal cross-sectional images of the region including the liver LV based on the data acquired by the coronal scan VS.

Candidate point determination means 92 is based on coronal image data CI _{h to} CI _m , a plurality of candidate points PL _{h to} PL _m that are candidates for the lower end of liver LV, and a plurality of candidate points that are candidates for the upper end of liver LV. to determine the PU _h ~PU _m.

The feature point determination means 93 determines the lower end of the liver based on the candidate points PL _{h to} PL _m and determines the upper end of the liver based on the candidate points PU _{h to} PU _m .

  The slice position setting means 94 sets the slice position of the liver based on the positions of the lower end and the upper end of the liver determined by the feature point determination means 93.

Since other configurations are the same as those of the first embodiment, description thereof is omitted.
Next, scanning executed when the subject 12 is imaged in the second embodiment will be described.

  FIG. 13 is an explanatory diagram of a scan executed when the subject 12 is imaged in the second embodiment.

  In the second mode, scout scan SC, coronal scan VS, and main scan MC are executed.

  The scout scan SC is a scan for acquiring scout image data, and is a scan in which the liver is rendered with a high signal. The scout image data is data used to specify the scan range of the next coronal scan VS. In the second embodiment, the scout scan SC is a 2D scan that scans the subject 12 using a pulse sequence for 2D imaging.

  Similar to the first embodiment, the coronal scan VS is a scan for acquiring coronal image data of a region including the liver. However, the scan range when executing the coronal scan VS in the second embodiment is determined based on the sagittal image data acquired by the scout scan SC.

  As in the first embodiment, the main scan MC is an imaging scan for collecting liver image data.

In the second form, the above scan is executed.
Next, a processing flow of the MRI apparatus 200 in the second embodiment will be described.

FIG. 14 is a diagram showing a processing flow of the MRI apparatus 200.
In step ST1, a scout scan SC is executed.

FIG. 15 is an explanatory diagram of slices set in the scout scan SC.
FIG. 15A illustrates the slice SL viewed from the RL direction, and FIG. 15B illustrates the slice SL viewed from the AP direction.

In the scout scan SC, the slice is selected so that the lower end PL of the liver LV is included in the slice SL. Since the lower end PL of the liver LV is located on the right hand side with respect to the median plane, the slice thickness t is set so that a certain range from the median plane to the right hand side is included in the slice SL. The lower end PL of the LV can be included. By executing the scout scan SC in this way, it is possible to acquire scout image data equivalent to the projection image data obtained by projecting data on the sagittal plane. Figure 16 schematically shows a scout image data DI _s obtained by scout scan SC. After obtaining the scout image data DI _s, the process proceeds to step ST2.

At step ST2, the scan range determining means 90 (see FIG. 1), based on the scout image data DI _s, determines the scan range of coronal scan VS executed in the next step ST11. A method for determining the scan range will be described below.

Scan range determining means 90, first, the scout image data DI _s, projected in the SI direction (craniocaudal), creating a 1D projection data (see FIG. 17).

Figure 17 is a diagram illustrating a 1D projection data PR ₁ created.
The scan range determining means 90 creates 1D projection data PR ₁ by projecting the integral value in the SI direction of the scout image data DI _s . Then, based on 1D projection data PR _1, obtains the AP direction of the range of liver LV. In the scout image data DI _s , since the liver is depicted as a high signal, in the 1D projection data PR ₁ , the portion with the large data value is the liver, and the portion with the low data value is the tissue around the liver. It is believed that there is. Accordingly, by setting a threshold TH for dividing the 1D projection data PR ₁ into a portion with a large data value and a portion with a small data value, the range of points a to b is specified as the range in the AP direction of the liver. Can do. The liver range specified in this way is set as the scan range RS in the AP direction of the coronal scan VS executed in the next step ST11. After setting the scan range RS, the process proceeds to step ST11.

  In step ST11, the coronal scan VS (see FIG. 13) is executed in accordance with the scan range RS set in step ST2.

  FIG. 18 is a diagram schematically showing image data acquired by coronal scan VS.

By executing the coronal scan VS, 3D image data TD of a region including the liver LV can be acquired. The cross-sectional image data creation unit 91 (see FIG. 12) creates coronal image data CI _{h to} CI _m from the 3D image data TD acquired by the coronal scan VS.

In the second embodiment, since the coronal scan VS is executed with the range in the AP direction of the liver as the scan range RS, the created coronal image data CI _{h to} CI _m include a section of the liver. Yes. After creating a coronal image data _CI h _{~CI m,} the process proceeds to step ST12.

In step ST12, candidate point determining unit 92 (see FIG. 12), each coronal image data _CI h _{~CI m,} determining a plurality of candidate points that are candidates for the lower end of the liver LV (see FIG. 19).

FIG. 19 is a diagram illustrating candidate points at the lower end of the liver determined for each of the coronal image data CI _{h to} CI _m .

As in the first embodiment, the segmentation method can be used as a method for determining the candidate points PL _{h to} PL _m . When the candidate points PL _{h to} PL _m are determined, the process proceeds to step ST13, and the candidate point PL _k having the lowest position in the SI direction is selected from the candidate points PL _{h to} PL _m as in the first embodiment. Determine the lower end of. If the lower end of the liver is determined, the process proceeds to step ST14.

In step ST14, the upper end of the liver is determined. As a method of determining the upper end of the liver, as in the first embodiment (see FIG. 10), candidate points PU _{h to} PU _m that are candidates for the upper end of the liver are determined, and a plurality of these candidate points PU _h to There is a method in which the candidate point PU _j located at the uppermost point among the PU _m is determined as the upper end of the liver.

Instead of determining the candidate points PU _{h to} PU _m , the scout image data DI _s (see FIG. 17) may be projected in the AP direction, and the upper end of the liver may be determined based on the projection data. Although the liver is rendered with a high signal, the lung adjacent to the liver is rendered with a low signal. Therefore, when the projection data is created, a position where the signal intensity rapidly changes appears in the projection data. Therefore, the position of the upper end of the liver can be detected by detecting the position where the signal intensity changes rapidly in the projection data.

After determining the upper end of the liver, the process proceeds to step ST15, (see FIG. 11) sets the slice position _SL 1 to SL _n, perform the main scanning in step ST16, the flow is terminated.

Also in the second form, as in the first form, the lower end of the liver can be determined by searching for the candidate point PL _k located at the lowest position among the candidate points PL _{h to} PL _m . Therefore, a slice position suitable for the position and size of the subject's liver can be set.

  In the second embodiment, as shown in FIG. 17, the range in the AP direction of the liver is set as the scan range RS of the coronal scan VS. However, since the lower end of the liver is normally located on the back side (posterior side), the abdomen side portion of the liver may be excluded from the scan range of the coronal scan VS (see FIG. 20).

FIG. 20 is a diagram showing a scan range RS ′ in which the abdominal part of the liver is excluded.
Since the scan range RS ′ does not include the abdomen portion of the liver, it can be made narrower than the scan range RS. Accordingly, by executing the coronal scan VS according to the scan range RS ′, the scan time of the coronal scan VS can be shortened.

  In the second embodiment, the scout scan SC is a 2D scan, but may be a 3D scan.

In the second mode, 1D projection data PR ₁ is created by projecting the integral value in the SI direction of the scout image data DI _s (see FIG. 17). However, instead of the integration value in the SI direction, 1D projection data PR ₁ may be generated by projecting a value other than the integration value, such as a maximum value, an average value, or a minimum value in the SI direction. Further, if it is possible to determine the scan range RS, the scout image data DI _s projected in different directions from the AP direction, may create a 1D projection data.

2 Magnetic field generator 3 Table 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 13 Operator 21 Bore 22 Superconducting coil 23 Gradient coil 24 Transmitting coil 31 Cradle 91 Cross-sectional image data creation means 92 Candidate point determination means 93 Feature point determination means 94 Slice position setting means 100 MRI apparatus

Claims (11)

A magnetic resonance imaging apparatus for imaging a predetermined part of a subject,
Cross-sectional image data creating means for creating a plurality of cross-sectional image data representing a cross-sectional image of an area including the feature point of the predetermined part;
Candidate point determination means for determining a plurality of candidate points that are candidate feature points of the predetermined part based on the plurality of cross-sectional image data;
Feature point determining means for determining a feature point of the predetermined part based on the plurality of candidate points;
Slice position setting means for setting a slice position of the predetermined part based on the position of the feature point of the predetermined part;
A magnetic resonance imaging apparatus.   The magnetic resonance imaging apparatus according to claim 1, wherein a first scan for acquiring the plurality of cross-sectional image data is executed. The candidate point determining means includes
3. The magnetic resonance according to claim 1, wherein cross-sectional image data crossing the predetermined part is specified from the plurality of cross-sectional image data, and the candidate point is determined for each specified cross-sectional image data. Imaging device. The candidate point determining means includes
The magnetic resonance imaging apparatus according to claim 3, wherein a histogram of each of the plurality of cross-sectional image data is created, and cross-sectional image data crossing the predetermined part is specified based on the histogram.   The magnetic resonance imaging apparatus according to claim 2, wherein a second scan for determining a scan range of the first scan is executed before the first scan.   The magnetic resonance imaging apparatus according to claim 5, further comprising a scan range determination unit that determines the scan range based on image data acquired by the second scan. The scan range determining means includes
The magnetic resonance imaging apparatus according to claim 6, wherein 1D projection data of image data acquired by the second scan is created, and the scan range is determined based on the 1D projection data.   The magnetic resonance imaging apparatus according to claim 1, wherein the cross-sectional image data is coronal image data. The predetermined site is the liver;
The magnetic resonance imaging apparatus according to claim 1, wherein the feature points are a lower end or an upper end of the liver, or both a lower end and an upper end of the liver. A slice position setting method for setting a slice position of a predetermined part of a subject,
A cross-sectional image data creating step for creating a plurality of cross-sectional image data representing a cross-sectional image of an area including the feature point of the predetermined part;
Candidate point determination step for determining a plurality of candidate points that are candidate feature points of the predetermined part based on the plurality of cross-sectional image data;
A feature point determining step for determining a feature point of the predetermined part based on the plurality of candidate points;
A slice position setting step for setting a slice position of the predetermined part based on the position of the feature point of the predetermined part;
A slice position setting method. A program of a magnetic resonance imaging apparatus for imaging a predetermined part of a subject,
Cross-sectional image data creation processing for creating a plurality of cross-sectional image data representing a cross-sectional image of an area including the feature point of the predetermined part;
Candidate point determination processing for determining a plurality of candidate points that are candidate feature points of the predetermined part based on the plurality of cross-sectional image data;
A feature point determination process for determining a feature point of the predetermined part based on the plurality of candidate points;
A slice position setting process for setting a slice position of the predetermined part based on the position of the feature point of the predetermined part;
A program to make a computer execute.

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