Abstract:
A blood flow dynamic analysis apparatus for determining a baseline indicative of a signal strength prior to an arrival of a contrast agent to a predetermined region of a subject, based on MR signals collected in time series from the predetermined region of the subject with the contrast agent injected therein, includes a time detection unit for detecting a time of data minimal in signal strength, of a first data sequence in which data of signal strengths of the MR signals are arranged in time series, a data fetch unit for fetching a second data sequence which appears prior to the time detected by the time detection unit, from within the first data sequence, a data detection unit for detecting centrally-located data from within a third data sequence obtained by sorting the second data sequence in the order of magnitudes of the signals strengths, a data extraction unit for extracting data from the third data sequence, based on the centrally-located data, and a baseline determination unit for determining the baseline, based on the data extracted by the data extraction unit.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of Japanese Patent Application No. 2008-304066 filed Nov. 28, 2008, which is hereby incorporated by reference in its entirety. 
       BACKGROUND OF THE INVENTION 
       [0002]    The embodiments described herein relate to a blood flow dynamic analysis apparatus for analyzing a blood flow dynamic state, and a magnetic resonance imaging system having the blood flow dynamic analysis apparatus. 
         [0003]    As a method for performing a diagnosis of brain infarction, there is known a method using a contrast agent. In order to carry out the diagnosis of the brain infarction using the contrast agent, the contrast agent is injected into a subject and MR signals are collected from slices set to the subject on a time-series basis. Thereafter, there is a need to determine a baseline indicative of a signal strength of each MR signal prior to the arrival of the contrast agent for each of regions lying in each slice. The baseline is a parameter essential for calculation of a change ΔR 2 * in transverse relaxation velocity or rate of each spin, and the like at the time that the contrast agent has passed through each region of the slice. Although a method for determining the baseline manually and a method for determining it automatically are known, the method for determining the baseline automatically has been in widespread use because it is necessary to carry out the diagnosis of the brain infarction promptly in a short period of time (refer to Japanese Unexamined Patent Publication No. 2004-57812). 
         [0004]    The method of described above is however accompanied by the problem that when an S/N ratio of each MR signal is small, the accuracy of a calculated value of the baseline is degraded. 
       BRIEF DESCRIPTION OF THE INVENTION 
       [0005]    A blood flow dynamic analysis apparatus for determining a baseline indicative of a signal strength prior to an arrival of a contrast agent to a predetermined region of a subject, based on MR signals collected in time series from the predetermined region of the subject with the contrast agent injected therein, includes a time detection unit for detecting a time of data minimal in signal strength, of a first data sequence in which data of signal strengths of the MR signals are arranged in time series; a data fetch unit for fetching a second data sequence which appears prior to the time detected by the time detection unit, from within the first data sequence; a data detection unit for detecting centrally-located data from within a third data sequence obtained by sorting the second data sequence in the order of magnitudes of the signals strengths; a data extraction unit for extracting data from the third data sequence, based on the centrally-located data; and a baseline determination unit for determining the baseline, based on the data extracted by the data extraction unit. 
         [0006]    A magnetic resonance imaging system of the invention is equipped with the blood flow dynamic analysis apparatus of the invention. 
         [0007]    A second data sequence that appears prior to the time of data minimal in signal strength is fetched from within a first data sequence arranged in time series. The second data sequence is sorted in the order of magnitude of the signal strength. Thereafter, centrally-located data is detected from the data sorted in the order of the magnitude of the signal strength. There is a tendency that when the data are sorted in the order of magnitude of the signal strength, data usable for determination of a baseline concentrate on the neighborhood of the center of the sorted data. Thus, the accuracy of the calculated value of the baseline can be enhanced even though the SN ratio of each MR signal is small, by using the data located in the center. 
         [0008]    Further objects and advantages of the present invention will be apparent from the following description of the preferred embodiments of the invention as illustrated in the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]      FIG. 1  is a schematic diagram of a magnetic resonance imaging system  1  according to one embodiment of the invention. 
           [0010]      FIG. 2  is a diagram showing a processing flow of the magnetic resonance imaging system. 
           [0011]      FIG. 3  is one example illustrative of slices set to a subject  8 . 
           [0012]      FIGS. 4A ,  4 B, and  4 C are conceptual diagrams showing frame images obtained from their corresponding slices S 1  through Sn. 
           [0013]      FIGS. 5A and 5B  are diagrams showing changes in signal strength with respect to time in a sectional area of a slice Sk set to a head  8   a  of the subject  8 . 
           [0014]      FIG. 6  is a diagram showing a data sequence DS 2  fetched from within a data sequence DS 1 . 
           [0015]      FIG. 7  is a diagram showing sorted data D 1  through D 24 . 
           [0016]      FIG. 8  is a diagram showing the positions of a lower limit value LC 1  and an upper limit value UC 1 . 
           [0017]      FIG. 9  is a diagram showing a confidence interval CI. 
           [0018]      FIG. 10  is a diagram for showing labeled data of a data sequence DS 2  arranged in time series. 
           [0019]      FIG. 11  is a diagram showing a baseline BL and an arrival time AT. 
           [0020]      FIGS. 12A and 12B  are diagrams showing one example of another method for determining an arrival time AT. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0021]      FIG. 1  is a schematic diagram of a magnetic resonance imaging system  1  according to one embodiment of the invention. 
         [0022]    The magnetic resonance imaging system (hereinafter called MRI (Magnetic Resonance Imaging) system)  1  has a coil assembly  2 , a table  3 , a reception coil  4 , a contrast agent injection device  5 , a control device  6  and an input device  7 . 
         [0023]    The coil assembly  2  has a bore  21  that accommodates a subject  8  therein, a superconducting coil  22 , a gradient coil  23  and a transmission coil  24 . The superconducting coil  22  applies a static magnetic field B 0 , the gradient coil  23  applies a gradient pulse and the transmission coil  24  transmits an RF pulse. 
         [0024]    The table  3  has a cradle  31 . The cradle  31  is configured so as to move in a z direction and a −z direction. With the movement of the cradle  31  in the z direction, the subject  8  is carried in the bore  21 . With the movement of the cradle  31  in the −z direction, the subject  8  carried in the bore  21  is carried out from the bore  21 . 
         [0025]    The contrast agent injection device  5  injects a contrast agent into the subject  8 . 
         [0026]    The reception coil  4  is attached to the head  8   a  of the subject  8 . An MR (Magnetic Resonance) signal received by the reception coil  4  is transmitted to the control device  6 . 
         [0027]    The control device  6  has coil control unit  61  through arrival time determination unit  69 . 
         [0028]    The coil control unit  61  controls the transmission coil  24  and the gradient coil  23  in such a manner that a pulse sequence for imaging the subject  8  is executed in response to an imaging command of the subject  8 , which has been inputted from the input device  7  by an operator  9 . 
         [0029]    The signal strength profile generation unit  62  generates a signal strength profile Ga of a data sequence DS 1  (refer to  FIGS. 5A and 5B ). 
         [0030]    The time detection unit  63  detects a time T 24  at data D 24  minimal in signal strength S, of the data sequence DS 1  (refer to  FIG. 5B ). 
         [0031]    The data fetch unit  64  fetches a data sequence DS 2  (refer to  FIG. 6 ) from within the data sequence DS 1  (refer to  FIG. 5B ) arranged in time series. 
         [0032]    The sort unit  65  rearranges or sorts the data sequence DS 2  in the order of magnitude of each signal strength. 
         [0033]    The data detection unit  66  detects data D 24  minimal in signal strength from within a data sequence DS 3  arranged in the order of magnitude of the signal strength. Further, the data detection unit  66  also detects data located in the center of the data sequence DS 3  arranged in the order of magnitude of the signal strength from within the data sequence DS 3 . 
         [0034]    The data extraction unit  67  has a data tentative extraction part  671 , a confidence interval determination part  672  and a data extraction part  673 . 
         [0035]    The data tentative extraction part  671  tentatively extracts data from within the data sequence DS 3  arranged in the order of magnitude of the signal strength, based on the data detected by the data detection unit  66 . 
         [0036]    The confidence interval determination part  672  determines a confidence interval CI at which data fitted to determine a baseline BL exist with respect to a set Dset 1  of the data tentatively extracted by the data tentative extraction part  671  (refer to  FIG. 9 ). 
         [0037]    The data extraction part  673  extracts a set Dset 2  of data contained in the confidence interval CI from within the set Dset 1  of the tentatively extracted data (refer to  FIG. 9 ). 
         [0038]    The baseline determination unit  68  has a labeling part  681 , a data determination part  682  and a baseline determination part  683 . 
         [0039]    The labeling part  681  labels data corresponding to the data (refer  FIG. 9 ) extracted from the confidence interval CI of the data sequence DS 3 , of the data (refer to  FIG. 6 ) contained in the data sequence DS 2  arranged in time series. 
         [0040]    The data determination part  682  determines data used to determine the baseline BL, based on the data labeled by the labeling part  681 . 
         [0041]    The baseline determination part  683  determines the baseline BL, based on the data determined by the data determination part  682 . 
         [0042]    The arrival time determination unit  69  determines an arrival time AT, based on the data labeled by the labeling part  681 . 
         [0043]    The input device  7  inputs various commands to the control device  6  in accordance with the operation of the operator  9 . 
         [0044]      FIG. 2  is a diagram showing a processing flow of the magnetic resonance imaging system  1 . 
         [0045]    At Step S 1 , contrast-enhanced or contrasting imaging is performed on the head  8   a  of the subject  8 . The operator manipulates the input device  7  to set slices to the subject  8 . 
         [0046]      FIG. 3  is one example illustrative of slices set to the subject  8 . 
         [0047]    n sheets of slices S 1  through Sn are set to the subject  8 . The number of slices is, for example, n=12. The number of the slices can be set to an arbitrary number of sheets as needed. An imaging area of the head  8   a  of the subject  8  is determined for each of the slices S 1  through Sn. 
         [0048]    After the slices S 1  through Sn have been set, the operator  9  transmits a contrast agent injection command to the contrast agent injection device  5  and transmits a command for imaging or obtaining the subject  8  to the coil control unit  61  of the MRI system (refer to  FIG. 1 ). The coil control unit  61  controls the transmission coil  24  and the gradient coil  23  in such a manner that a pulse sequence for imaging the head  8   a  of the subject  8  in response to the corresponding imaging command. 
         [0049]    In the present embodiment, a pulse sequence for obtaining m sheets of continuously-captured frame images from their corresponding slices is executed by a multi-slice scan. Thus, the m sheets of frame images are obtained per slice. For example, the number of frame images m=85. With the execution of the pulse sequence, data are collected from the head  8   a  of the subject  8 . 
         [0050]      FIGS. 4A ,  4 B, and  4 C are conceptual diagrams showing frame images obtained from their corresponding slices S 1  through Sn. 
         [0051]      FIG. 4A  is a schematic diagram showing that the n sheets of slices S 1  through Sn set to the head  8   a  of the subject  8  are arranged in time series in accordance with the order of collection thereof,  FIG. 4B  is a schematic diagram showing the manner in which the frame images of  FIG. 4A  are classified for each of the slices S 1  through Sn, and  FIG. 4C  is a schematic diagram showing frame images collected or acquired from the slice Sk, respectively. 
         [0052]    Frame images [S 1 , t 11 ] through [Sn, tnm] are acquired from the slices S 1  through Sn (refer to  FIG. 3 ) set to the head  8   a  of the subject  8  (refer to  FIG. 4A ). In  FIG. 4A , the left character of [,] indicative of each frame image represents a slice at which each frame image is acquired, and the right character thereof represents the time at which each frame image is acquired. 
         [0053]      FIG. 4B  shows the manner in which the frames images shown in  FIG. 4A  are classified for each of the slices S 1  through Sn.  FIG. 4B  shows by arrows, to which frame images of the frame images [S 1 , t 11 ] through [Sn, tnm] arranged in time series in  FIG. 4A  the frame images [Sk, tk 1 ] through [Sk, tkm] of the slice Sk of the slices S 1  through Sn correspond respectively. 
         [0054]    The section of the slice Sk and the m sheets of frame images [Sk, tk 1 ] through [Sk, tkm] acquired from the slice Sk are shown in  FIG. 4C . The section of the slice Sk is divided into α×β regions R 1 , R 2 , . . . Rz. The frame images [Sk, tk 1 ] through [Sk, tkm] have α×β pixels P 1 , P 2 , . . . Pz respectively. The pixels P 1 , P 2 , . . . Pz of the frame images [Sk, tk 1 ] through [Sk, tkm] are equivalent to those obtained by imaging or obtaining the regions R 1 , R 2 , . . . Rz of the slice Sk at times tk 1  through tkm (time intervals Δt). 
         [0055]    Incidentally, while only the frame images obtained at the slice Sk are shown in  FIG. 4C , m sheets of frame images are acquired even at other slices in a manner similar to the slice Sk. 
         [0056]    After the execution of Step S 1 , the processing flow proceeds to Step S 2 . 
         [0057]    At Step S 2 , the signal strength profile generation unit  62  (refer to  FIG. 1 ) generates a profile of a data sequence DS 1  (refer to  FIGS. 5A and 5B ). A description will hereinafter be made of how the signal strength profile generation unit  62  generates the profile of the data sequence DS 1 , with reference to  FIGS. 5A and 5B . 
         [0058]      FIGS. 5A and 5B  are diagrams showing changes in signal strength with time in a sectional area of the slice Sk set to the head  8   a  of the subject  8 . 
         [0059]    The section of the slice Sk of the subject  8  and the frame images [Sk, tk 1 ] through [Sk, tkm] of the slice Sk are shown in  FIG. 5A  (refer to  FIG. 4C ). 
         [0060]    A schematic diagram of a signal strength profile Ga indicative of changes in signal strength with time at a region Ra of the slice Sk is shown in  FIG. 5B . 
         [0061]    The horizontal axis indicates the time t at which each of the frame images [Sk, tk 1 ] through [Sk, tkm] is acquired from the slice Sk. The vertical axis indicates the signal strength S at each of pixels Pa of the frame images [Sk, tk 1 ] through [Sk, tkm]. Each of the pixels Pa of the frame images [Sk, tk 1 ] through [Sk, tkm] is equivalent to one obtained by capturing or imaging the region Ra of the slice Sk at each of the times tk 1  through tkm. The signal strength profile Ga shows a data sequence DS 1  in which data D 1  through Dm are arranged on a time-series basis. The data D 1  through Dm respectively indicate the signal strengths S at the pixels Pa of the frame images [Sk, tk 1 ] through [Sk, tkm]. For example, the data D 1  indicates the signal strength S at the pixel Pa of the frame image [Sk, tk 1 ], and the data Dg indicates the signal strength S at the pixel Pa of the frame image [Sk, tkg]. 
         [0062]    While the signal strength profile Ga at the region Ra of the slice Sk has been shown in  FIG. 5B , signal strength profiles Ga are generated or formed even at other regions in the slice Sk. Further, signal strength profiles Ga are generated similarly even at respective regions related to other slices other than the slice Sk. 
         [0063]    In the present embodiment, a baseline BL (refer to  FIG. 11 ) to be described later is determined from the data sequence DS 1  of the signal strength profile Ga. The baseline BL is of a line indicative of a signal strength S prior to the arrival of a contrast agent to the corresponding region Ra of the slice Sk. The baseline BL is a parameter necessary to calculate a change ΔR 2 * in transverse relaxation velocity or rate of each spin, and the like at the time that the contrast agent has passed through the region Ra of the slice Sk. The baseline BL is set to any position of a range A in which the signal strength S increases and decreases repeatedly in the first half of the signal strength profile Ga. Since, however, the optimal position of the baseline BL varies every signal strength profile Ga, it is necessary to determine the optimal position of the baseline BL every signal strength profile Ga. Thus, in the present embodiment, Steps S 3  through S 11  are executed in such a manner that the baseline BL can be set to the optimal position. Steps S 3  through S 11  will be explained below. 
         [0064]    At Step S 3 , the time detection unit  63  (refer to  FIG. 1 ) detects a time T 24  at data D 24  minimal in signal strength S, of the data sequence DS 1  of the signal strength profile Ga (refer to  FIG. 5B ). After the time T 24  has been detected, the processing flow proceeds to Step S 4 . 
         [0065]    At Step S 4 , the data fetch unit  64  (refer to  FIG. 1 ) fetches such a data sequence DS 2  (including the data D 24  at the time T 24  detected by the time detection unit  63  and data D 1  through D 23  prior to the time T 24 ) as shown in  FIG. 6  from within the data sequence DS 1  arranged in time series. 
         [0066]      FIG. 6  is a diagram showing the data sequence DS 2  fetched from within the data sequence DS 1 . 
         [0067]    The data sequence DS 2  contains the data D 1  through D 24 . In  FIG. 6 , only the data D 1  and D 24  are designated by reference symbols. Reference symbols for other data D 2  through D 23  are omitted. After the data D 1  through D 24  have been fetched, the processing flow proceeds to Step S 5 . 
         [0068]    At Step S 5 , the sort unit  65  (refer to  FIG. 1 ) sorts the fetched data sequence DS 2  (data D 1  through D 24 ) in the order of magnitude of the signal strength. 
         [0069]      FIG. 7  is a diagram showing the sorted data D 1  through D 24 . 
         [0070]    The horizontal axis of a graph indicates the positions of the sorted data D 1  through D 24 , and the vertical axis thereof indicates the signal strength S. With the sorting of the data sequence DS 2  (data D 1  through D 24 ) in the order of magnitude of the signal strength, a data sequence DS 3  arranged in the order of magnitude of the signal strength is obtained. After the data D 1  through D 24  have been sorted in the order of magnitude of the signal strength S, the processing flow proceeds to Step S 6 . 
         [0071]    At Step S 6 , the data detection unit  66  (refer to  FIG. 1 ) detects the data D 24  minimal in signal strength S from within the data sequence DS 3  arranged in the order of magnitude of the signal strength. 
         [0072]    Further, the data detection unit  66  detects data located in the center of the data sequence DS 3  arranged in the order of magnitude of the signal strength from within the data sequence DS 3 . In the present embodiment, however, the number of data contained in the data sequence DS 3  is 24, i.e., an even number. Thus, the position of the center of the data sequence DS 3  becomes a position E between twelfth data D 9  as counted from the side small in signal strength S and twelfth data D 5  as counted from the side large in signal strength S. However, no data exists in the position E. Therefore, in the present embodiment, the data D 9  adjacent to the side small in signal strength S is detected as the data located in the center with respect to the position E. However, the data D 5  adjacent to the side large in signal strength S may be detected as the data located in the center. Incidentally, when the number of data is an odd number, data located in the middle thereof is detected as the data located in the center. 
         [0073]    The data detection unit  66  detects the data D 24  and D 9  in the above-described manner. After the data D 24  and D 9  have been detected, the processing flow proceeds to Step S 7 . 
         [0074]    At Step S 7 , the data tentative extraction part  671  (refer to  FIG. 1 ) tentatively extracts data likely to be usable for determining a baseline BL from within the data sequence DS 3  arranged in the order of magnitude of the signal strength, based on the detected data D 24  and D 9 . 
         [0075]    In order to tentatively extract data, the data tentative extraction part  671  first determines a lower limit value LC 1  and an upper limit value UC 1  of a signal strength S defined as the reference for tentatively extracting the data. The lower limit value LC 1  and the upper limit value UC 1  are calculated from the following equations: 
         [0000]        LC 1 =Sm 1−( Sm 1 −S low)× k 1  Eq. (1) 
         [0000]        UC 1 =Sm 1+( Sm 1 −S low)× k 2  Eq. (2) 
         [0076]    where Sm 1  is a signal strength of data D 9  located in the center, Slow is a signal strength of data D 24 , and k 1  and k 2  are constants. 
         [0077]    Thus, the lower limit value LC 1  and the upper limit value UC 1  are calculated from the equations (1) and (2). 
         [0078]      FIG. 8  is a diagram showing the positions of the lower limit value LC 1  and the upper limit value UC 1 . 
         [0079]    After the lower limit value LC 1  and the upper limit value UC 1  have been calculated, a set Dset 1  of data (data D 6 , D 17 , D 3 , D 4 , D 19 , D 9 , D 5 , D 18 , D 12 , D 13  and D 15 ) located between the lower limit value LC 1  and the upper limit value UC 1  is tentatively extracted. 
         [0080]    Incidentally, the lower limit value LC 1  and the upper limit value UC 1  depend on the constants k 1  and k 2  along with 5 ml and Slow (refer to the equations (1) and (2)). The smaller the constants k 1  and k 2 , the narrower the interval between the lower limit value LC 1  and the upper limit value UC 1 . On the other hand, the larger the constants k 1  and k 2 , the wider the interval between the lower limit value LC 1  and the upper limit value UC 1 . Since the number of tentatively extracted data becomes small when the interval between the lower limit value LC 1  and the upper limit value UC 1  becomes too narrow, there is a need to wide the interval between the lower limit value LC 1  and the upper limit value UC 1  to some extent in such a manner that a certain number of data can be tentatively extracted. Since, however, the number of the tentatively extracted data increases when the interval between the lower limit value LC 1  and the upper limit value UC 1  becomes excessively wide, the ratio of the number of data unfitted to determine the baseline BL to the number of the tentatively extracted data also increases. It is thus necessary to set the constants k 1  and k 2  in such a way that the interval between the lower limit value LC 1  and the upper limit value UC 1  becomes a proper value. In the present embodiment, the constants are set to k 1 =k 2 =0.1. However, the values of k 1  and k 2  may be set to values other than 0.1 according to imaging conditions. 
         [0081]    In the present embodiment, a set Dset 1  of data is tentatively extracted. All data contained in the set Dset 1  of the tentatively extracted data are also usable as data for determining the baseline BL. There is however a possibility that data undesirable to be used as the data for determining the baseline BL will be contained in the set Dset 1  of the data depending on deviations in signal strength between the data contained in the set Dset 1  of the tentatively extracted data. Thus, in the present embodiment, the corresponding data used to determine the baseline BL is extracted from within the set Dset 1  of the tentatively extracted data. Therefore, the processing flow proceeds to Step S 8 . 
         [0082]    At Step S 8 , the confidence interval determination part  672  (refer to  FIG. 1 ) determines a confidence interval CI at which the corresponding data fitted to determine the baseline BL is likely to exist with respect to the set Dset 1  of the tentatively extracted data. The confidence interval CI is determined according to a lower limit value LC 2  and an upper limit value UC 2  of a signal strength S. The lower limit value LC 2  and the upper limit value UC 2  are calculated from, for example, the following equations: 
         [0000]        LC 2 =Sm 2−STD× k 3  Eq. (3) 
         [0000]        UC 2 =Sm 2−STD× k 4  Eq. (4) 
         [0000]    where Sm 2  is an average value of signal strengths of all data contained in set Dset 1  of tentatively extracted data, STD is a standard deviation, and k 3  and k 4  are constants. 
         [0083]    Thus, the lower limit value LC 2  and the upper limit value UC 2  are calculated from the equations (3) and (4). 
         [0084]      FIG. 9  is a diagram showing the confidence interval CI. 
         [0085]    The lower limit value LC 2  and the upper limit value UC 2  of the confidence interval CI are located between the lower limit value LC 1  and the upper limit value UC 1  used when the data is tentatively extracted. As a result, it is understood that data D 6  is omitted from the confidence section CI and low in reliability as the data used to determine the baseline BL. A set Dset 2  of data (data D 17 , D 3 , D 4 , D 19 , D 8 , D 9 , D 5 , D 18 , D 12 , D 13  and D 15 ) is contained in the confidence interval CI. 
         [0086]    Incidentally, the lower limit value LC 2  and the upper limit value UC 2  depend on the constants k 3  and k 4  along with Sm 2  and STD (refer to the equations (3) and (4)). While the values of the constants k 3  and k 4  take various values according to imaging conditions or the like, the constants are set to k 3 =k 4 =3 in the present embodiment. However, the values of the constants k 3  and k 4  may be set to values other than 3 according to the imaging conditions or the like. 
         [0087]    After the confidence interval CI has been determined, the processing flow proceeds to Step S 9 . 
         [0088]    At Step S 9 , the data extraction part  673  (refer to  FIG. 1 ) extracts the set Dset 2  of the data (data D 17 , D 3 , D 4 , D 19 , D 8 , D 9 , D 5 , D 18 , D 12 , D 13  and D 15 ) contained in the confidence interval CI from within the set Dset 1  of the tentatively extracted data. After the extraction of the data set Dset 2 , the processing flow proceeds to Step S 10 . 
         [0089]    At Step S 10 , the labeling part  681  (refer to  FIG. 1 ) labels data corresponding to the data extracted from the confidence interval CI of the data sequence DS 3 , of the data (refer to  FIG. 6 ) contained in the data sequence DS 2  arranged on a time series basis. 
         [0090]      FIG. 10  is a diagram for showing labeled data of the data sequence DS 2  arranged in time series. In  FIG. 10 , the labeled data (D 3 , D 4 , D 5 , D 8 , D 9 , D 12 , D 13 , D 15 , D 17 , D 18  and D 19 ) are shown with being surrounded by white circles. It is understood that when  FIGS. 10 and 9  are compared, the data contained in the set Dset 2  of the data shown in  FIG. 9  are labeled in  FIG. 10 . 
         [0091]    It is understood that referring to  FIG. 10 , the labeled data (D 3 , D 4 , D 5 , D 8 , D 9 , D 12 , D 13 , D 15 , D 17 , D 18  and D 19 ) appear in a range A in which an increase/decrease in signal strength is repeated. It is thus understood that the labeled data are data fitted to determine the baseline BL. After the data have been labeled, the processing flow proceeds to Step S 9 . 
         [0092]    At Step S 11 , the data determination part  682  (refer to  FIG. 1 ) determines data used to determine the baseline BL, based on the labeled data. Referring to  FIG. 10 , unlabeled data (D 2 , D 6 , D 7 , D 10 , D 11 , D 14  and D 16 ) also exist in the range A in which the increase/decrease in signal strength is repeated, in addition to the labeled data. However, the unlabeled data (D 6 , D 7 , D 10 , D 11 , D 14  and D 16 ) other than the data D 2  are interposed between the labeled data. In such a case, even the unlabeled data ((D 6 , D 7 , D 10 , D 11 , D 14  and D 16 ) are considered to be data fitted to determine the baseline BL. Therefore, the data determination part  682  determines both the labeled data (D 3 , D 4 , D 5 , D 8 , D 9 , D 12 , D 13 , D 15 , D 17 , D 18  and D 19 ) and the unlabeled data (D 6 , D 7 , D 10 , D 11 , D 14  and D 16 ) as the data used to determine the baseline BL. Thus, the data determination part  682  determines the data D 3  through D 19  as the data used to determine the baseline BL. Thereafter, the processing flow proceeds to Step S 12 . 
         [0093]    At Step S 12 , the baseline determination part  683  (refer to  FIG. 1 ) calculates an average value of signal strengths S of the data D 3  through D 19  determined by the data determination part  682  and determines the calculated average value as a baseline BL. The arrival time determination unit  69  (refer to  FIG. 1 ) determines a time AT (arrival time) at which the contrast agent has reached the region Ra of the slice Sk, based on the labeled data (D 3 , D 4 , D 5 , D 8 , D 9 , D 12 , D 13 , D 15 , D 17 , D 18  and D 19 )). 
         [0094]      FIG. 11  is a diagram sowing a baseline BL and an arrival time AT. 
         [0095]    In  FIG. 11 , reference symbols are omitted for data lying within a range A except for data D 19 . 
         [0096]    It is understood that referring to  FIG. 11 , the baseline BL is set within the range A in which an increase/decrease in signal strength S is repeated. A time T 19  of the data D 19  that appears finally on a time-series basis, of labeled data (D 3 , D 4 , D 5 , D 8 , D 9 , D 12 , D 13 , D 15 , D 17 , D 18  and D 19 ) is determined as the arrival time AT. It is understood that the signal strength S decreases suddenly from immediately after the data D 19 , and the time of the data D 19  is proper as the arrival time AT. 
         [0097]    The procedure for determining the baseline BL and the arrival time AT at the region Ra (refer to  FIG. 5A ) of the slice Sk has been explained up to now. However, baselines BL and arrival times AT at regions of other slices other than the slice Sk are also determined by an approach similar to above. 
         [0098]    In the present embodiment, the data sequence DS 2  (refer to  FIG. 6 ) including the data D 24  minimal in signal strength and the data D 1  through D 23  that appear prior to the data D 24  is fetched from within the data sequence DS 1  (refer to  FIG. 5B ) arranged in time series. The data sequence DS 2  is sorted in the order of magnitude of the signal strength. Thereafter, the data D 9  located in the center is detected from within the data D 1  through D 24  sorted in the order of magnitude of the signal strength. There is a tendency that when they are sorted in the order of magnitude of the signal strength, the data usable for determination of the baseline BL concentrate on the neighborhood of the center of the sorted data (refer to  FIG. 9 ). Thus, the accuracy of the calculated value of the baseline BL can be enhanced even though the SN ratio of an MR signal is large, by determining the data D 3  through D 19  used to determine the baseline BL finally, based on the data D 9  located in the center. 
         [0099]    Incidentally, in the present embodiment, the set Dset 2  of the data contained in the confidence interval CI is extracted from the set Dset 1  of the tentatively extracted data. The data D 3  through D 19  used to determine the baseline BL are determined based on the data set Dset 2 . However, the data used to determine the baseline BL may be determined based on the set Dset 1  of the tentatively extracted data. 
         [0100]    In the present embodiment, the data D 1  through D 24  are fetched as the data sequence DS 2 . However, the data D 1  through D 23  of the data D 1  through D 24  may be fetched out as the data sequence DS 2  without fetching the data  24  minimal in signal strength S. 
         [0101]    Although the time T 19  of the data D 19  is determined as the arrival time AT in the present embodiment, the arrival time AT can also be determined by another method. A description will hereinafter be made of a method for determining the arrival time AT by means of another method. 
         [0102]      FIGS. 12A and 12B  are diagrams showing one example of another method for determining the arrival time AT. 
         [0103]    As shown in  FIG. 12A , data D 19  through D 24  are first connected by straight lines and a line L 1  for connecting the data D 19  through D 24  is defined. 
         [0104]    Next, as shown in  FIG. 12B , the line L 1  is fitted using a predetermined function (gamma function or polynomial expression). With this fitting, the line L 1  changes to a line L 1 ′. A time T 19 ′ of a position corresponding to the data D 19  is calculated from the line L 1 ′. The time T 19 ′ calculated in this way may be determined as the arrival time AT. 
         [0105]    Many widely different embodiments of the invention may be configured without departing from the spirit and the scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments described in the specification, except as defined in the appended claims.