Magnetic resonance imaging apparatus and information processing apparatus

Abstract

To simultaneously collect and systematically evaluate information regarding free water and bound water within living organisms. [Solution] A magnetic resonance imaging apparatus according to one embodiment comprises a setting unit and a scanner. The setting unit sets a pulse sequence. The pulse sequence includes a plurality of sub-pulse sequences. The plurality of sub-pulse sequences collect MR (Magnetic Resonance) data corresponding to each of the plurality of echo signals generated after the application of MT (Magnetization Transfer) pulses and RF (radio frequency) pulses to the subject, using a UTE (Ultrashort Echo Time) sequence. The scanner collects MR data based on the pulse sequence set by the setting unit. The MR data corresponding to the first echo signal after the application of the RF pulse from among the plurality of echo signals of at least one sub-pulse sequence is used for the analysis of both bound water and free water in the subject.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to a magnetic resonance imaging (MRI) apparatus and an information processing apparatus.

Background Art

[0002] An MRI apparatus excites the nuclear spins of a subject placed in a static magnetic field with a high-frequency (RF: Radio Frequency) pulse at the Larmor frequency, and performs a scan to collect MR data based on the magnetic resonance (MR) signal generated from the subject accompanying the excitation, and generates an MR image based on the MR data collected by the scan.

[0003] In recent years, in order to enable imaging of a measurement object having a very short transverse relaxation time (T2 value, or T2 value considering magnetic field inhomogeneity), an UTE (UltraShort Echo Time) sequence has been used. * For example, for the purpose of quantitative evaluation of free water or pore water, the UTE sequence is utilized in clinical or research. Also, for the purpose of observing and evaluating bound water, which has a very short transverse relaxation time because it is strongly bound to biopolymers, clinical research using a UTE-MT (Magnetization Transfer) sequence has been progressing.

[0004] However, conventionally, free water and bound water in the living body have been observed separately, and a method for systematically evaluating information on free water and information on bound water, or for quantitative evaluation in the case of having multiple components of bound water has not been established.

[0005] However, conventionally, free water and bound water in the living body have been observed separately, and a method for systematically evaluating information on free water and information on bound water, or for quantitative evaluation in the case of having multiple components of bound water has not been established.

Prior Art Documents

Non-Patent Documents

[0006] [Non-Patent Document 1] Ya-Jun Ma et al., “Quantitative Magnetization Transfer Ultrashort Echo Time Imaging Using a Time-Efficient 3D Multispoke Cones Sequence”, Magnetic Resonance in Medicine Vol. 79, Issue 2, P.692-700, 2018 [Non-Patent Document 2] Eric Y. Chang et al., “Ultrashort Echo Time Magnetization Transfer (UTE-MT) Imaging of Cortical Bone”, NMR in Biomedicine Vol. 28, Issue 7, P.873-880, 2015 [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] One of the problems that the embodiments disclosed herein and in the drawings aim to solve is the simultaneous collection and systematic evaluation of information regarding free water and bound water within a living organism. However, the problems that the embodiments disclosed herein and in the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0008] An MRI apparatus according to one embodiment comprises a setting unit and a scanner. The setting unit sets a pulse sequence. The pulse sequence includes a plurality of sub-pulse sequences. The plurality of sub-pulse sequences collect MR data corresponding to each of the plurality of echo signals generated after the application of the MT pulse and RF pulse using the UTE sequence. The scanner collects MR data based on the pulse sequence set by the setting unit. The MR data corresponding to the first echo signal after the application of the RF pulse from among the plurality of echo signals of at least one sub-pulse sequence is used for the analysis of both bound water and free water in the subject. [Brief explanation of the drawing]

[0009] [Figure 1] A block diagram showing an example of the overall configuration of an MRI apparatus according to this embodiment. [Figure 2] An explanatory diagram of interstitial water, weak bound water, strong bound water, and structural water in cortical bone. [Figure 3] An NMR spectral diagram showing the difference in T2 values ​​between free water and bound water in living organisms. [Figure 4] A diagram illustrating regions dominated by bound water and regions dominated by free water. [Figure 5] A block diagram showing the processing circuit of an MRI apparatus according to the first embodiment. [Figure 6] A flowchart showing an example of operation of an MRI device according to the first embodiment. [Figure 7] A sequence chart showing an example of a pulse sequence according to the embodiment. [Figure 8] An explanatory diagram illustrating the improvement of data acquisition rate using an example pulse sequence according to the embodiment. [Figure 9] Figure 9(A) shows a first example of setting the imaging conditions for the pulse sequence according to the embodiment. Figure 9(B) shows a second example of setting the imaging conditions for the pulse sequence according to the embodiment. [Figure 10] An explanatory diagram illustrating the magnetization transfer of pore water, weakly bound water, and strongly bound water. [Figure 11] A flowchart showing an example of the operation of step ST20 of the MRI apparatus according to the first embodiment. [Figure 12] Figures 12(A), 12(B), and 12(C) are graphs showing examples of off-resonance frequency vs. OSR characteristics of cortical bone and rubber for different MT pulse intensities, respectively. [Figure 13] Figure 13(A) is a graph showing the relationship between off-resonance frequencies and OSR in a specific voxel. Figure 13(B) is a map image showing the OSR for each off-resonance frequency as pixel values. [Figure 14] Figure 14(A) is a graph showing an example of fitting off-resonance frequency vs. OSR characteristics to cortical bone using an exponential model. Figure 14(B) is a graph showing an example of fitting off-resonance frequency vs. OSR characteristics to rubber using an exponential model. Figure 14(C) is a map image showing off-resonance frequencies as pixel values ​​for weakly bound water and strongly bound water. [Figure 15] A graph showing the relationship between off-resonance frequencies of two types of phantoms and cortical bone and OSR. [Figure 16] Figure 16(A) is a graph showing the relationship between the off-resonance frequencies and component ratios of two types of phantoms and cortical bone. Figure 16(B) is a graph showing the component ratio gradient calculated based on Figure 16(A). [Figure 17] A flowchart showing an example of the operation of step ST30 of the information processing according to the first embodiment. [Figure 18] Graph of the T2* decay curve using the UTE sequence. [Figure 19] A block diagram showing an example of the overall configuration of the information processing device according to the embodiment. [Figure 20] A block diagram showing the processing circuit of the MRI apparatus according to the second embodiment. [Figure 21] A flowchart showing an example of operation of an MRI device according to the second embodiment. [Figure 22] A block diagram showing the processing circuit of an MRI apparatus according to a modified example of the second embodiment. [Figure 23]A flowchart showing an example of operation of an MRI device according to a modified example of the second embodiment. [Modes for carrying out the invention]

[0010] The MRI apparatus and information processing apparatus according to the embodiment will be described below with reference to the attached drawings. In each figure, the same elements are denoted by the same reference numerals, and redundant explanations are omitted.

[0011] (Overall configuration of the MRI system) Figure 1 is a block diagram showing an example of the overall configuration of an MRI apparatus 1 according to an embodiment. The MRI apparatus 1 comprises a pedestal 100, a control cabinet 300, a console 400, and a patient bed 500.

[0012] The mounting device 100 comprises a static magnetic field magnet 10, a gradient magnetic field coil 11, and a whole-body (WB) coil 12. These components are housed in a cylindrical casing.

[0013] The static magnetic field magnet 10 has a roughly cylindrical shape and generates a static magnetic field in the bore into which the patient being examined is brought. The bore is the examination space inside the cylinder of the static magnetic field magnet 10. The static magnetic field magnet 10 incorporates a superconducting coil, which is cooled to an extremely low temperature by liquid helium. In excitation mode, the static magnetic field magnet 10 generates a static magnetic field by applying a current supplied from a static magnetic field power supply (not shown) to the superconducting coil. After that, when the static magnetic field magnet 10 transitions to persistent current mode, the static magnetic field power supply is disconnected. Once the static magnetic field magnet 10 transitions to persistent current mode, it continues to generate a large static magnetic field for a long time, for example, for more than a year. The static magnetic field magnet 10 may also be made of a permanent magnet.

[0014] The gradient coil 11 has a generally cylindrical shape and is fixed inside the static magnetic field magnet 10 in the radial direction of the cylindrical shape. The gradient coil 11 generates a gradient magnetic field by receiving current from the gradient power supply 31. The gradient coil 11 is formed by combining three coils corresponding to the mutually orthogonal X, Y, and Z axes, and these three coils receive current individually from the gradient power supply 31 to generate a gradient magnetic field in which the magnetic field strength changes along the X, Y, and Z axes.

[0015] As shown in Figure 1, the left-right direction of the subject P positioned on the bed 500 is defined as the X-axis, the front-to-back direction (body thickness direction) as the Y-axis, and the head-to-foot direction as the Z-axis. The X, Y, and Z axes are orthogonal to each other.

[0016] The WB coil 12 is an RF coil that has a roughly cylindrical shape and is fixed inside the gradient magnetic field coil 11 so as to surround the subject. The WB coil 12 transmits RF pulses transmitted from the transmitter 32 to the subject and receives the MR signal emitted from the subject P by the excitation of hydrogen nuclei.

[0017] The MRI apparatus 1 may have a local coil 20 in addition to the WB coil 12. The local coil 20 is an RF coil positioned close to the subject and receives the MR signal emitted from the subject at a position close to the subject. The local coil 20 may also transmit RF pulses transmitted from the transmitter 32 to the subject. The local coil 20 comes in various types depending on the imaging area of ​​the subject, such as for the head, chest (e.g., Figure 1), spine, lower limbs, and whole body.

[0018] The control cabinet 300 comprises a gradient magnetic field power supply 31, a transmitter 32, a receiver 33, and a sequence controller 34. Under the control of the sequence controller 34, the gradient magnetic field power supply 31 supplies current to the gradient magnetic field coil 11, generating gradient magnetic fields along the X, Y, and Z axes.

[0019] Based on instructions from the sequence controller 34, the transmitter 32 generates an RF pulse train in the Larmor frequency band as an RF transmission wave and outputs it to the RF coil to excite the subject P.

[0020] The receiver 33 converts the MR signal received by the RF coil from analog to digital (AD) and outputs it to the sequence controller 34. The digitized MR signal is called raw data.

[0021] The sequence controller 34 performs a scan of the subject P by driving the gradient power supply 31, the transmitter 32, and the receiver 33, respectively, under the control of the console 400. The sequence controller 34 receives raw data via the receiver 33 and transmits that raw data to the console 400.

[0022] The sequence controller 34 includes a processing circuit (not shown). The processing circuit of the sequence controller 34 consists of, for example, a processor that executes a predetermined program, or hardware such as an FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit).

[0023] The bed 500 comprises a bed body 50 and a top plate 51. The bed body 50 allows the top plate 51 to move vertically and horizontally, moving the subject P, who is placed on the top plate 51, to a predetermined height and then into the bore.

[0024] The console 400 includes a processing circuit 40, a storage circuit 41, a display 42, an input interface 43, and a network interface 44.

[0025] The memory circuit 41 is a storage medium that includes ROM (Read Only Memory), RAM (Random Access Memory), and external storage devices such as HDD (Hard Disk Drive) and optical disc drives. The memory circuit 41 stores various information and data, and stores various programs that are executed by the processor equipped in the processing circuit 40.

[0026] The display 42 is a display device such as a liquid crystal display panel, a plasma display panel, or an organic EL panel. The display 42 may also be a GUI (Graphical User Interface) that displays various information and data under the control of the processing circuit 40 and also functions as an input device.

[0027] The input interface 43 includes various input devices for users, such as medical technologists, to input various types of information and data, and an input circuit that processes signals received from the input devices. Examples of input devices include a mouse, keyboard, trackball, and touch panel. When an input device is operated, the input circuit generates an instruction signal corresponding to that operation and outputs it to the processing circuit 40.

[0028] The network interface 44 communicates with various devices connected to the network via wired or wireless means, and exchanges various types of information and data.

[0029] The processing circuit 40 is, for example, a circuit equipped with a CPU or a dedicated or general-purpose processor. The processor executes various programs that are pre-stored in the memory circuit 41 or directly incorporated into the processing circuit 40.

[0030] These components allow the console 400 to control the entire MRI apparatus 1. Specifically, the processing circuit 40 receives instructions regarding imaging conditions through user input interface 43. The processing circuit 40 then causes the sequence controller 34 to perform a scan based on the input imaging conditions. In this specification, the configuration of the pallet unit 100, control cabinet 300, and patient bed 500 in the MRI apparatus 1, i.e., the configuration for performing a scan of the subject, is referred to as the "scanner." The processing circuit 40 also reconstructs the MR image based on the raw data transmitted from the sequence controller 34. The reconstructed MR image is displayed on the display 42 and stored in the memory circuit 41.

[0031] (Free water and bound water) Referring to Figure 2, we will now explain interstitial water, an example of free water, and loosely bound water (LBW), tightly bound water (TBW), and structural water, which are examples of non-free water, in cortical bone. While we will use cortical bone as an example below, the body sites to be observed are not limited to cortical bone; any site containing both free and non-free water is acceptable.

[0032] Cortical bone contains a central canal through which blood vessels and nerves pass, and a lacunar-tubular network that facilitates substance exchange and information transmission between bone cells. Interstitial water is present within the central canal and the lacunar-tubular network. Cortical bone also contains helical collagen fibers. Collagen fibers are mineralized by minerals (e.g., calcium salts) to form mineralized fibers. Weakly bound water exists at the interface between collagen fibers and mineral crystals, loosely bound to collagen or minerals. Strongly bound water binds to the triple helix structure of collagen, forming water bridges, and exists within the triple helix structure as fissure water and as water contributing to the interfacial monolayer. Structural water is incorporated around the carbonate apatite structural lattice and exists as water that forms hydrogen bonds between ions within the apatite crystal.

[0033] Figure 3 shows an example of the T2 spectrum obtained by proton NMR (Nuclear Magnetic Resonance) of cortical bone. The solid and dashed lines in Figure 3 indicate that the shape of the T2 spectrum differs depending on the subject. As shown in Figure 3, the T2 value of collagen is approximately 60 μs, the T2 value of bound water in collagen is approximately 400 μs, and the T2 values ​​of interstitial water and lipids are approximately 1 ms to 1 s.

[0034] Furthermore, the T2 values ​​for the periosteum are approximately 5-11 ms, the deep layer of articular cartilage is approximately 5-10 ms, the meniscus is approximately 5-8 ms, the ligaments are approximately 4-10 ms, the Achilles tendon is approximately 0.2-7 ms, the cortical bone is approximately 0.4-0.5 ms, the dentin is approximately 0.15 ms, the myelin T2 value, a major component of the central and peripheral nervous systems, is approximately 50-1000 μs, the enamel is approximately 70 μs, the protons in proteins are approximately 10 μs, and the protons in solids such as calcium hydroxyapatite are less than 1 μs. Thus, the dynamic range of the observed cortical bone is a predetermined range that includes protons in solids and protons in liquids, i.e., a predetermined range that includes both free water and non-free water, and is generally referred to as wide.

[0035] Figure 4 is an explanatory diagram of regions dominated by bound water and regions dominated by free water. Although there is a complementary relationship between the in vivo tissues of the observed subject, conventionally, regions dominated by bound water and regions dominated by free water have been observed separately, and the complementary relationship between the in vivo tissues of the subject has not been systematically observed. Specifically, methods for systematically evaluating information on free water and information on bound water, and for quantitative evaluation when multiple components of bound water are present, have not been established.

[0036] Therefore, the MRI apparatus 1 according to this embodiment makes it possible to simultaneously collect and systematically evaluate information on free water and bound water in the living body. Firstly, the MRI apparatus 1 collects MR data and T2 data, which reflect the binding force in the part of the living body being observed, which were conventionally collected separately. *Secondly, MRI device 1 performs analysis to systematically evaluate information on free water and information on bound water, and to quantitatively evaluate the presence of multiple components of bound water.

[0037] (First Embodiment) Figure 5 is a block diagram showing the processing circuit 40 of the MRI apparatus 1 according to the first embodiment. The processing circuit 40 of the first embodiment includes a setting function F1, an acquisition function F2, a classification function F3, an OSR calculation function F4, a first map generation function F5, an OSR component calculation function F6, a second map generation function F7, a component ratio gradient calculation function F8, a third map generation function F9, an elastic modulus estimation function F10, a fourth map generation function F11, and T2 * The analysis function F12 and the fifth map generation function F13 are executed. An example of the operation of the MRI apparatus 1 according to the first embodiment will be described with reference to the flowchart in Figure 6.

[0038] In step ST11, the setting function F1 sets the pulse sequence for acquiring MR data.

[0039] Figure 7 is a sequence chart showing an example of the timing between RF pulse application and data acquisition in a pulse sequence according to the embodiment. The pulse sequence consists of multiple sub-pulse sequences. Figure 7 shows a pulse sequence consisting of three sub-pulse sequences, namely the first, second, and third, but the number of sub-pulse sequences is not limited. For example, the pulse sequence may consist of N sub-pulse sequences, from the first sub-pulse sequence to the Nth sub-pulse sequence (where N is a natural number greater than or equal to 2).

[0040] Multiple subpulse sequences collect MR data corresponding to each of the multiple echo signals generated after the application of MT pulses and RF pulses to the subject, using a UTE sequence. In other words, the UTE sequence in this embodiment collects data with multiple echoes. Figure 7 shows a UTE sequence with 4 echoes, but the number of multi-echoes is not particularly limited; any number is acceptable, and it may be 4 or more.

[0041] A UTE sequence, for example, is a gradient echo (GRE) sequence that allows for a very short first echo time (TE) from the application of an RF excitation pulse to the acquisition of MR data corresponding to the first echo signal, enabling clearer visualization of hard tissues such as bone, tendons, and lung tissue, as well as tissues with low water content.

[0042] The MR data collected at the first of multiple TEs, i.e., the echo time corresponding to the first echo signal after the application of the RF pulse, is used in the analysis of both the bound water-dominant region and the free water-dominant region.

[0043] MR data collected during the second and subsequent TEs (Time Effects) among multiple TEs, i.e., the echo time corresponding to the second and subsequent echo signals after the application of the RF pulse, is used, along with the MR data collected during the first TE, in the analysis of regions dominated by free water. In other words, within the TR of the subpulse sequence, the RF excitation pulse for data acquisition of the first echo, the data acquisition phase for the first echo, and the spoiler are used in common.

[0044] Figure 8 is an explanatory diagram illustrating the improvement in data acquisition rate by an example pulse sequence according to the embodiment. As shown in Figure 8, Section 1 covers the RF excitation pulse for data acquisition of the first echo using an MT pulse, up to the completion of MR data acquisition. Section 2 includes the application of spoiler gradient pulses.

[0045] In Section 2, after applying spoiler gradient magnetic field pulses, a recovery time of at least a predetermined amount of time is provided for the MR signal to return the atomic nuclei to their original energy levels for each subpulse sequence. Then, the next repetition time TR begins. This ensures the image quality and desired image contrast of the MR image. Note that this recovery time may differ for each subpulse sequence.

[0046] Thus, Sections 1 and 2 are used in common for both the first echo data acquisition phase using MT pulses for analyzing regions dominated by bound water, and the multi-echo data acquisition phase for analyzing regions dominated by free water. Therefore, a significant improvement in data acquisition efficiency can be expected compared to acquiring data in separate scans for the first echo data acquisition phase using MT pulses and the multi-echo data acquisition phase.

[0047] For example, if each scan is performed individually, and the data acquisition scan using MT pulses takes about 5 minutes, and the multi-echo data acquisition scan takes about 10 minutes, the total scan time would be about 15 minutes. In contrast, according to the pulse sequence of the embodiment, the scan time required to acquire the same amount of data is about 10 minutes, which is about 3 / 4 of the scan time when each scan is performed individually.

[0048] Also, the conventional T2 * For UTE sequencing used for analysis, for example, very short T2 sequences of less than 1 ms are used. * To obtain a sufficient amount of data to represent the attenuation of the component MR signal, it is necessary to acquire data while varying the first TE of multiple TEs in each of multiple subpulse sequences.

[0049] On the other hand, in the pulse sequence of the embodiment, for example, a very short T2 of less than 1 ms. *The component responsible for this is the data acquisition phase of the first echo using the MT pulse, that is, the OS (Off-resonance Saturation) data acquisition phase using the MT pulse.

[0050] For example, if you set the TR to 20ms and the number of multi-echoes to 5, and acquire data for 20 echoes, then the conventional T2 * For the UTE sequence used for analysis, the first TE of multiple TEs is set to vary for each of the multiple sub-pulse sequences, as shown below. For example, the multiple TEs for the first sub-pulse sequence may be set to 0.18, 2.61, 5.06, 7.46, 9.89; the multiple TEs for the second sub-pulse sequence to 0.40, 2.83, 5.26, 7.68, 10.11; the multiple TEs for the third sub-pulse sequence to 0.80, 3.23, 5.66, 8.08, 10.51; and the multiple TEs for the fourth sub-pulse sequence to 1.6, 4.03, 6.46, 8.88, 11.31.

[0051] On the other hand, in the pulse sequence of the embodiment, the number of sub-pulse sequences can be reduced by setting the first TE of multiple TEs in each of the multiple sub-pulse sequences to a predetermined TE, as shown below. For example, when acquiring data for 20 echoes with a TR of 20ms and a multi-echo count of 5 as described above, the multiple TEs of the first sub-pulse sequence may be set to 0.18, 2.61, 5.06, 7.46, 9.89, the multiple TEs of the second sub-pulse sequence to 0.18, 2.83, 5.26, 7.68, 10.11, and the multiple TEs of the third sub-pulse sequence to 0.18, 4.03, 6.46, 8.88, 11.31. In this case, the number of sub-pulse sequences can be reduced to 3 / 4 of the conventional number, and the scan time can be shortened by at least 3 / 4. Note that the multiple TEs are not limited to the examples and can be set as appropriate.

[0052] Furthermore, the number of multi-echoes may differ for each of the multiple sub-pulse sequences. Additionally, TR may be set as appropriate. T2 *By enabling the collection of analysis data, reducing the number of multi-echoes, or appropriately setting the TR, the scan time can be further shortened.

[0053] In this way, the pulse sequence according to the embodiment performs multi-echo data collection together with OS data collection for each repetition time TR, so that the data for OS analysis and T2 * analysis data are collected in a single sub-pulse sequence. As described above, compared with a sequence in which the data for OS analysis and the data for T2 * analysis are collected separately, the efficiency of data collection is improved, and components derived from bound water, which are difficult to measure in the conventional UTE sequence for T2 * analysis, can be obtained with higher accuracy. That is, T2 ―5 analysis data collection in a wide dynamic range of 10 * to 1 s can be realized.

[0054] For each TR of the sub-pulse sequence, imaging conditions such as the flip angle θ of the MT pulse, the off-resonance frequency, TE, and the echo spacing can be set. In addition, as imaging conditions, the number of multi-echoes, TR, the number of additions, the matrix size used for resolution setting, the slice thickness, the FOV (Field Of View), etc. may be set. The imaging conditions of the pulse sequence are set, for example, by the user's operation via the input interface 43 or by reading out the imaging conditions previously stored in the memory circuit.

[0055] Figures 9(A) and 9(B) show examples of setting imaging conditions for pulse sequences according to the embodiment. As shown in Figures 9(A) and 9(B), the number of pulses of the MT pulse, the flip angle θ, the off-resonance frequency, the first TE of the multiple TEs, the echo space, and the number of multi-echoes may be set for each TR of the sub-pulse sequence and displayed on the GUI. The number of sub-pulse sequences may also be set. For example, Figure 9(A) shows that the number of sub-pulse sequences is 3, and Figure 9(B) shows that the number of sub-pulse sequences is 4. Note that the imaging conditions that can be set on the GUI are not limited to these, and may include some of the imaging conditions shown in Figures 9(A) and 9(B), or other imaging conditions not shown in Figures 9(A) and 9(B).

[0056] Figure 9(A) shows a typical configuration example, while Figure 9(B) shows a configuration example for obtaining information from multiple biological tissues or for higher accuracy. For example, by increasing the number of sub-pulse sequences and increasing the number of settings that vary the off-resonance frequency and echo space of the MT pulse, it is possible to obtain information from multiple biological tissues or for higher accuracy.

[0057] In the pulse sequence according to this embodiment, the off-resonance frequencies of the MT pulses differ from each other among a plurality of sub-pulse sequences. Figure 10 is an explanatory diagram of magnetization transfer (MT) of pore water, weakly bound water, and strongly bound water. By utilizing the differences in the MT effect caused by changing the off-resonance frequency of the MT pulse, the contrast between strongly bound water, weakly bound water, and pore water can be enhanced. For example, the T2 of weakly bound water * The value is approximately 0.2-0.4 ms, T2 of strongly bound water. * The value is 10μ or less, and the T2 of the pore water. * The value is approximately 1-5ms.

[0058] The data collected by the first of multiple TEs is used as OS analysis data, so it is preferable that it is collected in such a way that it is only affected by the MT pulse between subpulse sequences. In other words, it is preferable that the first of multiple TEs is the same across the multiple subpulse sequences. However, the first of multiple TEs may be different across the multiple subpulse sequences. For example, if the other subpulse sequences of the first subpulse sequence do not collect OS analysis data, * A subpulse sequence that collects only data for analysis may also be used.

[0059] Furthermore, the data collected by the first TE is T2 * This data is used for analysis. The data collected by the first TE is affected by the MT pulse. However, if there is a subpulse sequence in which the MT pulse is applied at an off-resonance frequency sufficiently far from the on-resonance frequency that the MT pulse can be assumed to be ineffective, then a correction can be made to reduce the effect of the MT pulse.

[0060] Furthermore, T2 * For analysis, data collected by the second and subsequent TEs among multiple TEs are also used. To minimize the effect of MT pulses returning to the original magnetization over time, it is preferable to set the pulse sequence using setting function F1 such that the smaller the off-resonance frequency, the larger the echo space, which is the acquisition interval for each of the multiple echo signals. For example, in Figure 7, the relative magnitudes of the off-resonance frequencies of the first, second, and third sub-pulse sequences are Δf1>Δf2>Δf3, and the relative magnitudes of the echo spaces of the first, second, and third sub-pulse sequences are δ1<δ2<δ3.

[0061] Furthermore, it is preferable that the second and subsequent TEs among the multiple TEs differ between the multiple subpulse sequences. If the second and subsequent TEs differ between the multiple subpulse sequences, different T2s will be observed for each in vivo tissue. * Because the number of TEs used in the analysis to calculate the value increases, T2* The accuracy of the analysis will improve.

[0062] Thus, the pulse sequence according to the embodiment provides OS analysis data and T2 data for a given voxel in k space. * Analysis data can be obtained. Furthermore, by repeating the same pulse sequence with varying encoding amounts, OS analysis data and T2 data for other voxels in k-space can be obtained. * Data for analysis can be obtained. Data in k-space can be collected by changing the encoding amount so that it is filled radially in 2D and in a kush ball shape in 3D.

[0063] Returning to Figure 6, in step ST12 of Figure 6, the scanner performs a scan. That is, the scanner applies a set pulse sequence to the subject and collects MR data corresponding to each of the multiple echo signals for each of the multiple sub-pulse sequences. The scanner collects MR data from the subject's in vivo tissues that contain free water and bound water as components. In vivo tissues include, for example, deep radial cartilage, calcified cartilage, meniscus, ligaments, tendons, cortical bone, lungs, and tissues of the cranial nervous system, central nervous system, and peripheral nervous system.

[0064] In step ST13, the acquisition function F2 acquires the MR data collected by the pulse sequence.

[0065] In step ST14, the classification function F3 classifies the MR data into first MR data corresponding to different off-resonance frequencies and second MR data corresponding to different TEs. The first MR data consists of multiple data points collected at the first of multiple TEs in the UTE sequence for each TR in the subpulse sequence. The second MR data consists of multiple data points collected at multiple TEs in the UTE sequence, i.e., multi-echoes in the UTE sequence, for each TR in the subpulse sequence.

[0066] In step ST20, all or some of the various maps may be generated based on the first MR data. That is, the processing circuit 40 only needs to have the functions of the maps to be generated from among the first map generation function F5, second map generation function F7, third map generation function F9, fourth map generation function F11, and fifth map generation function F13. Figure 11 is a flowchart that specifically shows an example of the operation of step ST20 of the MRI apparatus 1 according to the first embodiment.

[0067] In step ST201, the OSR calculation function F4 acquires the first MR data.

[0068] In step ST202, the OSR calculation function F4 calculates the OSR (Off-resonance Saturation Ratio) for each different off-resonance frequency based on the first MR data. The OSR(θ, Δf) for an MT pulse with a flip angle θ and off-resonance frequency Δf is given by Equation 1. OSR(θ, Δf)=(S0-Ssat(θ, Δf)) / S0...Equation 1

[0069] Here, θ is the flip angle of the MT pulse, i.e., the intensity of the MT pulse. Δf is the frequency shift from the on-resonance resonance frequency, i.e., the off-resonance frequency. S0 is the average signal intensity obtained when no MT pulse is applied, i.e., when the flip angle θ=0 and the off-resonance frequency Δf=0. Ssat(θ, Δf) is the average signal intensity obtained when an MT pulse with a flip angle θ and off-resonance frequency Δf is applied. Ssat(θ, Δf) is saturated by the saturation effect, so its signal intensity is lower than S0.

[0070] Figures 12(A) to 12(C) are graphs showing examples of off-resonance frequency vs. OSR characteristics for cortical bone and rubber for different MT pulse intensities. The MT pulse intensity is proportional to the flip angle of the MT pulse, decreasing in the order of Figure 12(A), Figure 12(B), and Figure 12(C). The higher the MT pulse intensity, the higher the OSR, and the higher the off-resonance frequency, the lower the OSR. This trend is more pronounced in cortical bone than in rubber. Because rubber lacks the proton pools, such as bound water from minerals and protons from collagen, that contribute to the OSR of cortical bone, the OSR of rubber is consistently lower than that of cortical bone.

[0071] In step ST203, the first map generation function F5 generates an OSR map that represents the OSR for each different off-resonance frequency using pixel values.

[0072] Figure 13(A) is a graph showing the relationship between off-resonance frequency and OSR in a specific voxel. Figure 13(B) is a map image showing the OSR as pixel values ​​for each off-resonance frequency for phantoms A1, A2, and A3 with different elastic moduli.

[0073] In step ST204, the OSR component calculation function F6 calculates the dominant off-resonance frequency for each component of the in vivo tissue based on the OSR for each different off-resonance frequency. Here, the components of the in vivo tissue include components related to weakly bound water and components related to strongly bound water.

[0074] Specifically, the OSR component calculation function F6 separates and calculates the dominant off-resonance frequency for each component of the in vivo tissue by fitting the binding model of all components of the in vivo tissue with an exponential function model.

[0075] Figure 14(A) is a graph showing an example of fitting the off-resonance frequency vs. OSR characteristics for cortical bone using a single-component exponential model and a two-component exponential model. For cortical bone, the fitting error is larger when fitting as a single component compared to when fitting as a two-component model. Figure 14(B) is a graph showing an example of fitting the OSR for rubber. For rubber, the fitting accuracy is high even when fitting as a single component. The following explanation will use the case of a two-component binding model of weakly bound water and strongly bound water as an example, but the components of in vivo tissues may be three or more components, and even with three or more components, the off-resonance frequencies for each component of the in vivo tissue can be separated and calculated.

[0076] The exponential decay S(Δf) of the OSR with respect to the off-resonance frequency Δf is separated into α·exp(-Δf / β), which is the OSR in weakly bound water, γ·exp(-Δf / δ), which is the OSR in strongly bound water, and a noise component ε, as shown in Equation 2. Then, by curve fitting using a two-component exponential function model, the parameters α, β, γ, and δ in Equation 2 can be calculated. In this case, the dominant off-resonance frequency of the weakly bound water component is calculated as β, and the dominant off-resonance frequency of the strongly bound water component is calculated as δ. S(Δf)=α·exp(-Δf / β)+γ·exp(-Δf / δ)+ε...Equation 2

[0077] In step ST205, the second map generation function F7 generates an OSR component map in which the dominant off-resonance frequencies for each component of the in vivo tissue are represented by pixel values.

[0078] Figure 14(C) is a map image separated into two components for the same phantom as in Figure 13(B): an OSR-LBW map representing the off-resonance frequencies of the weakly bound water component as pixel values, and an OSR-TBW map representing the off-resonance frequencies of the strongly bound water component as pixel values. In other words, the OSR at the off-resonance frequency close to the on-resonance frequency, i.e., α·exp(-Δf / β), for weakly bound water, and the OSR at the off-resonance frequency far from the on-resonance frequency, i.e., γ·exp(-Δf / δ), for strongly bound water, i.e., for off-resonance frequencies far from the on-resonance frequency, are represented separately. Thus, in step ST205, a map image is generated for each component of the in vivo tissue.

[0079] In step ST206, the component ratio gradient calculation function F8 calculates the component ratio gradient based on the dominant off-resonance frequency for each component of the in vivo tissue.

[0080] Figure 15 shows a graph illustrating the relationship between off-resonance frequency and OSR in specific voxels for two types of phantoms, A1 and A2, and cortical bone. The two types of phantoms, A1 and A2, have different elastic moduli, with the elastic modulus of phantom A1 being nearly twice as high as that of phantom A2. In other words, phantom A1 is harder than phantom A2.

[0081] For each of the two types of phantoms A1 and A2 and the cortical bone in Figure 15, the OSR in weak bound water is separated into α·exp(-Δf / β) and the OSR in strong bound water is separated into γ·exp(-Δf / δ) according to step ST204. Then, the component ratios are calculated by determining α and γ such that the sum of the components related to weak bound water and the components related to strong bound water equals 100%. That is, if the component ratio related to weak bound water corresponding to the off-resonance frequency β is calculated as α(%) by curve fitting, then the component ratio γ related to strong bound water corresponding to the off-resonance frequency δ is calculated as γ(%) = 100 - α(%).

[0082] Figure 16(A) is a graph showing the relationship between off-resonance frequency and component ratio in a specific voxel for the two types of phantoms and cortical bone shown in Figure 15. Figure 16(B) is a graph showing the component ratio gradient calculated based on Figure 16(A). The component ratio gradient is the slope of the component ratio with respect to the off-resonance frequency. The component ratio gradient becomes gentler as the difference between the off-resonance frequency β, which is dominant for weakly bound water components, and the off-resonance frequency δ, which is dominant for strongly bound water components, increases.

[0083] Here, the component ratio gradient can be considered to represent the relative elastic moduli of the two types of phantoms A1 and A2 and the cortical bone.

[0084] Therefore, in step ST207, the third map generation function F9 generates a component ratio gradient map that represents the component ratio gradient for each component of the in vivo tissue using pixel values.

[0085] Then, in step ST208, the elastic modulus estimation function F10 estimates the elastic modulus of each component of the in vivo tissue based on the component ratio gradient. In the first embodiment, the elastic modulus estimation function F10 estimates the elastic modulus of each component of the in vivo tissue based on the first MR data.

[0086] Since elastic modulus is a primary physical indicator of hardness, estimating the elastic modulus allows for the estimation of bone density based on a database of relationships between bone density and elastic modulus. Furthermore, if a database exists showing the correlation between bone diseases and elastic modulus, it becomes possible to estimate diseases based on information about elastic modulus, i.e., hardness, in lesions.

[0087] In step ST209, the fourth map generation function F11 generates an elastic modulus map in which the elastic modulus of each component of the in vivo tissue is represented by a pixel value.

[0088] Returning to Figure 6, in step ST30 of Figure 6, a map is generated based on the second MR data. Figure 17 is a flowchart specifically showing an example of the operation of step ST30 of the MRI apparatus 1 according to the first embodiment.

[0089] In step ST301, T2 * Analysis function F12 acquires the second MR data.

[0090] In step ST302, T2 * The analysis function F12 uses the second MR data to determine T2 * The value is calculated. Figure 18 shows T2 by UTE sequencing. * This is a graph of the decay curve. In step ST11, by making the second and subsequent TEs different among multiple subpulse sequences, T2 * Because the number of TEs used in the analysis to calculate the value increases, T2 * The accuracy of the analysis will improve.

[0091] In step ST303, the fifth map generation function F13 is T2 * T2 values ​​are expressed as pixel values. * Generate a map.

[0092] According to the MRI apparatus 1 of the first embodiment, as described above, 10 ―5 T2 with a wide dynamic range of ~1s * This enables data collection for analysis while also improving data collection efficiency. Furthermore, it allows for the collection of OS analysis data and T2 data from multiple biological tissues. * Because it is possible to collect data for analysis simultaneously, data collection efficiency is improved, and it becomes possible to evaluate special interactions in which mechanical, biochemical, and metabolic interactions continuously occur in multiple biological tissues. Regarding the tissue properties of the measurement target, it is possible to systematically evaluate information on free water and bound water within a wide dynamic range, and to quantitatively evaluate the case when there is bound water of multiple components.

[0093] For example, in osteoporosis, it is known that the clinical evaluation of both bone and tendon is important. Here, tendons and bone constitute a special interaction in which mechanical, biochemical, and metabolic interactions occur continuously, and bone loss in osteoporosis and its precursor, osteopenia, is associated with a decline in tendon quality. In contrast, in the first embodiment, there is a very short T2 of deep radial cartilage, calcified cartilage, meniscus, ligamentous tendons, cortical bone, etc. * In tissues with specific values, a systematic evaluation including both high-molecular-weight and water-based components can be performed. Furthermore, this systematic evaluation can contribute to the early detection, diagnostic support, and monitoring of treatment effectiveness for conditions such as osteoarthritis, osteoporosis, and osteogenesis imperfecta.

[0094] (Information processing device) Figure 19 is a block diagram showing an example of the overall configuration of the information processing device 600 according to the first embodiment. The information processing device 600 comprises a processing circuit 60, a storage circuit 41, a display 42, an input interface 43, and a network interface 44. The processing circuit 60 of the information processing device 600 according to the first embodiment differs from the processing circuit 40 of the MRI device 1 according to the first embodiment in that it does not have a setting function F1. The processing circuit 60 of the information processing device 600 can perform the same processing as the processing circuit 40 of the MRI device 1, from step ST13 onwards in the flowchart of Figure 6, independently of the scanner of the MRI device 1. Since other various functions are substantially the same, redundant explanations are omitted.

[0095] In other words, in step ST13, the information processing device 600's acquisition function F2 acquires MR data collected by a pulse sequence that includes multiple sub-pulse sequences as a UTE sequence, which collect MR data corresponding to each of the multiple echo signals generated after the application of MT pulses and RF pulses to the subject. Here, the off-resonance frequencies of the MT pulses are applied to the subject at different frequencies between the multiple sub-pulse sequences. The MR data is acquired, for example, via the memory circuit 41, the input interface 43, and the network interface 44.

[0096] (Second Embodiment) Figure 20 is a block diagram showing the processing circuit 40 of the MRI apparatus 1 according to the second embodiment. In the second embodiment, the processing circuit 40 is T2 * This differs from the first embodiment in that it further performs the estimation function F14, the sixth map generation function F15, the synthesis function F16, and the seventh map generation function F17. An example of the operation of the MRI apparatus 1 according to the second embodiment will be described with reference to the flowchart in Figure 21.

[0097] In step ST41, T2 * The estimated function F14 is based on the first MR data and the first T2 * Estimate the value. First T2 * The values ​​are physical indicators such as OSC values ​​and T2 * It can be estimated using conversion formulas based on correlations with values ​​or known methods.

[0098] After step ST41, the sixth map generation function F15 is performed on the first T2 * Based on the value, T2 * You may generate a map (the 6th map).

[0099] In step ST42, the synthesis function F16 is performed on the first T2 * Value and second T2 * The values ​​are combined. Here, the second T2 * The value is T2 in step ST302 of Figure 17. * Analysis function F12 calculated T2 based on the second MR data. * This is the value. The first T2 * Value and second T2 * By combining values, for example, 10 ―5 T2 with a wide dynamic range of ~1s * It becomes possible to calculate the value.

[0100] In step ST43, the seventh map generation function F17 generates the first T2 * Value and second T2 * Based on the values, the synthesized T2 * Composite T2 representing values ​​as pixel values* Generate the map (Map 7).

[0101] (Modified version of the second embodiment) The elastic modulus estimation function F10 only needs to estimate the elastic modulus for each component of the in vivo tissue based on at least one of the first MR data and the second MR data. In a modified version of the second embodiment, the elastic modulus estimation function F10 estimates the elastic modulus for each component of the in vivo tissue based on both the first MR data and the second MR data.

[0102] Figure 22 is a block diagram showing the processing circuit 40 of the MRI apparatus 1 according to a modified example of the second embodiment. The modified example of the second embodiment differs from the second embodiment in that the processing circuit 40 executes the elastic modulus estimation function F10 after the processing of the synthesis function F16, rather than after the processing of the component ratio gradient calculation function F8, and further executes the eighth map generation function F18. An example of the operation of the MRI apparatus 1 according to the modified example of the second embodiment will be explained with reference to the flowchart in Figure 23. In step ST42, the synthesis function F16 calculates the first T2 estimated based on the first MR data. * The second T2 was calculated based on the values ​​and the second MR data. * After the process of combining the values ​​is performed, the process proceeds to step ST51.

[0103] In step ST51, the elastic modulus estimation function F10 determines the first T2 * Value and second T2 * Based on the values, the elastic modulus of each component of the in vivo tissue is estimated with a wide dynamic range. Here, the first T2 * Value and second T2 * The value refers to the T2 of both free water and bound water components. * The values ​​are included. In a modified version of the second embodiment, the elastic modulus estimation function F10 estimates the elastic modulus for each component of the in vivo tissue based on both the first MR data and the second MR data.

[0104] Furthermore, the elastic modulus estimation function F10 can also estimate the elastic modulus of each component of in vivo tissue based on the second MR data. However, using only the second MR data results in a component analysis mainly of free water, and therefore does not take into account the influence of components bound to biomolecules. For this reason, it is preferable to estimate the elastic modulus of each component of in vivo tissue based on both the first and second MR data.

[0105] In step ST52, the eighth map generation function F18 generates an elastic modulus map in which the elastic modulus of each component of the in vivo tissue is represented by a pixel value.

[0106] The processing circuit 60 of the information processing device 600 according to the second embodiment and its modified form differs from the processing circuit 40 of the MRI device 1 according to the second embodiment and its modified form in that it does not have a setting function F1. Since other functions are substantially the same, redundant explanations are omitted. Furthermore, the second embodiment and its modified form have the same effects as the first embodiment.

[0107] According to the magnetic resonance imaging apparatus and information processing apparatus of at least one embodiment described above, it is possible to simultaneously collect and systematically evaluate information on free water and bound water in a living organism.

[0108] In the above embodiment, the term "processor" refers to circuits such as a dedicated or general-purpose CPU (Central Processing Unit), GPU (Graphics Processing Unit), or Application Specific Integrated Circuit (ASIC), or a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)).

[0109] If the processor is, for example, a CPU, it implements various functions by reading and executing programs stored in memory circuits. Alternatively, if the processor is, for example, an ASIC, instead of storing programs in memory circuits, functions equivalent to those programs are directly incorporated as logic circuits within the processor's circuitry. In this case, the processor implements various functions through hardware processing that reads and executes the programs incorporated within the circuitry. Furthermore, a processor can also implement various functions by combining software and hardware processing.

[0110] Furthermore, although the above embodiment shows an example where a single processor in the processing circuit implements each function, a processing circuit may be configured by combining multiple independent processors, with each processor implementing each function. Also, when multiple processors are provided, the memory circuit for storing programs may be provided individually for each processor, or a single memory circuit may store programs corresponding to the functions of all processors together.

[0111] Note that the setting function F1 in the description of the embodiment is an example of the setting unit in the claims. The acquisition function F2 in the description of the embodiment is an example of the setting unit in the claims. The classification function F3 in the description of the embodiment is an example of the classification unit in the claims. The OSR calculation function F4, OSR component calculation function F6, and component ratio gradient calculation function F8 in the description of the embodiment are examples of the OSR analysis unit in the claims. T2 in the description of the embodiment * The analysis function F12 is used in the description of the claims, T2 * This is an example of an analysis unit. The analysis unit consists of an OSR analysis unit and a T2 * This is an example of an analysis unit. T2 in the description of the embodiment * Estimated function F14 is T2 in the claims. *This is an example of an estimation unit. The synthesis function F16 and elastic modulus estimation function F10 in the description of the embodiment are examples of an elastic modulus estimation unit as described in the claims. The first to eighth map generation functions F5, F7, F9, F11, F13, F15, F17, and F18 in the description of the embodiment are examples of a map generation unit as described in the claims.

[0112] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. With respect to the above embodiments, the following additional notes are disclosed as aspects of the invention and selective features. (Note 1) An MRI apparatus according to one embodiment comprises a setting unit and a scanner. The setting unit sets a pulse sequence. The pulse sequence includes a plurality of sub-pulse sequences. The plurality of sub-pulse sequences collect MR data corresponding to each of the plurality of echo signals generated after the application of MT pulses and RF pulses to the subject, using a UTE sequence. The scanner collects MR data based on the pulse sequence set by the processing circuit. The MR data corresponding to the first echo signal after the application of the RF pulse from among the plurality of sub-pulse sequences, of which there are plurality of echo signals, is used for the analysis of both bound water and free water in the subject. (Note 2) Preferably, the off-resonance frequency of the MT pulse in the first sub-pulse sequence included in the plurality of sub-pulse sequences is different from the off-resonance frequency of the MT pulse in the second sub-pulse sequence included in the plurality of sub-pulse sequences. (Note 3) The setting unit may set the pulse sequence such that the TE corresponding to the first echo signal after the application of the RF pulse among the plurality of echo signals in the first subpulse sequence included in the plurality of subpulse sequences is the same as the TE corresponding to the first echo signal after the application of the RF pulse among the plurality of echo signals in the second subpulse sequence included in the plurality of subpulse sequences. (Note 4) The setting unit may set the pulse sequence such that the TE corresponding to the second and subsequent echo signals after the application of the RF pulse in the first subpulse sequence included in the plurality of subpulse sequences is different from the TE corresponding to the second and subsequent echo signals after the application of the RF pulse in the second subpulse sequence included in the plurality of subpulse sequences. (Note 5) The scanner may collect the MR data from the in vivo tissue of the subject, which contains free water and bound water as components. (Note 6) The aforementioned in vivo tissue may be any of the following: deep radial cartilage, calcified cartilage, meniscus, ligament, tendon, cortical bone, lung, cranial nervous system, central nervous system, or peripheral nervous system. (Note 7) The setting unit may set the pulse sequence such that the smaller the off-resonance frequency, the larger the echo space, which is the acquisition interval for each of the multiple echo signals. (Note 8) An information processing apparatus according to one embodiment includes an acquisition unit that acquires MR data collected by a pulse sequence including a plurality of sub-pulse sequences as a UTE sequence that collects MR data corresponding to each of a plurality of echo signals generated after the application of an MT pulse and an RF pulse to a subject, and an analysis unit that performs analysis of both bound water and free water in the subject based on the MR data collected at the first echo time after the application of the RF pulse from a plurality of echo signals of at least one of the plurality of sub-pulse sequences. (Note 9) The MRI apparatus and the information processing apparatus may further include a classification unit that classifies the MR data collected by the scanner into first MR data corresponding to different off-resonance frequencies and second MR data corresponding to different TEs. (Note 10) The MRI apparatus and the information processing apparatus may further include an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue based on at least one of the first MR data and the second MR data. (Note 11) The MRI apparatus and the information processing device determine the first T2 based on the first MR data. * Estimate the value of T2 * Estimation unit and second T2 based on the second MR data * T2 calculates the value * Analysis unit and the aforementioned T2 * Value and the second T2 * The system may further include an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue based on the values. (Note 12) The first T2 * Value and the second T2 * The value refers to the T2 of both free water and bound water components. * It may include a value. (Note 13) The system may further include a map generation unit that generates an elastic modulus map in which the elastic modulus is represented by a pixel value for each component of the in vivo tissue. (Note 14) The system may further include an OSR analysis unit that calculates OSR for each of the different off-resonance frequencies based on the first MR data, calculates the dominant off-resonance frequency for each component of the in vivo tissue based on the OSR for each of the different off-resonance frequencies, and calculates a component ratio gradient based on the dominant off-resonance frequency for each component of the in vivo tissue, and an elastic modulus estimation unit that estimates the elastic modulus for each component of the in vivo tissue based on the component ratio gradient. (Note 15) The components of the aforementioned in vivo tissue may include components related to weakly bound water and components related to strongly bound water. (Note 16) The OSR analysis unit may separate and calculate the dominant off-resonance frequency for each component of the in vivo tissue by fitting the binding model of all components of the in vivo tissue with an exponential function model. (Note 17) The OSR analysis unit may calculate the component ratio gradient, which is the slope of the component ratio with respect to the off-resonance frequency, based on the dominant off-resonance frequency for each component of the in vivo tissue. (Note 18) The MRI apparatus and the information processing apparatus may further include a map generation unit that generates at least one of the following maps: an OSR map representing the OSR for each different off-resonance frequency in pixel values; an OSR component map representing the dominant off-resonance frequency for each component of the in vivo tissue in pixel values; a component ratio gradient map representing the component ratio gradient for each component of the in vivo tissue in pixel values; and an elastic modulus map representing the elastic modulus for each component of the in vivo tissue in pixel values. (Note 19) The MRI apparatus and the information processing device determine T2 based on the second MR data. * T2 calculates the value * Analysis unit and the T2 * T2 values ​​are expressed as pixel values. * The system may further include a map generation unit that generates a map. (Note 20) The MRI apparatus and the information processing device determine the first T2 based on the first MR data.* T2 that estimates a value * An estimation unit that calculates a second T2 based on the second MR data * value * An analysis unit, the first T2 * value and the second T2 * Based on the value, T2 * A map generation unit that represents the value as a pixel value may further be provided * for the T2 map.

Description of Signs

[0113] [[ID=​​​​​

Claims

1. A setting unit sets a pulse sequence that includes multiple sub-pulse sequences that collect MR (Magnetic Resonance) data corresponding to each of the multiple echo signals generated after the application of MT (Magnetization Transfer) pulses and RF (radio frequency) pulses to a subject, using an UTE (Ultrashort Echo Time) sequence. The system includes a scanner that collects MR data based on the pulse sequence set by the setting unit, The MR data corresponding to the first echo signal after the application of the RF pulse, from among the multiple echo signals of at least one subpulse sequence among the multiple subpulse sequences, is used for the analysis of both bound water and free water in the subject. Magnetic resonance imaging device.

2. The off-resonance frequency of the MT pulse in the first sub-pulse sequence included in the plurality of sub-pulse sequences is different from the off-resonance frequency of the MT pulse in the second sub-pulse sequence included in the plurality of sub-pulse sequences. The magnetic resonance imaging apparatus according to claim 1.

3. The setting unit sets the pulse sequence such that the echo time (TE) corresponding to the first echo signal after the application of the RF pulse among the plurality of echo signals in the first subpulse sequence included in the plurality of subpulse sequences is the same as the TE corresponding to the first echo signal after the application of the RF pulse among the plurality of echo signals in the second subpulse sequence included in the plurality of subpulse sequences. The magnetic resonance imaging apparatus according to claim 1.

4. The setting unit sets the pulse sequence such that the TE corresponding to the second and subsequent echo signals after the application of the RF pulse in the first subpulse sequence included in the plurality of subpulse sequences is different from the TE corresponding to the second and subsequent echo signals after the application of the RF pulse in the second subpulse sequence included in the plurality of subpulse sequences. The magnetic resonance imaging apparatus according to claim 1.

5. A classification unit that classifies the MR data collected by the scanner into first MR data corresponding to different off-resonance frequencies and second MR data corresponding to different TEs. The system further includes an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue based on at least one of the first MR data and the second MR data. The magnetic resonance imaging apparatus according to claim 1.

6. A classification unit that classifies the MR data collected by the scanner into first MR data corresponding to different off-resonance frequencies and second MR data corresponding to different TEs. Based on the first MR data, the first T2 ＊ Estimate the value of T2 ＊ Estimation unit, Based on the second MR data, the second T2 ＊ T2 to write value ＊ Analysis unit, The first T2 ＊ Value and the second T2 ＊ The system further comprises an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue based on the values, The magnetic resonance imaging apparatus according to claim 1.

7. The first T2 ＊ value and the second T2 ＊ value are the T2 ＊ values including the components of both free water and bound water The magnetic resonance imaging apparatus according to claim 6.

8. The setting unit sets the pulse sequence such that the smaller the off-resonance frequency, the larger the echo space, which is the acquisition interval for each of the multiple echo signals. The magnetic resonance imaging apparatus according to claim 2.

9. The scanner collects the MR data from the in vivo tissue of the subject, which contains the free water and the bound water as components. The aforementioned in vivo tissue is one of the following: deep radial cartilage, calcified cartilage, meniscus, ligament, tendon, cortical bone, lung, cranial nervous system, central nervous system, or peripheral nervous system. The magnetic resonance imaging apparatus according to claim 1.

10. An acquisition unit that acquires the MR data collected by a pulse sequence including a plurality of sub-pulse sequences as a UTE sequence that collects MR data corresponding to each of the plurality of echo signals generated after the application of MT pulses and RF pulses to a subject, The system includes an analysis unit that performs analysis of both bound water and free water in the subject based on MR data collected at the first echo time after the application of the RF pulse from among a plurality of echo signals of at least one subpulse sequence among the plurality of subpulse sequences. Information processing device.

11. The off-resonance frequency of the MT pulse in the first sub-pulse sequence included in the plurality of sub-pulse sequences is different from the off-resonance frequency of the MT pulse in the second sub-pulse sequence included in the plurality of sub-pulse sequences. The information processing apparatus according to claim 10.

12. A classification unit that classifies the aforementioned MR data into first MR data corresponding to each of different off-resonance frequencies and second MR data corresponding to each of different TEs, The system further includes an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue based on at least one of the first MR data and the second MR data. The information processing apparatus according to claim 10.

13. A classification unit that classifies the aforementioned MR data into first MR data corresponding to each of different off-resonance frequencies and second MR data corresponding to each of different TEs, Based on the first MR data, the first T2 ＊ Estimate the value of T2 ＊ Estimation unit, Based on the second MR data, the second T2 ＊ T2 to write value ＊ Analysis unit, The first T2 ＊ Value and the second T2 ＊ The system further comprises an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue of the subject based on the values, The information processing apparatus according to claim 10.

14. The system further comprises a map generation unit that generates an elastic modulus map in which the elastic modulus is represented by a pixel value for each component of the in vivo tissue. The information processing apparatus according to claim 12 or claim 13.

15. A classification unit that classifies the aforementioned MR data into first MR data corresponding to each of different off-resonance frequencies and second MR data corresponding to each of different TEs, An OSR analysis unit calculates the OSR (Off-resonance Saturation Ratio) for each of the different off-resonance frequencies based on the first MR data, calculates the dominant off-resonance frequency for each component of the in vivo tissue based on the OSR for each of the different off-resonance frequencies, and calculates the component ratio gradient based on the dominant off-resonance frequency for each component of the in vivo tissue. The system further includes an elastic modulus estimation unit that estimates the elastic modulus of each component of the in vivo tissue based on the aforementioned component ratio gradient. The information processing apparatus according to claim 10.

16. The components of the aforementioned in vivo tissue include components relating to weakly bound water and components relating to strongly bound water. The information processing apparatus according to claim 15.

17. The OSR analysis unit separates and calculates the dominant off-resonance frequency for each component of the in vivo tissue by fitting the binding model of all components of the in vivo tissue with an exponential function model. The information processing apparatus according to claim 15.

18. The OSR analysis unit calculates the component ratio gradient, which is the slope of the component ratio with respect to the off-resonance frequency, based on the dominant off-resonance frequency for each component of the in vivo tissue. The information processing apparatus according to claim 15.

19. The system further comprises a map generation unit that generates at least one of the following maps: an OSR map representing the OSR for each different off-resonance frequency as pixel values; an OSR component map representing the dominant off-resonance frequency for each component of the in vivo tissue as pixel values; a component ratio gradient map representing the component ratio gradient for each component of the in vivo tissue as pixel values; and an elastic modulus map representing the elastic modulus for each component of the in vivo tissue as pixel values. The information processing apparatus according to claim 15.

20. A classification unit that classifies the aforementioned MR data into first MR data corresponding to each of different off-resonance frequencies and second MR data corresponding to each of different TEs, Based on the second MR data, T2 ＊ T2 to write value ＊ Analysis unit, The aforementioned T2 ＊ T2 values ​​are represented by pixel values. ＊ It further comprises a map generation unit that generates a map, The information processing apparatus according to claim 10.

21. A classification unit that classifies the aforementioned MR data into first MR data corresponding to each of different off-resonance frequencies and second MR data corresponding to each of different TEs, Based on the first MR data, the first T2 ＊ Estimate the value of T2 ＊ Estimation unit, Based on the second MR data, the second T2 ＊ T2 to write value ＊ Analysis unit, The first T2 ＊ Value and the second T2 ＊ Based on the value, T2 ＊ T2 values ​​are represented by pixel values. ＊ It further comprises a map generation unit that generates a map, The information processing apparatus according to claim 10.

Images