Spectral analysis apparatus, method, and program

Abstract

To improve the accuracy of spectral analysis using MRS or NMR. [Solution] The spectral analysis apparatus according to the embodiment comprises a first acquisition unit, a second acquisition unit, and an analysis unit. The first acquisition unit acquires the spectrum of the substance to be analyzed, collected by MRS (magnetic resonance spectroscopy) or NMR (nuclear magnetic resonance). The second acquisition unit is an acquisition unit that acquires a plurality of basis sets with different generation conditions, each of the plurality of basis sets includes a reference spectrum relating to each of the plurality of substances. The analysis unit analyzes the spectrum of the substance to be analyzed using the plurality of basis sets and calculates analysis results relating to the abundance of the plurality of substances.

Description

[Technical Field]

[0001] Embodiments disclosed herein and in the drawings relate to spectral analyzers, methods, and programs. [Background technology]

[0002] In MR spectroscopy (MRS: magnetic resonance spectroscopy) or nuclear magnetic resonance (NMR: nuclear magnetic resonance), spectra (hereinafter referred to as acquired spectra) are collected by magnetic resonance scanning of subjects such as the human body or samples. Spectral analyzers analyze the acquired spectra using a basis set containing multiple reference spectra corresponding to multiple substances. Specifically, the spectral analyzer performs regression calculations on the basis set for the acquired spectra and calculates a regression spectrum that includes the weighted sum of multiple reference spectra and residual components, thereby outputting analysis results such as concentration values ​​for each substance constituting the subject. Therefore, the quality of the basis set affects the accuracy of the analysis results. However, since it is difficult to perfectly match the environment in which the basis set is generated with the environment in which the magnetic resonance scan is performed on the subject, the reference spectra contain errors from the ideal, which leads to a deterioration in the accuracy of the analysis results. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2019-90747 [Overview of the project] [Problems that the invention aims to solve]

[0004] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to improve the accuracy of spectral analysis by MRS or NMR. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the respective effects of the respective configurations shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0005] The spectral analysis apparatus according to the embodiment includes a first acquisition unit, a second acquisition unit, and an analysis unit. The first acquisition unit acquires a spectrum of an analysis target collected by MRS (magnetic resonance spectroscopy) or NMR (nuclear magnetic resonance). The second acquisition unit is an acquisition unit that acquires a plurality of basis sets having different generation conditions, and each of the plurality of basis sets includes a reference spectrum for each of a plurality of substances. The analysis unit analyzes the spectrum of the analysis target using the plurality of basis sets, and calculates an analysis result regarding the abundance of the plurality of substances.

Brief Description of the Drawings

[0006] [Figure 1] FIG. 1 is a diagram showing a configuration example of a magnetic resonance imaging apparatus according to the first embodiment. [Figure 2] FIG. 2 is a diagram showing an example of a plurality of basis sets. [Figure 3] FIG. 3 is a diagram showing an example of a processing example of spectral analysis according to the first embodiment. [Figure 4] FIG. 4 is a diagram schematically showing a generation process of a mixed basis set. [Figure 5] FIG. 5 is a diagram schematically showing an example of parallelization of spectral analysis. [Figure 6] FIG. 6 is a diagram showing an example of a display screen display related to manual selection of an analysis result. [Figure 7] FIG. 7 is a diagram showing a configuration example of an NMR apparatus according to the second embodiment. [Figure 8] Figure 8 shows an example of the configuration of a spectral analyzer according to the third embodiment. [Modes for carrying out the invention]

[0007] The spectral analysis apparatus, method, and program according to this embodiment will be described in detail below with reference to the drawings.

[0008] The spectral analysis device according to this embodiment includes a computer for analyzing spectra collected by MRS or NMR. The spectral analysis device may be included in a magnetic resonance imaging device that performs MRS, or in an NMR device that performs NMR, or it may be a workstation separate from the magnetic resonance imaging device or NMR device.

[0009] (First Embodiment) Figure 1 shows an example of the configuration of a magnetic resonance imaging apparatus 1 according to the first embodiment. As shown in Figure 1, the magnetic resonance imaging apparatus 1 includes a frame 11, a bed 13, a gradient magnetic field power supply 21, a transmitting circuit 23, a receiving circuit 25, a bed drive device 27, a sequence control circuit 29, and a spectrum analyzer 50.

[0010] The mounting base 11 includes a static magnetic field magnet 41 and a gradient magnetic field coil 43. The static magnetic field magnet 41 and the gradient magnetic field coil 43 are housed in the casing of the mounting base 11. The casing of the mounting base 11 has a hollow bore. A transmitting coil 45 and a receiving coil 47 are arranged inside the bore of the mounting base 11.

[0011] The static magnetic field magnet 41 has a hollow, approximately cylindrical shape and generates a static magnetic field inside the approximately cylindrical body. For example, a permanent magnet, a superconducting magnet, or a normal conducting magnet can be used as the static magnetic field magnet 41. Here, the central axis of the static magnetic field magnet 41 is defined as the Z-axis, the axis perpendicular to the Z-axis is defined as the Y-axis, and the axis perpendicular to the Z-axis horizontally is defined as the X-axis. The X-axis, Y-axis, and Z-axis constitute an orthogonal three-dimensional coordinate system.

[0012] The gradient magnetic field coil 43 is mounted inside the static magnetic field magnet 41 and is a hollow, substantially cylindrical coil unit. The gradient magnetic field coil 43 generates a gradient magnetic field by receiving current from the gradient magnetic field power supply 21. More specifically, the gradient magnetic field coil 43 has three coils corresponding to the mutually orthogonal X, Y, and Z axes. These three coils form a gradient magnetic field in which the magnetic field strength changes along each of the X, Y, and Z axes. The gradient magnetic fields along each of the X, Y, and Z axes are combined to form mutually orthogonal slice-selective gradient magnetic field Gs, phase-encoded gradient magnetic field Gp, and frequency-encoded gradient magnetic field Gr in the desired direction. The slice-selective gradient magnetic field Gs is used to arbitrarily determine the imaging cross-section (slice). The phase-encoded gradient magnetic field Gp is ​​used to change the phase of the magnetic resonance signal (hereinafter referred to as the MR signal) according to the spatial position. The frequency-encoded gradient magnetic field Gr is used to change the frequency of the MR signal according to the spatial position. In the following explanation, the gradient direction of the slice selection gradient magnetic field Gs is assumed to be the Z-axis, the gradient direction of the phase encoding gradient magnetic field Gp is ​​assumed to be the Y-axis, and the gradient direction of the frequency encoding gradient magnetic field Gr is assumed to be the X-axis.

[0013] The gradient power supply 21 supplies current to the gradient coil 43 according to the sequence control signal from the sequence control circuit 29. By supplying current to the gradient coil 43, the gradient power supply 21 generates gradient magnetic fields along the X, Y, and Z axes. These gradient magnetic fields are superimposed on the static magnetic field formed by the static magnetic field magnet 41 and applied to the subject S. In the first embodiment, the subject S is assumed to be a human body.

[0014] The transmitting coil 45 is, for example, positioned inside the gradient magnetic field coil 43 and receives current from the transmitting circuit 23 to generate high-frequency pulses (hereinafter referred to as RF pulses).

[0015] The transmitting circuit 23 supplies current to the transmitting coil 45 in order to apply an RF pulse to the subject S via the transmitting coil 45 in order to excite the target proton present in the subject S. The RF pulse oscillates at a resonant frequency unique to the target proton, thereby exciting the target proton. An MR signal is generated from the excited target proton and detected by the receiving coil 47. The transmitting coil 45 is, for example, a whole-body coil (WB coil). A whole-body coil may also be used as a transmitting and receiving coil.

[0016] The receiving coil 47 receives MR signals emitted from target protons present in the subject S in response to the RF pulse. The receiving coil 47 has multiple receiving coil elements capable of receiving MR signals. The received MR signals are supplied to the receiving circuit 25 via wired or wireless connection. Although not shown in Figure 1, the receiving coil 47 has multiple receiving channels implemented in parallel. Each receiving channel has a receiving coil element that receives the MR signal and an amplifier that amplifies the MR signal. The MR signal is output for each receiving channel. The total number of receiving channels and the total number of receiving coil elements may be the same, or the total number of receiving channels may be greater than or less than the total number of receiving coil elements.

[0017] The receiving circuit 25 receives the MR signal generated from the excited target proton via the receiving coil 47. The receiving circuit 25 processes the received MR signal to generate a digital MR signal. The digital MR signal can be represented in k-space, which is defined by the spatial frequency. Hereinafter, the digital MR signal will be referred to as k-space data. k-space data is digital data that expresses the signal intensity value of the MR signal as a function of time. The k-space data is supplied to the spectral analyzer 50 via wired or wireless connection.

[0018] The transmitting coil 45 and receiving coil 47 described above are merely examples. Instead of the transmitting coil 45 and receiving coil 47, a transmitting and receiving coil equipped with both transmitting and receiving functions may be used. Furthermore, the transmitting coil 45, receiving coil 47, and the transmitting and receiving coil may be combined.

[0019] A bed 13 is installed adjacent to the frame 11. The bed 13 has a top plate 131 and a base 133. The subject S is placed horizontally on the top plate 131. The base 133 supports the top plate 131 so that it can slide along the X, Y, and Z axes. A bed drive device 27 is housed in the base 133. The bed drive device 27 moves the top plate 131 under control from a sequence control circuit 29. The bed drive device 27 may include any motor, such as a servo motor or a stepping motor.

[0020] The sequence control circuit 29 has a processor such as a CPU (Central Processing Unit) or MPU (Micro Processing Unit) and memory such as ROM (Read Only Memory) or RAM (Random Access Memory) as hardware resources. The sequence control circuit 29 synchronously controls the gradient magnetic field power supply 21, the transmission circuit 23 and the reception circuit 25 based on preset acquisition conditions, and performs signal acquisition on the subject S according to the acquisition conditions to collect k-space data about the subject S.

[0021] The sequence control circuit 29 according to this embodiment performs a magnetic resonance scan for MRS. MRS measures chemical shifts, which are minute differences in the resonance frequencies of target protons, such as hydrogen nuclei, that occur in response to differences in the chemical environment. MRS includes the single-voxel method, which acquires signals from a single voxel, and the multi-voxel method, which acquires signals from multiple voxels, and this embodiment is applicable to either method. By applying an RF pulse or the like to the subject S, an MR signal, such as a free induction decay (FID) signal or a spin echo signal, is generated from the region of interest of the subject S. The receiving circuit 25 receives the MR signal via the receiving coil 47, processes the received MR signal, and collects k-space data related to the measurement target. The sequence control circuit 29 can also perform a magnetic resonance scan for MR image acquisition to perform MR image acquisition.

[0022] As shown in Figure 1, the spectral analyzer 50 is a computer having a processing circuit 51, memory 52, display 53, input interface 54, and communication interface 55. Data communication between the processing circuit 51, memory 52, display 53, input interface 54, and communication interface 55 is performed via a bus.

[0023] The processing circuit 51 has a processor such as a CPU as a hardware resource. The processing circuit 51 functions as the central hub of the magnetic resonance imaging apparatus 1. For example, the processing circuit 51 implements a scan control function 511, a first acquisition function 512, a second acquisition function 513, a mixing function 514, an analysis function 515, and a display control function 516 by executing various programs.

[0024] The scan control function 511 causes the processing circuit 51 to transmit a command to the sequence control circuit 29 to perform MRS on the subject S according to separately defined imaging conditions. In response to this command, the sequence control circuit 29 synchronously controls all or part of the patient table drive unit 27, gradient magnetic field power supply 21, transmitting circuit 23, and receiving circuit 25 according to the imaging conditions to perform MRS. The MR signal acquired by MRS is converted into k-space data by the receiving circuit 25.

[0025] The pulse sequences used in this embodiment of MRS include basic sequences such as PRESS (Point resolved spectroscopy), STEAM (Stimulated echo acquisition mode), LASER (Localization by adiabatic selective refocusing), semi-LASER, ISIS (Image-Selected in vivo spectroscopy), and their variations. Alternatively, the MRS pulse sequence in this embodiment may be an edited MRS with frequency-selective pulses added to the basic sequences. Examples of frequency-selective pulses include MEGA (Mescher-Garwood) pulses, BASING (Band-selective inversion with gradient dephasing) pulses, SLOW (SLOtboom-Weng) pulses, and their variations. Regarding the use of BASING pulses, Single-BASING pulses and Double-BASING pulses are known. The imaging conditions include imaging parameters such as the type of basic sequence, the type of frequency selection pulse, the frequency band of the frequency selection pulse (hereinafter referred to as the editing frequency band), the repetition time (TR), the echo time (TE), the pulse width of the excitation pulse, and the number of excitations (NEX). These imaging conditions can be set manually by the user or automatically according to an algorithm.

[0026] The processing circuit 51 acquires the spectrum to be analyzed (hereinafter referred to as the target spectrum) collected by the MRS using the first acquisition function 512. For example, the processing circuit 51 collects k-space data via the receiving circuit 25 and generates the target spectrum (hereinafter referred to as the target spectrum) based on the k-space data. More specifically, since the MRS pulse sequence is repeated a number of times equal to the number of integrations (NEX), NEX k-space data points are generated. The processing circuit 51 integrates the NEX k-space data points. Noise can be reduced by integration. The processing circuit 51 generates the target spectrum by applying a Fourier transform to the integrated k-space data. The processing circuit 51 may perform various correction processes such as zero-filling, phase correction, and baseline correction during the target spectrum generation process. The spectrum represents a signal intensity distribution in which the first axis is defined by signal intensity and the second axis, orthogonal to the first axis, is defined by the chemical shift frequency.

[0027] The processing circuit 51 acquires multiple basis sets with different generation conditions through the second acquisition function 513. Each of the multiple basis sets contains a reference spectrum for each of the multiple substances. The reference spectrum is also called the spectral basis. "Substance" refers to molecules and metabolites contained in the human body.

[0028] Figure 2 shows an example of multiple basis sets. As shown in Figure 2, a basis set is a collection of reference spectra for multiple metabolites (substances) such as GABA, Glu (glucose), 2HG, and GSH. A reference spectrum is, for example, data representing the spectral waveform of the substance. Another example is that a reference spectrum may be one or more parameter values ​​that characterize the spectral waveform. Parameter values ​​that characterize a spectral waveform include, for example, the chemical shift frequency of the peak of the spectral waveform, the signal intensity of the peak, the full width at half maximum of the peak, and the standard deviation of the peak. The signal intensity of the peak corresponds to the concentration of the metabolite. Multiple basis sets are generated under different conditions. Due to these differences in generation conditions, the waveforms or parameter values ​​of the reference spectra for the same substance will differ among multiple basis sets.

[0029] Multiple basis sets include basis sets collected by MRS or NMR on samples with known material compositions (hereinafter referred to as collected basis sets) and / or basis sets calculated by simulation (hereinafter referred to as calculated basis sets).

[0030] The generation conditions for the acquisition basis set are called imaging conditions. For example, imaging conditions include the manufacturer and / or product name of the instrument used to perform MRS or NMR. Other examples of imaging conditions include the type of basic sequence, the type of frequency-selective pulse, the editing frequency band of the frequency-selective pulse, the repetition time (TR), the echo time (TE), the number of integrations (NEX), etc. The echo time and / or the pulse width of the excitation pulse are factors that determine the imaging timing. In the case of echo time, for example, the first echo time of the PRESS method, which is a basic sequence, can be made different, for example, "10 ms" and "12 ms". In the case of pulse width of the excitation pulse, for example, it can be made different, for example, "4 ms" and "6 ms".

[0031] The generation conditions for the calculation basis set are called calculation conditions. The calculation conditions may include the type of calculation algorithm and / or parameters. The type of calculation algorithm may be any type, such as density matrix simulation. The parameters of the calculation algorithm may include the frequency bands of water suppression pulses and / or MEGA pulses. In the case of the water suppression pulse frequency band, for example, the difference in the water suppression frequency band between a press without MEGA pulses and a press with MEGA pulses may be set to "0 ppm" in the first basis set and to "0.1 ppm" in the second basis set. Note that the frequency bands of the water suppression pulses for each press, both with and without MEGA pulses, may be set to different values ​​in the first and second basis sets. In the case of the MEGA pulse frequency band, for example, the difference in the editing frequency bands of two MEGA pulses may be set to "2.8 ppm" in the first basis set and to "2.9 ppm" in the second basis set. Furthermore, the editing frequency bands of the two MEGA pulses may be set to different values ​​for the first basis set and the second basis set. A calculation basis set is included, as an example, in FID-A, open-source software for MRS simulation.

[0032] A collection basis set is generated by the following process, as an example. First, imaging conditions are determined. Next, according to the determined imaging conditions, an MRS or NMR scan is performed on a sample containing one type of metabolite to be generated to collect k-space data, and a spectrum is generated by performing an FFT or similar operation on the collected k-space data. Then, a reference spectrum for that metabolite is generated by removing the water component from the generated spectrum. One collection basis set is generated by repeating this process for different metabolites. Multiple collection basis sets are generated by repeating the above process for different imaging conditions.

[0033] A computational basis set is generated by the following process, as an example: First, the computational conditions are determined. Next, an MRS or NMR simulation is performed according to the determined computational conditions to generate reference spectra for one or more types of metabolites to be generated. By repeating this process for different metabolites, one computational basis set is generated. Furthermore, by repeating the above process for different computational conditions, multiple collection basis sets are generated.

[0034] Multiple basis sets are pre-generated by the magnetic resonance imaging apparatus 1 or a workstation or other device and stored in memory 52 in the form of a table (LUT: Look Up Table) that associates a substance identifier with a reference spectrum.

[0035] In the following explanation, unless otherwise specified, the collected basis set and the computed basis set will simply be referred to as the basis set. Furthermore, to distinguish them from the basis sets generated by the mixing function 514, the basis sets generated by the mixing function 514 will be referred to as the mixed basis set, and the basis sets not mixed by the mixing function 514 will be referred to as the original basis set.

[0036] The mixing function 514 causes the processing circuit 51 to mix two or more original basis sets from among several original basis sets in a predetermined ratio to generate a mixed basis set. The mixing may involve collecting basis sets, calculating basis sets, or a combination of collecting and calculating basis sets. A mixed basis set does not need to be generated if unnecessary. Hereafter, when original basis sets and mixed basis sets are not specifically distinguished, they will simply be referred to as basis sets.

[0037] The processing circuit 51, using the analysis function 515, analyzes the target spectrum using multiple basis sets and calculates analysis results regarding the abundance of multiple substances. The abundance may be the concentration value of the substance, or it may be a value used to calculate the concentration value, such as the signal intensity or peak area of ​​the peak. The processing circuit 51 calculates multiple analysis results corresponding to each of the multiple basis sets, and also calculates multiple goodness-of-fit values ​​corresponding to each of the multiple basis sets. The processing circuit 51 calculates a regression spectrum as an analysis result by performing a regression calculation of the basis set on the target spectrum, and calculates a goodness-of-fit value that represents the degree of fit of the regression spectrum to the target spectrum. Here, the regression spectrum means a composite spectrum obtained by performing a regression calculation of multiple reference spectra included in the basis set on the target spectrum. The regression spectrum is an example of an analysis result because it reflects the abundance of multiple substances contained in the subject S. The processing circuit 51 selects one analysis result from among the multiple analysis results based on the multiple goodness-of-fit values ​​and outputs the selected analysis result.

[0038] The display control function 516 causes the processing circuit 51 to display various information on a display device such as the display 53. For example, the processing circuit 51 displays multiple fitness scores on the display 53 along with multiple analysis results obtained by the analysis function 515.

[0039] Memory 52 is a storage device such as an HDD (Hard Disk Drive), SSD (Solid State Drive), or integrated circuit storage device that stores various types of information. Alternatively, memory 52 may be a drive device that reads and writes various types of information to and from portable storage media such as CD-ROM drives, DVD drives, or flash memory.

[0040] The display 53 displays various information according to the control of the display control function 516. For example, the display 53 can be a CRT display, a liquid crystal display, an organic EL display, an LED display, a plasma display, or any other display known in the art.

[0041] The input interface 54 includes an input device that receives various commands from the user. Possible input devices include keyboards, mice, various switches, touchscreens, and touchpads. However, the input device is not limited to those with physical operating components such as mice and keyboards. For example, an electrical signal processing circuit that receives electrical signals corresponding to input operations from an external input device separate from the magnetic resonance imaging apparatus 1 and outputs the received electrical signals to various circuits is also an example of the input interface 54. Furthermore, the input interface 54 may also be a speech recognition device that converts audio signals collected by a microphone into instruction signals.

[0042] The communication interface 55 is an interface that connects the magnetic resonance imaging apparatus 1 to workstations, PACS (Picture Archiving and Communication System), HIS (Hospital Information System), RIS (Radiology Information System), etc., via a LAN (Local Area Network) or the like. The communication interface 55 transmits and receives various types of information between the connected workstation, PACS, HIS, and RIS.

[0043] The following describes an example of spectral analysis processing according to the first embodiment.

[0044] Figure 3 shows an example of spectral analysis processing according to the first embodiment. It is assumed that, prior to the start of the spectral analysis shown in Figure 3, multiple basis sets are generated in advance and stored in memory 52 in a searchable table format such as a LUT.

[0045] As shown in Figure 3, the processing circuit 51 first acquires the target spectrum of the subject S using the first acquisition function 512 (step S1). Specifically, the sequence control circuit 29 synchronously controls the bed drive device 27, gradient magnetic field power supply 21, transmission circuit 23, and reception circuit 25 to perform MRS on the subject S. The reception circuit 25 receives the MR signal from the subject S and generates k-space data by performing signal processing such as A / D conversion. The generated k-space data is transmitted to the processing circuit 51. The processing circuit 51 generates the target spectrum of the subject S based on the transmitted k-space data.

[0046] When step S1 is performed, the processing circuit 51 acquires multiple original basis sets using the second acquisition function 513 (step S2). The processing circuit 51 in step S2 does not need to acquire all of the original basis sets stored in memory 52, but may acquire only some of the original basis sets. The processing circuit 51 acquires at least two or more original basis sets. The processing circuit 51 may automatically select a basis set from among the multiple original basis sets stored in memory 52 that is associated with imaging conditions or calculation conditions that are the same as or similar to the imaging conditions of the target spectrum, or it may select a basis set specified by the operator via the input interface 54, or it may select a basis set randomly.

[0047] When step S2 is performed, the processing circuit 51 uses the mixing function 514 to mix the two or more original basis sets obtained in step S2 in a predetermined ratio to generate a mixed basis set (step S3).

[0048] Figure 4 schematically illustrates the process of generating a mixed basis set. In the example in Figure 4, it is assumed that there are two original basis sets to be mixed: basis set #1 and basis set #2. As shown in Figure 4, the processing circuit 51 generates a mixed basis set #3 based on basis set #1 and basis set #2. Specifically, the processing circuit 51 multiplies basis set #1 by a mixing ratio α%, multiplies basis set #2 by a mixing ratio (100-α)%, and adds basis set #1 multiplied by a mixing ratio of α% and basis set #2 multiplied by a mixing ratio of (100-α)% to generate a mixed basis set #3 (=#1*α%+#2*(100-α%)). The value of the mixing ratio α can be set arbitrarily.

[0049] The process of generating a mixed basis set will be described in more detail below. As described above, the original basis set contains multiple reference spectra corresponding to multiple substances. The processing circuit 51 selects reference spectra #1iX and #2jX (where iX and jX represent the subscripts i and j, respectively, representing substance X) from reference spectrum #1i (where i is a subscript identifying a substance) included in basis set #1 and reference spectrum #2j (where j is a subscript identifying a substance) included in basis set #2, and mixes the selected reference spectra #1iX and #2jX for the same substance X. Specifically, the processing circuit 51 multiplies the reference spectrum #1iX by the mixing ratio α%, multiplies the reference spectrum #2jX by the mixing ratio (100-α)%, adds the reference spectrum #1iX multiplied by the mixing ratio α%, and the reference spectrum #2jX multiplied by the mixing ratio (100-α)%, to generate reference spectrum #3 (=#1iX*α%+#2jX*(100-α%)). Note that if a reference spectrum of the same substance does not exist in the other basis set, the reference spectrum does not need to be mixed.

[0050] The number of basis sets that make up a mixed basis set is not limited to two; it can be three or more. In this case, the mixing ratio of each basis set #k (where k is an index that identifies the basis set) is represented by α_k. Here, it is desirable to adjust the mixing ratios so that Σα_k=1, i.e., the sum of the mixing ratios α_k is 1.

[0051] The number of mixed basis sets is not limited to one; there may be two or more. The processing circuit 51 may generate multiple mixed basis sets by multiplying two or more identical original basis sets by different mixing ratios, or it may generate multiple mixed basis sets by multiplying two or more different original basis sets by the same or different mixing ratios.

[0052] Once step S3 is performed, the processing circuit 51 analyzes the target spectrum using multiple basis sets via the analysis function 515 (step S4). The basis sets used include the original basis set acquired in step S2 and the mixed basis set generated in step S3. The analysis process in step S4 will be described in detail below.

[0053] In step S4, the processing circuit 51 performs regression calculations on the target spectrum for multiple original basis sets and mixed basis sets and calculates multiple analysis results. Next, the processing circuit 51 calculates multiple goodness-of-fits for the target spectrum for multiple original basis sets and mixed basis sets. For N basis sets, N analysis results and N goodness-of-fits are calculated. Then, the processing circuit 51 selects one analysis result from the multiple goodness-of-fits and outputs the selected analysis result.

[0054] The processing circuit 51 calculates a regression spectrum as an analysis result by performing regression calculations for each of multiple basis sets on the target spectrum. There are various mathematical expressions for regression spectra. As an example, the regression spectrum Y(ν) is obtained by taking multiple reference spectra M corresponding to multiple substances l, as shown in equation (1) below. l It includes the weighted sum component of (ν) and the residual component R(ν). ν represents frequency. Reference spectrum M l (ν) weight C l This represents the concentration value. Note that the reference spectrum M l (ν) is designed to include not only frequency ν, but also regression parameters such as phase shift, full width at half maximum, T2 variation, and chemical shift frequency variation as variables.

[0055] Y(ν)=C l Σ l M l (ν)+R l (ν) (1)

[0056] Specifically, the processing circuit 51 generates multiple regression spectra by changing regression parameters and concentration values, and outputs the regression spectrum with the smallest residual component size as the optimal regression spectrum. This results in one regression spectrum for each basis set. The optimal regression spectrum and its concentration value are used as analysis results. The processing circuit 51 calculates multiple analysis results by performing the above steps for multiple basis sets. For each of the multiple basis sets, the processing circuit 51 calculates the degree of fit based on the magnitude of the residual component for the target spectrum in the frequency range for which the fit is to be determined, each time it outputs the optimal regression spectrum. The degree of fit is calculated using the sum of absolute values ​​or the sum of squares of the residual components. In this case, the degree of fit is designed so that the value increases as the magnitude of the residual component decreases. The frequency range for which the fit is to be determined is, for example, often set to 1 ppm to 4.1 ppm, which is used in analysis, but it may be set to other frequency ranges depending on the analysis target, or it may be set to the entire frequency range. The degree of fit and regression spectrum for each basis set are stored in memory 52 in association with each other.

[0057] When the processing circuit 51 uses a mixed basis set as the basis set, the reference spectrum M in the above formula (1) l (ν) can be replaced with a mixed reference spectrum to calculate the fitness and the regression spectrum. For example, when generating a mixed basis set based on two original basis sets, the reference spectrum of the first basis set is M l (1) (ν), and the reference spectrum of the second basis set is M l (2) (ν). Then, the reference spectrum M in the above formula (1) l (ν) is replaced by αM l (1) (ν)+(1-α)M l (2) (ν).

[0058]

[0059]

Equation

[0060] As shown in the following formula (2), the spectral signal model Y^(ν(2) As shown in the formula, the spectral signal model Y^(ν k ) is represented by the sum of the first term and the second term. The first term represents the baseline B(ν) of the first reference spectrum M l . The second term represents the sum term. The sum term represents the sum over all substances of the product value of the concentration C l of substance l and the reference spectrum M l . A phase shift exp[i(φ0 + νφ1)] is applied to both the first term and the second term. The parameter γ l included in the reference spectrum M l represents the variation of T2, and the parameter ε lrepresents the variation in chemical shift. In equation (2), when a mixed basis set is generated based on two original basis sets, the reference spectrum of the first basis set is M l (1) (ν k-n γ l ,ε l ), the reference spectrum of the second basis set is M l (2) (ν k-n γ l ,ε l ) If so, the reference spectrum M in equation (2) above l (ν k-n γ l ,ε l ) is αM l (1) (ν k-n γ l ,ε l )+(1-α)M l (2) (ν k-n γ l ,ε l You can extend the model like this.

[0061] The spectral analysis calculation performed by the analysis function 515 can be parallelized. Parallelization can also be achieved by assigning spectral analysis using different basis sets to the CPU cores of the processing circuit 51.

[0062] Figure 5 schematically illustrates an example of parallelization of spectral analysis. As shown in Figure 5, the CPU of the processing circuit 51 is assumed to have three cores, namely processors #1, #2, and #3. Each processor #1, #2, and #3 is assigned spectral analysis using a different basis set. For example, processor #1 is assigned spectral analysis using basis set #1, processor #2 is assigned spectral analysis using basis set #2, and processor #3 is assigned spectral analysis using basis set #3. By assigning spectral analysis of different basis sets to different CPU cores, it becomes possible to improve the processing speed of spectral analysis using multiple basis sets by parallelizing the spectral analysis checks.

[0063] The method of allocating spectral analysis to CPU cores is not limited to the above. For example, spectral analysis of two or more basis sets may be assigned to a single CPU core.

[0064] When step S4 is performed, the processing circuit 51 uses the analysis function 515 to select one analysis result based on the multiple fitness scores calculated in step S4 (step S5). For example, the processing circuit 51 identifies the highest fitness score among the multiple fitness scores calculated in step S4 and selects the analysis result having that fitness score.

[0065] When step S5 is performed, the processing circuit 51 outputs one analysis result selected in step S5 using the analysis function 515 (step S6). Subsequently, the processing circuit 51 displays the outputted analysis result on the display 53. At this time, the processing circuit 51 may also display the identifier and generation conditions of the basis set corresponding to the analysis result selected in step S5.

[0066] This concludes the spectral analysis according to the first embodiment.

[0067] According to the spectral analysis described above, regression calculations (fitting) of the reference spectrum are performed on the target spectrum for multiple basis sets with different generation conditions, thereby increasing the likelihood of obtaining analysis results with a high degree of fit to the target spectrum. This is expected to improve the accuracy of the analysis results. Therefore, according to the first embodiment, the accuracy of spectral analysis by MRS can be improved.

[0068] Let's consider the case where MEGA-PRESS is used to acquire the target spectrum. Gaussian functions and Gaussian-Hamming functions are often used as MEGA pulses. Because the frequency characteristics are Gaussian, there are relatively many frequency regions where the expected gain cannot be obtained, for example, when a 180-degree pulse is applied, the spin phase actually changes by only about 40 to 130 degrees. In the frequency regions where the expected gain cannot be obtained, the deviation of the reference spectrum from the target spectrum tends to become large due to the effects of magnetic field inhomogeneity, etc. Even if the spectrum is shifted in the frequency direction as in the LCModel, it is not possible to correct the above phenomenon in which the frequency characteristics themselves change. This phenomenon is more likely to occur in MRSI (MRS Imaging) with a large measurement area. By using the spectral analysis according to this embodiment, it is possible to deal with changes in the frequency characteristics themselves and thus absorb the deviation of the reference spectrum from the target spectrum.

[0069] The spectral analysis described above is merely an example, and various steps can be added, deleted, and / or modified without departing from the spirit of the invention.

[0070] <Example 1> In the spectral analysis described above, the processing circuit 51 automatically selects one analysis result based on multiple goodness-of-fit criteria (S5). However, the processing circuit 51 may also manually select one goodness-of-fit criterion via the input interface 54. In this case, the processing circuit 51 displays the multiple goodness-of-fit criterion criteria along with the multiple analysis results calculated in step S4 on the display 53 using the display control function 516. The processing circuit 51 then uses the analysis function 515 to select the analysis result specified by the operator from among the displayed multiple analysis results and outputs the selected analysis result.

[0071] Figure 6 shows an example of the display screen I1 for manual selection of analysis results. As shown in Figure 6, the display screen I1 includes a display area I1n for basis set identification information (where n is the basis set identification number), a display area I2n for the regression spectrum, a display area I3n for the concentration value, a display area I4n for the degree of fit, and a selection button I5n. Note that in Figure 6, the display screen I1 is an example with three basis sets, but the number of display areas can be increased or decreased depending on the number of basis sets.

[0072] As shown in Figure 6, display area I1n displays the basis set number, information indicating whether the basis set is a basis set collected by MRS or NMR (collected basis set), a basis set calculated by simulation (calculated basis set), or a mixed basis set (hereinafter referred to as type information), and the generation conditions. For example, display area I11 displays "#1" as the number, "Collected" representing a collected basis set, "Company A" as the name of the manufacturer, "PRESS" as the pulse sequence type, and "TE=12" as the echo time as type information. As another example, display area I12 displays "#2" as the number, "Calculated" representing a calculated basis set as type information, and "Density Matrix Simulation" as the type of calculation algorithm. Display area I13 displays "#3" as the number, "Mixed" representing a mixed basis set as type information, and "#1:#2=3:7" as the mixing ratio.

[0073] Display area I2n displays the regression spectrum generated in step S4. Display area I3n displays the concentration values ​​for each substance calculated in step S4. Step SI4n displays the goodness of fit calculated in step S4. For example, display area I41 displays "Medium" representing a moderate goodness of fit, display area I42 displays "Low" representing a low goodness of fit, and display area I43 displays "High" representing a high goodness of fit. Note that the goodness of fit may also be displayed as a numerical value.

[0074] The operator decides which analysis result to adopt by comprehensively considering the various items displayed on the display screen I1. The display screen shows GUI (Graphical User Interface) buttons (hereinafter referred to as selection buttons) I5n that instruct the operator to select the corresponding analysis result. If the analysis result of basis set #1 is adopted, selection button I51 is pressed via the input interface 54; if the analysis result of basis set #2 is adopted, selection button I52 is pressed; and if the analysis result of basis set #3 is adopted, selection button I53 is pressed. The processing circuit 51 selects the analysis result corresponding to the pressed selection button I5n (S5). Thus, according to Modification 1, the operator can evaluate multiple analysis results corresponding to multiple basis sets and adopt the analysis result and basis set that the operator judges to be the best among these multiple analysis results.

[0075] <Modification 2> In the spectral analysis shown in Figure 3, a mixed basis set is generated with an arbitrarily determined mixing ratio, and a regression spectrum of equation (1) or (2) is generated using the mixed basis set. However, this embodiment is not limited thereto. The processing circuit 51 according to Modification 2 generates a second basis set by adding two or more original basis sets from among a plurality of original basis sets, weighted by a variable representing the mixing ratio (hereinafter referred to as the mixing ratio variable), using the mixing function 514. The processing circuit 51 calculates the optimal value of the mixing ratio variable and the analysis result at that optimal value by performing a regression calculation of the mixed basis set for the spectrum to be analyzed using the analysis function 515.

[0076] For example, the processing circuit 51 uses the M of equation (1) which represents the regression spectrum. l (ν) to αM l (1) (ν)+(1-α)M l (2) Replace with (ν). Here, α represents the mixing ratio variable. The processing circuit 51 in Modification 2 calculates the regression spectrum while changing the values ​​of the regression parameters, concentration values, and mixing ratio variable, and searches for the combination of regression parameters, concentration values, and mixing ratio variable values ​​that minimizes the magnitude of the residual component. This makes it possible to calculate the optimal value of the mixing ratio variable using regression calculation. According to Modification 2, since the optimal value of the mixing ratio variable can be calculated using regression calculation, it becomes possible to obtain the optimal mixing ratio accurately. Furthermore, according to Modification 2, it becomes possible to obtain analysis results such as concentration values ​​along with the optimal value of the mixing ratio variable.

[0077] <Variation 3> In the spectral analysis shown in Figure 3, a mixed basis set is generated. However, this embodiment is not limited to this. The processing circuit 51 does not have to generate a mixed basis set. In this case, the processing circuit 51 analyzes the target spectrum using multiple original basis sets (S4). Specifically, the processing circuit 51 calculates multiple analysis results corresponding to each of the multiple original basis sets and selects one analysis result from among the multiple analysis results. The method for selecting one analysis result is the same as in the above embodiment; the analysis result corresponding to the best fit may be selected from among multiple fit scores corresponding to each of the multiple original basis sets, or the analysis result specified by the operator via the input interface 54 may be selected.

[0078] <Modification 4> In the spectral analysis shown in Figure 3, the acquisition of the target spectrum (S1) and the acquisition of the original basis set (S2) are assumed to be performed in this order. However, the acquisition of (S1) may be performed after the acquisition of (S2), or the acquisition of (S1) and the acquisition of (S2) may be performed in parallel.

[0079] (Second Embodiment) The spectral analyzer 50 according to the first embodiment described above is included in the magnetic resonance imaging apparatus 1 capable of performing MRS. The spectral analyzer according to the second embodiment is included in the NMR apparatus capable of performing NMR. In the following description, components having substantially the same function as those in the first embodiment are denoted by the same reference numerals and described redundantly only when necessary.

[0080] Figure 7 shows an example of the configuration of the NMR apparatus 2 according to the second embodiment. As shown in Figure 7, the NMR apparatus 2 includes a housing 61, a transmitting circuit 71, a sequence control circuit 72, a receiving circuit 73, and a spectral analyzer 50.

[0081] The housing 61 is a vacuum vessel with a roughly cylindrical space (bore) 62 formed along its axis. A superconducting magnet 63 is housed inside the housing 61. To maintain the superconductivity of the superconducting magnet 63, a coolant such as liquid helium is contained inside the housing 61. The superconducting magnet 63 has a hollow, roughly cylindrical shape and contains a superconducting coil that generates a magnetic field inside the roughly cylindrical interior.

[0082] A cylindrical probe 64 containing a subject S, such as a sample, is detachably positioned in the bore 62. In the second embodiment, the subject S is assumed to be a sample taken from a human body. The probe 64 is provided with a transmitting and receiving coil (not shown). The transmitting and receiving coil generates an RF pulse by receiving current from, for example, a transmitting circuit 71.

[0083] The transmitting circuit 71 supplies current to the transmitting and receiving coils in order to apply RF pulses to the subject S via the transmitting and receiving coils in order to excite the target atomic nuclei present in the subject S. The RF pulses oscillate at a resonance frequency unique to the target atomic nuclei, thereby exciting the target atomic nuclei. An NMR signal is generated from the excited target atomic nuclei and detected by the transmitting and receiving coils. The transmitting and receiving coils receive the NMR signal emitted from the target atomic nuclei present in the subject S in response to the RF pulses. The received NMR signal is supplied to the receiving circuit 73 via wired or wireless connection. The receiving circuit 73 includes a receiving coil element for receiving the NMR signal and an amplifier for amplifying the NMR signal.

[0084] The receiving circuit 73 receives the NMR signal generated from the excited target proton via the transmitting and receiving coil. The receiving circuit 73 processes the received NMR signal to generate a digital NMR signal. The digital NMR signal is supplied to the spectral analyzer 50 via wired or wireless connection.

[0085] The sequence control circuit 72 has a processor such as a CPU or MPU and memory such as ROM or RAM as hardware resources. The sequence control circuit 72 synchronously controls the transmission circuit 71 and the reception circuit 73 based on preset acquisition conditions, and performs a magnetic resonance scan on the subject S according to the acquisition conditions to collect NMR signals related to the subject S.

[0086] The spectral analyzer 50 implements the same functions as in the first embodiment. The spectral analyzer 50 generates a target spectrum from the NMR signal and can perform the same processing on the generated target spectrum as in the first embodiment and modifications 1 to 4. Therefore, according to the second embodiment, the accuracy of spectral analysis by NMR can be improved.

[0087] (Third embodiment) In the first embodiment, the spectral analyzer 50 is included in the magnetic resonance imaging apparatus 1, and in the second embodiment, the spectral analyzer 50 is included in the NMR apparatus 2. In the third embodiment, the spectral analyzer is a workstation separate from the magnetic resonance imaging apparatus and the NMR apparatus. The spectral analyzer according to the third embodiment will be described below. In the following description, components having substantially the same functions as those in the first and second embodiments will be denoted by the same reference numerals and will be described redundantly only when necessary.

[0088] Figure 8 shows an example of the configuration of the spectral analyzer 50 according to the third embodiment. As shown in Figure 8, the spectral analyzer 50 is a computer such as a workstation connected via a network to the magnetic resonance imaging device 3, the NMR device 4, and the PACS (Picture Archiving and Communication System) 5. The magnetic resonance imaging device 3 performs MRS on a subject and collects the target spectrum of MRS, similar to the magnetic resonance imaging device 1 according to the first embodiment. The NMR device 4 performs NMR on a subject and collects the target spectrum of NMR, similar to the NMR device 2 according to the second embodiment. The PACS 5 is a computer or computer network system that manages and stores various medical data, and as an example, stores the target spectra collected by the magnetic resonance imaging device 1 and the NMR device 4.

[0089] As shown in Figure 8, the processing circuit 51 of the spectral analyzer 50 has a first acquisition function 512, a second acquisition function 513, a mixing function 514, an analysis function 515, and a display control function 516. The processing circuit 51 acquires the target spectrum from the magnetic resonance imaging apparatus 3, NMR apparatus 4, PACS 5, etc., using the first acquisition function 512. The spectral analyzer 50 according to the third embodiment can perform the same processing on the acquired target spectrum as in the first embodiment, modifications 1 to 4, and the second embodiment. Therefore, according to the third embodiment, the accuracy of spectral analysis by MRS and NMR can be improved.

[0090] (Summary) According to some of the embodiments described above, the spectral analyzer 50 has a processing circuit 51. The processing circuit 51 acquires a target spectrum collected by MRS or NMR. The processing circuit 51 acquires a plurality of basis sets with different generation conditions. Here, each of the plurality of basis sets includes a reference spectrum for each of the plurality of substances. The processing circuit 51 analyzes the target spectrum using the plurality of basis sets and calculates analysis results regarding the abundance of the plurality of substances.

[0091] According to the above configuration, the degree of discrepancy between the target spectrum and the reference spectrum can be reduced, thereby improving the analysis results of the target spectrum.

[0092] According to at least one embodiment described above, the accuracy of spectral analysis by MRS or NMR can be improved.

[0093] In the above description, the term "processor" refers to circuits such as CPUs, GPUs, or Application Specific Integrated Circuits (ASICs), programmable logic devices (e.g., Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs)). A processor functions by reading and executing a program stored in a memory circuit. Alternatively, instead of storing the program in a memory circuit, the program may be directly incorporated into the processor's circuitry. In this case, the processor functions by reading and executing the program incorporated into the circuitry. On the other hand, if the processor is an ASIC, for example, the program is not stored in a memory circuit, but rather the function is directly incorporated into the processor's circuitry as a logic circuit. In this embodiment, each processor is not limited to being configured as a single circuit; multiple independent circuits may be combined to form a single processor and realize its functions. Furthermore, the multiple components shown in Figures 1, 7, and 8 may be integrated into a single processor to realize their functions.

[0094] While several embodiments 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 implemented in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments are possible 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. [Explanation of Symbols]

[0095] 1. Magnetic Resonance Imaging System 2 NMR device 3. Magnetic Resonance Imaging System 4 NMR device 5 PACS 11. Stand 13 berths 21 Gradient magnetic field power supply 23 Transmitter Circuit 25 Receiving Circuit 27 Bed drive mechanism 29 Sequence control circuit 41 Static magnetic field magnet 43. Gradient field coil 45 Transmitter coil 47 Receiving coil 50. Spectral analyzer 51 Processing Circuit 52 memory 53 displays 54 Input Interfaces 55 Communication Interfaces 61 cabinets 62 Bore 63 Superconducting Magnets 64 probes 71 Transmitter Circuit 72 Sequence control circuit 73 Receiving Circuit 131 Top plate 133 Base 511 Scan control function 512 First Acquisition Function 513 Second acquisition function 514 Mixed functions 515 Analysis Function 516 Display control function

Claims

1. A first acquisition unit that acquires the spectrum to be analyzed, collected by MRS (magnetic resonance spectroscopy) or NMR (nuclear magnetic resonance), An acquisition unit that acquires a plurality of basis sets having different generation conditions, wherein each of the plurality of basis sets includes a reference spectrum relating to each of the plurality of substances, and a second acquisition unit An analysis unit that analyzes the spectrum of the substance to be analyzed using the plurality of basis sets and calculates analysis results regarding the abundance of the plurality of substances, A spectral analyzer equipped with the following features.

2. The aforementioned analysis unit, Multiple analysis results corresponding to each of the multiple basis sets are calculated, and multiple goodness-of-fit values ​​corresponding to each of the multiple basis sets are calculated, and here, a regression spectrum is calculated as an analysis result by performing a regression calculation of the basis set on the spectrum to be analyzed, and the goodness-of-fit value representing the degree of fit of the regression spectrum to the spectrum to be analyzed is calculated. Based on the multiple degrees of fit, one analysis result is selected from the multiple analysis results, and the selected one analysis result is output. The spectral analyzer according to claim 1.

3. It also includes a mixing section, The plurality of basis sets include a plurality of initial first basis sets and a mixed second basis set. The mixing unit mixes two or more of the plurality of first basis sets in a predetermined ratio to generate the second basis set. The spectral analyzer according to claim 2.

4. The aforementioned analysis unit, Regression calculations are performed on the spectrum of the object to be analyzed for the plurality of first basis sets and the second basis set to calculate the plurality of analysis results. The plurality of goodness-of-fit values ​​for the spectrum to be analyzed are calculated for the plurality of first basis sets and the second basis set. The spectral analyzer according to claim 3.

5. The regression spectrum includes a weighted sum component and a residual component of a plurality of reference spectra corresponding to each of the plurality of substances. The analysis unit calculates the degree of fit based on the magnitude of the residual component with respect to the spectrum of the object to be analyzed in the frequency range for which the suitability is to be determined. The spectral analyzer according to claim 2.

6. It further comprises a display control unit, The analysis unit calculates multiple analysis results corresponding to each of the multiple basis sets and calculates multiple goodness-of-fit values ​​corresponding to each of the multiple basis sets, and here, by performing regression calculation of the basis set on the spectrum to be analyzed, it calculates a regression spectrum as the analysis result and calculates a goodness-of-fit value that represents the degree of fit of the regression spectrum to the spectrum to be analyzed. The display control unit displays the plurality of fitness scores together with the plurality of analysis results on the display device. The spectral analyzer according to claim 1.

7. The spectral analyzer according to claim 6, wherein the analysis unit selects one analysis result specified by the operator from among the displayed plurality of analysis results and outputs the selected one analysis result.

8. It also includes a mixing section, The plurality of basis sets include a plurality of initial first basis sets and a mixed second basis set. The mixing unit generates the second basis set by adding two or more first basis sets from the plurality of first basis sets, weighted by a variable representing the mixing ratio. The analysis unit calculates the optimal value of the variable and the analysis result at that optimal value by performing a regression calculation of the second basis set on the spectrum to be analyzed. The spectral analyzer according to claim 1.

9. The spectral analyzer according to claim 1, wherein the plurality of basis sets include collected basis sets obtained by MRS or NMR on a sample having a known material composition, and / or calculated basis sets calculated by simulation.

10. The spectral analyzer according to claim 9, wherein the generation conditions relating to the acquisition basis set are the manufacturer and / or product name of the apparatus that performs MRS or NMR.

11. The spectral analyzer according to claim 9, wherein the generation conditions relating to the acquired basis set include the type of basic sequence, the type of frequency selection pulse, the editing frequency band of the frequency selection pulse, the repetition time, the echo time, and / or the number of integrations.

12. The spectral analyzer according to claim 9, wherein the generation conditions relating to the calculation basis set include the type of calculation algorithm and / or parameters.

13. The spectral analysis apparatus according to claim 12, wherein the parameters of the calculation algorithm include the frequency bands of the water suppression pulse and / or MEGA pulse.

14. A first acquisition step involves acquiring the spectrum to be analyzed, which is collected by MRS (magnetic resonance spectroscopy) or NMR (nuclear magnetic resonance). A process for obtaining multiple basis sets having different production conditions, wherein each of the multiple basis sets includes a reference spectrum relating to each of the multiple substances, a second acquisition step, An analysis step of analyzing the spectrum of the substance to be analyzed using the plurality of basis sets and calculating the analysis results regarding the abundance of the plurality of substances, A spectral analysis method comprising the following:

15. On the computer, A first acquisition function that acquires the spectrum of the target to be analyzed, collected by MRS (magnetic resonance spectroscopy) or NMR (nuclear magnetic resonance), A function for obtaining multiple basis sets with different generation conditions, wherein each of the multiple basis sets includes a reference spectrum relating to each of the multiple substances, and a second acquisition function. An analytical function that analyzes the spectrum of the target of analysis using the aforementioned multiple basis sets and calculates analytical results regarding the abundance of the aforementioned multiple substances, A spectral analysis program that enables this.

Images