WO2024042614A1 - 磁気粒子イメージングシステム、磁気粒子イメージング方法、および磁気粒子イメージングプログラム - Google Patents

磁気粒子イメージングシステム、磁気粒子イメージング方法、および磁気粒子イメージングプログラム Download PDF

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WO2024042614A1
WO2024042614A1 PCT/JP2022/031728 JP2022031728W WO2024042614A1 WO 2024042614 A1 WO2024042614 A1 WO 2024042614A1 JP 2022031728 W JP2022031728 W JP 2022031728W WO 2024042614 A1 WO2024042614 A1 WO 2024042614A1
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magnetic field
magnetic
detection signal
particle imaging
magnetic particle
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English (en)
French (fr)
Japanese (ja)
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一輝 山内
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Mitsubishi Electric Corp
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Mitsubishi Electric Corp
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Priority to PCT/JP2022/031728 priority Critical patent/WO2024042614A1/ja
Priority to DE112022007688.7T priority patent/DE112022007688T5/de
Priority to US18/996,342 priority patent/US20260029493A1/en
Priority to JP2023579520A priority patent/JP7520261B1/ja
Priority to CN202280099013.1A priority patent/CN119677458A/zh
Publication of WO2024042614A1 publication Critical patent/WO2024042614A1/ja
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/12—Measuring magnetic properties of articles or specimens of solids or fluids
    • G01R33/1276—Measuring magnetic properties of articles or specimens of solids or fluids of magnetic particles, e.g. imaging of magnetic nanoparticles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/0515—Magnetic particle imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/0023—Electronic aspects, e.g. circuits for stimulation, evaluation, control; Treating the measured signals; calibration

Definitions

  • the present disclosure relates to a magnetic particle imaging system, a magnetic particle imaging method, and a magnetic particle imaging program.
  • the magnetic particle imaging system includes a selection means for generating a selection magnetic field having a low magnetic field region and a high magnetic field region within an examination region, and a driving means for changing the relative positional relationship of two regions within the examination region with respect to the magnetic particles. , an excitation unit that applies an excitation magnetic field so that the magnetization of the magnetic particles changes, and a reception unit that receives the change in magnetization of the magnetic particles as a detection signal.
  • the value of the detection signal of a magnetic particle imaging system is the value obtained by convolving and integrating the system function with the spatial distribution of magnetic particles, so imaging the spatial distribution of magnetic particles from the detection signal involves deconvolution of the system function. Perform image reconstruction.
  • the system function includes information on the magnetization curve of the magnetic particle and the system (selecting means, driving means, excitation means, and receiving means), it is necessary to select the particles used for inspection and the system function data for each system setting condition. It is common to obtain this information before the test.
  • a method using actual measurement is known as a method for obtaining system functions.
  • the measured value is obtained as a system function by moving a minute point-shaped calibration sample point by point within the inspection area. Since the amount of magnetic particles contained in the calibration sample is minute, it is necessary to take time to measure each point in order to obtain a sufficient S/N ratio. As a result, it takes an enormous amount of time to obtain the system functions.
  • Patent Document 1 uses a method based on numerical calculation.
  • the spatial symmetry that a system function theoretically has is utilized. For example, if the system function distribution is symmetrical with respect to two axes, a point-like calibration sample is installed and moved in only one quarter of the area, and the signal intensity at each position is obtained. The measured signal is then duplicated to match the symmetry of the system function to create the system function.
  • Patent Document 1 cannot deal with the asymmetry of the system function caused by manufacturing errors. As a result, the quality of the reconstructed image deteriorates due to the occurrence of artifacts in the reconstructed image, a decrease in spatial resolution, a decrease in quantitative performance, and the like.
  • an object of the present disclosure is to provide a magnetic particle imaging system that can obtain system functions in a shorter time than a method based on actual measurements, and that can generate high-quality reconstructed images.
  • a method and a magnetic particle imaging program are provided.
  • the magnetic particle imaging system for imaging the spatial distribution of magnetic particles within an examination region of the present disclosure includes a first partial region having a low magnetic field strength and a second partial region having a higher magnetic field strength within the examination region.
  • the sensor includes a receiver that receives a change in magnetization of the sensor as a detection signal, and a processor.
  • the detection signal is expressed as a convolution of the spatial distribution of magnetic particles and a system function.
  • the processor operates the system by a first deconvolution operation based on a first detection signal data set obtained when the calibration sample is placed in the test area and a numerical model of the spatial distribution of magnetic particles contained in the calibration sample.
  • the spatial distribution of magnetic particles contained in the test sample is determined by a second deconvolution operation based on the system function and the second detection signal data set obtained when the function is calculated and the test sample is placed in the test area. get.
  • the magnetic particle imaging method of imaging the spatial distribution of magnetic particles within an examination region of the present disclosure includes a selector that selects a first partial region having a low magnetic field strength and a second partial region having a higher magnetic field strength. generating a selective magnetic field having a spatial pattern of magnetic field strengths to be formed within an examination region; an exciter applying an excitation magnetic field such that the magnetization of magnetic particles present in the selective magnetic field changes; and a receiver. receiving as a detection signal a magnetization change of the magnetic particles excited by the excitation magnetic field, the detection signal being represented by a convolution of the spatial distribution of the magnetic particles and a system function.
  • the magnetic particle imaging method includes a first detection signal data set obtained when a processor places a calibration sample in an examination region, and a numerical model of the spatial distribution of magnetic particles contained in the calibration sample. a step of calculating a system function by a deconvolution operation, and a second deconvolution operation based on the system function and a second detection signal data set obtained when the processor places the test sample in the inspection area; , obtaining a spatial distribution of magnetic particles contained in the test sample.
  • the magnetic particle imaging system includes a first subregion having a low magnetic field strength and a first subregion having a higher magnetic field strength. generating a selection magnetic field with a spatial pattern of magnetic field strength such that a second sub-region having Changes in magnetization of magnetic particles excited by the magnetic field are received as detection signals.
  • a magnetic particle imaging system includes a processor. The detection signal is expressed as a convolution of the spatial distribution of magnetic particles and a system function.
  • the magnetic particle imaging program causes the processor to generate a first detection signal data set based on a data set of first detection signals obtained when a calibration sample is placed in an examination region and a numerical model of the spatial distribution of magnetic particles contained in the calibration sample.
  • a system function can be acquired in a short time, and a high-quality reconstructed image can be generated.
  • FIG. 1 is a diagram showing an example of the overall configuration of a magnetic particle imaging system.
  • 1 is a diagram illustrating an example of a hardware configuration of an information processing device 11.
  • FIG. 1 is a flowchart showing a procedure of a magnetic particle imaging method according to an embodiment.
  • 4 is a flowchart showing a procedure for measuring a calibration sample in step S104 in FIG. 3.
  • FIG. 3 is a diagram showing an example of arrangement of calibration samples.
  • 5 is a flowchart showing the procedure of a subroutine for system function generation (first deconvolution calculation) in step S206 in FIG. 4.
  • FIG. 4 is a flowchart showing the procedure of diagnostic measurement (test sample measurement) in step S107 in FIG. 3.
  • FIG. 8 is a flowchart showing the procedure of the spatial distribution imaging process (second deconvolution calculation) in step S406 of FIG. 7.
  • FIG. 2 is a flowchart showing a procedure for measuring a calibration sample in Patent Document 1.
  • FIG. 3 is a diagram showing an example of arrangement of calibration samples in Patent Document 1.
  • 2 is a flowchart showing the procedure of a system function generation subroutine in Patent Document 1.
  • FIG. 1 is a diagram showing an example of the overall configuration of a magnetic particle imaging system.
  • the magnetic particle imaging system includes an exciter 2, a first selector 3a, a second selector 3b, a receiver 4, a power source 5 for applying an excitation magnetic field, a power source 7 for a first selective magnetic field, and a power source 7 for a second selective magnetic field. It includes a power source 8, a filter 9, a signal amplifier 10, and an information processing device 11.
  • the first selector 3a and the second selector 3b are arranged so that a first partial area having a low magnetic field strength and a second partial area having a higher magnetic field strength are arranged in an examination area in which a subject is placed.
  • a selective magnetic field having a spatial pattern of magnetic field strengths is generated so as to form a selective magnetic field.
  • the region of magnetic particles that can contribute to the measurement signal among the magnetic particles present in the inspection region is limited to the vicinity of the first partial region.
  • a region in which the magnetic field strength takes a value particularly close to zero is called a field free region (FFR).
  • FFR field free region
  • the zero magnetic field region FFR is also called a field free point (FFP), a field free line (FFL), or a planar zero magnetic field region depending on its shape.
  • the FFL only needs to have a zero magnetic field region extending in one direction, and may have a rectangular shape (in this case, the length direction of the long side is the extending direction) or an elliptical shape.
  • the planar zero magnetic field region and FFL have a larger region that can contribute to a signal than FFP, so they have the advantage of being able to obtain a sufficient SN ratio for measurement and image reconstruction in a short time.
  • the first selector 3a includes a first electromagnet.
  • the second selector 3b includes a second electromagnet.
  • the first electromagnet and the second electromagnet are arranged to face each other so as to generate opposite magnetic fields in the examination area.
  • the first electromagnet is connected to a first selection magnetic field power supply 7.
  • the second electromagnet is connected to a second selection magnetic field power source 8.
  • a selection magnetic field is generated by passing current from the first selection magnetic field power supply 7 to the first electromagnet and from the second selection magnetic field power supply 8 to the second electromagnet.
  • the position of the zero magnetic field region FFR within the inspection region can be moved in the translational direction or rotational direction.
  • the first partial region particularly the zero magnetic field region FFR
  • the method of generating the selection magnetic field is not limited to electromagnets.
  • the first electromagnet and the second electromagnet two permanent magnets arranged opposite to each other, or a combination of a permanent magnet and an electromagnet may be used.
  • the scanning method of the magnetic field in this example, zero magnetic field region FFR is not limited to the above.
  • the magnetic field may be scan-driven by physical movement of the first selector 3a and the second selector 3b, or by a combination of electrical movement and physical movement.
  • the magnetic field may be scanned relative to the subject by fixing the position of the magnetic field and moving the subject.
  • the exciter 2 applies an excitation magnetic field so that the magnetization of the magnetic particles present in the selected magnetic field of the test region where a subject such as a calibration sample or a test sample is placed changes.
  • the calibration sample is used to specify the system function and is a sample with a known magnetic particle distribution.
  • the test sample is a sample whose magnetic particle distribution is tested.
  • the exciter 2 is configured by a coil connected to a power source 5 for applying an excitation magnetic field.
  • a power source 5 for applying an excitation magnetic field.
  • the alternating current magnetic field is applied as an excitation magnetic field to the examination area where the subject is placed.
  • the magnetic particles contained in the object By applying an excitation magnetic field to the object, the magnetic particles contained in the object generate a fundamental wave magnetic signal having the same frequency as the excitation magnetic field and a higher-order harmonic magnetic signal.
  • the magnetic particles are modified with a substance such as a protein that binds to a target substance contained in an analyte through an antigen-antibody reaction.
  • the receiver 4 receives the magnetization change of the magnetic particles excited by the excitation magnetic field as a detection signal.
  • the receiver 4 is composed of, for example, a coil.
  • the receiver 4 uses a variable sensor such as a Hall element, a magnetoresistive element (AMR (Anisotropic Magneto Resistive) element, SMR (Semiconductor Magneto Resistive) element, TMR (Tunnel Magneto Resistive) element, etc.), an MI (Magneto Impedance) sensor, etc. Any material that can detect a magnetic field may be used.
  • the detection signal is input to the information processing device 11 via, for example, a noise removal filter 9 and a signal amplifier 10.
  • FIG. 2 is a diagram showing an example of the hardware configuration of the information processing device 11.
  • the information processing device 11 includes a processor 21 , a RAM (Random Access Memory) 25 , a reading section 26 , an internal storage section 27 , a display section 22 , an operation section 23 , and a communication interface 24 .
  • the processor 21 is, for example, a CPU (Central Processing Unit) and executes arithmetic processing.
  • a CPU Central Processing Unit
  • the RAM 25 stores temporary information generated as a result of arithmetic processing by the processor 21.
  • the processor 21 reads programs (including a system function generation program and a spatial distribution imaging program) stored in the internal storage unit 27, loads the programs into the RAM 25, and executes the programs.
  • the reading unit 26 reads information recorded on an optical storage medium such as a CD-ROM (Compact Disk Read Only Memory).
  • an optical storage medium such as a CD-ROM (Compact Disk Read Only Memory).
  • the internal storage unit 27 is, for example, a hard disk drive.
  • the internal storage unit 27 stores various programs such as a system function generation program and a spatial distribution imaging program, and various data such as a numerical model of a calibration sample.
  • the display unit 22 is, for example, a liquid crystal display.
  • the display unit 22 displays a screen generated according to the calculation processing of the processor 21.
  • the operation unit 23 includes, for example, a keyboard, a mouse, and the like.
  • the operation unit 23 accepts operation input by an operator.
  • the communication interface 24 communicates with an external device (for example, a server device) via a network.
  • an external device for example, a server device
  • the system function generation program includes a group of processing instructions for generating a system function based on the measurement signal of the calibration sample.
  • the spatial distribution imaging program includes a group of processing instructions related to imaging of the spatial distribution of magnetic particles present in a test sample such as a living body of a patient. These programs are recorded on, for example, an optical recording medium, read by the reading section 26, and stored in the internal storage section 27. Alternatively, these programs may be downloaded from the server device through the communication interface 24 and stored in the internal storage section 27.
  • Processor 21 stores the detection signal from receiver 4.
  • the k-th harmonic component U k (r i , ⁇ j ) of the detection signal when the zero magnetic field region exists at the translational position r i and the angle ⁇ j is expressed by the system function S It is expressed as a convolution of k (p) and magnetic particle distribution c(p).
  • p is a vector representing a three-dimensional position (x, y, z).
  • the convolution operation is expressed, for example, by the following formula.
  • the system function S k (p) is determined by the influence of the magnetization curve of the magnetic particles, the characteristics of the equipment calibrating the magnetic particle imaging system.
  • d 3 p represents dx ⁇ dy ⁇ dz.
  • the system function S k (p) is a transfer function when the input is the magnetic particle distribution c(p) and the output is the k-th harmonic component U k (r i , ⁇ j ) of the detection signal.
  • An example of the first deconvolution operation is to perform an inverse Fourier transform on the value obtained by dividing the Fourier transform value of U k (r i , ⁇ j ) by the Fourier transform value of c(p). This is the way to obtain.
  • the processor 21 executes a deconvolution operation using a data set of the first detection signal, which is a detection signal collected with a change in the relative position of the calibration sample and the selection magnetic field.
  • the deconvolution operation performed using the data set of the first detection signal will be referred to as a first deconvolution operation.
  • the "first detection signal data set” refers to a collection of first detection signal data collected at a plurality of positions. Therefore, each element of the data set of the first detection signal is associated with the collection position of the first detection signal. For example, each time the position is changed, data of the first detection signal associated with the position is collected. A collection of the collected first detection signal data for each position becomes a first detection signal data set.
  • the first deconvolution operation may be performed using the first detection signal data set corresponding to all positions at the end of the scan, or for each change in the scan position, for each position before and after the change. It may be performed sequentially using corresponding data sets of first detection signals.
  • the processor 21 processes, for example, a data set of the first predicted detection signal obtained by a convolution operation of the magnetic particle distribution of the calibration sample and the predicted system function, and a data set of the first detection signal.
  • the first deconvolution operation may be performed by updating the expected system function so that the values are as close as possible to the data set.
  • the processor 21 updates the prediction system function so that the sum of squared errors between each element of the first prediction detection signal data set and each element of the first detection signal data set decreases. You may.
  • An example of the second deconvolution operation is to perform an inverse Fourier transform on the value obtained by dividing the Fourier transform value of U k (r i , ⁇ j ) by the Fourier transform value of S k (p). This is the way to obtain.
  • the processor 21 executes a deconvolution operation using a data set of the second detection signal, which is a detection signal collected with a change in the relative position of the test sample and the selection magnetic field.
  • the deconvolution operation performed using the second detection signal data set will be referred to as a second deconvolution operation.
  • the "data set of second detection signals” refers to a collection of data of a plurality of collected second detection signals. Therefore, each element of the data set of the second detection signal is associated with a collection position of the second detection signal. For example, each time the position is changed, data of the second detection signal associated with the position is collected. A collection of the collected second detection signal data for each position becomes a second detection signal data set.
  • the second deconvolution operation may be performed using the second detection signal data set corresponding to all positions at the end of the scan, or for each change in the scan position, for each position before and after the change. It may be performed sequentially using the corresponding second detection signal data set.
  • the test sample may be placed stationary at one location within the inspection area, may be placed stationary at multiple locations, or may be moved relative to the selection magnetic field.
  • the processor 21 calculates the expected magnetic particle distribution so that the data set of the second expected detection signal obtained by the convolution calculation of the system function and the expected magnetic particle distribution has a value as close as possible to the data set of the second detected signal.
  • a second deconvolution operation may be performed by updating the particle distribution.
  • the processor 21 adjusts the predicted magnetic particle distribution such that the sum of squares of errors between each element of the second detection signal data set and each element of the second expected detection signal data set decreases. May be updated.
  • FIG. 3 is a flowchart showing the procedure of the magnetic particle imaging method according to the embodiment.
  • step S101 a test agent corresponding to the target substance to be imaged is selected.
  • test agents are superparamagnetic magnetic particles modified with proteins that bind to target substances through antigen-antibody reactions.
  • the magnetization characteristics of the test agent differ depending on the type of test agent. Furthermore, different magnetization properties result in different system functions. Therefore, it is necessary to use a system function that corresponds to the selected test agent. Magnetization properties are determined not only by the characteristics of the magnetic particles themselves, such as the size of the core particle size and core particle size distribution, but also by the hydrodynamic properties of the magnetic particles due to differences in the types of antibody molecules that are modified on the magnetic particles. It is also affected by the surrounding environment, such as changes in diameter and viscosity near the lesion.
  • System conditions include excitation conditions such as excitation intensity distribution and excitation frequency, selection conditions such as intensity distribution of the selection magnetic field, driving conditions for driving the selection magnetic field, sensitivity distribution of the receiving coil, filter characteristics, and signal amplifier characteristics. Reception conditions are included as typical conditions. System conditions are system settings that contribute to the measured signal strength.
  • step S103 it is determined whether the system function acquired under the same conditions as the diagnostic measurement (test sample measurement) is stored in the information processing device 11. If no such system function is stored, the process proceeds to step S104.
  • step S104 calibration sample measurement is performed. This results in the calibration sample measurement being performed before the diagnostic measurement (test sample).
  • step S105 the acquisition timing of the system function is determined. If the system function has not been acquired within a certain period of time, the process returns to step S104. Even if the required system functions are already stored in the information processing device 11, the state of the magnetic particle imaging system changes over time, so periodic inspections that perform calibration sample measurements at certain intervals are necessary. This is because implementation is desirable. If a diagnostic measurement is performed using a system function that does not match the type of test agent or system conditions, the reconstructed image obtained by spatial distribution imaging will contain artifacts and the quantification of magnetic particles will be impaired. or As a result, diagnostic measurements are adversely affected.
  • step S106 a system function is selected.
  • the latest system function is selected.
  • diagnostic measurement is performed in step S107.
  • diagnostic measurement test sample measurement
  • FIG. 4 is a flowchart showing the calibration sample measurement procedure in step S104 in FIG.
  • steps S201 to S206 in the flowchart shown in FIG. 4 is realized by the processor 21 executing the program loaded in the RAM 25.
  • FIG. 5 is a diagram showing an example of the arrangement of calibration samples.
  • one grid in FIG. 5 corresponds to one pixel of the reconstructed image of the spatial distribution of magnetic particles.
  • the size of the calibration sample only needs to be larger than the pixel size of the reconstructed image.
  • a linear zero magnetic field region FLL
  • the calibration sample is placed, for example, approximately in the center of the test area.
  • step S201 the processor 21 generates a command to control power supply to the first electromagnet and the second electromagnet, and transmits the generated command to the first selected magnetic field power source 7 and the second selected magnetic field power source 7. Output to 8.
  • the first selection magnetic field power supply 7 and the second selection magnetic field power supply 8 start supplying power to the first electromagnet and the second electromagnet in response to the command. As a result, a selective magnetic field is generated in the examination area.
  • step S202 the processor 21 generates a command to control power supply to the exciter 2, and outputs the generated command to the excitation magnetic field application power supply 5.
  • the excitation magnetic field application power supply 5 starts supplying power to the exciter 2 in response to the command. As a result, an alternating current excitation magnetic field is applied to the subject.
  • step S203 the processor 21 controls the selection in the inspection area by adjusting the current balance from the first selection magnetic field power supply 7 and the second selection magnetic field power supply 8 to the first electromagnet and the second electromagnet.
  • step S204 the receiver 4 receives the change in the magnetization moment of the magnetic particles excited by the excitation magnetic field as a detection signal.
  • the detection signal is input to the information processing device 11 via the noise removal filter 9 and the signal amplifier 10.
  • step S205 the processor 21 determines whether the scanning of the selected magnetic field in the inspection area has been completed based on preset termination conditions. If the scanning is not completed, the process returns to step S203. If the scanning is completed, the process advances to step S206.
  • the zero magnetic field region FFR is a linear zero magnetic field region (FFL)
  • the FFL is rotated in specified angle increments from 0 degrees to 180 degrees, and at each angle, the entire range within the inspection region is rotated.
  • the zero magnetic field region FFR has a different shape, the scan end conditions are different.
  • step S206 the processor 21 uses the set of detection signals stored in step S204 to execute a process for generating a system function (first deconvolution calculation).
  • step S201 the order of generating the selection magnetic field in step S201 and generating the excitation magnetic field in step S202 may be reversed.
  • the order of scanning drive of the selection magnetic field in step S203 and signal detection in step S204 may be reversed.
  • FIG. 6 is a flowchart showing the procedure of the system function generation (first deconvolution calculation) subroutine of step S206 in FIG. 4.
  • step S301 the processor 21 generates a calibration measurement sinogram from the set of detection signals stored in step S204 of FIG. 4 and information indicating the scanning position of the zero magnetic field region.
  • the calibration measurement sinogram is a map representing the k-th harmonic component U k (r, ⁇ ) of the detection signal at the order k of the harmonic component, the translational position r, and the angle ⁇ .
  • U k (r i , ⁇ j ) is expressed by the convolution of the system function S k (p) and the magnetic particle distribution c(p) of the calibration sample.
  • step S302 the processor 21 sets a predicted system function S2 k (p).
  • a predetermined initial value is set for the predicted system function S2 k (p).
  • step S303 the processor 21 performs a convolution operation of the expected system function S2 k (p) set in step S202 and the magnetic particle distribution c(p) of the calibration sample to obtain the k-th harmonic component U2 of the expected detection signal. Calculate k (r, ⁇ ).
  • the magnetic particle distribution c(p) of the calibration sample is expressed by a numerical model representing the shape and magnetic particle concentration of the calibration sample.
  • the numerical model is expressed as follows using a step function H(p). For example, when the calibration sample has a cylindrical shape (a circle with a diameter R in the YZ plane and a length L in the X direction), the numerical model is expressed by the following formula.
  • c(r) ct ⁇ [H(Lx/2-(x-x0)) ⁇ H(Ly/2-(y-y0)) ⁇ H(Lz/2-(z-z0))]...(A2 )
  • x0, y0, and z0 are the center coordinates of the calibration sample.
  • the calibrated expected sinogram is a map representing the k-th harmonic component U2 k (r, ⁇ ) of the expected detection signal at the order k of the harmonic component, the translational position r, and the angle ⁇ .
  • step S304 the processor 21 calculates the sum of squares E1 of the errors between each element of the calibrated measurement sinogram and each element of the calibrated predicted sinogram.
  • the tensor representing the expected system function S2 k (p) is Sass
  • the tensor representing the numerical model of the magnetic particle distribution of the calibration sample is Cmodel
  • the tensor Uass representing the calibration expected sinogram is expressed by the following formula: Ru.
  • Sass has dimensions k, r i , ⁇ j , x, y, z.
  • Cmodel has x, y, and z dimensions.
  • Uass has dimensions k, r i , ⁇ j .
  • Uass Sass ⁇ Cmodel...(B1) Letting Ucal be the tensor representing the calibration measurement sinogram, E1 is expressed by the following equation. Ucal has dimensions k, r i , ⁇ j . The following equation is the sum of squared errors of each element of the two tensors.
  • step S305 the processor 21 determines whether E1 is less than or equal to a predetermined convergence condition. If E1 is less than or equal to the predetermined reference value, the process returns to step S302. If E1 exceeds the predetermined reference value, the process advances to step S306.
  • step S302 the processor 21 updates the expected system function S2 k (p).
  • the processor updates the expected system function S2 k (p) by gradient descent as in the following equation.
  • ⁇ is an acceleration coefficient that determines the update speed.
  • step S306 the processor 21 determines the predicted system function S2 k (p) corresponding to the calibrated predicted sinogram that satisfies the convergence condition as the system function S k (p).
  • system functions have a smooth distribution, but if the set expected system function S2 k (p) has a non-smooth distribution that includes noise, the spatial distribution image obtained as a result of spatial distribution imaging may This also results in noise. Therefore, it is preferable to perform smoothing on the determined system function S k (p) in post-processing, or to give constraints during the convergence calculation for determining the system function S k (p).
  • FIG. 7 is a flowchart showing the procedure of diagnostic measurement (test sample measurement) in step S107 in FIG.
  • steps S401 to S406 in the flowchart shown in FIG. 7 is realized by the processor 21 executing the program loaded in the RAM 25.
  • step S400 a test sample is placed in the test area.
  • the processing in steps S401 to S405 is similar to the processing in steps S201 to S205 in FIG. 4, so the description will not be repeated.
  • step S406 the processor 21 executes spatial distribution imaging processing (second deconvolution calculation) using the set of detection signals stored in step S404.
  • FIG. 8 is a flowchart showing the procedure of the spatial distribution imaging process (second deconvolution calculation) in step S406 in FIG.
  • step S501 the processor 21 generates a test measurement sinogram from the set of detection signals stored in step S404 of FIG. 7 and information indicating the scanning position of the zero magnetic field region.
  • the test measurement sinogram is a map representing the k-th harmonic component U k (r, ⁇ ) of the detection signal at the order k of the harmonic component, the translational position r, and the angle ⁇ .
  • U k (r i , ⁇ j ) is expressed by the convolution of the system function S k (p) and the magnetic particle distribution c(p) of the test sample.
  • step S502 the processor 21 sets the expected magnetic particle distribution c2(p).
  • the expected magnetic particle distribution c2(p) is set to a predetermined initial value.
  • step S503 the processor 21 calculates the k -th harmonic component U2 k (r, ⁇ ) is calculated.
  • Test expected sinogram is a map representing the k-th harmonic component U2 k (r, ⁇ ) of the expected detection signal at the order k of the harmonic component, the translational position r, and the angle ⁇ .
  • step S504 the processor 21 calculates the sum of squares E2 of the errors between each element of the test measurement sinogram and each element of the test predicted sinogram.
  • the tensor representing the system function S k (p) is S and the tensor representing the expected magnetic particle distribution is Cexp
  • the tensor Uexp representing the expected test sinogram is expressed by the following formula.
  • S has dimensions k, r i , ⁇ j , x, y, z.
  • Cexp has dimensions x, y, and z.
  • Uexp has dimensions k, r i , ⁇ j .
  • Uexp S ⁇ Cexp...(C1) Letting Uins be the tensor representing the test measurement sinogram, E2 is expressed by the following formula. Uins has dimensions k, r i , ⁇ j . The following equation is the sum of squared errors of each element of the two tensors.
  • step S505 the processor 21 determines whether E2 is less than or equal to a predetermined convergence condition. If E2 is less than or equal to the predetermined reference value, the process returns to step S502. If E2 exceeds the predetermined reference value, the process advances to step S506.
  • step S502 the processor 21 updates the predicted magnetic particle distribution c2(p).
  • the processor 21 updates the predicted magnetic particle distribution c2(p) using the gradient descent method as shown in the following equation.
  • step S506 the processor 21 determines the predicted magnetic particle distribution c2(p) that satisfies the convergence condition as the magnetic particle distribution c(p).
  • An image representing this magnetic particle distribution c(p) is spatial distribution imaging data, that is, a reconstructed image.
  • FIG. 9 is a flowchart showing a procedure for measuring a calibration sample in Patent Document 1.
  • the flowchart in FIG. 9 differs from the flowchart in FIG. 4 in that the flowchart in FIG. 9 includes steps S800 and S806 instead of steps S200 and S206.
  • FIG. 10 is a diagram showing an example of arrangement of calibration samples in Patent Document 1.
  • a point-shaped calibration sample is placed at a spatial position corresponding to one pixel within the inspection area.
  • the calibration sample is sequentially moved by a width corresponding to one pixel.
  • FIG. 11 is a flowchart showing the procedure of the system function generation subroutine of Patent Document 1.
  • step S901 the symmetry of the system function is read.
  • step S902 a system function is created by duplicating the data of the measurement signal (detection signal) so as to match the symmetry of the system function.
  • step S903 system function data is output.
  • the distribution of the calibration sample is deconvoluted, unlike the method of Patent Document 1, there is no need to match the size of the calibration sample to the size corresponding to the pixels of the reconstructed image.
  • the sample size can be made larger than the pixel size of the reconstructed image. Since the calibration sample is large, the measurement signal in the measurement of the calibration sample becomes large, so that a sufficient SN ratio can be obtained even in a short measurement time. As a result, measurement of the calibration sample can be performed in a shorter time. Therefore, in this embodiment, periodic inspections of the magnetic particle imaging system can be carried out more frequently than in the past, so the image quality of spatial distribution imaging is improved.
  • the measurement of the calibration sample can be performed using the same procedure as the diagnostic measurement (measurement of the test sample).
  • measurement of the calibration sample may be performed on a certain calibration sample within the inspection region by measurement involving translational scanning and rotational scanning of the FFL. In such a case, mechanical scanning of the calibration sample is not required, resulting in shorter calibration measurements and also eliminating the need for a drive mechanism to perform the mechanical scanning.
  • a reconstructed image is generally represented by pixels divided into a grid, it is better to use a rectangular prism shape than a cylindrical shape in numerical modeling of a calibration sample because it reduces discretization errors at the edges of the calibration sample. becomes smaller.
  • By reducing the error in numerical modeling it is possible to reduce the error in the generated system function and furthermore the error in the spatial distribution image.

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PCT/JP2022/031728 2022-08-23 2022-08-23 磁気粒子イメージングシステム、磁気粒子イメージング方法、および磁気粒子イメージングプログラム Ceased WO2024042614A1 (ja)

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US18/996,342 US20260029493A1 (en) 2022-08-23 2022-08-23 Magnetic particle imaging system, magnetic particle imaging method, and non-transitory computer-readable storage medium storing magnetic particle imaging program
JP2023579520A JP7520261B1 (ja) 2022-08-23 2022-08-23 磁気粒子イメージングシステム、磁気粒子イメージング方法、および磁気粒子イメージングプログラム
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