The present invention relates to the field of magnetic nanoparticle imaging, and particularly relates to a hysteresis effect-based Field Free Point-Magnetic Particle Imaging (FFP-MPI) method.
MPI is a novel imaging method. A Field Free Point (FFP) or a Field Free Line (FFL) is constructed, so that Superparamagnetic Iron Oxide Nanoparticles (SPIOs) in an FFP or FFL area generate a response to an excitation magnetic field, while the SPIOs in other areas are in a magnetic saturation state and do not have a response to the excitation magnetic field, and the purpose of performing spatial coding reconstruction on magnetic particle distribution information is achieved, thereby performing precise positioning on a detection object, such as a tumor. The MPI has the characteristics of high time resolution, high spatial resolution, and no harmful radiation, so it has a very good application prospect in the field of medicine.
At present, image reconstruction theoretical methods of the MPI are all based on Langevin function. It is considered that magnetic particles are adiabatically aligned with an externally applied excitation magnetic field, so as not to produce a hysteresis effect. However, when the particle sizes of the SPIOs in actual application are less than 14 nm, the hysteresis effect of the particles is very low and can be ignored; but when the particle sizes are greater than 14 nm, the hysteresis effect of the particles is obvious. The basic assumptions of a Langevin magnetization curve-based image reconstruction algorithm are no longer satisfied, and artifacts and deviations appear in a constructed image. Most SPIOs used for biomedical MPI at present are about 20 nm, so the hysteresis effect cannot be ignored. Therefore, an image reconstruction algorithm considering the hysteresis effect of the magnetic particles is needed.
In order to solve artifacts of a reconstructed image caused by a hysteresis effect of SPIOs in the prior art, the present disclosure provides a hysteresis effect-based FFP-MPI method. Starting from constructing a hysteresis loop model of the SPIOs, the variations of the characteristics of a Point Spread Function (PSF) are analyzed on the basis of the hysteresis effect, a voltage signal received by an induction coil is reconstructed according to a scanning track of an FFP-MPI system to obtain an original reconstructed image, and finally, deconvolution is performed according to the PSF of the hysteresis effect, so as to obtain a high-quality final reconstructed image. Specific technical solutions are as follows.
A hysteresis effect-based FFP-MPI method includes the following steps:
Further, the method for acquiring the hysteresis loop of the SPIOs in S1 includes:
measuring a plurality of sets of feature point data of the SPIOs in an Alternating Current (AC) magnetic field, substituting the data into an M-H hysteresis curve model, solving to obtain parameters: saturation magnetization vector Ms, magnetic field coupling strength α, magnetic domain density a, average energy k, and magnetization reversibility c, and substituting the parameters into the M-H hysteresis curve model to obtain the hysteresis loop of the SPIOs.
Further, the M-H hysteresis curve model is:
where, H is an externally applied excitation magnetic field, M is a magnetization vector of the SPIOs, μ0 is the permeability of vacuum; when the externally applied excitation magnetic field increases positively, δ=1; when the externally applied excitation magnetic field decreases positively, δ=−1.
Further, the method for calculating to obtain the PSF of the SPIOs in S2 is that:
when the SPIOs are excited by a sinusoidal excitation magnetic field,
H(t)=A cos(ωt)
where, t is time, A is a magnetic field amplitude value, and ω is an angular frequency of an excitation magnetic field;
the above formula is substituted into the hysteresis loop model of the SPIOs to obtain a function M(t) of a magnetization vector of the SPIOs varying along with the time; a derivative of M(t) is taken with respect to the time, and the obtained PSF of the SPIOs is as follows:
Further, a method for acquiring an original reconstructed image of the FFP-MPI on the basis of the FFP moving track and the voltage signal in S3 includes:
moving an FFP according to a scanning track, where the moving speed is v, and the position is r; scanning the overall view field by using MPI to obtain a voltage signal u(t) of an induction coil,
where a relationship between the original reconstructed image and the voltage signal is as follows:
where, c(r) is a variation distribution matrix of concentration along with the position, and *** is a three-dimensional convolution symbol;
dividing the voltage signal by a scanning speed, and splicing the image according to the scanning track to obtain an original reconstructed image.
Further, a method for performing deconvolution on the original reconstructed image with respect to the PSF considering the hysteresis effect to obtain a final reconstructed image in S4 is that:
where, {tilde over (*)}{tilde over (*)}{tilde over (*)} is three-dimensional deconvolution.
The present disclosure has the following beneficial effects.
The present disclosure will be further described in detail below with reference to accompanying drawings and embodiments.
A hysteresis effect-based FFP-MPI method includes the following steps.
S1: a hysteresis loop of SPIOs is acquired by measuring the parameters through an MPS and combining an M-H curve model of a hysteresis effect.
The SPIOs are excited by a high-frequency (20-45 kHz) sinusoidal excitation magnetic field in an MPI device, and a magnetization process of the SPIOs follows an M-H hysteresis curve model:
Where, H is an externally applied excitation magnetic field, M is a magnetization vector of the SPIOs, and μ0 is the permeability of vacuum. In the model of Formula (1.1), when the externally applied excitation magnetic field increases positively, δ=1; when the externally applied excitation magnetic field decreases positively, δ=−1. Three sets of data of the SPIOs in an AC magnetic field can be measured through the MPS: the residual hysteresis Mr when H=0, the coercive field intensity Hc when M=0, and the maximum magnetization vector Mmax at the maximum magnetic field intensity Hmax. The three sets of data are substituted into (1.1) to solve the following parameters: saturation magnetization vector Ms, magnetic field coupling strength α, magnetic domain density a, average energy k, and magnetization reversibility c. The parameters are substituted into the M-H hysteresis curve model to obtain the hysteresis loop of the SPIOs. For example, when the diameter of the SPIOs is 30 nm, the hysteresis loop considering the hysteresis effect is shown as the hysteresis loop with the hysteresis effect in
S2: a response magnetization vector of the SPIOs is obtained in combination with the hysteresis loop of the SPIOs on the basis of the sinusoidal excitation magnetic field, and a derivative is solved with respect to time, so as to obtain a PSF of the SPIOs.
When the SPIOs are excited by the sinusoidal excitation magnetic field:
H(t)=A cos(ωt) (1.2)
Where, t is time, A is a magnetic field amplitude value, and w is an angular frequency of an excitation magnetic field. Formula (1.2) is substituted into Formula (1.1) to obtain a function M(t) of the magnetization vector of the SPIOs along with time. As shown in
PSF is a derivative of the magnetization vector with respect to time, so:
The variation curve of PSF along with time is as shown in
S3: an original reconstructed image of the FFP-MPI is acquired on the basis of an FFP moving track and a voltage signal.
The scanning track of the FFP generally includes: a Cartesian scanning track and a Lissajous scanning track. The FFP is moved according to a certain scanning track, where the moving speed is v, and the position varying along with time is r(t); the view field of the MPI is scanned once to obtain a voltage signal u(t) of an induction coil. A relationship between the original reconstructed image and the voltage signal is as follows:
IMGraw=u(t)/v=c(r)***PSF (1.4)
Where, c(r) is a variation distribution matrix of concentration along with the position, and *** is a three-dimensional convolution symbol. The voltage signal is divided by a scanning speed, and the image is spliced according to the scanning track to obtain the original reconstructed image. At present, an image result used in the MPI field is the original reconstructed image. The original reconstructed image can reflect the general situation of concentration distribution of the SPIOs, but not an accurate concentration distribution result. In addition, the resolution is poor, the phase backward shift of the PSF caused by the hysteresis effect is ignored, and further image optimization is needed. For example, MPI scans two parallel strip samples, and the original reconstructed image is as shown in
S4: deconvolution is performed on the original reconstructed image through the PSF considering the hysteresis effect, so as to obtain a final reconstructed image.
The original reconstructed image ignores the phase backward shift of the hysteresis effect of the SPIOs, and a position error is generated during corresponding from a time domain voltage signal to each FFP. Deconvolution is performed on the original reconstructed image with respect to the PSF, a phase backward shift error caused by the hysteresis effect is corrected by using the PSF, and the result after the deconvolution is a final reconstructed result.
Where, {tilde over (*)}{tilde over (*)}{tilde over (*)} is three-dimensional deconvolution. For example, MPI scans two parallel strip samples, and the final reconstructed image after the deconvolution is as shown in
The above is merely a specific implementation mode of the present disclosure and is not intended to limit the scope of protection of the present disclosure. Any modifications, equivalent replacements, improvements and the like made within the spirit and principle of the present disclosure shall fall within the scope of protection of the present disclosure.
Number | Name | Date | Kind |
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20110221438 | Goodwill | Sep 2011 | A1 |
20220260655 | Conolly | Aug 2022 | A1 |
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WO-2011116229 | Sep 2011 | WO |
WO 2021016473 | Jan 2021 | WO |
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20230024179 A1 | Jan 2023 | US |