Magnetic resonance imaging pulse sequence design method
A technology of magnetic resonance imaging and sequence design, applied in magnetic resonance measurement, measurement using MRI system, and magnetic variable measurement, etc., can solve problems such as limitations, difficulty in exploring possibilities, etc., to improve safety and patient safety. The effect of comfort
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Embodiment 1
[0081] Embodiment 1 is a modification of the basic embodiment. On the basis of the basic embodiment, the technical solution of embodiment 1 is specifically:
[0082] The steps of the magnetic resonance noise prediction method provided by this embodiment further include:
[0083] 1. Construction of sequence parameter model: define parameters for optimization, and establish the mapping relationship between parameters and K-space and pulse sequence gradient;
[0084] 2. Limitation of K-space trajectory: use the physical principles of magnetic resonance imaging, hardware parameters of the imaging system and other conditions to impose constraints on parameters and sequences;
[0085] 3. Sequence noise prediction: For the generated pulse sequence gradient waveform, quantify the acoustic characteristics of the noise generated during the scanning process;
[0086] 4. Parameter optimization using machine learning methods.
Embodiment 2
[0088] Embodiment 2 is a variation of Embodiment 1. On the basis of Embodiment 1, the technical solution of Embodiment 2 is specifically:
[0089] Step 3: Sequence noise prediction, the specific method is as follows:
[0090] In this embodiment, the signal system theory is used to estimate the noise generated by the magnetic resonance scanner. The magnetic resonance scanner is approximated as a linear system, and the acoustic response s(t) can be regarded as the convolution of the gradient waveform g(t) and the impulse response function (IRF) h(t), which is equivalent to the frequency domain Multiplication: S(f) = G(f)·H(f). where H(f) represents the frequency response function (FRF).
[0091] In order to predict the noise generated during the scanning of sequences generated by the AI Agent, the frequency response function of the MR scanner needs to be obtained. The experiment was carried out in a 3T magnetic resonance imaging system (uMR790, Shanghai United Imaging Medic...
Embodiment 3
[0094] Embodiment 3 is a variation of Embodiment 1 or Embodiment 2. On the basis of Embodiment 1 or Embodiment 2, the technical solution of Embodiment 3 is specifically:
[0095] The specific methods of step 1, step 2, and step 4 can be flexibly defined in different embodiments. E.g:
[0096] The position coordinate k(T) of the sampled data point in K-space and the acceleration a(T) of its traveling in K-space are defined as parameters for optimization. The relationship between the position coordinate k(T) of the sampled data point in K space and its acceleration a(T) traveling in K space can be obtained by the following formula:
[0097] v(T+1)=v(T)+a(T)ΔT, v(0)=0
[0098] k(T+1)=k(T)+v(T)ΔT, k(0)=0
[0099] ΔT=nΔt
[0100] where Δt is the ADC sampling interval. because Among them, γ is the gyromagnetic ratio, and for hydrogen nuclei, γ=42.6MHz / T; s(T) is the rate of change of the gradient amplitude at time T, that is, the slewrate; g(T) is the gradient amplitude at ti...
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