A method and apparatus for optimizing bandwidth using multi-nuclear magnetic resonance imaging

Abstract

The application discloses a kind of sampling methods for optimizing bandwidth using multi-nuclear magnetic resonance imaging, determine magnetic resonance scanning track and encoding gradient;The density compensation coefficient corresponding to k value is calculated and normalized to obtain the normalized density compensation coefficient corresponding to k value;The relaxation compensation coefficient corresponding to k value is calculated;Based on sampling bandwidth, the normalized density compensation coefficient corresponding to k value, the relaxation compensation coefficient corresponding to k value calculates image noise power;Optimize sampling bandwidth based on minimizing image noise power;Optimal sampling bandwidth is obtained;Sampling is carried out using optimized sampling bandwidth setting, and the sampling signal is compensated for density and relaxation time, after the sampling signal after compensation is reconstructed by gridding, the magnetic resonance image is obtained.The application also relates to a corresponding device.Using the sampling method and the device for optimizing bandwidth using multi-nuclear magnetic resonance imaging of the application can improve the image signal-to-noise ratio of multi-nuclear magnetic resonance imaging and improve the image quality.

Description

Technical Field

[0001] This invention belongs to the field of magnetic resonance imaging (MRI) methods, and particularly relates to a sampling method and device for optimizing the bandwidth of magnetic resonance imaging. It is suitable for setting the sampling bandwidth parameters for magnetic resonance imaging, improving the signal-to-noise ratio of magnetic resonance imaging, and optimizing image quality. Background Technology

[0002] Magnetic resonance imaging (MRI) is an important tool in the field of modern medical imaging. Its advantages of being non-invasive, radiation-free, and providing excellent soft tissue contrast make it widely used in clinical diagnosis and research. The human body contains more than 60 elements, and all atomic nuclei with non-zero spin can generate magnetic resonance signals. With the development of multinucleus MRI technology, the imaging capabilities for different elements have further expanded the application range of MRI, providing rich multi-element information for detecting vital activities.

[0003] In multinucleus magnetic resonance imaging, the T2 or T2* relaxation times of various nuclides are relatively short (e.g., 23 Na, 129 Due to the short sampling time window (e.g., Xe), conventional magnetic resonance imaging (MRI) sequences often struggle to capture sufficient signals. Therefore, ultrashort echo time (UTE) imaging sequences are commonly employed. UTE sequences are a key technology for addressing imaging challenges with short T2 or T2* relaxation times. Sampling bandwidth is a crucial parameter in sequence parameter settings, closely related to the image signal-to-noise ratio (SNR). Theoretically, the noise power of the sampled signal is directly proportional to the sampling bandwidth; a larger bandwidth results in higher noise power. Conversely, the readout time is inversely proportional to the sampling bandwidth; a smaller bandwidth leads to a longer readout time. During signal readout, signal attenuation due to T2* relaxation causes image blurring, thus reducing the true resolution of the image.

[0004] Therefore, finding the optimal sampling bandwidth setting by balancing the impact of image signal-to-noise ratio and true resolution is a crucial issue in multi-nuclear magnetic resonance imaging (MMRI). However, research on this issue is limited, and currently, there is a lack of an efficient and reliable solution that can optimize bandwidth settings under different imaging parameters to meet practical application requirements. Sampling bandwidth setting remains largely empirical, with an appropriate sampling bandwidth selected based on image quality after acquiring multiple images. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned problems in the prior art by providing a sampling method and device that optimizes bandwidth using multinuclear magnetic resonance imaging.

[0006] The above-mentioned objectives of the present invention are achieved by the following technical means:

[0007] A sampling method for optimizing bandwidth using multinuclear magnetic resonance imaging, characterized by comprising the following steps:

[0008] Step 1: Determine the magnetic resonance scan trajectory and coding gradient;

[0009] Step 2: Sample under the coding gradient selected in Step 1, calculate the density compensation coefficient dc(k) corresponding to the k value, and perform normalization to obtain the normalized density compensation coefficient dcn(k) corresponding to the k value;

[0010] Step 3: Calculate the relaxation compensation coefficient rc(k) corresponding to the value of k;

[0011] Step 4: Calculate the image noise power based on the sampling bandwidth BW, the normalized density compensation coefficient dcn(k) corresponding to the k value, and the relaxation compensation coefficient rc(k) corresponding to the k value;

[0012] Step 5: Optimize the sampling bandwidth based on minimizing image noise power to obtain the optimal sampling bandwidth.

[0013] The formula for calculating the sampling bandwidth in step 4 is as follows:

[0014] BW = γG P FOV1

[0015] Where BW is the sampling bandwidth, γ is the gyromagnetic ratio of the imaging kernel, and G... P FOV1 represents the strength of the encoding gradient during the plateau period, and FOV1 represents the size of the first dimension of the sampling field of view FOV.

[0016] The formula for calculating the density compensation coefficient dc(k) corresponding to the k value in step 2 is as follows:

[0017]

[0018] in,

[0019]

[0020] Where BW is the sampling bandwidth, FOV1 is the size of the first dimension of the sampling field of view FOV, t(k) is the sampling time corresponding to the coding gradient reaching value k, and T ramp Let k be the gradient ascent time, and k be the k-value in the k-space corresponding to the magnetic resonance image. max Let Matrix1 be the maximum k-value in the k-space corresponding to the magnetic resonance image, and T be the size of the first dimension of the sampling matrix Matrix. acq For k values ​​in k-space to reach k max The sampling time at that time.

[0021] The relaxation compensation coefficient rc(k) corresponding to the k value in step 3 is calculated using the following formula:

[0022]

[0023] Where e is the natural constant, t(k) is the sampling time corresponding to the coding gradient reaching the value k, and T2* is the relaxation time corresponding to T2* relaxation.

[0024] The formula for calculating image noise power in step 4 is as follows:

[0025]

[0026] Among them, P σ (BW) represents the image noise power, σ k Let k be the noise of the signal in k-space. max is the maximum k-value in the k-space corresponding to the magnetic resonance image.

[0027] The formula for minimizing image noise power in step 5 is as follows:

[0028]

[0029] in, It is to seek The minimum sampling bandwidth BW, P σi Let T2 be the relaxation time of the i-th time. * The corresponding image noise power is shown below, where N represents the relaxation time T2. * The number of possible values.

[0030] It also includes step 6:

[0031] Using the optimized sampling bandwidth setting obtained in step 5, sampling is performed. The obtained sampling signal is multiplied by the updated normalized density compensation coefficient dcn(k) to obtain the density-compensated sampling signal. Then, the density-compensated sampling signal is multiplied by the updated relaxation compensation coefficient rc(k) to obtain the compensated sampling signal. After the compensated sampling signal is reconstructed by gridding, a magnetic resonance image is obtained.

[0032] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement all steps of the above-described sampling method using multinuclear magnetic resonance imaging to optimize bandwidth, except for step 6.

[0033] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements all steps of the above-described sampling method using multinuclear magnetic resonance imaging to optimize bandwidth, except for step 6.

[0034] A computer program product includes a computer program that, when executed by a processor, implements all steps of the above-described sampling method using multinuclear magnetic resonance imaging with optimized bandwidth, except for step 6.

[0035] Compared with existing methods, the present invention has the following advantages:

[0036] By establishing a method for calculating image noise power under different sampling bandwidths, the sampling bandwidth setting is optimized with the goal of minimizing image noise power, thereby enhancing the signal-to-noise ratio of multinuclear magnetic resonance imaging and improving image quality. Attached Figure Description

[0037] Figure 1 This is a flowchart of the present invention;

[0038] Figure 2 This is a graph showing the relationship between the intensity of the coding gradient and the time of the coding gradient for different sampling bandwidths (BW) of the present invention; the horizontal axis represents the time of the coding gradient, and the vertical axis represents the intensity of the coding gradient.

[0039] Figure 3 This is a graph showing the relationship between the k-value and sampling time for signals under different sampling bandwidths (BW) according to the present invention; the horizontal axis represents the magnitude of the k-value corresponding to the signal, and the vertical axis represents the sampling time corresponding to that k-value.

[0040] Figure 4 This is a graph showing the relationship between the normalized density compensation coefficient and the corresponding k value in this invention; the horizontal axis represents the magnitude of the k value corresponding to the signal, and the vertical axis represents the magnitude of the normalized density compensation coefficient.

[0041] Figure 5 This is a graph showing the relationship between the relaxation compensation coefficient and the corresponding k value under different sampling bandwidths (BW) of the present invention; the horizontal axis represents the magnitude of the corresponding k value of the signal, and the vertical axis represents the magnitude of the density compensation coefficient.

[0042] Figure 6 This is a graph showing the relationship between image noise power and sampling bandwidth BW in this invention; the horizontal axis represents the sampling bandwidth BW, and the vertical axis represents the logarithmic value of the image noise power.

[0043] Figure 7 The relaxation time T2 of this invention * The graph shows the relationship between the corresponding optimized sampling bandwidth (BW) value and the horizontal axis; the horizontal axis represents the relaxation time (T2). * The size is represented by the vertical axis, which is the optimized sampling bandwidth (BW) value. Detailed Implementation

[0044] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to embodiments. The embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0045] Example 1:

[0046] A sampling method for optimizing bandwidth using multinuclear magnetic resonance imaging includes the following steps:

[0047] Step 1: Determine the magnetic resonance scan trajectory and coding gradient.

[0048] In magnetic resonance imaging (MRI), the sampling bandwidth (BW) is related to the encoding gradient of the MRI hardware. In this embodiment, MRI sampling uses a radial trajectory for radial sampling, and the encoding gradient adopts a fixed rise-time pattern. That is, during a single sampling period, the intensity of the encoding gradient first rises with a fixed rise time, and then enters a plateau phase. During the plateau phase, the intensity of the encoding gradient remains unchanged, and after sampling, the intensity of the encoding gradient decreases to 0 with the same decay time as the rise time.

[0049] In this embodiment, the sampling parameters are as follows: the sampling field of view (FOV) is set to 32mm×32mm, the sampling matrix is ​​set to 64×64, and the number of radial sampling excitations is 202.

[0050] Based on the above sampling parameters, the maximum k value k in the k-space corresponding to the magnetic resonance image is calculated. max for:

[0051]

[0052] Where FOV1 represents the size of the first dimension of the sampling field of view (FOV), and Matrix1 represents the size of the first dimension of the sampling matrix (Matrix). Because radial sampling results in a square field of view and isotropic resolution, the same result can be obtained by taking any value for either the sampling field of view (FOV) or the sampling matrix (Matrix). In this embodiment, k... max 1000m -1 .

[0053] The value of k in k-space is the integral of the encoding gradient, and the value of k(t) that changes with the sampling time t can be expressed as:

[0054]

[0055] Where G(τ) is the value of the coding gradient as a function of time τ, γ is the gyromagnetic ratio of the imaging kernel, and T is the total sampling time.

[0056] According to the Nyquist sampling theorem, the maximum distance between adjacent sampling points in k-space should be equal to 1 / FOV1, i.e., γG P Δt = 1 / FOV1, where G PLet Δt be the intensity of the coding gradient during the plateau period, and Δt be the time interval between sampling points. Simultaneously, the sampling bandwidth BW is the reciprocal of the sampling time interval, i.e., BW = 1 / Δt. The relationship between the sampling bandwidth BW and the intensity of the coding gradient during the plateau period can be calculated as follows:

[0057] BW = γG P FOV1 (3)

[0058] In this embodiment, under the fixed climb time mode, the climb time is set to a fixed 100 μs, and the maximum climb rate does not exceed 2000 T / m / s. The maximum coding gradient intensity achievable during the plateau phase in this mode is 0.2 T / m. The corresponding maximum sampling bandwidth (BW) under these sampling parameters is 2.72 × 10⁻⁶. 5 Hz.

[0059] In this fixed climb time mode, the k value in the k-space reaches k. max Sampling time T acq for:

[0060]

[0061] Among them, T ramp Let T be the gradient ascent time. Sampling and encoding gradients are initiated simultaneously; the gradient ascent time T for encoding the gradient is... ramp During this period, the encoded gradient ascends linearly, and the gradient ascent time T ramp At the end, the strength of the encoded gradient reaches G. P During the plateau period, the strength of the encoded gradient remains constant. After sampling, the encoded gradient undergoes another period equal to the gradient ramp time T. ramp The same time decay is 0.

[0062] Figure 2 This is a graph showing the relationship between the intensity of the coding gradient and the time of the coding gradient for different sampling bandwidths (BW).

[0063] Step 2: Sample under the encoding gradient selected in Step 1, calculate the density compensation coefficient dc(k) corresponding to the k value, and perform normalization to obtain the normalized density compensation coefficient dcn(k) corresponding to the k value.

[0064] Sampling is performed under the encoding gradient described in step 1, and the sampled signal fills the k-space. In radial sampling, the sampling density is high at the center of the k-space and low at the outer periphery of the k-space.

[0065] In regions with high sampling density, the averaged sampled signal exhibits lower noise, while in regions with low sampling density, the averaged sampled signal exhibits higher noise, and the noise power is inversely proportional to the sampling density. Therefore, after radial sampling, density compensation can be performed on the k-space sampled signal by multiplying the sampled signal by a density compensation coefficient. The density compensation coefficient is the reciprocal of the sampling density, i.e., dc(k) = 1 / D(k), where dc(k) is the density compensation coefficient corresponding to the k value, and D(k) is the sampling density corresponding to the k value.

[0066] In the fixed climb mode, the value k(t) that varies with the sampling time t is calculated and expressed as:

[0067]

[0068] The sampling time t(k) corresponding to the gradient reaching value k can be expressed as:

[0069]

[0070] Figure 3 The graph shows the relationship between the k value and the sampling time t for different sampling bandwidths (BW).

[0071] In radial sampling mode, the sampling density is inversely proportional to the value of k and also inversely proportional to the rate of change of k over time. Based on this, the density compensation coefficient corresponding to radial sampling can be calculated. The density compensation coefficient is the reciprocal of the sampling density.

[0072]

[0073] In radial sampling mode, the sampling density at the outermost periphery of k-space is typically the lowest, and it precisely satisfies the Nyquist sampling theorem. Therefore, the density compensation coefficient is largest at the outermost periphery. Based on this, the density compensation coefficient can be normalized as dcn(k) = dc(k) / max(dc(k)), where max represents the maximum value, and dcn(k) represents the normalized density compensation coefficient corresponding to the value of k, facilitating subsequent calculations. Figure 4 The graph shows the relationship between the normalized density compensation coefficient and the signal k value.

[0074] Step 3: Calculate the relaxation compensation coefficient rc(k) corresponding to the value of k.

[0075] The sampling bandwidth BW is inversely proportional to the sampling time t. During sampling, T2* relaxation leads to signal attenuation, resulting in image blurring and reduced true resolution. Therefore, to compare images with different sampling bandwidths BW, a comparison standard needs to be established to correct for the effects of T2* relaxation. In this embodiment, relaxation time compensation is performed on the sampling signal before image reconstruction to ensure that the true resolution of the magnetic resonance images remains consistent. The attenuation of the sampling signal caused by relaxation is compensated by multiplying it by a relaxation compensation coefficient, thus offsetting the effect of relaxation and allowing magnetic resonance images with different sampling bandwidths BW to be compared at the same true resolution.

[0076] During the sampling process, the signal corresponding to the value of k can be expressed as follows under the relaxation time T2*:

[0077]

[0078] Where SI0 is the signal strength without considering T2* relaxation, SI(k) is the signal strength of the sampled signal at the value of k, and e is the natural constant.

[0079] The relaxation compensation coefficient rc(k) for a k-valued signal is the reciprocal of the signal attenuation caused by relaxation, i.e.:

[0080]

[0081] Where rc(k) is the relaxation compensation coefficient corresponding to the value of k, and T2* is the relaxation time corresponding to T2* relaxation. Figure 5 The graph shows the relationship between the relaxation compensation coefficient rc(k) and the value of k. It can be seen that the smaller the bandwidth and the longer the sampling time, the larger the relaxation compensation coefficient.

[0082] Step 4: Calculate the image noise power based on the sampling bandwidth BW, the normalized density compensation coefficient dcn(k) corresponding to the k value, and the relaxation compensation coefficient rc(k) corresponding to the k value.

[0083] Noise in magnetic resonance imaging (MRI) images originates from sampling noise across the entire k-space. During sampling, the noise power in the sampled signal is proportional to the sampling bandwidth (BW). After applying density and relaxation compensation coefficients, the sampled signal is reconstructed using a gridding method to obtain the MRI image. During gridding reconstruction, regions with high sampling density have lower noise power after averaging, while regions with low sampling density have higher noise power. The noise power distribution corresponding to different sampling densities is corrected using density compensation coefficients; similarly, the effect of relaxation on the sampled signal is corrected using relaxation compensation coefficients when the sampling bandwidth (BW) is different. During the correction process, the noise power is amplified or reduced accordingly with the compensation coefficients.

[0084] In this embodiment, the image noise power P σ(BW) can be expressed as follows under the combined effect of density compensation coefficient and relaxation compensation coefficient:

[0085]

[0086] Where, σ k Let P be the noise of the signal in k-space, where the noise is a measured value. σ (BW) represents the power of the image noise, dcn(k) is the normalized density compensation coefficient, and rc(k) is the relaxation compensation coefficient.

[0087] Step 5: Optimize the sampling bandwidth based on minimizing image noise power to obtain the optimal sampling bandwidth.

[0088] As can be seen from the calculation method of image noise power, image noise power is the sum of the sampling bandwidth BW and the relaxation time T2. * A function with equal parameters. The relaxation time T2 can be determined experimentally. * The value of , or the relaxation time T2 is determined based on prior knowledge. * The approximate range, and then the relaxation time T2 * The values ​​of are discretized. The objective function for optimizing the sampling bandwidth is:

[0089]

[0090] In the formula, It is to seek The minimum sampling bandwidth BW is calculated to make The minimum sampling bandwidth BW is the optimal bandwidth. σi Let T2 be the relaxation time of the i-th time. * The corresponding image noise power is shown below, where N represents the relaxation time T2. * The number of possible values.

[0091] The image noise power varies significantly with the sampling bandwidth BW, so by analyzing the image noise power P... σ (BW) takes the logarithm to reduce the numerical range and determines the minimum image noise power. Figure 6 Relaxation time T2 * The relationship between the logarithm of the noise power at a 2ms sampling time and the sampling bandwidth BW is shown. At this point, the sampling bandwidth corresponding to the minimum image noise power is set to 12900Hz. With a sampling bandwidth of 12900Hz, the noise power is 88% of the noise power at a sampling bandwidth of 8000Hz and 91% of the noise power at a sampling bandwidth of 20000Hz.

[0092] According to Formula 11, the relaxation time T2 can be obtained. * Correspondence with the optimal sampling bandwidth BW ( Figure 7It can be seen that the optimal sampling bandwidth BW and relaxation time T2 are... * It is inversely related.

[0093] Step 6: Sample using the optimal sampling bandwidth setting to obtain a magnetic resonance image.

[0094] Sampling is performed using the optimized sampling bandwidth setting obtained in step 5. The obtained sampled signal is multiplied by the updated normalized density compensation coefficient dcn(k) to obtain the density-compensated sampled signal. Then, the density-compensated sampled signal is multiplied by the updated relaxation compensation coefficient rc(k) to obtain the compensated sampled signal. After the compensated sampled signal is reconstructed by gridding, the magnetic resonance image is obtained.

[0095] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0096] Example 2:

[0097] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0098] Example 3:

[0099] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0100] Example 4:

[0101] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0102] It should be noted that the embodiments described in this invention are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains can make various modifications or additions to the described embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging, characterized in that, Includes the following steps: Step 1: Determine the magnetic resonance scan trajectory and coding gradient; Step 2: Sample under the coding gradient selected in Step 1, calculate the density compensation coefficient dc(k) corresponding to the k value, and perform normalization to obtain the normalized density compensation coefficient dcn(k) corresponding to the k value; Step 3: Calculate the relaxation compensation coefficient rc(k) corresponding to the value of k; Step 4: Calculate the image noise power based on the sampling bandwidth BW, the normalized density compensation coefficient dcn(k) corresponding to the k value, and the relaxation compensation coefficient rc(k) corresponding to the k value; Step 5: Optimize the sampling bandwidth based on minimizing image noise power to obtain the optimal sampling bandwidth.

2. The sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging according to claim 1, characterized in that, The formula for calculating the sampling bandwidth in step 4 is as follows: BW=γG P FOV1 Where BW is the sampling bandwidth, γ is the gyromagnetic ratio of the imaging kernel, and G... P FOV1 represents the strength of the encoding gradient during the plateau period, and FOV1 represents the size of the first dimension of the sampling field of view FOV.

3. The sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging according to claim 1, characterized in that, The formula for calculating the density compensation coefficient dc(k) corresponding to the k value in step 2 is as follows: in, Where BW is the sampling bandwidth, FOV1 is the size of the first dimension of the sampling field of view FOV, t(k) is the sampling time corresponding to the coding gradient reaching value k, and T ramp Let k be the gradient ascent time, and k be the k-value in the k-space corresponding to the magnetic resonance image. max Let Matrix1 be the maximum k-value in the k-space corresponding to the magnetic resonance image, and T be the size of the first dimension of the sampling matrix Matrix. acq For k values ​​in k-space to reach k max The sampling time at that time.

4. A sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging according to claim 1, characterized in that, The relaxation compensation coefficient rc(k) corresponding to the k value in step 3 is calculated using the following formula: Where e is the natural constant, t(k) is the sampling time corresponding to the coding gradient reaching the value k, and T2* is the relaxation time corresponding to T2* relaxation.

5. A sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging according to claim 1, characterized in that, The formula for calculating image noise power in step 4 is as follows: Among them, P σ (BW) represents the image noise power, σ k Let k be the noise of the signal in k-space. max is the maximum k-value in the k-space corresponding to the magnetic resonance image.

6. A sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging according to claim 1, characterized in that, The formula for minimizing image noise power in step 5 is as follows: in, It is to seek The minimum sampling bandwidth BW, P σi Let T2 be the relaxation time of the i-th time. * The corresponding image noise power is shown below, where N represents the relaxation time T2. * The number of possible values.

7. A sampling method for optimizing bandwidth using multi-nuclear magnetic resonance imaging according to claim 1, characterized in that, It also includes step 6: Using the optimized sampling bandwidth setting obtained in step 5, sampling is performed. The obtained sampling signal is multiplied by the updated normalized density compensation coefficient dcn(k) to obtain the density-compensated sampling signal. Then, the density-compensated sampling signal is multiplied by the updated relaxation compensation coefficient rc(k) to obtain the compensated sampling signal. After the compensated sampling signal is reconstructed by gridding, a magnetic resonance image is obtained.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of a sampling method using multinuclear magnetic resonance imaging to optimize bandwidth, as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a sampling method using multinuclear magnetic resonance imaging to optimize bandwidth, as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of a sampling method using multinuclear magnetic resonance imaging to optimize bandwidth, as described in any one of claims 1 to 6.

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