GOVERNMENT INTEREST STATEMENT

[0001] The present subject matter was made with U.S. government support. The U.S. government has certain rights in this subject matter.

FIELD

[0002] The present technology pertains to probing in-vivo metabolite relaxation, and more specifically to a multiple flip angle pulse-driven ratio of longitudinal steady states magnetization preparation that is echo time independent (TE-independent).

BACKGROUND

[0003] Nuclear magnetic resonance spectroscopy (MRS) allows noninvasive detection of endogenous metabolites in the human brain and can provide valuable metabolic and physiological information. MRS is also a promising tool for diagnosing metabolic disorders and other various pathological conditions in human brain, including epilepsy, multiple sclerosis, stroke, cancer, and psychiatric disease. Using MRS, the

concentrations and relaxation times of certain metabolites can be used to detect abnormalities in brain regions that otherwise appear normal under magnetic resonance imaging (MRI). MRS can additionally be used to characterize the pathology underlying MRI-visible abnormalities. For example, many neurological and psychiatric diseases alter their surrounding cellular environment, alterations which may be reflected in changes of metabolite T ₂ relaxation time.

[0004] Conventional measurement sequences of MRS all generally measure signal changes at different echo times (TEs). As such, these sequences are referred to as TE- dependent, or multi-TE. However, because these conventional measurement sequences measure at multiple TEs, various TE-dependent effects such as diffusion, macromolecule baseline, and J-coupling modulation are introduced into the quantification results, which is almost always undesirable.

[0005] In general, two types of techniques are conventionally adopted for T ₂

quantification of metabolites in the brain. In a first technique, sequences with simple spin or stimulated echoes are used to measure signals at step-incremented echo times (i.e. multi-TE) for T ₂ quantification. In a second technique, Carr-Purcell-Meiboom-Gill (CPMG) sequences are used in a similar manner by applying multiple Carr-Purcell (CP) blocks. However, these conventional methods provide quantification results that are markedly different and exhibit significant dependence upon the applied sequence type. In some circumstances, the quantification results may differ even when the same sequence is used, due to the selection of measurement parameters such as the number of TE steps or the inter-pulse delay.

[0006] Thus, given that the magnetic resonance exponential decay signal from known multi-TE approaches can be complicated under many scenarios (e.g., confounding spin evolution due to scalar couplings, significant diffusion weighting during readout, or very fast T ₂ relaxation of many Proton-MRS signals), it would be highly desirable to provide a technique capable of generating variable T ₂ weighting at a single TE value.

SUMMARY OF THE INVENTION

[0007] Disclosed are systems, methods, and computer readable media for probing in- vivo metabolite relaxation by linear quantification of spatially modulated magnetization. A magnetic resonance spectroscopy (MRS) readout sequence is selected and a plurality of interleaved pulsed field gradient (iPFG) pulse trains are applied to a subject of measurement. The iPFG trains generate longitudinal steady-state magnetization of the subject, and each iPFG train is applied at a different flip angle and a constant echo time (TE) of readout sequence.

[0008] Using the MRS readout sequence, MRS spectra are obtained for each applied iPFG pulse train, such that the MRS spectra are associated with the corresponding different flip angles of the iPFG pulse trains. For one or more metabolites detected in the MRS spectra, a T-i value is predetermined from various techniques, such as inversion recovery or saturation recovery. Based at least in part on the MRS spectra, a ratio of longitudinal steady states is determined for each of the one or more metabolites. Using the ratio of longitudinal steady states and the T-i value, a T ₂ value is calculated for each of the one or more metabolites.

[0009] In some embodiments, the ratio of longitudinal steady states for each of the one or more metabolites is determined as R _zss = , where a represents a flip angle.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[0011] FIG. 1 depicts a diagram of an RF pulse train and MARzss module;

[0012] FIGS. 2A-D depict results of Bloch simulation and equation validation with N- acetylaspartate (NAA) as the selected metabolite;

[0013] FIGS. 3A-D depict results of phantom metabolite T ₂ determination using the disclosed MARzss technique;

[0014] FIGS. 4A-D depict a comparison of phantom results of J-coupled metabolites, such as aspartate (Asp ⁺ ), with contributions from NAA aspartyl moiety, glutamine (Gin), glutamate (Glu), and lactate (Lac), between the disclosed MARzss technique and conventional multi-TE PRESS techniques;

[0015] FIGS. 5A-D depict results of in-vivo metabolite T ₂ determination using the disclosed MARzss technique;

[0016] FIG. 6 depicts in-vivo results for Glu obtained using the disclosed MARzss technique;

[0017] FIG.7 depicts a Bloch simulation of longitudinal steady state magnetization (Mzss) temporal variation in one TR at different FA (unit: degree); [0018] FIG. 8 depicts typical raw in vivo spectra acquired from the medial frontal lobe voxel of a healthy subject using MARzss method at four different FAs;

[0019] FIG. 9A depicts the mean value of Glu intensity (red cross) from 100 Monte Carlo simulations at 50 different FAs;

[0020] FIG. 9B depicts the contour map of Glu amplitude-to-noise ratio as a function of number of signal averages and FA;

[0021] FIG. 10A depicts individual fit of in vivo spectra acquired from the medial frontal lobe region of a healthy subject using MARzss method at FA=0°;

[0022] FIG. 10B depicts individual fit of in vivo spectra acquired from the medial frontal lobe region of a healthy subject using MARzss method at FA=12°;

[0023] FIG. 10C depicts individual fit of in vivo spectra acquired from the medial frontal lobe region of a healthy subject using MARzss method at FA=24°;

[0024] FIG. 10D depicts individual fit of in vivo spectra acquired from the medial frontal lobe region of a healthy subject using MARzss method at FA=36°;

[0025] FIG. 10E depicts the signal amplitude ratio (Rzss) of Glu as a function of FA is plotted as red circle and linear fitting is shown as black dashed line; Yellow squares overlaid on the high resolution T1 -weighted MRPAGE images at right side of the spectra indicates the position of the MRS voxel in a GM dominated brain region;

[0026] FIGS. 1 1 A, 1 1 B, and 1 1 C depict density matrix simulated Glu spectra with (blue) and without (red) iPFG train without line-broadening at different FA (12°, 24°, 26° respectively);

[0027] FIGS. 1 1 D, 1 1 E, and 1 1 F depict density matrix simulated Glu spectra with (blue) and without (red) iPFG train with line-broadening at different FA (12°, 24°, 26° respectively);

[0028] FIG. 12A depicts a stack view of four in vivo spectra acquired from the left frontal lobe region of a healthy subject using MARzss method at four different FA;

[0029] FIG. 12B depicts a linear combination of fitting plots for individual spectra at FA=0°;

[0030] FIG. 12C depicts a linear combination of fitting plots for individual spectra at FA=12°; [0031] FIG. 12D depicts the signal amplitude ratio (Rzss) of Glu as a function of FA plotted as red circle and linear fitting is shown as black dashed line;

[0032] FIG. 12E depicts a linear combination of fitting plots for individual spectra at

FA=24°;

[0033] FIG. 12F depicts a linear combination of fitting plots for individual spectra at FA=36°;

[0034] FIG. 13A depicts a stack view of four in vivo spectra acquired from the occipital lobe region of a healthy subject using MARzss method at four different FA;

[0035] FIG. 13B depicts a linear combination of fitting plots for individual spectra at FA=0°;

[0036] FIG. 13C depicts a linear combination of fitting plots for individual spectra at FA=12°;

[0037] FIG. 13D depicts the signal amplitude ratio (Rzss) of Glu as a function of FA plotted as red circle and linear fitting is shown as black dashed line;

[0038] FIG. 13E depicts a linear combination of fitting plots for individual spectra at

FA=24°;

[0039] FIG. 13F depicts a linear combination of fitting plots for individual spectra at FA=36°;

[0040] FIGS. 14A and 14B illustrate schematic diagrams of example computing systems for use with example system embodiments; and

[0041] FIG. 15 depicts an example system of the present disclosure.

[0042] Corresponding reference characters indicate corresponding elements among the view of the drawings. The headings used in the figures do not limit the scope of the claims.

DETAILED DESCRIPTION

[0043] Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description.

References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments.

[0044] Disclosed are systems, methods, and computer-readable media for quantifying spatially modulated magnetization of one or more metabolites. In various embodiments, the one or more metabolites may include, but are not limited to glutamate (Glu), glutamine (Gin), lactate (Lac), N-acetylaspartate (NAA; acetyl moiety, aspartyl moiety), N-acetylaspartylglutamate (NAAG acetyl moiety, NAAG aspartyl and glutamate moieties), total Cr, total Cho, Aspartate (Asp), glutathione (GSH), and y-aminobutyric acid (GABA).

[0045] A novel technique, MARzss, is used to probe metabolite T ₂ relaxation in vivo using an interleaved pulse field gradient (iPFG) train to generate RF-driven longitudinal steady-state magnetization without the need to vary TE, and a new linear equation is derived from Bloch equations. The application of the disclosed MARzss technique is demonstrated by Bloch simulations, phantom experiments, and in-vivo experiments on the human brain, and is useful in a variety of applications and experiments involving relaxation characterization of tissue properties. The prepared longitudinal magnetization of longitudinal steady states (M _Z ss) weighted by T ₂ /Ti can be acquired from any readout sequence as desired. When T-iS of metabolites are predetermined from various techniques, such as inversion recovery or saturation recovery, T ₂ s and spin densities of metabolites can be correspondingly calculated. Because the disclosed technique is a quantifiable contrast preparation technique, it does not require varying TE for metabolite relaxation quantification and furthermore is independent of the readout module. Using the disclosed technique in lieu of conventional multi-TE or CPMG methods avoids readout sequence-dependent variations entirely, including but not limited to those associated with diffusion weighting, macromolecule baseline, and J-coupling

mechanism. [0046] Tissue signals at steady states driven by rapid pulse trains are frequently proposed in magnetic resonance imaging (MRI) techniques for quantifying different components of tissue water. Balanced steady-state free precession (b-SSFP) serves as a high signal-to-noise ratio (SNR) imaging tool to generate images with T ₂ /Ti weighted contrasts. Quantitative maps can be extracted from T ₂ /Ti weighted images acquired with two or multiple flip angles (multi-FA). However, b-SSFP cannot be directly used in magnetic resonance spectroscopy (MRS) to quantify MRS spectra, as b-SSFP may have undesirable frequency selective effects which previously were attributed to the periodic nature of delay alternating with nutation for tailored excitation (DANTE) pulses.

[0047] More specifically, DANTE pulses interspersed with gradients were previously employed as a preparation module for the spatial tagging of magnetic resonance images as well as for black blood contrast imaging. When DANTE preparation is used for black blood imaging applications, spatial modulation of longitudinal magnetization (M _z ) can give rise to periodic intensity variations in images, which manifest as dark bandings. To avoid visualizing these dark bandings in black blood images, the gradient moment in DANTE pulses can be adjusted such that the banding size is smaller than the voxel size. In that case, the voxel signal amplitude may be quantified. This DANTE prepared imaging application indicates that the spatial modulation of metabolites at steady state M _Z ss may also be quantifiable in a similar fashion.

[0048] In order to best describe the disclosed techniques, FIG. 1 depicts an example train of RF pulses with a gradient G along the z-direction, where the gradient is further associated with an interleaved pulse field gradient (iPFG) train. Also shown are a water saturation/suppression module 'Wat Sat' and a readout module 'PRESS' (Point

RESolved Spectroscopy), which generates the basic PRESS pulse sequence of a double spin-echo with slice selective pulses in orthogonal planes, although it is understood that various other readout modules and pulse sequences may be employed without departing from the scope of the present disclosure. As seen in FIG. 1 , the RF pulses can be characterized by a phase Θ, which alternates between a base frequency shift (bfs) given by the frequency offset of the pulse and bfs + 180°, an interpulse delay T _d , and a flip angle (FA) a. In order to quantify the longitudinal M _Z ss of metabolites at steady state using different flip angles, a new linear equation derived from Bloch equations is disclosed.

[0049] For the train of RF pulses seen in FIG. 1 , the resulting steady state

magnetization (M _Z ss) is given by:

rr((nn++li))nn M M ₀₀ (( ll--Et ₁ )[E ₂ (E ₂ --ccoosseti)) ++ 0(l---Et ₂₂ ccoosseti) )ccoossaa]\

mZSS = J -( ^η +1)π {i-E ₁ cosa){l-E ₂ cose)-E ₁ cosa{E ₂ -cose)E ₂

(1 )

-Td -Td

where E ₁ = e ^T i , E ₂ = e , n is an integer, M ₀ is the thermal equilibrium signal intensity, and as noted above, a is the flip angle (FA) of a single RF pulse and T _d is the inter-pulse delay time. Note that the inter-pulse delay time T _d is much shorter than Ti and T ₂ of the metabolite; E ₁ = 1 -— and E ₂ = 1 -— ; and Θ is the position-dependent linear phase angle induced inside the voxel by the applied field gradient and the local field

inhomogeneity.

[0050] For Equation 1 to be valid, the minimal gradient moment Gd _G in FIG. 1 must be much greater than where γ is the gyromagnetic ratio, d _G is the duration of the applied gradient, and ΔΓ is the voxel size. A train of identical RF pulses may be applied and field gradients with the same amplitude and duration may be inserted between RF pulses. Depending on the FA, each RF pulse in the iPFG train puts a portion of the longitudinal magnetization onto the transverse plane. The gradient immediately following the RF pulse dephases spin in the transverse plane before the application of next RF pulse. After applying a considerable number of RF pulses and field gradients, the longitudinal magnetization is attenuated from its thermal equilibrium state ( ₀ ) to a RF-driven steady state closed-form expression for M _Z ss:

M _zss = ₀ - (l - ₇₇ ¾=) (2)

[0051] where K = I— and X = tan (—) (0°≤ FA≤ 90°). By measuring the steady state magnetization (M _Z ss) at several different FAs, a linear relationship between the relaxation weighting factor A of metabolites and the ratio of signal amplitudes (R _zss ) can be established:

R _ZSS = K - X (3) [0052] where R _zss = which is the ratio of signal amplitudes from the

readout sequence. The slope A of the above function can be solved by linear regression:

min \ \R _zss - K - X\ \ ² (4)

Equation 3 suggests that T-|/T ₂ of metabolites can be calculated by linear fitting. More importantly, Equation 3 is independent of readout sequence and local inhomogeneity. Because TE is a fixed parameter in readout, the real or complex exponential terms and

-TE

effects induced by readout sequence (e.g. e ^T 2 , e ^~bD , or J evolution f(J, a)) are common factors that are cancelled in the ratio of longitudinal steady states R _Z ss- [0053] Due to delay of the water suppression module, the spatial modulation of metabolites at steady state M _Z ss experiences a slight magnetization recovery which can be corrected using T-i recovery:

^~ tyvs ^~ tyvs

Mmeas_z = M _zss e * ⁱ + (1 - e r ⁱ ) ₀ (5) where M _mea s_z is the amplitude from measurement and t _ws is the delay time of the water suppression module. For purposes of discussion, it is assumed that Ti of the

metabolites is predetermined from other approaches, such as inversion recovery or saturation recovery. Therefore, M _Z ss can be calculated.

[0054] With the predetermined metabolite Ti value, normally through TE-independent conventional techniques, such as inversion recovery, metabolite T ₂ values are determined:

T ₂ = T K ² (6)

Relaxation-weighting preparation and readout are uncoupled in the MAR _ZSS method. The RF-driven steady state magnetization M _Z ss can be measured without changing the readout TE and is independent of the readout sequence.

[0055] A new technique, MAR _ZS s, for probing metabolite T ₂ relaxation by generating RF- driven longitudinal steady-state magnetization without the need to vary TE is disclosed. Multiple flip angle iPFG trains are utilized as preparation modules to generate T ₂ /T-i weighting of the steady state longitudinal magnetization. In contrast, a PRESS sequence serves only as a readout module. A new linear equation for quantifying longitudinal magnetization and metabolite relaxation (T ₂ /T-i) at steady state, derived from Bloch equations, is disclosed. The newly derived equations are validated through Bloch simulations, phantom experiments, and in-vivo experiments

[0056] Using conventional techniques such as multi-TE PRESS, spins evolve on the transverse plane with differing evolution times between measurements, (i.e. echo time TE), which may result in confounding signal decay from diffusion, physiological motion, and/or signal modulation from J-coupling. Further, it is known that metabolite T ₂ s measured by multi-TE PRESS methods are substantially different from those measured by CPMG-type methods (see, for example, Table 2). In contrast, when used as a preparation module, the proposed new technique MAR _Z ss is independent from the undesirable effects noted above. These undesirable effects are traditionally generated by the readout sequence and the disclosed MARzss technique offers results that are substantially independent of the readout sequence. Therefore, the readout sequence parameters, especially TE, can advantageously be kept consistent between

measurements.

[0057] There are several significantly different characteristics among the three techniques for T ₂ measurement. First, it is noted that metabolite quantification is complicated by the baseline contributions of broad resonances from macromolecules when spectra are measured at short or even medium TEs (echo times). Without a priori knowledge of the concentrations or relaxation times of these macromolecules, accurate extraction of the true signal amplitude of the metabolites of interest can be difficult or even impossible for short TEs. However, using short TE data is unavoidable when measuring T ₂ using conventional multi-TE PRESS or CPMG techniques.

[0058] Because the fitting results of signal amplitudes acquired with short TE steps are typically less accurate than those acquired with longer TE steps, due to the stronger macromolecule baseline present at short TE, systematic errors in metabolite T ₂ measurements are difficult to eliminate when using either of the two conventional techniques. In contrast, the disclosed MARzss method completely separates relaxation weighting from readout. As such, an optimized readout sequence with fixed TE can be used. For example, in the examples discussed below, the readout sequence was optimized to minimize macromolecule baseline and to separate Glu H4 resonance from overlapping signals of Gin and the aspartyl moiety of NAA.

[0059] Second, for spin echo type sequences, metabolite transverse relaxation measurements can be affected by extra signal attenuation resulting from diffusion effects. More specifically, variations in TE effectively alter diffusion times. Additionally, the spoiling gradients used in the sequences and the intrinsic microscopic internal gradient associated with susceptibility inhomogeneity can introduce diffusion

attenuation. Furthermore, in cases of in-vivo measurements obtained in the presence of internal gradients at different TEs, the potential signal decay (pseudo-diffusion) induced by physiological motion such as cardiac and respiration cycles can be even stronger than diffusion per se, introducing more errors. Implementing CPMG-type sequences for quantification can reduce various diffusion effects. However, in order to shorten the diffusion time, the inter-pulse delay of CPMG has to be decreased while increasing the number of 180° refocusing pulses. These changes may result in safety concerns relating to specific absorption rates in clinical settings and applications. Finally, metabolite T ₂ relaxation measured using CPMG-type sequences can be contaminated by relaxation during the refocusing pulses.

[0060] Third, for spin echo and stimulated echo sequences, the signal amplitude of coupled resonances is complicated due to scalar coupling effects. Introducing T ₂ relaxation by varying TE, which is necessary for both multi-TE PRESS and CPMG methods, introduces additional signal modulation due to scalar coupling but is again unavoidable in both conventional methods. The experimenter is often forced to use multiple suboptimal TEs in order to generate different amount of T ₂ weighting for T ₂ quantification. These suboptimal TEs can cause significant signal overlapping for metabolites of interest. For example, the spectra of FIG. 4A, acquired using the disclosed MAR _Z ss technique, demonstrate smooth and monotonic signal amplitude attenuation by the progressively larger saturation effects of iPFG trains. In comparison, the spectra of FIG. 4B, obtained using a conventional multi-TE PRESS method, demonstrate strong interference between the J-coupling effect and the exponential attenuation of transverse relaxation. [0061] While multi-TE PRESS experiments place magnetization entirely on the transverse plane to introduce necessary T ₂ weighting, the disclosed pulse-driven steady state technique uses a combination of rapid refocusing with 10 ms intervals between adjacent iPFG pulses and longitudinal storage of magnetization. Given that in the MARzss method, relaxation weighting is prepared longitudinally, the method is expected to be substantially less susceptible to diffusion and motion effects.

[0062] The phantom measurements listed in Table 1 demonstrate that the T ₂ of NAA, Cr, and Cho as determined by the disclosed MARzss technique agree reasonably well with the conventional multi-TE PRESS technique. This is likely due to the fact that internal local susceptibility gradients (i.e. internal gradients) are not present in the phantom liquid. The free diffusion decay of metabolites is very small with multi-TE PRESS because the time difference of the two spoil gradients surrounding the two 180° degree pulses is short and because diffusion evolution times are not altered in response to different TE values in the multi-TE measurements.

[0063] However, the situation would be completely different in-vivo, where internal local susceptibility gradients (internal gradients) are present. TE changes in multi-TE measurements cause variations in the evolution time of diffusion, thereby creating different diffusion decay rates that depend on TE. As a result, substantial, statistically significant differences were found between the disclosed MARzss technique and the conventional multi-TE technique in in-vivo experiments. Indeed, the measurements listed in Table 2 demonstrate that the T ₂ s of NAA, Cr, and Cho measured by MARzss are significantly longer than those measured by multi-TE PRESS. Interestingly, the in- vivo T2 values measured by the MARzss technique— which has a mechanism of rapid partial refocusing with an inter-pulse interval of only 10 ms— were closer to values obtained using CPMG, which is known to be far less sensitive to diffusion-related effects.

[0064] The experimental results presented below conceptually validate the disclosed MARzss technique. In some embodiments, further optimization of this technique may be possible. For example, the first measurement with a flip angle of 0° may not be necessary because the same data is acquired (or extracted from fitting) in the separate T-i measurements with the same readout sequence. In addition, the simulation results shown in FIG. 2B indicate that it may be possible to shorten both recovery delay time and iPFG pulse train duration in order to speed up the overall experiment. The Bloch simulations discussed below suggest that recovery delay time TR may be shortened to 4-5 seconds for flip angles larger than 15°.

[0065] The MARzss method may be optimized to measure relaxation properties of one or more metabolites in a subject. In an embodiment, the duration of the iPFG train and TR may be varied based on the FA of the RF pulses used for relaxation weighting preparation. For example, as discussed below, Bloch and Monte Carlo simulations may be used to optimize FA, duration of the iPFG train and TR to significantly shorten the time required for measuring a metabolite T ₂ , such as Glu T ₂ . Compared to the non- optimized MARzss technique, the scan time per voxel for measuring Glu transverse relaxation may be shortened by more than 50%.

[0066] Bloch equation numerical simulations may be performed to demonstrate the spatial modulation and attenuation of magnetization in the longitudinal direction with different parameterizations. Linear regression of Equation 3 with R _Z ss measured at multiple FA solves T-|/T ₂ ratio. T ₂ may be quantified using four-FA measurements. The first FA is chosen to be zero (the smallest FA). The last FA should be chosen to have the furthest distance from 0, while the signal intensity of the metabolite is not too small to be reliably determined. In various embodiments, the FA may be between about 0° and about 70°. For example, the four FAs may be 0°, 12°, 24°, and 36°. Because signal measured at the last FA corresponds to the lowest SNR, T ₂ quantification accuracy is highly dependent on the metabolite signal intensity with the largest FA.

[0067] In one embodiment, the MARzss technique may reliably measure Glu transverse relaxation in the frontal cortex, where structural and functional abnormalities are strongly associated with psychiatric symptoms. In other embodiments, the MARzss technique may measure transverse relaxation in the medial frontal lobe, the left frontal lobe, and/or the occipital lobe. The principal excitatory neurotransmitter Glu plays an important role in many central nervous system disorders. Glu also is a key metabolite linking carbon and nitrogen metabolism. The dual roles of Glu as a neurotransmitter and metabolite are intricately connected. Altered concentration of Glu has been found in many pathologies, such as head trauma, pain, ageing and many neurodegenerative diseases. For the majority of psychiatric disorders, abnormal glutamatergic activities are believed to play major roles in pathophysiology and therapeutics

[0068] Since Glu resides predominantly in glutamatergic neurons instead of astroglial cells, relaxation properties of Glu reflect the intracellular environment of glutamatergic neurons. A method that can reliably measure Glu T ₂ relaxation using a clinical scanner therefore can be used to study many CNS disorders involving abnormal glutamatergic activities. For psychiatric disorders, probing intracellular environment of glutamatergic neurons in the frontal cortex, where structural lesions, disturbed function and

morphology are strongly associated with psychiatric symptoms, is expected to be very useful for characterizing the diseases as well as for providing potential target for monitoring treatment. Furthermore, measurement of Glu relaxation times is of importance for its quantification by in vivo MRS because many Glu MRS methods require medium to long TE.

[0069] In an embodiment, an optimized MAR _Z ss technique can reliably measure Glu relaxation from the frontal cortex with consistent quality and high precision in a typical clinical setting. The average peak linewidths in the frontal lobe voxels may be 21 -40% greater than the occipital lobe voxel. In one embodiment, the medial frontal, left frontal and occipital voxels the Glu T ₂ may be 1 17.5+12.9 ms (mean+SD, n=9), 107.3+12.1 (mean+SD, n=9) and 124.4+16.6 ms (mean+SD, n=8), respectively. The optimized MARzss technique may be useful for studying intracellular environment of glutamatergic neurons in a variety of brain disorders and providing potential glutamatergic targets for monitoring treatment.

[0070] The optimized acquisition of MARzss data may be highly accelerated by using less FAs and a different TR _mi n for each FA, where TR _min for RF-driven steady state may be pre-determined by Bloch simulation. The transverse relaxation information may be computed based on linear regression of Equation 3. As seen in FIG. 7, the oscillatory behavior of the longitudinal magnetization quickly diminishes after two seconds for the largest FA of 70° in FIG. 7. For larger FAs, the Mz vs. iPFG duration curves stop oscillating at an even earlier time. This suggests that one can further reduce the duration of the iPFG train and rely on Bloch or density matrix simulation to predict a non-steady state Mz to extract relaxation information by nonlinear data fitting. [0071] Phantom spectra of coupled spins including Glu and lactate when subjected to an iPFG train as implemented in MARzss show no discernible changes in spectral pattern within the limits of experimental accuracy. Without being limited to a particular theory, this may be due to the combined effects of inhibition of scalar evolution during the RF pulses, the little time each spin packet spends on the transverse plane. Because of the vast amount different coherence pathways generated by the very large number of RF pulses in the iPFG train, it is not surprising that no other scalar evolution pattern may dominate at the end of the iPFG train. To investigate this issue in a quantitative manner, in the examples below performed full density matrix simulation of the PRESS readout sequence in the presence of an iPFG train as implemented experimentally. As a result, subsequent Monte Carlo simulation found no significant difference between quantification results with and without considering scaler evolution during the iPFG train. FIG. 1 1 shows that the difference between the lineshape of Glu signal with and without the application of an iPFG train consisted of 500 Sine-Gaussian pulses and interleaved field gradient pulses was indeed very small for the FAs investigated.

[0072] Taken together, the complete separation between relaxation preparation and readout sequence that is offered by the disclosed MARzss technique opens many interesting possibilities for practical applications in metabolite relaxation experiments. For example, many spectral editing experiments require a fixed TE to maximize editing yield, making it very difficult to effectively measure transverse relaxation of the edited signals using conventional multi-TE PRESS or CPMG methods. The MARzss technique, however, allows the straightforward inclusion of various editing schemes (e.g. two-step J editing or multiple quantum filtering) into the readout sequence because TE can be arbitrarily selected and fixed while relaxation weighting is completely generated and quantified by the disclosed MARzss technique. In addition, with the freedom to select an arbitrary TE for readout, TE can be set to zero or close to zero when measuring T ₂ relaxation, which can be very convenient for investigating MRS signals with very short T ₂ s. Examples include ATP signals in ³¹ P MRS and labile proton signals that are in fast exchange with water. The separation of the relaxation preparation module and the readout module that is enabled by the disclosed MARzss technique further provides additional dimensions over which analysis may be performed. For example, a first dimension can consist of multiple T ₂ relaxation measurements and a second dimension can consist of multiple diffusion (D) measurements, thereby providing a T ₂ -D dataset for metabolite quantification and disease diagnosis and characterization. A similar approach can be used to generate a T ₂ -J (J-coupling) quantification, further

demonstrating the usefulness of the MARzss technique, as neither of these dimensional datasets could be produced using conventional techniques.

[0073] FIGS. 14A and FIG. 14B illustrate example system embodiments. The more appropriate embodiment will be apparent to those of ordinary skill in the art when practicing the present technology. Persons of ordinary skill in the art will also readily appreciate that other system embodiments are possible.

[0074] FIG. 14B illustrates a conventional system bus computing system architecture 700 wherein the components of the system are in electrical communication with each other using a bus 705. Exemplary system 700 includes a processing unit (CPU or processor) 710 and a system bus 705 that couples various system components including the system memory 715, such as read only memory (ROM) 720 and random access memory (RAM) 725, to the processor 710. The system 700 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 710. The system 700 can copy data from the memory 715 and/or the storage device 730 to the cache 712 for quick access by the processor 710. In this way, the cache can provide a performance boost that avoids processor 710 delays while waiting for data. These and other modules can control or be configured to control the processor 710 to perform various actions. Other system memory 715 may be available for use as well. The memory 715 can include multiple different types of memory with different performance characteristics. The processor 710 can include any general purpose processor and a hardware module or software module, such as module 1 732, module 2 734, and module 3 736 stored in storage device 730, configured to control the processor 710 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 710 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. [0075] To enable user interaction with the computing device 700, an input device 745 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 735 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device 700. The communications interface 740 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

[0076] Storage device 730 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 725, read only memory (ROM) 720, and hybrids thereof.

[0077] The storage device 730 can include software modules 732, 734, 736 for controlling the processor 710. Other hardware or software modules are contemplated. The storage device 730 can be connected to the system bus 705. In one aspect, a hardware module that performs a particular function can include the software

component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 710, bus 705, display 735, and so forth, to carry out the function.

[0078] FIG. 14A illustrates an example computer system 750 having a chipset architecture that can be used in executing the described method and generating and displaying a graphical user interface (GUI). Computer system 750 is an example of computer hardware, software, and firmware that can be used to implement the disclosed technology. System 750 can include a processor 755, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 755 can communicate with a chipset 760 that can control input to and output from processor 755. In this example, chipset 760 outputs information to output device 765, such as a display, and can read and write information to storage device 770, which can include magnetic media, and solid state media, for example. Chipset 760 can also read data from and write data to RAM 775. A bridge 780 for interfacing with a variety of user interface components 785 can be provided for interfacing with chipset 760. Such user interface components 785 can include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to system 750 can come from any of a variety of sources, machine generated and/or human generated.

[0079] Chipset 760 can also interface with one or more communication interfaces 790 that can have different physical interfaces. Such communication interfaces can include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein can include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 755 analyzing data stored in storage 770 or 775. Further, the machine can receive inputs from a user via user interface components 785 and execute appropriate functions, such as browsing functions by interpreting these inputs using processor 755.

[0080] It can be appreciated that example systems 700 and 750 can have more than one processor 710 or be part of a group or cluster of computing devices networked together to provide greater processing capability.

[0081] FIG. 15 illustrates an example block diagram of an illustrative system 800 of the present disclosure. A processor 802 and a memory 804, provided for example via one or more computing devices, are communicatively coupled between a database 806 and an MRS module 810. As discussed previously, the MRS module 810 is utilized to generate and transmit a plurality of pulse trains 812 in order to generate measurements corresponding to a subject 820. More particularly, these measurements of subject 820 are MRS spectra 814, wherein a given pulse train will produce a corresponding one or more MRS spectra. With the MRS spectra 814 obtained, the system 800 can be utilized to determine T ₂ s of various metabolites of interest utilizing the MARzss technique discussed previously. In some embodiments, database 806 might be utilized to store pre-determined T-i values in order to reduce the computational load experienced by processor 802. Additionally, database 806 (and/or memory 804) can be used to store instructions which, when executed by processor 802, cause the system 800 to

implement the MAR _Z ss method of the present disclosure.

EXAMPLES

Example 1: MARzss

[0082] To demonstrate the spatial modulation of metabolite magnetization and its attenuation in the longitudinal direction by iPFG, Bloch equation numerical simulations were performed in MATLAB. The M _Z ss of N-zcetylaspartate (NAA) was simulated based on the protocol time series in a single repetition time (TR). For NAA, T-i and T ₂ literature values at 7T of 1730 ms and 170 ms, respectively, were used.

[0083] All scans were acquired using a 32-channel head coil on a 7T Siemens scanner. A doped water-based sphere phantom was used for phantom measurement of metabolites. Written informed consent was obtained from five healthy volunteers (four females and one male, between the ages of 24 and 40 years) for in-vivo measurement of metabolites.

[0084] The iPFG train applied in the measurements shown in FIG. 1 consisted of 500 sine-Gauss pulses with gradient G _z (along the z-direction) interspersed between the RF pulses, although other iPFG trains may be employed. The duration of each RF pulse was 4 ms with a frequency offset at 2.2 ppm for optimal determination of NAA. A constant inter-pulse delay, T _d , of 10 ms was used. The phases of the iPFG RF pulses alternated between base frequency shift (bfs) and bfs + 180°. Base frequency shift was decided based on the frequency offset used in the measurement, which, again, was 2.2 ppm. The flip angle of the iPFG train was incremented by 10° from 0° to 60° for a total of seven measurements. Gradient amplitude and duration were G _z =2mT/m and 5 ms, respectively. Spoil gradients immediately after the preparation module were set along all three directions, G _x =G _y =G _z , with amplitude 8 mT/m and duration = 1.2 ms, respectively.

[0085] The sine-Gaussian pulse shown in FIG. 1 has a somewhat non-uniform

frequency profile across the metabolites of interest. As a result, different chemical shifts experience slightly different flip angles. Although small, the flip angle at each chemical shift can be corrected for data fitting based on Bloch simulation of the sine-Gaussian pulse.

[0086] A water suppression module with an overall duration of 120 ms was applied. Nine sine-Gauss RF pulses implemented in the water suppression module were all set to an RF duration of 9 ms with 12 ms interpulse delay time. The RF flip angle, RF phase and spoil gradient amplitudes used after each of the pulses were (Flip Angle/Phase/G): 80.4 234 G _x =32mT/m, 80.475857G _Z =32 mT/m, 152.8710537G _X =32 mT/m,

80.4/16387G _z =16 mT/m, 152.8723407G _y =16 mT/m, 80.4731597G _X =16 mT/m,

152.8740957G _z =40 mT/m, 152.8751487G _y =40 mT/m and 152.8763187G _x =G _y =G _z =40 mT/m. Spoil gradient durations were all set to 1 .2 ms.

[0087] The PRESS pulse sequence was implemented with an isotropic voxel (2 cm x 2 cm x 2 cm) for both in-vivo and phantom experiments. The chosen location of in-vivo measurements was in the occipital (OC) lobe, as illustrated in the upper right quadrant of FIG. 5D. Parameters were selected such that TR (repetition time) = 8.5 s, TE-i (the first PRESS TE) = 69 ms, and TE ₂ (TE-TE-i) = 37 ms. These parameters were also utilized for both the in-vivo and the phantom experiments. TEi and TE ₂ were chosen to minimize contamination of the glutamate (Glu) H4 resonance by glutamine (Gin) and NAA. 2,048 data points were acquired for each spectrum, with phantom data acquired with 12 averages and in-vivo data acquired with four averages. The use of at least four in-vivo averages was expected to provide adequate SNR for fitting NAA, creatine (Cr), and choline (Cho), although in some embodiments a greater number of in-vivo averages may be taken. For example, when Glu, which has an inherently lower signal intensity, is the target metabolite, increasing the number of in-vivo averages from four to 12 or more can significantly improve the fitting results. The total scan time for measuring Ti/T ₂ using seven flip angles was approximately 18 minutes for phantom experiments and six minutes for in-vivo experiments.

[0088] Conventional standard inversion recovery was implemented for independent T-i determination at the same location. With no change in the water saturation or PRESS modules in the MARzss sequence, the iPFG train was replaced with an inversion recovery pulse. Inversion recovery (Tl) times of 425, 625, 825, 1 125, 1425, 1625, 2725, 4725, and 6725 were implemented for T-i measurements. Four scans were conducted for each Tl measurement, again with TR set to 8.5 s. For comparison purposes, a multi- TE PRESS sequence for T ₂ quantification was also implemented at the same location.

[0089] The metabolite signal amplitudes for MAR _Z ss, IR, and multi-TE measurements were determined using LCModel fitting. The basis set of metabolites for fitting included N-acetylaspartate (NAA; acetyl moiety, aspartyl moiety), N-acetylaspartylglutamate (NAAG acetyl moiety, NAAG aspartyl and glutamate moieties), total Cr, total Cho, Aspartate (Asp), Glu, Gin, Lactate (Lac), glutathione (GSH), and y-aminobutyric acid (GABA). Two-tailed, unpaired Student's t-tests were used to compare T ₂ s obtained via the proposed MARzss technique and the multi-TE PRESS pulse sequence technique.

[0090] Bloch simulations were implemented to demonstrate changes in longitudinal magnetization (M _z ) as a function of different RF flip angles, both spatially at steady state and temporally at transition state, shown in FIG. 2A and FIG. 2B, respectively. More particularly, FIG. 2A illustrates the resulting spatial variations of M _z when iPFG trains with different flip angles were applied to saturate NAA to steady states. Although only one of the full spatial modulation periods was simulation in FIG. 2A, for multiple half periods, signal attenuation would be identical in the voxel due to symmetry.

[0091] In the simulation illustrated in FIG. 2A, there is only one period of modulation in the voxel, which corresponds to a gradient moment of 0.23mT/m χ 5ms in the iPFG train, given the 2 cm voxel size. In practical applications, significantly larger gradient moment of 2mT/m χ 5ms could be employed to avoid the potential quantification error introduced by a non-integer value of n (or a non-half integer value due to the

symmetrical shape of the even function cos Θ) in Equation 1 .

[0092] Approximately seventeen half-periods of modulation were present in the 2 cm voxel, which minimized the quantification error introduced at the edges of the voxel. The evolution of M _z over the span of an entire TR was numerically simulated using Bloch equations, where each signal point as a function of time was averaged over Θ values. FIG. 2B illustrates the M _z of NAA evolving from transition states to steady states at different flip angles. Note that because of the 120 ms duration of the water suppression module, a slight magnetization recovery occurred between the time immediately after the iPFG train and the time before the 90° readout pulse. [0093] FIGS. 2C and 2D present Bloch numerical simulations that validate Equations 2 and 3, respectively. The dotted data points seen in FIG. 2C were extracted from steady state amplitude at the dotted line P in FIG. 2B. Alternatively, the black dots could be identically extracted by integrating the areas under the symmetrical M _Z ss spatial variation curves in FIG. 2A. The solid curved line in FIG. 2C, 'M _Z ss Equation', represents the signal change as a function of flip angle calculated from Equation 2, indicating excellent agreement between the full Bloch simulation and Equation 2. Linear fitting of the black dots based on Equation 3 is shown in FIG. 2D. The T ₂ value determined from the slope is 167 ms, which is in good agreement with the true value of 170 ms. The small observed deviation was introduced primarily due to the approximation E ₁ = 1 -— and E ₂ = 1 -— used to obtain the linear relationship of Equation 3.

[0094] For phantom measurements, signal amplitude ratios R _Z ss (defined in Equation 3) of NAA, Cr, and Cho are illustrated as functions of tan Q in FIGS. 3A, 3B, and 3C, respectively. FIG. 3D shows the phantom spectra acquired by the MARzss method at different flip angles. The linear relationships between R _Z ss and tan ( ) predicted by

Equation 3 are clearly confirmed for phantom measurements of all three metabolites. The T ₂ s of NAA, Cr, and Cho were determined by least squares linear fitting, as indicated by the dotted lines in each of FIGS. 3A-3C. The phantom metabolite T ₂ results obtained from the proposed MARzss technique are listed in Table 1 , below, and compared in side-by-side fashion with the phantom metabolite T ₂ results obtained from a conventional multi-TE PRESS method.

Table 1

[0095] In the context of the phantom measurements presented in Table I, metabolite T-iS of the doped phantom were pre-determined to be 759 ms, 449 ms, and 297 ms for NAA, creatine, and choline, respectively. For each metabolite, the phantom was tested three times, with the average result (and respective uncertainty) presented above. Note that NAA is an abbreviation for N-acetylaspartate, and that the superscript ^a indicates a preparation module while the superscript ^b indicates a readout module.

[0096] Comparisons of the spectra of metabolites with J-couplings (ASP ⁺ , partially contributed from NAA), Gin, Glu, and lactate (Lac) obtained via MARzss versus multi-TE are demonstrated in FIGS. 4A and 4B. FIG. 4A clearly shows that the proposed MARzss technique can be implemented with no discernable modulation from J-coupling evolutions because the readout module uses a fixed TE. In contrast, strong signal modulations of metabolites of ASP ⁺ , Gin, Glu, and LAC were observed as expected because of J-coupling effects with TE variations, as seen in FIG. 4B. Changes in R _Z ss for Glu and Lac as a function of flip angle were extracted from LCModel and plotted in FIGS. 4C and 4D, respectively. T ₂ values for Glu and Lac in the water-based sphere phantom were determined to be 217 ms and 533 ms, based on measured Ti values of 626 ms and 658 ms.

[0097] For in-vivo measurements, which were acquired from a single subject, signal amplitude ratios R _Z ss (defined in Equation 3) of NAA, Cr, and Cho are illustrated as functions of tan ^ in FIGS. 5A, 5B, and 5C, respectively. FIG. 5D shows the in-vivo spectra acquired by the MARzss method using flip angles from 0° to 60°, adjusted in 10° increments. The linear relationships between R _Z ss and tan ( ) predicted by Equation 3 are clearly confirmed for in-vivo measurements of all three metabolites. The T ₂ s of NAA, Cr, and Cho were determined by least squares linear fitting, via the slopes of the dotted straight lines in each of FIGS. 5A-5C. The in-vivo metabolite T ₂ results obtained from the proposed MARzss technique are listed in Table 2, below, and compared in side-by- side fashion with the in-vivo metabolite T ₂ results obtained from a conventional multi-TE PRESS method.

Table 2

[0098] The in-vivo T ₂ metabolite measurements presented above were obtained at 7T for both measurement techniques. Measurement results from multi-TE PRESS approximate the values obtained in the literature at 7T. The T-i S of individual subjects were pre-determined for the calculation of T ₂ s. The averaged in-vivo T-| S in the OC were 1409 ms, 1396 ms, 1 160 ms, and 1 198 ms for NAA, creatine, choline, and glutamate, respectively. Note that OC is an abbreviation for the occipital lobe; PA is an abbreviation for the parietal lobe; and P represents a t-test of differences between MAR _Z ss and multi- TE PRESS.

[0099] FIG. 6A illustrates the fitted in-vivo spectra of Glu obtained using the disclosed MARzss technique. Here, the preparation pulses of MARzss monotonically affected the signal attenuation of Glu. R _Z ss of Glu as a function of tan was also extracted, with the result shown in FIG. 6B. The linear relationship between Rzss and tan predicted by Equation 3 is clearly observable, with a fitting coefficient of determination of R ² = .0982

Example 2: Optimized MARzss for Glu

[00100] Bloch equation numerical simulations were performed to demonstrate the spatial modulation and attenuation of magnetization in the longitudinal direction with different parameterizations. Linear regression of Equation 3 with R _Z ss measured at multiple FA solves T-|/T ₂ ratio. T ₂ may be quantified using four-FA measurements. The first FA is chosen to be zero (the smallest FA). The last FA may be chosen to have the furthest distance from 0, while the signal intensity of Glu is not too small to be reliably determined. Because signal measured at the last FA corresponds to the lowest SNR, T ₂ quantification accuracy is highly dependent on the Glu signal intensity with the largest FA.

[00101] Monte Carlo simulation was used to evaluate the estimation accuracy of Glu at different signal to noise ratio (SNR) levels. Metabolite concentrations, T ₂ values and baseline measured from a healthy volunteer (vide infra) were used to synthesize FIDs using density matrix simulations. Random white noise with Gaussian distribution was added to the synthesized FIDs. The noise level was determined from the in vivo data. Quantification results from synthesized FIDs were computed using the LCModel method. The whole procedure was repeated 100 times with different noise realizations at each of the 50 different SNR levels, corresponding to different attenuation ratio of Glu signal at 50 different FAs. The deviation from ground truth value at each SNR level was calculated. Because SNR increases when more signal averages are used, a contour plot as function of FA and number of signal averages were generated. The numerical simulation of spin density matrix was implemented in Java on Eclipse Java Oxygen integrated development environment (IDE). All other mathematic computations were programmed in MATLAB.

Example 3: In vivo studies

[00102] Nine healthy volunteers (six females and three males between the age of 27-49 years) were recruited and scanned on a Siemens 7T scanner (Siemens Medical Solutions, Malvern, PA, USA) equipped with a 32-channel receiver head coil. All procedures were approved by the institutional review board of the NIMH. Written informed consent was obtained from each participant.

[00103] High resolution T1 -weighted magnetization prepared rapid gradient echo (MPRAGE) images were acquired from each subject to position a 2*2*2 cm ³ MRS voxel in three different locations: 1 ) A GM-dominated medial frontal lobe region; 2) A WM-dominated left frontal lobe region; and 3) a GM-dominated medial occipital lobe region. Locations 1 ) and 2) are known to be involved in many psychiatric disorders. Acquisition parameters were TR=3 s, TE= 3.9 ms, matrix=256x256x256, resolution= 1x1x1 mm ³ . First- and second-order B ₀ field inhomogeneties within the selected voxel were corrected. Threshold-based segmentation was performed to calculate the percentage of GM, WM and CSF in each voxel.

[00104] For in vivo measurement of Glu T ₂ , TR of 8.5 s, 7.5 s, 6.0 s and 4.5 s were used for FA=0, 12, 24 and 36 degrees, respectively. The corresponding iPFG train consisted of 750, 700, 550 and 350 identical sine-Gauss RF pulses, respectively. The duration of each RF pulse was 4 ms and the inter-pulse delay was 10 ms. The interspersed gradient amplitude was 2 mT/m with a duration of 5 ms. A water suppression module, consisted of nine sine-Gauss RF pulses with a duration of 9 ms and inter-pulse delay of 12 ms followed by spoiling gradient after each RF pulse, was placed immediately after the iPFG train. The duration of all spoiling gradients was set to 1 .2 ms. The relaxation weighted spatially modulated longitudinal magnetization was acquired with a PRESS sequence optimized for Glu detection at 7 Tesla. Acquisition parameters of the PRESS sequence were TEi=69 ms, TE ₂ =37 ms, data points = 2048, and number of averages = 16. Unsuppressed water signal was also acquired for each FA. The totally scan time for measuring T-|/T ₂ using four FA with 16 averages was about 7 minutes per voxel. Glu T-i was determined using the conventional inversion recovery method. Inversion recovery times of 255, 355, 455, 655, 1225, 1825, 2825, 3825 and 5025 ms were used.

[00105] Concentrations of Glu were determined using LCModel fitting. To generate the basis set for LCModel fitting, the exact PRESS readout sequence used in the in vivo studies was simulated using the highly accelerated one-dimensional projection technique. Density matrix simulated metabolites used in LCModel included NAA, N- acetylaspartyglutamate (NAAG), total Cr (tCr), total Cho (tCho), aspartate (ASP), Glu, glutamine (Gin), glutathione (GSH), γ-Aminobutyric acid (GABA) and myo-inositol (ml). For each FA, the 32-channel water-suppressed FIDs were averaged from 16

acquisitions, and subsequently combined into single-channel FIDs using a generalized least square method. The single unsuppressed water FID was similarly processed and used to correct eddy current effects. Metabolite concentrations were determined using the LCModel. All spectra data were zero filled 8 times and apodized using a

combination of Lorenztian and Gaussian function before fitting. Zero-order phase, first- order phase and spline baselines were coded as fitting parameters in the fitting algorithm. Glu T-i values were pre-determined from fitting the inversion recovery data. Glu T ₂ values were subsequently determined by linearly fitting the in vivo signal amplitude ratio (R _zss , defined in Equation 3) as a function of tan(FA/2). Delay due to water suppression was compensated as a slight magnetization recovery and corrected in Equation 2. Fitting coefficients of determination (R ² ) were calculated for each brain regions of individual subjects.

[00106] Bloch simulation of temporal variation of longitudinal magnetization at different FA during one TR is plotted in FIG. 7. M _zss achieved steady state much more quickly as FA increases. RD is the recovery delay between PRESS and the iPFG train. Short recovery of longitudinal magnetization during water suppression was

compensated in Equation 2. As expected, signal attenuates more from the thermal equilibrium signal intensity as FA increases. The time for the longitudinal magnetization to achieve steady states also varied for each FA. For example, only 4.5 s of TR is required when FA is at 36°. Overall, the time required for magnetization to achieve RF- driven steady state gradually decreases as FA increases. Thus, a shorter TR can be used for a larger FA. The total scan time can be reduced significantly by using different TRmin for different FA, where TR _mi n is defined as the minimum time required for magnetization to achieve RF-driven steady state (when the amplitude change of magnetization is less than 0.1 %) in a single scan.

[00107] FIG. 8 shows typical in vivo spectra without line-broadening acquired at four different FAs. Although the noise intensities remain the same among different FAs, SNR decreases significantly as the signal intensities decrease when FA increases. Fifty different SNR levels, corresponding to 50 different FAs varying between 20° to 70° degrees, were analyzed using Monte Carlo simulation. The mean Glu concentration and its standard deviation obtained from the 100 Monte Carlo simulations at each SNR level were calculated. Mean values were plotted using red x marks in FIG. 9A. The ground truth Glu concentration values (blue line) were also plotted for comparison. Significantly more deviations of mean Glu concentrations from corresponding ground truth values were observed when Glu amplitude-to-noise ratio was below 5. Standard deviations of Glu concentration gradually increased when SNR decreased as expected. A contour plot of Glu amplitude-to-noise as a function of the number of signal averages and FA was shown in FIG. 9B. Values of the ratio are labeled on each contour line. FIG. 9B provides guidance for choosing the largest FA for measuring Glu T ₂ . When 16 averages are used in the acquisition, the largest FA can be set to 36° (at the intersection of the two dashed lines in FIG. 9B) so that the Glu amplitude-to-noise ratio is larger than 5.

[00108] The four FAs chosen for in vivo study were 0, 12, 24, 36° withl 6 acquisition averages for each FA. By using a different TR _mi n for each FA, the total scan time for measuring Glu T ₂ was about 7 minutes. FIGS. 10A-D shows linear combination fitting plots for individual spectra acquired from a voxel placed in medial frontal lobe of a healthy subject at four different FAs. The voxel location was indicated by the yellow box overlaid on the axial and sagittal high resolution T-i-weighted images. Threshold based- segmentation indicates that the averaged fraction of GM within the medial frontal lobe voxel is 65.0%. The original data was zero filled and apodized with Lorentzian and Gaussian function. Glu concentrations were used to calculate in vivo signal amplitude ratio, R _zss , defined in Equation 3. FIG. 10E displays R _zss as a function of tan(FA/2). With a pre-determined Glu Ti values from inversion recovery technique, Glu T ₂ were solved using Equation 4 and Equation 6. The linear fitting coefficient of determination averaged over all subjects is 0.988.

[00109] Similar spectral results from left frontal lobe and occipital lobe are obtained, (see FIGS. 12A-F and FIGS. 13A-F). FIGS. 12A-F show typical spectra obtained from a voxel placed in left frontal lobe of a healthy subject at four different FA. FIG. 12 A shows the stack view of four spectra. In FIG. 12A, the voxel location is indicated by the yellow box overlaid on the axial and sagittal high resolution Ti -weighted images on top of the spectra. Linear combination of fitting plots for individual spectra are displayed in FIGS. 12B, 12C, 12E, and 12F. Glu concentrations were used to calculate in vivo signal amplitude ratio, R _zss , defined in Equation 3. FIG. 12D displays R _zss as a function of tan(FA/2). With a pre-determined Glu Ti values from inversion recovery technique, Glu T ₂ were solved using Equations 4 and Equation 6. FIGS. 13A-F show similar spectral results from occipital lobe. One data from occipital lobe was excluded because motion artifacts were observed. Threshold based-segmentation indicates the averaged fraction of WM in the voxel placed in the left frontal lobe is 73%, while the averaged fraction of GM in the voxel placed in the occipital lobe is 55%. [00110] Mean values and standard deviations for the Ti and T ₂ values of Glu in from the frontal lobe GM-, frontal lobe WM- and occipital lobe GM-dominated regions of healthy subjects are listed in Table 3, below.

Ti (s) T ₂ (ms) R ² GM(%) WM(%) CSF(%)

Medial frontal lobe 1.15+0.14 117.5+12.9 0.988+0.012 65+4 22+3 13+2

Left frontal lobe 1.24+0.11 107.3+12.1 0.982+0.016 14+2 73+3 13+3

Occipital lobe 1.11+0.09 124.4+16.6 0.987+0.017 55+3 25+3 20+3

* mean + standard deviation. For both frontal lobe studies, n=9. For occipital lobe study, n=8.

Table 3

[00111] The averaged linear fitting coefficients of determination R ² are also listed in Table 3. The mean T ₂ values of Glu from GM dominated voxels are found to be longer than mean T ₂ values of Glu from WM dominated voxels, consistent with previous studies. Paired student t-test shows that T ₂ values of Glu from GM dominated frontal lobe voxel and GM dominated occipital voxel region are significantly longer than T ₂ values of Glu from WM dominated frontal lobe voxel (p=0.0067 and p=0.0040, respectively). Oppositely, mean Ti values of Glu from GM dominated voxels are found to be shorter than mean T-i values of Glu from WM dominated voxels. Paired student t- test shows that T-i values of Glu from GM dominated occipital lobe voxel are significantly shorter than Ti values of Glu from WM dominated frontal lobe voxel (p=0.021 ). No significant difference was found between Ti values of Glu from GM dominated frontal lobe voxel and T-i values of Glu from WM dominated frontal lobe voxel (p=0.089). The standard deviations of T ₂ values of Glu are found to be smaller in the frontal lobe region than the occipital lobe region and are slightly greater than 10% across all three regions. The average peak linewidths were 13.8 Hz, 16.0 Hz and 1 1.4 Hz at location 1 ), 2) and 3), respectively.

Example 4: Scaler evolution

[00112] To investigate the effect of scaler evolution during the iPFG pulse train, numerical simulations of Glu density matrix was performed to calculate the Glu spectrum. This simulation was performed using the one-dimensional projection method that converts the three-dimensional pixel-by-pixel density matrix simulation problem into three one-dimensional ones. The iPFG train consisted of five hundred sine-Gauss shaped RF pulses with field gradients interspersed between the RF pulses. The readout sequence is a standard point-resolved spectroscopy (PRESS) sequence with

amplitude-modulated excitation and refocusing RF pulses. TE was optimized as [69, 37] ms. The parameters of the simulated pulse sequence including RF and gradient pulses of the iPFG train as well as delays between them were identical to those used experimentally. A one-dimensional projection method was used to dramatically speed up the full density matrix simulation. The results were compared with Glu spectrum generated without the iPFG train. Monte Carlo simulations are used to evaluate the effect of scaler evolution on Glu quantification by fitting a synthetic dataset with known Glu concentration using basis sets simulated with and without the iPFG train. The synthetic dataset used experimentally measured baseline and metabolite

concentrations as described in Method.

[00113] FIG. 1 1 compares density matrix simulated Glu spectra with and without the MARzss preparation module at three different FAs (12°, 24 ⁰ and 36°). The density matrix simulated Glu spectra were also line-broadened to match the in vivo linewidth. FIG. 1 1 A shows Glu spectra with and without the MARzss preparation module at FA of 12° without line-broadening and FIG. 1 1 D show the Glu spectra with and without the MARzss preparation module at FA of 12° with line-broadening. FIG. 1 1 B shows Glu spectra with and without the MARzss preparation module at FA of 24° without line- broadening and FIG. 1 1 E show the Glu spectra with and without the MARzss

preparation module at FA of 24° with line-broadening. FIG. 1 1 C shows Glu spectra with and without the MARzss preparation module at FA of 36° without line-broadening and FIG. 1 1 F show the Glu spectra with and without the MARzss preparation module at FA of 36° with line-broadening. The difference between simulated Glu spectra with and without iPFG train are represented by the residual lines in black at the bottom. As shown in FIGS. 1 1 A-F, the lineshapes of the simulated Glu spectra with and without the iPFG train are very similar. Monte Carlo simulation was performed to compare the Glu T ₂ obtain from fitting spectra calculated with and without the iPFG train, respectively. Noise level was determined from in vivo measurements with 16 averages. Mean values and standard deviation of Glu T ₂ from 100 Monte Carlo simulations with different noise realizations are listed in Table 4. The ground truth Glu T ₂ was set to 98.0 ms. Two- tailed, unpaired Student's t-tests showed no significant difference between Glu T ₂ obtained by using density matrix simulated with and without the iPFG train (p=0. 16).

with iPFG train without iPFG train

Mean+SD Err % Mean+SD Err %

Glu T2 (ms) 95.7+5.9 2.4 96.6+6.1 1.4

Table 4

[00114] For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

[00115] In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

[00116] Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

[00117] Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

[00118] The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

[00119] Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of

implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. Moreover, claim language reciting "at least one of" a set indicates that one member of the set or multiple members of the set satisfy the claim.

[00120] Reference to "one embodiment", "an embodiment", or "some

embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others.

[00121] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term.

Likewise, the disclosure is not limited to various embodiments given in this specification.

[00122] Without intent to limit the scope of the disclosure, examples of

instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given above. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.

[00123] Additional features and advantages of the disclosure are set forth in the description above, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims.

[00124] While several particular embodiments of the present invention have been described herein, it will be appreciated by those skilled in the art that changes and modifications may be made thereto without departing from the invention in its broader aspects and as set forth in the following claims.