Acquisition and quantification pipeline for in vivo hyperpolarized 13 C MR spectroscopy

Abstract

Purpose: The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized ¹³ C-labeled compounds in vivo. In this study, we tested our approach on [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T.

Methods: We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic ¹³ C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites.

Results: Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute.

Conclusion: The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized ¹³ C data enhanced our ability to monitor the metabolism of [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting.

Keywords: LCModel; brain tumor; denoising; hyperpolarized 13C MRS; multiband excitation; variable flip angle correction.

Figure 1

Spectral-spatial RF excitation pulse with independent flip angle control over two excitation bands. (A and B) gradient and RF waveforms (blue is real and red is imaginary part of the RF pulse). This 0.93 ms pulse was designed for a 10 mm slab, with the following resonances and their corresponding bandwidths and flip angles: 207.8 ppm ± 1 ppm ([2-¹³C]pyruvate), 4°; 183.9 ppm ± 1 ppm ([5-¹³C]glutamate), 30°. (C) The magnitude of the spectral-spatial profile of the pulse at different shifts from the iso-center and over a range of frequencies. (D) Spectral profile at the center of the slab (at 0 cm).

Figure 2.

LCModel results for simulated spectra of different SNR levels (A). Quantified concentrations (B) and corresponding CRLBs (C) for simulated spectra with various SNRs. Note that dotted lines on Figure2B indicate true concentrations of each metabolite. As SNR increases, estimated concentrations are converging to their true concentrations.

Figure 3.

A comparison of in vivo spectra between pre- (A and B) and post-denoising (C and D) using the SVD and following [2-¹³C]pyruvate injection (A and C) and [1-¹³C]α-ketoglutarate injection (B and D). After denoising, lower concentration product signals are more clearly visible (C and D).

Figure 4.

Examples of in vivo spectra following [2-¹³C]pyruvate (A and B) and [1-¹³C]αKG (C) injections with individual metabolite fitting lines. (A): [2-¹³C]pyruvate (orange), [2-¹³C]pyruvate-hydrate (blue), and [2-¹³C]lactate (yellow). (B): [5-¹³C]glutamate (red), [5-¹³C]glutamine (navy), [1-¹³C]acetoacetate (light blue), [1-¹³C]acetylcarnitine (green), [1-¹³C]pyruvate (purple), and residual signal (above). Note that Figure 4B shows the fitting result after excluding [2-¹³C]pyruvate to enable phasing of all metabolite. (C): [5-¹³C]α-ketoglutarate + [1-¹³C]2-hydroxyglutarate (yellow), [1-¹³C]α-ketoglutarate-hydrate (blue), [1-¹³C]glutamate (purple), [1-¹³C]α-ketoglutarate (orange), and residual signal (above).

Figure 5.

Comparison of the mean CRLB over all time points between the pre- (blue) and post-denoised (orange) results for [2-¹³C]pyruvate (A: U87IDHmut tumors), [1-¹³C]α-ketoglutarate (B: BT257 and C: U87 tumors). Error bars indicate 95% confidence intervals and asterisks indicate a significant difference between the two groups: p<0.05.

Figure 6.

(A) Expected magnetization variations over flip angles determined by quantum mechanical calculation for each RF pulse for the metabolites observed following [2-¹³C]pyruvate (upper panel) and [1-¹³C]α-ketoglutarate (lower panel) injections. Example of pre- (B) and post-VFA correction (C) for metabolite concentrations quantified by LCModel for [2-¹³C]pyruvate (upper panels) and [1-¹³C]α-ketoglutarate (lower panels) injections. The correction results in metabolite dynamic curves that reflect the expected increase due to metabolic conversion followed by a decrease due to magnetization decay. Note that metabolite signals in Figures 6B and 6C were normalized to the maximum substrate signal to compare the correction effect directly. Note that time indicates the elapsed time after starting the substate injection.

Figure 7.

Averaged metabolic kinetics following LCModel fitting and VFA correction based on spectra pre-denoising (blue) and post-denoising (orange) for [2-¹³C]pyruvate (A: U87IDHmut model) and [1-¹³C]α-ketoglutarate (B: BT257 and C: U87 models). For the BT257 model, the [5-¹³C]αKG + [1-¹³C]2HG peak was significantly increased at 38 s, whereas, the U87 model did not show this second peak. Note the error bars indicate standard deviations.