Joint image and field map estimation for multi‐echo hyperpolarized ¹³C metabolic imaging of the heart

Abstract

Image reconstruction of metabolic images from hyperpolarized 13 C multiecho data acquisition is sensitive to susceptibility-induced phase offsets, which are particularly challenging in the heart. A model-based framework for joint estimation of metabolite images and field map from echo shift-encoded data is proposed. Using simulations, it is demonstrated that correction of signal spilling due to incorrect decomposition of metabolites and geometrical distortions over a wide range of offresonance gradients is possible. In vivo feasibility is illustrated using hyperpolarized [1-13 C]pyruvate in the pig heart. Methods: The model-based reconstruction for multi-echo, multicoil data was implemented as a nonconvex minimization problem jointly optimizing for metabolic images and B 0. A comprehensive simulation framework for echo shift-encoded hyperpolarized [1-13 C]pyruvate imaging was developed and applied to assess reconstruction performance and distortion correction of the proposed method. In vivo data were obtained in four pigs using hyperpolarized [1-13 C]pyruvate on a clinical 3T MR system with a six-channel receiver coil. Dynamic images were acquired during suspended ventilation using cardiac-triggered multi-echo single-shot echo-planar imaging in short-axis orientation. Results: Simulations revealed that off-resonance gradients up to ±0.26 ppm/pixel can be corrected for with reduced signal spilling and geometrical distortions yielding an accuracy of ≥90% in terms of Dice similarity index. In vivo, improved geometrical consistency (10% Dice improvement) compared to image reconstruction without field map correction and with reference to anatomical data was achieved. Conclusion: Joint image and field map estimation allows addressing off-resonanceinduced geometrical distortions and metabolite spilling in hyperpolarized 13 C metabolic imaging of the heart.

Cardiac measurement of hyperpolarized 13C metabolites using metabolite‐selective multi‐echo spiral imaging

“…Reed et al demonstrated improved B 0 shimming to mitigate the nonuniform circumferential HP [ 13 C]bicarbonate signal from the Myo 13 . Traechtler et al proposed a model‐based reconstruction to correct the B 0 ‐induced phase offsets in HP 13 C multi‐echo acquisition 12 . Considering the other contributing factors besides off‐resonance effect, direct measurements of in vivo would further benefit the cardiac HP 13 C studies.…”

“…13 Traechtler et al proposed a model-based reconstruction to correct the B 0 -induced phase offsets in HP 13 C multi-echo acquisition. 12 Considering the other contributing factors besides off-resonance effect, direct measurements of in vivo T * 2 would further benefit the cardiac HP 13 C studies.…”

“…However, cardiac HP acquisition schemes are still considered suboptimal, 11 and the HP 13 C images are sensitive to acquisition parameters such as TE times, B 0 field inhomogeneity, 12,13 and blood flow 14 . For proper assessment of HP metabolites, in vivo MR characteristics such as the s of the HP 13 C‐metabolites need to be understood.…”

Considerations for hyperpolarized 13C MR at reduced field: Comparing 1.5T versus 3T

“…Hence, as T * 2 increases at lower field strengths, the available signal persists longer, which, together with the reduced spectral span of the metabolites at lower fields, can be utilized to adaptively change the acquisition bandwidth proportional to 𝜔, that is, BW ∝ 𝜔. For the field-dependent SNR scaling according to Equations ( 9) and (10)…”

“…10,30 Reconstruction was performed in MATLAB using nonlinear conjugate gradient-based reconstruction with ground truth B 0 correction based on a measured 1 H field map, and total variation regularization with 𝜆 = 0.3. 10 Image SNR was calculated as the magnitude of the metabolite (m) image 𝝆 m over pixels v of each dynamic frame t d divided by the SD over all pixels from the last dynamic frame t noise (noise only) of the reconstruction without regularization ρm . Metabolic SNR time curves were extracted as the average signal over all pixels of the left ventricular blood pool (pyruvate and pyruvate hydrate) and myocardium (lactate, alanine, and bicarbonate), respectively (V ROI ):…”