TY - JOUR
T1 - Super-resolution hyperpolarized13c imaging of human brain using patch-based algorithm
AU - Ma, Junjie
AU - Park, Jae Mo
N1 - Funding Information:
This study was supported by The Texas Institute for Brain Injury and Repair; Mobility Foundation; National Institutes of Health of the United States (R01 NS107409-01A1, P41 EB015908, S10 OD018468); The Welch Foundation (I-2009-20190330); and UT Dallas Collaborative Biomedical Research Award (UTD 1907789).
Publisher Copyright:
© 2020 The Authors. Published by Grapho Publications, LLC.
PY - 2020
Y1 - 2020
N2 - Spatial resolution of metabolic imaging with hyperpolarized13C-labeled substrates is limited owing to the multidimensional nature of spectroscopic imaging and the transient characteristics of dissolution dynamic nuclear polarization. In this study, a patch-based algorithm (PA) is proposed to enhance spatial resolution of hyperpolarized13C human brain images by exploiting compartmental information from the corresponding high-resolution1H images. PA was validated in simulation and phantom studies. Effects of signal-to-noise ratio, upsampling factor, segmentation, and slice thickness on reconstructing13C images were evaluated in simulation. PA was further applied to low-resolution human brain metabolite maps of hyperpolarized [1-13 C] pyruvate and [1-13C] lactate with 3 compartment segmentations (gray matter, white matter, and cerebrospi-nal fluid). The performance of PA was compared with other conventional interpolation methods (sinc, nearest-neighbor, bilinear, and spline interpolations). The simulation and the phantom tests showed that PA improved spatial resolution by up to 8 times and enhanced the image contrast without compromising quantification accuracy or losing the intracompartment signal inhomogeneity, even in the case of low signal-to-noise ratio or inaccurate segmentation. PA also improved spatial resolution and image contrast of human13C brain images. Dynamic analysis showed consistent performance of the proposed method even with the signal decay along time. In conclusion, PA can enhance low-resolution hyperpolarized13 C images in terms of spatial resolution and contrast by using a priori knowledge from high-resolution1H magnetic resonance imaging while preserving quantification accuracy and intracompartment signal inhomogeneity.
AB - Spatial resolution of metabolic imaging with hyperpolarized13C-labeled substrates is limited owing to the multidimensional nature of spectroscopic imaging and the transient characteristics of dissolution dynamic nuclear polarization. In this study, a patch-based algorithm (PA) is proposed to enhance spatial resolution of hyperpolarized13C human brain images by exploiting compartmental information from the corresponding high-resolution1H images. PA was validated in simulation and phantom studies. Effects of signal-to-noise ratio, upsampling factor, segmentation, and slice thickness on reconstructing13C images were evaluated in simulation. PA was further applied to low-resolution human brain metabolite maps of hyperpolarized [1-13 C] pyruvate and [1-13C] lactate with 3 compartment segmentations (gray matter, white matter, and cerebrospi-nal fluid). The performance of PA was compared with other conventional interpolation methods (sinc, nearest-neighbor, bilinear, and spline interpolations). The simulation and the phantom tests showed that PA improved spatial resolution by up to 8 times and enhanced the image contrast without compromising quantification accuracy or losing the intracompartment signal inhomogeneity, even in the case of low signal-to-noise ratio or inaccurate segmentation. PA also improved spatial resolution and image contrast of human13C brain images. Dynamic analysis showed consistent performance of the proposed method even with the signal decay along time. In conclusion, PA can enhance low-resolution hyperpolarized13 C images in terms of spatial resolution and contrast by using a priori knowledge from high-resolution1H magnetic resonance imaging while preserving quantification accuracy and intracompartment signal inhomogeneity.
KW - Human brain
KW - Hyperpolarized 13C imaging
KW - Patch-based algorithm
KW - Super-resolution
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U2 - 10.18383/j.tom.2020.00037
DO - 10.18383/j.tom.2020.00037
M3 - Article
C2 - 33364424
AN - SCOPUS:85098182283
SN - 2379-1381
VL - 6
SP - 343
EP - 355
JO - Tomography
JF - Tomography
IS - 4
ER -