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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
Open AccessArticle

Super-Resolution Hyperpolarized 13C Imaging of Human Brain Using Patch-Based Algorithm

by 1 and 1,2,3,*
1
Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
2
Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
3
Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USA
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(4), 343-355; https://doi.org/10.18383/j.tom.2020.00037
Received: 12 September 2020 / Revised: 8 October 2020 / Accepted: 5 November 2020 / Published: 1 December 2020
Spatial resolution of metabolic imaging with hyperpolarized 13C-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 hyperpolarized 13C human brain images by exploiting compartmental information from the corresponding high-resolution 1H images. PA was validated in simulation and phantom studies. Effects of signal-to-noise ratio, upsampling factor, segmentation, and slice thickness on reconstructing 13C images were evaluated in simulation. PA was further applied to low-resolution human brain metabolite maps of hyperpolarized [1-13C] pyruvate and [1-13C] lactate with 3 compartment segmentations (gray matter, white matter, and cerebrospinal 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 human 13C 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 hyperpolarized 13C images in terms of spatial resolution and contrast by using a priori knowledge from high-resolution 1H magnetic resonance imaging while preserving quantification accuracy and intracompartment signal inhomogeneity.
Keywords: hyperpolarized 13C imaging; super-resolution; patch-based algorithm; human brain hyperpolarized 13C imaging; super-resolution; patch-based algorithm; human brain
MDPI and ACS Style

Ma, J.; Park, J.M. Super-Resolution Hyperpolarized 13C Imaging of Human Brain Using Patch-Based Algorithm. Tomography 2020, 6, 343-355. https://doi.org/10.18383/j.tom.2020.00037

AMA Style

Ma J, Park JM. Super-Resolution Hyperpolarized 13C Imaging of Human Brain Using Patch-Based Algorithm. Tomography. 2020; 6(4):343-355. https://doi.org/10.18383/j.tom.2020.00037

Chicago/Turabian Style

Ma, Junjie; Park, Jae Mo. 2020. "Super-Resolution Hyperpolarized 13C Imaging of Human Brain Using Patch-Based Algorithm" Tomography 6, no. 4: 343-355. https://doi.org/10.18383/j.tom.2020.00037

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