Specialized computational methods for denoising, B1 correction, and kinetic modeling in hyperpolarized 13C MR EPSI studies of liver tumors
Corresponding Author
Philip M. Lee
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Correspondence
Philip M. Lee, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 1700 Fourth Street, Byers Hall Suite 102, San Francisco, CA 94158, USA.
Email: [email protected]
Search for more papers by this authorHsin-Yu Chen
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorJeremy W. Gordon
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorZihan Zhu
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorPeder E.Z. Larson
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorNicholas Dwork
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorMark Van Criekinge
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorLucas Carvajal
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorMichael A. Ohliger
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorZhen J. Wang
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorDuan Xu
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorJohn Kurhanewicz
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorRobert A. Bok
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorRahul Aggarwal
Department of Medicine, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorPamela N. Munster
Department of Medicine, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorDaniel B. Vigneron
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorCorresponding Author
Philip M. Lee
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Correspondence
Philip M. Lee, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 1700 Fourth Street, Byers Hall Suite 102, San Francisco, CA 94158, USA.
Email: [email protected]
Search for more papers by this authorHsin-Yu Chen
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorJeremy W. Gordon
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorZihan Zhu
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorPeder E.Z. Larson
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorNicholas Dwork
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorMark Van Criekinge
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorLucas Carvajal
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorMichael A. Ohliger
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorZhen J. Wang
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorDuan Xu
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorJohn Kurhanewicz
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorRobert A. Bok
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorRahul Aggarwal
Department of Medicine, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorPamela N. Munster
Department of Medicine, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorDaniel B. Vigneron
UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
Search for more papers by this authorFunding information
National Institutes of Health, Grant/Award Numbers: R01CA183071, U01EB026412, R01DK115987, and P41EB013598
Abstract
Purpose
To develop a novel post-processing pipeline for hyperpolarized (HP) 13C MRSI that integrates tensor denoising and 
correction to measure pyruvate-to-lactate conversion rates (kPL) in patients with liver tumors.
Methods
Seven HP 13C MR scans of progressing liver tumors were acquired using a custom 13C surface transmit/receive coil and the echo-planar spectroscopic imaging (EPSI) data analysis included B0 correction, tensor rank truncation, and zero- and first-order phase corrections to recover metabolite signals that would otherwise be obscured by spectral noise as well as a correction for inhomogeneous transmit (
) using a 
map aligned to the coil position for each patient scan. Processed HP data and corrected flip angles were analyzed with an inputless two-site exchange model to calculate kPL.
Results
Denoising averages SNR increases of pyruvate, lactate, and alanine were 37.4-, 34.0-, and 20.1-fold, respectively, with lactate and alanine dynamics most noticeably recovered and better defined. In agreement with Monte Carlo simulations, over-flipped regions underestimated kPL and under-flipped regions overestimated kPL. 
correction addressed this issue.
Conclusion
The new HP 13C EPSI post-processing pipeline integrated tensor denoising and 
correction to measure kPL in patients with liver tumors. These technical developments not only recovered metabolite signals in voxels that did not receive the prescribed flip angle, but also increased the extent and accuracy of kPL estimations throughout the tumor and adjacent regions including normal-appearing tissue and additional lesions.
Supporting Information
| Filename | Description |
|---|---|
| mrm28901-sup-0001-Supinfo.docxWord document, 387.8 KB | FIGURE S1 (A) The simulated pyruvate and lactate signals. Following similar approaches,1 the inputless 2-site exchange model was used, an approach that only fits the lactate magnetization and not the pyruvate magnetization while the measured pyruvate magnetization is used as the input at each time point. The nominal simulation values were kPL = 0.03 s−1, T1 of pyruvate = 30 s, and T1 of lactate = 25 s. The noise standard deviation was set at 0.004 relative to a fixed total magnetization input normalized to 1. This value was chosen to approximately match the SNR of the simulated data with typical empirical data. The γ-distribution function to simulate the pyruvate and lactate signals had an arrival time of 4 s and a full width at half-maximum of 8 s. All excitations of each time point were consolidated into a single effective flip angle.1 (B) Monte Carlo simulations of the 2-site inputless model demonstrated an inverse relationship between the relative  error and the fractional kPL error, the simulated kPL values (kPL,fit) against the input kPL of 0.03 s−1 (kPL,truth). The solid and dashed lines indicate the mean and ±1 standard deviation, respectively. Positive errors in  resulted in underestimations of kPL and negative errors resulted in overestimations, agreeing with acquired data. This relationship corresponded with the computed kPL maps before and after  correction. The selected voxels presented in Table 1 are indicated with blue circles and all but 1 are within the 1 SD bounds |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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