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Guo R, Yang S, Wiesner HM, Li Y, Zhao Y, Liang ZP, Chen W, Zhu XH. Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla. Front Neurosci 2024; 18:1389111. [PMID: 38911598 PMCID: PMC11190064 DOI: 10.3389/fnins.2024.1389111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/20/2024] [Indexed: 06/25/2024] Open
Abstract
Introduction Nicotinamide adenine dinucleotide (NAD) is a crucial molecule in cellular metabolism and signaling. Mapping intracellular NAD content of human brain has long been of interest. However, the sub-millimolar level of cerebral NAD concentration poses significant challenges for in vivo measurement and imaging. Methods In this study, we demonstrated the feasibility of non-invasively mapping NAD contents in entire human brain by employing a phosphorus-31 magnetic resonance spectroscopic imaging (31P-MRSI)-based NAD assay at ultrahigh field (7 Tesla), in combination with a probabilistic subspace-based processing method. Results The processing method achieved about a 10-fold reduction in noise over raw measurements, resulting in remarkably reduced estimation errors of NAD. Quantified NAD levels, observed at approximately 0.4 mM, exhibited good reproducibility within repeated scans on the same subject and good consistency across subjects in group data (2.3 cc nominal resolution). One set of higher-resolution data (1.0 cc nominal resolution) unveiled potential for assessing tissue metabolic heterogeneity, showing similar NAD distributions in white and gray matter. Preliminary analysis of age dependence suggested that the NAD level decreases with age. Discussion These results illustrate favorable outcomes of our first attempt to use ultrahigh field 31P-MRSI and advanced processing techniques to generate a whole-brain map of low-concentration intracellular NAD content in the human brain.
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Affiliation(s)
- Rong Guo
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Siemens Medical Solutions USA, Inc., Urbana, IL, United States
| | - Shaolin Yang
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Hannes M. Wiesner
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Yudu Li
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Yibo Zhao
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Zhi-Pei Liang
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Wei Chen
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Xiao-Hong Zhu
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
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Wang Y, Saha U, Rubakhin SS, Roy EJ, Smith AM, Sweedler JV, Lam F. High-resolution 1H-MRSI at 9.4 T by integrating relaxation enhancement and subspace imaging. NMR IN BIOMEDICINE 2024:e5161. [PMID: 38715469 DOI: 10.1002/nbm.5161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 07/12/2024]
Abstract
Achieving high-resolution and high signal-to-noise ratio (SNR) in vivo metabolic imaging via fast magnetic resonance spectroscopic imaging (MRSI) has been a longstanding challenge. This study combines the methods of relaxation enhancement (RE) and subspace imaging for the first time, enabling high-resolution and high-SNR in vivo MRSI of rodent brains at 9.4 T. Specifically, an RE-based chemical shift imaging sequence, which combines a frequency-selective pulse to excite only the metabolite frequencies with minimum perturbation of the water spins and a pair of adiabatic pulses to spatially localize the slice of interest, is designed and evaluated in vivo. This strategy effectively shortens the apparent T1 of metabolites, thereby increasing the SNR during relatively short repetition time ((TR) compared with acquisitions with only spatially selective wideband excitations, and does not require water suppression. The SNR was further enhanced via a state-of-the-art subspace reconstruction method. A novel subspace learning strategy tailored for 9.4 T and RE acquisitions is developed. In vivo, high-resolution (e.g., voxel size of 0.6 × 0.6 × 1.5 mm3) MRSI of both healthy mouse brains and a glioma-bearing mouse brain in 12.5 min has been demonstrated.
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Affiliation(s)
- Yizun Wang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Urbi Saha
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Stanislav S Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Edward J Roy
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Andrew M Smith
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Holonyak Micro and Nanotechnology Laboratory, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, Urbana, Illinois, USA
| | - Jonathan V Sweedler
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, Urbana, Illinois, USA
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Lam F, Peng X, Liang ZP. High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions. IEEE SIGNAL PROCESSING MAGAZINE 2023; 40:101-115. [PMID: 37538148 PMCID: PMC10398845 DOI: 10.1109/msp.2022.3203867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid, high-resolution, quantitative MRSI. This paper provides a systematic review of these progresses in the context of MRSI physics and offers perspectives on promising future directions.
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Affiliation(s)
- Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA
- Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering and Cancer Center at Illinois, University of Illinois Urbana-Champaign
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering and Cancer Center at Illinois, University of Illinois Urbana-Champaign
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Stamatelatou A, Scheenen TWJ, Heerschap A. Developments in proton MR spectroscopic imaging of prostate cancer. MAGMA (NEW YORK, N.Y.) 2022; 35:645-665. [PMID: 35445307 PMCID: PMC9363347 DOI: 10.1007/s10334-022-01011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 10/25/2022]
Abstract
In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Tom W J Scheenen
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
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Li Y, Wang Z, Lam F. SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints. IEEE Trans Biomed Eng 2022; 69:3087-3097. [PMID: 35320082 DOI: 10.1109/tbme.2022.3161417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoen-coder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE 1 H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE 1 H-MRSI of the brain, potentially useful for various clinical applications.
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Li Y, Zhao Y, Guo R, Wang T, Zhang Y, Chrostek M, Low WC, Zhu XH, Liang ZP, Chen W. Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3879-3890. [PMID: 34319872 PMCID: PMC8675063 DOI: 10.1109/tmi.2021.3101149] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a potentially powerful tool for noninvasive imaging of brain energy metabolism and tumor. However, the low sensitivity of DMRSI has significantly limited its utility for both research and clinical applications. This work presents a novel machine learning-based method to address this limitation. The proposed method synergistically integrates physics-based subspace modeling and data-driven deep learning for effective denoising, making high-resolution dynamic DMRSI possible. Specifically, a novel subspace model was used to represent the dynamic DMRSI signals; deep neural networks were trained to capture the low-dimensional manifolds of the spectral and temporal distributions of practical dynamic DMRSI data. The learned subspace and manifold structures were integrated via a regularization formulation to remove measurement noise. Theoretical analysis, computer simulations, and in vivo experiments have been conducted to demonstrate the denoising efficacy of the proposed method which enabled high-resolution imaging capability. The translational potential was demonstrated in tumor-bearing rats, where the Warburg effect associated with cancer metabolism and tumor heterogeneity were successfully captured. The new method may not only provide an effective tool to enhance the sensitivity of DMRSI for basic research and clinical applications but also provide a framework for denoising other spatiospectral data.
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