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Alzaidi AA, Panek R, Blockley NP. Quantitative BOLD (qBOLD) imaging of oxygen metabolism and blood oxygenation in the human body: A scoping review. Magn Reson Med 2024; 92:1822-1837. [PMID: 39072791 DOI: 10.1002/mrm.30165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE There are many approaches to the quantitative BOLD (qBOLD) technique described in the literature, differing in pulse sequences, MRI parameters and data processing. Thus, in this review, we summarized the acquisition methods, approaches used for oxygenation quantification and clinical populations investigated. METHODS Three databases were systematically searched (Medline, Embase, and Web of Science) for published research that used qBOLD methods for quantification of oxygen metabolism. Data extraction and synthesis were performed by one author and reviewed by a second author. RESULTS A total of 93 relevant papers were identified. Acquisition strategies were summarized, and oxygenation parameters were found to have been investigated in many pathologies such as steno-occlusive diseases, stroke, glioma, and multiple sclerosis disease. CONCLUSION A summary of qBOLD approaches for oxygenation measurements and applications could help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.
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Affiliation(s)
- Ahlam A Alzaidi
- David Greenfield Human Physiology Unit, School of Life Sciences, University of Nottingham, Nottingham, UK
- Radiology Department, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Rafal Panek
- Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Nicholas P Blockley
- David Greenfield Human Physiology Unit, School of Life Sciences, University of Nottingham, Nottingham, UK
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2
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Kahali S, Kothapalli SVVN, Xu X, Kamilov US, Yablonskiy DA. Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient-Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties. NMR IN BIOMEDICINE 2023; 36:e4883. [PMID: 36442839 DOI: 10.1002/nbm.4883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 06/16/2023]
Abstract
The purpose of the current study was to introduce a Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, R2t*, and hemodynamic-specific, R2', metrics of quantitative gradient-recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation of R2t* and R2' maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B0 ) magnetic field inhomogeneities directly from the gradient-recalled echo (GRE) magnitude images. The R2t* and R2' maps for training were generated by means of a voxel-by-voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B0 -inhomogeneities contributions to multigradient-echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise-suppression feature of DANSE is especially prominent for data with low signal-to-noise ratio (SNR ~ 50-100), where DANSE-generated R2t* and R2' maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time-dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications.
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Affiliation(s)
- Sayan Kahali
- Department of Radiology, Washington University in Saint Louis, St. Louis, Missouri, USA
| | | | - Xiaojian Xu
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ulugbek S Kamilov
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Dmitriy A Yablonskiy
- Department of Radiology, Washington University in Saint Louis, St. Louis, Missouri, USA
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Review of the Research Progress of Human Brain Oxygen Extraction Fraction by Magnetic Resonance Imaging. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:4554271. [PMID: 36304964 PMCID: PMC9596244 DOI: 10.1155/2022/4554271] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/02/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022]
Abstract
In recent years, the incidence of cerebrovascular diseases (CVD) is increasing, which seriously endangers human health. The study on hemodynamics of cerebrovascular disease can help us to understand, prevent, and treat the disease. As one of the important parameters of human cerebral hemodynamics and tissue metabolism, OEF (oxygen extraction fraction) is of great value in central nervous system diseases. The use of BOLD (blood oxygen level dependent) effect offers the possibility to study cerebral hemodynamic and metabolic characteristics by MRI (magnetic resonance imaging) measurements. Therefore, this paper reviews the hemodynamic parameters of brain tissue, discusses the principles and methods of quantitative BOLD-based MRI measurements of OEF, and discusses the advantages and disadvantages of each method.
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4
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Jiang D, Lu H. Cerebral oxygen extraction fraction MRI: Techniques and applications. Magn Reson Med 2022; 88:575-600. [PMID: 35510696 PMCID: PMC9233013 DOI: 10.1002/mrm.29272] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/20/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022]
Abstract
The human brain constitutes 2% of the body's total mass but uses 20% of the oxygen. The rate of the brain's oxygen utilization can be derived from a knowledge of cerebral blood flow and the oxygen extraction fraction (OEF). Therefore, OEF is a key physiological parameter of the brain's function and metabolism. OEF has been suggested to be a useful biomarker in a number of brain diseases. With recent advances in MRI techniques, several MRI-based methods have been developed to measure OEF in the human brain. These MRI OEF techniques are based on the T2 of blood, the blood signal phase, the magnetic susceptibility of blood-containing voxels, the effect of deoxyhemoglobin on signal behavior in extravascular tissue, and the calibration of the BOLD signal using gas inhalation. Compared to 15 O PET, which is considered the "gold standard" for OEF measurement, MRI-based techniques are non-invasive, radiation-free, and are more widely available. This article provides a review of these emerging MRI-based OEF techniques. We first briefly introduce the role of OEF in brain oxygen homeostasis. We then review the methodological aspects of different categories of MRI OEF techniques, including their signal mechanisms, acquisition methods, and data analyses. The strengths and limitations of the techniques are discussed. Finally, we review key applications of these techniques in physiological and pathological conditions.
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Affiliation(s)
- Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
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5
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Zibetti MVW, Sharafi A, Regatte RR. Optimization of spin-lock times in T 1ρ mapping of knee cartilage: Cramér-Rao bounds versus matched sampling-fitting. Magn Reson Med 2022; 87:1418-1434. [PMID: 34738252 PMCID: PMC8822470 DOI: 10.1002/mrm.29063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T1ρ ) mapping. METHODS Optimization criteria for TSLs based on Cramér-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T1ρ mapping on synthetic data, model phantoms, and knee cartilage. The MSF approaches are optimized using robust methods for noisy cost functions. The MSF approaches assume that optimal TSLs depend on the chosen fitting method. An iterative non-linear least squares (NLS) and artificial neural networks (ANN) are tested as two possible T1ρ fitting methods for MSF approaches. RESULTS All optimized criteria were better than non-optimized ones. However, we observe that a modified CRLB and an MSF based on the mean of the normalized absolute error (MNAE) were more robust optimization approaches, performing well in all tested cases. The optimized TSLs obtained the best performance with synthetic data (3.5-8.0% error), model phantoms (1.5-2.8% error), and healthy volunteers (7.7-21.1% error), showing stable and improved quality results, comparing to non-optimized approaches (4.2-13.3% error on synthetic data, 2.1-6.2% error on model phantoms, 9.8-27.8% error on healthy volunteers). CONCLUSION A modified CRLB and the MSF based on MNAE are robust optimization approaches for choosing TSLs in T1ρ mapping. All optimized criteria allowed good results even using rapid scans with two TSLs when a complex-valued fitting is done with iterative NLS or ANN.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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Takeshima H. Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview. Magn Reson Med Sci 2021; 21:553-568. [PMID: 34544924 DOI: 10.2463/mrms.rev.2021-0040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers develop various applications of DL. DL has made a large impact on the literature of many medical sciences, including MR. However, its technical details are not easily understandable for non-experts of machine learning (ML).The first part of this article presents an overview of DL and its related technologies, such as artificial intelligence (AI) and ML. AI is explained as a function that can receive many inputs and produce many outputs. ML is a process of fitting the function to training data. DL is a kind of ML, which uses a composite of many functions to approximate the function of interest. This composite function is called a deep neural network (DNN), and the functions composited into a DNN are called layers. This first part also covers the underlying technologies required for DL, such as loss functions, optimization, initialization, linear layers, non-linearities, normalization, recurrent neural networks, regularization, data augmentation, residual connections, autoencoders, generative adversarial networks, model and data sizes, and complex-valued neural networks.The second part of this article presents an overview of the applications of DL in MR and explains how functions represented as DNNs are applied to various applications, such as RF pulse, pulse sequence, reconstruction, motion correction, spectroscopy, parameter mapping, image synthesis, and segmentation.
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Affiliation(s)
- Hidenori Takeshima
- Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation
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Torop M, Kothapalli SVVN, Sun Y, Liu J, Kahali S, Yablonskiy DA, Kamilov US. Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact-free and denoised R 2 * images. Magn Reson Med 2020; 84:2932-2942. [PMID: 32767489 DOI: 10.1002/mrm.28344] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/12/2020] [Accepted: 05/13/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R 2 * maps from multi-gradient recalled echo (mGRE) MRI data. METHODS RoAR trains a convolutional neural network (CNN) to generate quantitative R 2 ∗ maps free from field inhomogeneity artifacts by adopting a self-supervised learning strategy given (a) mGRE magnitude images, (b) the biophysical model describing mGRE signal decay, and (c) preliminary-evaluated F-function accounting for contribution of macroscopic B0 field inhomogeneities. Importantly, no ground-truth R 2 * images are required and F-function is only needed during RoAR training but not application. RESULTS We show that RoAR preserves all features of R 2 * maps while offering significant improvements over existing methods in computation speed (seconds vs. hours) and reduced sensitivity to noise. Even for data with SNR = 5 RoAR produced R 2 * maps with accuracy of 22% while voxel-wise analysis accuracy was 47%. For SNR = 10 the RoAR accuracy increased to 17% vs. 24% for direct voxel-wise analysis. CONCLUSIONS RoAR is trained to recognize the macroscopic magnetic field inhomogeneities directly from the input magnitude-only mGRE data and eliminate their effect on R 2 ∗ measurements. RoAR training is based on the biophysical model and does not require ground-truth R 2 * maps. Since RoAR utilizes signal information not just from individual voxels but also accounts for spatial patterns of the signals in the images, it reduces the sensitivity of R 2 * maps to the noise in the data. These features plus high computational speed provide significant benefits for the potential usage of RoAR in clinical settings.
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Affiliation(s)
- Max Torop
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Yu Sun
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jiaming Liu
- Department of Electrical and Systems Engineering, University in St. Louis, St. Louis, MO, USA
| | - Sayan Kahali
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Ulugbek S Kamilov
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Department of Electrical and Systems Engineering, University in St. Louis, St. Louis, MO, USA
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Jung S, Lee H, Ryu K, Song JE, Park M, Moon WJ, Kim DH. Artificial neural network for multi-echo gradient echo-based myelin water fraction estimation. Magn Reson Med 2020; 85:380-389. [PMID: 32686208 DOI: 10.1002/mrm.28407] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 02/01/2023]
Abstract
PURPOSE To demonstrate robust myelin water fraction (MWF) mapping using an artificial neural network (ANN) with multi-echo gradient-echo (GRE) signal. METHODS Multi-echo gradient-echo signals simulated with a three-pool exponential model were used to generate the training data set for the ANN, which was designed to yield the MWF. We investigated the performance of our proposed ANN for various conditions using both numerical simulations and in vivo data. Simulations were conducted with various SNRs to investigate the performance of the ANN. In vivo data with high spatial resolutions were applied in the analyses, and results were compared with MWFs derived by the nonlinear least-squares algorithm using a complex three-pool exponential model. RESULTS The network results for the simulations show high accuracies against noise compared with nonlinear least-squares MWFs: RMS-error value of 5.46 for the nonlinear least-squares MWF and 3.56 for the ANN MWF at an SNR of 150 (relative gain = 34.80%). These effects were also found in the in vivo data, with reduced SDs in the region-of-interest analyses. These effects of the ANN demonstrate the feasibility of acquiring high-resolution myelin water images. CONCLUSION The simulation results and in vivo data suggest that the ANN facilitates more robust MWF mapping in multi-echo gradient-echo sequences compared with the conventional nonlinear least-squares method.
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Affiliation(s)
- Soozy Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hongpyo Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kanghyun Ryu
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jae Eun Song
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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Glang F, Deshmane A, Prokudin S, Martin F, Herz K, Lindig T, Bender B, Scheffler K, Zaiss M. DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks—application to CEST imaging of the human brain at 3T. Magn Reson Med 2019; 84:450-466. [DOI: 10.1002/mrm.28117] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/23/2019] [Accepted: 11/18/2019] [Indexed: 01/07/2023]
Affiliation(s)
- Felix Glang
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
| | - Anagha Deshmane
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
| | - Sergey Prokudin
- Department of Perceiving Systems Max Planck Institute for Intelligent Systems Tübingen Germany
| | - Florian Martin
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
| | - Kai Herz
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
| | - Tobias Lindig
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
- Department of Diagnostic and Interventional Neuroradiology Eberhard Karls University Tübingen Tübingen Germany
| | - Benjamin Bender
- Department of Diagnostic and Interventional Neuroradiology Eberhard Karls University Tübingen Tübingen Germany
| | - Klaus Scheffler
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
- Department of Biomedical Magnetic Resonance Eberhard Karls University Tübingen Tübingen Germany
| | - Moritz Zaiss
- Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany
- Department of Neuroradiology University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU) Erlangen Germany
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10
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Cherukara MT, Stone AJ, Chappell MA, Blockley NP. Model-based Bayesian inference of brain oxygenation using quantitative BOLD. Neuroimage 2019; 202:116106. [PMID: 31430532 PMCID: PMC7334042 DOI: 10.1016/j.neuroimage.2019.116106] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/30/2019] [Accepted: 08/16/2019] [Indexed: 11/17/2022] Open
Abstract
Streamlined Quantitative BOLD (sqBOLD) is an MR technique that can non-invasively measure physiological parameters including Oxygen Extraction Fraction (OEF) and deoxygenated blood volume (DBV) in the brain. Current sqBOLD methodology rely on fitting a linear model to log-transformed data acquired using an Asymmetric Spin Echo (ASE) pulse sequence. In this paper, a non-linear model implemented in a Bayesian framework was used to fit physiological parameters to ASE data. This model makes use of the full range of available ASE data, and incorporates the signal contribution from venous blood, which was ignored in previous analyses. Simulated data are used to demonstrate the intrinsic difficulty in estimating OEF and DBV simultaneously, and the benefits of the proposed non-linear model are shown. In vivo data are used to show that this model improves parameter estimation when compared with literature values. The model and analysis framework can be extended in a number of ways, and can incorporate prior information from external sources, so it has the potential to further improve OEF estimation using sqBOLD.
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Affiliation(s)
- Matthew T Cherukara
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Alan J Stone
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Nicholas P Blockley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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11
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Hubertus S, Thomas S, Cho J, Zhang S, Wang Y, Schad LR. Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD. Magn Reson Med 2019; 82:2199-2211. [PMID: 31273828 DOI: 10.1002/mrm.27882] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/28/2019] [Accepted: 06/05/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization. METHODS Random combinations of OEF, deoxygenated blood volume ( ν ), R2 , and nonblood magnetic susceptibility ( χ nb ) with each parameter following a Gaussian distribution that represented physiological gray matter and white matter values were used to simulate quantitative BOLD signals and QSM values. An ANN was trained with the simulated data with added Gaussian noise. The ANN was applied to multigradient echo brain data of 7 healthy subjects, and the reconstructed parameters and maps were compared to QN results using Student t test and Bland-Altman analysis. RESULTS Intersubject means and SDs of gray matter were OEF = 43.5 ± 0.8 %, R 2 = 13.5 ± 0.3 Hz, ν = 3.4 ± 0.1 %, χ nb = - 25 ± 5 ppb for ANN; and OEF = 43.8 ± 5.2 %, R 2 = 12.2 ± 0.8 Hz, ν = 4.2 ± 0.6 %, χ nb = - 39 ± 7 ppb for QN, with a significant difference ( P < 0.05 ) for R 2 , ν , and χ nb . For white matter, they were OEF = 47.5 ± 1.1 %, R 2 = 17.1 ± 0.4 Hz, ν = 2.5 ± 0.2 %, χ nb = - 38 ± 5 ppb for ANN; and OEF = 42.3 ± 5.6 %, R 2 = 16.7 ± 0.7 Hz, ν = 2.9 ± 0.3 %, χ nb = - 45 ± 9 ppb for QN, with a significant difference ( P < 0.05 ) for OEF and ν . ANN revealed more gray-white matter contrast but less intersubject variation in OEF than QN. In contrast to QN, the ANN reconstruction did not need an additional sequence for parameter initialization and took approximately 1 s rather than roughly 1 h. CONCLUSION ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.
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Affiliation(s)
- Simon Hubertus
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Thomas
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Junghun Cho
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York.,Department of Radiology, Tongji Hospital, Wuhan, China
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York.,Department of Radiology, Weill Cornell Medical College, New York, New York
| | - Lothar Rudi Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Hubertus S, Thomas S, Cho J, Zhang S, Wang Y, Schad LR. Comparison of gradient echo and gradient echo sampling of spin echo sequence for the quantification of the oxygen extraction fraction from a combined quantitative susceptibility mapping and quantitative BOLD (QSM+qBOLD) approach. Magn Reson Med 2019; 82:1491-1503. [DOI: 10.1002/mrm.27804] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Simon Hubertus
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Sebastian Thomas
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Junghun Cho
- Department of Biomedical Engineering Cornell University Ithaca New York
| | - Shun Zhang
- Department of Radiology Weill Cornell Medical College New York New York
- Department of Radiology Tongji Hospital Wuhan China
| | - Yi Wang
- Department of Biomedical Engineering Cornell University Ithaca New York
- Department of Radiology Weill Cornell Medical College New York New York
| | - Lothar Rudi Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim Heidelberg University Mannheim Germany
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13
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Affiliation(s)
- Doohee Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Korea
| | - Jingu Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Korea
| | - Jingyu Ko
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Korea
| | - Jaeyeon Yoon
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Korea
| | - Kanghyun Ryu
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Yoonho Nam
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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14
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Bertleff M, Domsch S, Weingärtner S, Zapp J, O'Brien K, Barth M, Schad LR. Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T. NMR IN BIOMEDICINE 2017; 30:e3833. [PMID: 28960549 DOI: 10.1002/nbm.3833] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 08/17/2017] [Accepted: 08/17/2017] [Indexed: 06/07/2023]
Abstract
Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times.
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Affiliation(s)
- Marco Bertleff
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Domsch
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Weingärtner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Jascha Zapp
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kieran O'Brien
- Center for Advanced Imaging, University of Queensland, St Lucia, QLD, Australia
| | - Markus Barth
- Center for Advanced Imaging, University of Queensland, St Lucia, QLD, Australia
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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