Purpose: To improve the accuracy of QSM plus quantitative blood oxygen leveldependent magnitude (QSM + qBOLD or QQ)-based mapping of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO 2 ) using cluster analysis of time evolution (CAT). Methods: 3D multi-echo gradient echo and arterial spin labeling images were acquired in 11 healthy subjects and 5 ischemic stroke patients. DWI was also carried out on patients. CAT was developed for analyzing signal evolution over TE.QQ-based OEF and CMRO 2 were reconstructed with and without CAT, and results were compared using region of interest analysis and a paired t-test. Results: Simulations demonstrated that CAT substantially reduced noise error in QQ-based OEF. In healthy subjects, QQ-based OEF appeared less noisy and more uniform with CAT than without CAT; average OEF with and without CAT in cortical gray matter was 32.7 ± 4.0% and 37.9 ± 4.5%, with corresponding CMRO 2 of 148.4 ± 23.8 and 171.4 ± 22.4 μmol/100 g/min, respectively. In patients, regions of low OEF were confined within the ischemic lesions defined on DWI when using CAT, which was not observed without CAT. Conclusion: The cluster analysis of time evolution (CAT) significantly improves the robustness of QQ-based OEF against noise.
K E Y W O R D Scerebral metabolic rate of oxygen, cluster analysis of time evolution, K-means, machine learning, oxygen extraction fraction, quantitative blood oxygenation level-dependent imaging, quantitative susceptibility mapping
Background:
Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC.
Purpose:
To develop a rapid, robust and automated liver QSM for clinical practice.
Study Type:
Prospective
Population:
13 healthy subjects and 22 patients.
Field strength/Sequences
1.5T and 3T / 3D multi-echo gradient-recalled echo (GRE) sequence.
Assessment:
Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2*-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (SPURS) in healthy subjects (n=5). Reproducibility was assessed over 4 scanners at 2 field strengths from 2 manufacturers using healthy subjects (n=8). Clinical feasibility was evaluated in patients (n=22).
Statistical Tests:
IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and ROI measurements. Reproducibility of QSM, R2*, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC).
Results:
Liver QSM using the IP method was found to be approximately 5.5 times faster than SPURS (P< 0.05) in initializing T2*-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between −0.06 to 0.07 ppm, ICC 0.97).
Conclusion:
Use of IP echo-based initialization, enables robust water/fat separation and field estimation for automated, rapid and reproducible liver QSM for clinical applications.
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over explicit image feature extractions in defining the needed prior. However, DL typically does not incorporate the precise physics of data generation or data fidelity. Instead, DL networks are trained to output some average response to an input. Consequently, DL image reconstruction contains errors, and may perform poorly when the test data deviates significantly from the training data, such as having new pathological features. To address this lack of data fidelity problem in DL image reconstruction, a novel approach, which we call fidelity-imposed network edit (FINE), is proposed. In FINE, a pre-trained prior network's weights are modified according to the physical model, on a test case. Our experiments demonstrate that FINE can achieve superior performance in two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and undersampled reconstruction in MRI.
Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods: The current T * 2-IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T * 2-IDEAL. Results: All DNN methods generated consistent water/fat separation results that agreed well with T * 2-IDEAL under proper initialization. Conclusion: The water/fat separation problem can be solved using unsupervised deep neural networks.
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