The changes of oxygen extraction fraction in different types of lesions in relapsing-remitting multiple sclerosis: A cross-sectional and longitudinal study.

OBJECTIVES: To explore the oxygen metabolism level of different types of lesions in relapsing-remitting multiple sclerosis (RRMS) patients by oxygen extraction fraction (OEF) both cross-sectionally and longitudinally. METHODS: Forty-six RRMS patients and forty-one healthy controls (HC) went MRI examination. The quantitative susceptibility mapping (QSM) and OEF map were reconstructed from a 3D multi-echo gradient echo sequence. MS lesions in white matter were classified as contrast-enhancing lesions (CELs) on post-gadolinium T1-weighted sequence, paramagnetic rim lesions (PRLs), hyperintense lesions and non-hyperintense lesions on QSM, respectively. The susceptibility and OEF of different types of lesions were compared. The susceptibility and OEF values were measured and compared among different types of lesions. Among these RRMS patients, seventeen had follow-up MRI and 232 lesions, and baseline to follow-up longitudinal changes in susceptibility and OEF were measured. RESULTS: PRLs had higher susceptibility and lower OEF than CELs, hyperintense lesions, and non-hyperintense lesions. The hyperintense lesions had higher susceptibility and lower OEF than non-hyperintense lesions. In longitudinal changes, PRLs had susceptibility increased (P < 0.001) and OEF decreased (P < 0.001). The hyperintense lesions showed significant decreases in susceptibility (P = 0.020), and non-hyperintense lesions showed significant increases in OEF during follow-up (P = 0.005). Notably, hyperintense lesions may convert to PRLs or non-hyperintense lesions as time progresses, accompanied by changes of OEF and susceptibility in the lesions. CONCLUSION: This study revealed tissue damage and oxygen metabolism level in different types of MS lesions. The OEF may contribute to further understanding the evolution of MS lesions.

Cerebral oxygen metabolism from MRI susceptibility.

This article provides an overview of MRI methods exploiting magnetic susceptibility properties of blood to assess cerebral oxygen metabolism, including the tissue oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2). The first section is devoted to describing blood magnetic susceptibility and its effect on the MRI signal. Blood circulating in the vasculature can have diamagnetic (oxyhemoglobin) or paramagnetic properties (deoxyhemoglobin). The overall balance between oxygenated and deoxygenated hemoglobin determines the induced magnetic field which, in turn, modulates the transverse relaxation decay of the MRI signal via additional phase accumulation. The following sections of this review then illustrate the principles underpinning susceptibility-based techniques for quantifying OEF and CMRO2. Here, it is detailed whether these techniques provide global (OxFlow) or local (Quantitative Susceptibility Mapping - QSM, calibrated BOLD - cBOLD, quantitative BOLD - qBOLD, QSM+qBOLD) measurements of OEF or CMRO2, and what signal components (magnitude or phase) and tissue pools they consider (intravascular or extravascular). Validations studies and potential limitations of each method are also described. The latter include (but are not limited to) challenges in the experimental setup, the accuracy of signal modeling, and assumptions on the measured signal. The last section outlines the clinical uses of these techniques in healthy aging and neurodegenerative diseases and contextualizes these reports relative to results from gold-standard PET.

Spatiotemporal patterns of brain iron-oxygen metabolism in patients with Parkinson's disease.

OBJECTIVES: Iron deposition and mitochondrial dysfunction are closely associated with the genesis and progression of Parkinson's disease (PD). This study aims to extract susceptibility and oxygen extraction fraction (OEF) values of deep grey matter (DGM) to explore spatiotemporal progression patterns of brain iron-oxygen metabolism in PD. METHODS: Ninety-five PD patients and forty healthy controls (HCs) were included. Quantitative susceptibility mapping (QSM) and OEF maps were computed from MRI multi-echo gradient echo data. Analysis of covariance (ANCOVA) was used to compare mean susceptibility and OEF values in DGM between early-stage PD (ESP), advanced-stage PD (ASP) patients and HCs. Then Granger causality analysis on the pseudo-time-series of MRI data was applied to assess the causal effect of early altered nuclei on iron content and oxygen extraction in other DGM nuclei. RESULTS: The susceptibility values in substantia nigra (SN), red nucleus, and globus pallidus (GP) significantly increased in PD patients compared with HCs, while the iron content in GP did not elevate obviously until the late stage. The mean OEF values for the caudate nucleus, putamen, and dentate nucleus were higher in ESP patients than in ASP patients or/and HCs. We also found that iron accumulation progressively expands from the midbrain to the striatum. These alterations were correlated with clinical features and improved AUC for early PD diagnosis to 0.824. CONCLUSIONS: Abnormal cerebral iron deposition and tissue oxygen utilization in PD measured by QSM and OEF maps could reflect pathological alterations in neurodegenerative processes and provide valuable indicators for disease identification and management. CLINICAL RELEVANCE STATEMENT: Noninvasive assessment of cerebral iron-oxygen metabolism may serve as clinical evidence of pathological changes in PD and improve the validity of diagnosis and disease monitoring. KEY POINTS: • Quantitative susceptibility mapping and oxygen extraction fraction maps indicated the cerebral pathology of abnormal iron accumulation and oxygen metabolism in Parkinson's disease. • Iron deposition is mainly in the midbrain, while altered oxygen metabolism is concentrated in the striatum and cerebellum. • The susceptibility and oxygen extraction fraction values in subcortical nuclei were associated with clinical severity.

Subsecond accurate myelin water fraction reconstruction from FAST-T2 data with 3D UNET.

PURPOSE: To develop a 3D UNET convolutional neural network for rapid extraction of myelin water fraction (MWF) maps from six-echo fast acquisition with spiral trajectory and T2 -prep data and to evaluate its accuracy in comparison with multilayer perceptron (MLP) network. METHODS: The MWF maps were extracted from 138 patients with multiple sclerosis using an iterative three-pool nonlinear least-squares algorithm (NLLS) without and with spatial regularization (srNLLS), which were used as ground-truth labels to train, validate, and test UNET and MLP networks as a means to accelerate data fitting. Network testing was performed in 63 patients with multiple sclerosis and a numerically simulated brain phantom at SNR of 200, 100 and 50. RESULTS: Simulations showed that UNET reduced the MWF mean absolute error by 30.1% to 56.4% and 16.8% to 53.6% over the whole brain and by 41.2% to 54.4% and 21.4% to 49.4% over the lesions for predicting srNLLS and NLLS MWF, respectively, compared to MLP, with better performance at lower SNRs. UNET also outperformed MLP for predicting srNLLS MWF in the in vivo multiple-sclerosis brain data, reducing mean absolute error over the whole brain by 61.9% and over the lesions by 67.5%. However, MLP yielded 41.1% and 51.7% lower mean absolute error for predicting in vivo NLLS MWF over the whole brain and the lesions, respectively, compared with UNET. The whole-brain MWF processing time using a GPU was 0.64 seconds for UNET and 0.74 seconds for MLP. CONCLUSION: Subsecond whole-brain MWF extraction from fast acquisition with spiral trajectory and T2 -prep data using UNET is feasible and provides better accuracy than MLP for predicting MWF output of srNLLS algorithm.

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET). METHODS: The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test. RESULTS: In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps. CONCLUSION: QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.

Quantitative susceptibility mapping and R1 measurement: Determination of the myelin volume fraction in the aging ex vivo rat corpus callosum.

In studies of the white matter (WM) in aging brains, both quantitative susceptibility mapping (QSM) and direct R1 measurement offer potentially useful ex vivo MRI tools that allow volumetric characterization of myelin content changes. Despite the technical importance of such MRI methods in numerous age-related diseases, the supposed linear relationship between the estimates of either the QSM or R1 method and age-affected myelin contents has not been validated. In this study, the absolute myelin volume fraction (MVF) was determined by transmission electron microscopy (TEM) as a gold standard measure for comparison with the values obtained by the aforementioned MR methods. To theoretically evaluate and understand the MR signal characteristics, QSM simulations were performed using the finite perturber method (FPM). Specifically, the simulation geometry modeling was based on TEM-derived structures aligned orthogonally to the main magnetic field, the construct of which was used to estimate the magnetic field shift (ΔB) changes arising from the conjectured myelin structures. Experimentally, ex vivo corpus callosum (CC) samples from rat brains obtained at 6 weeks (n = 3), 4 months (n = 3), and 20 months (n = 3) after birth were used to establish the relationship between changes quantified by either QSM or R1 with the absolute MVF by TEM. From the ex vivo brain samples, the scatterplot of mean MVF versus R1 was fitted to a linear equation, where R1mean = 0.7948 × MVFmean + 0.8118 (Pearson's correlation coefficient r = 0.9138; p < 0.01), while the scatterplot of mean MVF versus MRI-derived magnetic susceptibility (χ) was also fitted to a line where χmeasured,mean = -0.1218 × MVFmean - 0.006345 (r = -0.8435; p < 0.01). As a result of the FPM-based QSM simulations, a linearly proportional relationship between the simulated magnetic susceptibility, χsimulated,mean , and MVF (r = -0.9648; p < 0.01) was established. Such a statistically significant linear correlation between MRI-derived values by the QSM (or R1 ) method and MVF demonstrated that variable myelin contents in the WM (i.e., CC) can be quantified across multiple stages of aging. These findings further support that both techniques based on QSM and R1 provide an efficient means of studying the brain-aging process with accurate volumetric quantification of the myelin content in WM.

Oxygen extraction fraction (OEF) assesses cerebral oxygen metabolism of deep gray matter in patients with pre-eclampsia.

OBJECTIVES: The objective of this study was to compare oxygen extraction fraction (OEF) values in the deep gray matter (GM) of pre-eclampsia (PE) patients, pregnant healthy controls (PHCs), and non-pregnant healthy controls (NPHCs) to explore their brain oxygen metabolism differences in GM. METHODS: Forty-seven PE patients, forty NPHCs, and twenty-one PHCs were included. Brain OEF values were computed from quantitative susceptibility mapping (QSM) plus quantitative blood oxygen level-dependent magnitude (QSM + qBOLD = QQ)-based mapping. One-way ANOVA was used to compare mean OEF values in the three groups. The area under the curve of the mean OEF value in each region of interest was estimated using a receiver operating characteristic curve analysis. RESULTS: We found that the mean OEF values in the thalamus, putamen, caudate nucleus, pallidum, and substantia nigra were significantly different in these three groups (F = 5.867, p = 0.004; F = 5.142, p = 0007; F = 6.158, p = 0.003; F = 6.319, p = 0.003; F = 5.491, p = 0.005). The mean OEF values for these 5 regions were higher in PE patients than in NPHCs and in PHCs (p < 0.05). The AUC of these ROIs ranged from 0.673 to 0.692 (p < 0.01) and cutoff values varied from 35.1 to 36.6%, indicating that the OEF values could discriminate patients with and without PE. Stepwise multivariate analysis revealed that the OEF values correlated with hematocrit in pregnant women (r = 0.353, p = 0.003). CONCLUSION: OEF values in the brains of pregnant women can be measured in clinical practice using QQ-based OEF mapping for noninvasive assessment of hypertensive disorders. KEY POINTS: • Pre-eclampsia is a hypertensive disorder associated with abnormalities in brain oxygen extraction. • Oxygen extraction fraction (OEF) is an indicator of brain tissue viability and function. QQ-based mapping of OEF is a new MRI technique that can noninvasively quantify brain oxygen metabolism. • OEF values in the brains of pregnant women can be measured for noninvasive assessment of hypertensive disorders in clinical practice.

Cerebral oxygen extraction fraction: Comparison of dual-gas challenge calibrated BOLD with CBF and challenge-free gradient echo QSM+qBOLD.

PURPOSE: To compare cortical gray matter oxygen extraction fraction (OEF) estimated from 2 MRI methods: (1) the quantitative susceptibility mapping (QSM) plus quantitative blood oxygen level dependent imaging (qBOLD) (QSM+qBOLD or QQ), and (2) the dual-gas calibrated-BOLD (DGCB) in healthy subjects; and to investigate the validity of iso-cerebral metabolic rate of oxygen consumption assumption during hypercapnia using QQ. METHODS: In 10 healthy subjects, 3 tesla MRI including a multi-echo gradient echo sequence at baseline and hypercapnia for QQ, as well as an EPI dual-echo pseudo-continuous arterial spin labeling for DGCB, were performed under a hypercapnic and a hyperoxic condition. OEFs from QQ and DGCB were compared using region of interest analysis and paired t test. For QQ, cerebral metabolic rate of oxygen consumption = cerebral blood flow*OEF*arterial oxygen content was generated for both baseline and hypercapnia, which were compared. RESULTS: Average OEF in cortical gray matter across 10 subjects from QQ versus DGCB was 35.5 ± 6.7% versus 38.0 ± 9.1% (P = .49) at baseline and 20.7 ± 4.4% versus 28.4 ± 7.6% (P = .02) in hypercapnia: OEF in cortical gray matter was significantly reduced as measured in QQ (P < .01) and in DGCB (P < .01). Cerebral metabolic rate of oxygen consumption (in µmol O2 /min/100 g) was 168.2 ± 54.1 at baseline from DGCB and was 153.1 ± 33.8 at baseline and 126.4 ± 34.2 (P < .01) in hypercapnia from QQ. CONCLUSION: The differences in OEF obtained from QQ and DGCB are small and nonsignificant at baseline but are statistically significant during hypercapnia. In addition, QQ shows a cerebral metabolic rate of oxygen consumption decrease (17.4%) during hypercapnia.

Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD.

PURPOSE: To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using temporal clustering, tissue composition, and total variation (CCTV). METHODS: Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO2 were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test. RESULTS: In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001). CONCLUSION: The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training.

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 T2∗ -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 T2∗ -IDEAL. RESULTS: All DNN methods generated consistent water/fat separation results that agreed well with T2∗ -IDEAL under proper initialization. CONCLUSION: The water/fat separation problem can be solved using unsupervised deep neural networks.

Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping.

PURPOSE: Typical quantitative susceptibility mapping (QSM) reconstruction steps consist of first estimating the magnetization field from the gradient-echo images, and then reconstructing the susceptibility map from the estimated field. The errors from the field-estimation steps may propagate into the final QSM map, and the noise in the estimated field map may no longer be zero-mean Gaussian noise, thus, causing streaking artifacts in the resulting QSM. A multiecho complex total field inversion (mcTFI) method was developed to compute the susceptibility map directly from the multiecho gradient echo images using an improved signal model that retains the Gaussian noise property in the complex domain. It showed improvements in QSM reconstruction over the conventional field-to-source inversion. METHODS: The proposed mcTFI method was compared with the nonlinear total field inversion (nTFI) method in a numerical brain with hemorrhage and calcification, the numerical brains provided by the QSM Challenge 2.0, 18 brains with intracerebral hemorrhage scanned at 3T, and 6 healthy brains scanned at 7T. RESULTS: Compared with nTFI, the proposed mcTFI showed more accurate QSM reconstruction around the lesions in the numerical simulations. The mcTFI reconstructed QSM also showed the best image quality with the least artifacts in the brains with intracerebral hemorrhage scanned at 3T and healthy brains scanned at 7T. CONCLUSION: The proposed multiecho complex total field inversion improved QSM reconstruction over traditional field-to-source inversion through better signal modeling.

Quantification of brain oxygen extraction fraction using QSM and a hyperoxic challenge.

PURPOSE: To use hyperoxia in combination with QSM to quantify microvascular oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) in healthy subjects and to cross-validate results with those from hypercapnia QSM-OEF. METHODS: Ten healthy subjects were scanned on a 3T MRI scanner. At baseline normoxia and during hyperoxia (PetO2 = +300 mmHg), QSM data were acquired using a multi-echo gradient-echo (GRE) sequence, and cerebral blood flow data were acquired using a pseudocontinuous arterial spin labeling sequence. The OEF and CMRO2 maps were computed and compared with those from hypercapnia QSM-OEF, acquired in the same subjects, using correlation and Bland-Altman analysis in 16 vascular territories. RESULTS: Hyperoxia QSM-OEF produced physiologically reasonable OEF and CMRO2 values in all subjects (gray-matter region of interest average OEF = 0.42 ± 0.04, average CMRO2 = 181 ± 34 µmol O2 /min/100 g). When compared with hypercapnia QSM-OEF, Bland-Altman plots revealed small deviations (mean OEF difference = 0.015, mean CMRO2 difference = 4.9 µmol O2 /min/100 g, P < .05). Good and excellent correlations of regional OEF and CMRO2 were found for the two methods. In addition, hyperoxia had minimal impact on cerebral blood flow (average gray-matter cerebral blood flow was reduced by 7.5 ± 5.4%). CONCLUSIONS: Hyperoxia in combination with QSM is a robust approach to measure OEF. Compared with hypercapnia, hyperoxia is more comfortable and has minimal impact on cerebral blood flow.

Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual-gas calibrated BOLD imaging.

PURPOSE: To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2 ) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual-gas calibrated BOLD imaging (DGC-BOLD) in healthy subjects. METHODS: Ten healthy subjects were scanned using a 3T MR system. The QSM data were acquired with a multi-echo gradient-echo sequence at baseline and hypercapnia. Cerebral blood flow data were acquired using the pseudo-continuous arterial spin labeling technique. Baseline OEF and CMRO2 were calculated using QSM and cerebral blood flow measurements. The DGC-BOLD data were also collected under a hypercapnic and a hyperoxic condition to yield baseline OEF and CMRO2 . The QSM-OEF and CMRO2 maps were compared with DGC-BOLD OEF and CMRO2 maps using region of interest (vascular territories) analysis and Bland-Altman plots. RESULTS: Hypercapnia is a robust stimulus for mapping OEF in combination with QSM. Average OEF in 16 vascular territory regions of interest across 10 subjects was 0.40 ± 0.04 by QSM-OEF and 0.38 ± 0.09 by DGC-BOLD. The average CMRO2 was 176 ± 35 and 167 ± 53 µmol O2 /min/100g by QSM-OEF and DGC-BOLD, respectively. A Bland-Altman plot of regional OEF and CMRO2 in regions of interest revealed a statistically significant but small difference (OEF difference = 0.02, CMRO2 difference = 9 µmol O2 /min/100g, p < .05) between the 2 methods for the 10 healthy subjects. CONCLUSION: Hypercapnic challenge-assisted QSM-OEF is a feasible approach to quantify regional brain OEF and CMRO2 . Compared with DGC-BOLD, hypercapnia QSM-OEF results in smaller intersubject variability and requires only 1 gas challenge.

Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent magnitude (qBOLD)-based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) mapping.

PURPOSE: To improve the accuracy of QSM plus quantitative blood oxygen level-dependent magnitude (QSM + qBOLD or QQ)-based mapping of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) 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 CMRO2 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 CMRO2 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.

Dipole modeling of multispectral signal for detecting metallic biopsy markers during MRI-guided breast biopsy: a pilot study.

PURPOSE: During MRI-guided breast biopsy, a metallic biopsy marker is deployed at the biopsy site to guide future interventions. Conventional MRI during biopsy cannot distinguish such markers from biopsy site air, and a post-biopsy mammogram is therefore performed to localize marker placement. The purpose of this pilot study is to develop dipole modeling of multispectral signal (DIMMS) as an MRI alternative to eliminate the cost, inefficiency, inconvenience, and ionizing radiation of a mammogram for biopsy marker localization. METHODS: DIMMS detects and localizes the biopsy marker by fitting the measured multispectral imaging (MSI) signal to the MRI signal model and marker properties. MSI was performed on phantoms containing titanium biopsy markers and air to illustrate the clinical challenge that DIMMS addresses and on 20 patients undergoing MRI-guided breast biopsy to assess DIMMS feasibility for marker detection. DIMMS was compared to conventional MSI field map thresholding, using the post-procedure mammogram as the reference standard. RESULTS: Biopsy markers were detected and localized in 20 of 20 cases using MSI with automated DIMMS post-processing (using a threshold of 0.7) and in 18 of 20 cases using MSI field mapping (using a threshold of 0.65 kHz). CONCLUSION: MSI with DIMMS post-processing is a feasible technique for biopsy marker detection and localization during MRI-guided breast biopsy. With a 2-min MSI scan, DIMMS is a promising MRI alternative to the standard-of-care post-biopsy mammogram.

Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD.

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 ( χnb ) 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 %, R2=13.5±0.3 Hz, ν=3.4±0.1 %, χnb=-25±5 ppb for ANN; and OEF = 43.8±5.2 %, R2=12.2±0.8 Hz, ν=4.2±0.6 %, χnb=-39±7 ppb for QN, with a significant difference ( P<0.05 ) for R2 , ν , and χnb . For white matter, they were OEF = 47.5±1.1 %, R2=17.1±0.4 Hz, ν=2.5±0.2 %, χnb=-38±5 ppb for ANN; and OEF =42.3±5.6 %, R2=16.7±0.7 Hz, ν=2.9±0.3 %, χnb=-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.

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.

PURPOSE: To compare gradient echo (GRE) and gradient echo sampling of spin echo (GESSE) sequences for the quantification of the oxygen extraction fraction (OEF) from combined quantitative BOLD and quantitative susceptibility mapping (QSM) with regard to accuracy, precision and parameter initialization. METHODS: GRE and GESSE data were acquired from 7 healthy volunteers. QSM was applied to the GRE data and used as a regularization for the single-compartment quantitative BOLD fit to the GESSE and GRE data, respectively, to quantify OEF, deoxygenated blood volume (ν), R2 , and non-blood susceptibility (χnb ). Intersubject means within gray and white matter, respectively, were compared between GESSE and GRE (Student's t) and gray-white matter contrast was determined for each sequence separately. A single- and multi-compartment simulation was used to compare reconstruction accuracy. RESULTS: Intersubject means and SDs for gray and white matter were OEF = 32.4 ± 1.6%, ν = 2.9 ± 0.1%, R2 = 14.2 ± 0.5 Hz, χnb = -43 ± 5 ppb for GESSE and OEF = 43.0 ± 5.4%, ν = 3.5 ± 0.4%, R2 = 14.4 ± 0.7 Hz, χnb = -43 ± 8 ppb for GRE with a significant difference (P < 0.05) for OEF and ν. Gray-white matter contrast was significant (P < 0.05) in all parameters for GESSE but only in ν and R2 for GRE. All parameters reconstructed from GESSE had higher accuracy than from GRE in the single- but not multi-compartment simulation. CONCLUSION: GESSE yields higher parameter accuracy in simulated gray matter but produces unphysiological gray-white matter contrast in OEF in vivo. GRE produces uniform OEF maps in vivo and is more efficient, which could facilitate a clinical implementation, but revealed biases in simulation. The appropriate sequence should be chosen depending on application.

Quantitative susceptibility mapping-based cerebral metabolic rate of oxygen mapping with minimum local variance.

PURPOSE: The objective of this study was to demonstrate the feasibility of a cerebral metabolic rate of oxygen (CMRO2 ) mapping method based on its minimum local variance (MLV) without vascular challenge using quantitative susceptibility mapping (QSM) and cerebral blood flow (CBF). METHODS: Three-dimensional multi-echo gradient echo imaging and arterial spin labeling were performed in 11 healthy subjects to calculate QSM and CBF. Minimum local variance was used to compute whole-brain CMRO2 map from QSM and CBF. The MLV method was compared with a reference method using the caffeine challenge. Their agreement within the cortical gray matter (CGM) was assessed on CMRO2 and oxygen extraction fraction (OEF) maps at both baseline and challenge states. RESULTS: Mean CMRO2 (in µmol/100 g/min) obtained in CGM using the caffeine challenge and MLV were 142 ± 16.5 and 139 ± 14.8 µmol/100 g/min, respectively; the corresponding baseline OEF were 33.0 ± 4.0% and 31.8 ± 3.2%, respectively. The MLV and caffeine challenge methods showed no statistically significant differences across subjects with small ( < 4%) biases in CMRO2 and OEF values. CONCLUSIONS: Minimum local variance-based CMRO2 mapping without vascular challenge using QSM and arterial spin labeling is feasible in healthy subjects. Magn Reson Med 79:172-179, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Coherence enhancement in quantitative susceptibility mapping by means of anisotropic weighting in morphology enabled dipole inversion.

PURPOSE: To investigate an anisotropic structural prior in morphology enabled dipole inversion (MEDI) for improving accuracy in quantitative susceptibility mapping (QSM). THEORY AND METHODS: Anisotropic weighting (AW) was devised and implemented to incorporate orientation information into the edge agreement in the MEDI method. AW performance was compared with isotropic weighting by testing and validating on in vivo brain multiple orientation MRI data using COSMOS and the (33) component of the susceptibility tensor as reference. RESULTS: Suppressing streaking artifacts, AW improved not only QSM image quality but also accuracy in terms of RMSE (root mean square error), HFEN (high frequency error norm), SSIM (structural similarity index), and GDA (gradient direction agreement). In addition, it outperformed isotropic weighting in region of interest-based analysis. From a computational perspective, AW was as fast as isotropic weighting, taking approximately the same central processing unit times. CONCLUSION: Using AW in MEDI improves QSM accuracy compared with isotropic weighting. Magn Reson Med 79:1172-1180, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cerebral metabolic rate of oxygen (CMRO2 ) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD).

PURPOSE: To map the cerebral metabolic rate of oxygen (CMRO2 ) by estimating the oxygen extraction fraction (OEF) from gradient echo imaging (GRE) using phase and magnitude of the GRE data. THEORY AND METHODS: 3D multi-echo gradient echo imaging and perfusion imaging with arterial spin labeling were performed in 11 healthy subjects. CMRO2 and OEF maps were reconstructed by joint quantitative susceptibility mapping (QSM) to process GRE phases and quantitative blood oxygen level-dependent (qBOLD) modeling to process GRE magnitudes. Comparisons with QSM and qBOLD alone were performed using ROI analysis, paired t-tests, and Bland-Altman plot. RESULTS: The average CMRO2 value in cortical gray matter across subjects were 140.4 ± 14.9, 134.1 ± 12.5, and 184.6 ± 17.9 µmol/100 g/min, with corresponding OEFs of 30.9 ± 3.4%, 30.0 ± 1.8%, and 40.9 ± 2.4% for methods based on QSM, qBOLD, and QSM+qBOLD, respectively. QSM+qBOLD provided the highest CMRO2 contrast between gray and white matter, more uniform OEF than QSM, and less noisy OEF than qBOLD. CONCLUSION: Quantitative CMRO2 mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude.