ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation

Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.

DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1α, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to noninvasively detect expression of hypoxia inducible factor-1-alpha (HIF-1α), epidermal growth factor receptor (EGFR), and Ki-67 in nasopharyngeal carcinoma (NPC) by quantitatively measuring tumor blood flow, vascularity, and permeability.

PURPOSE

We aim to explore the utility of DCE-MRI in detecting HIF-1α, EGFR, and Ki-67 expression levels using traditional Kety's/Tofts' modeling and quantitative transport mapping (QTM).

MATERIALS AND METHODS

Eighty-nine NPC patients underwent DCE-MRI before treatment were enrolled. DCE-MRI was processed to generate the following kinetic parameters: |u| and D from the QTM model, tumor blood flow (TBF) from Kety's model, and K^trans, V_e, and K_ep from Tofts' model. Pretreatment levels of HIF-1α, EGFR, and Ki-67 were assessed by immunohistochemistry and classified into low and high expression groups.

RESULTS

|u| (p < 0.001) and TBF (p = 0.015) values were significantly higher in the HIF-1α high-expression group compared to low-expression group. Only K^trans (p = 0.016) was significantly higher in the EGFR high-expression group. Only |u| (p < 0.001) values were significantly higher in the Ki-67 high-expression group compared to low-expression group. Multiple linear regression analyses showed that |u| independently correlated with HIF-1α and Ki-67 expression, and K^trans independently correlated with EGFR. The areas under the ROC curves of |u| for HIF-1α and Ki-67, and K^trans for EGFR were 0.83, 0.74, and 0.70, respectively.

CONCLUSION

|u| and K^trans derived from DCE-MRI may be considered as noninvasive imaging markers for detecting hypoxia and proliferation in NPC patients.

Increased risk for cerebral small vessel disease is associated with quantitative susceptibility mapping in HIV infected and uninfected individuals

The aim of this study was to assess, in the context of cerebral small vessel disease (CSVD), whether cardiovascular risk factors and white matter hyperintensities (WMHs) were associated with brain tissue susceptibility as measured by quantitative susceptibility mapping (QSM). Given that CSVD is diagnosed by the presence of lacunar strokes, periventricular and deep WMHs, increased perivascular spaces, and microbleeds, we expected that QSM could capture changes in brain tissue due to underlying CSVD pathology. We compared a cohort of 101 HIV-infected individuals (mean age ± SD = 53.2 ± 10.9 years) with mild to moderate cardiovascular risk scores, as measured by the Reynolds risk score, to 102 age-matched controls (mean age (SD) = 50.3 (15.7) years) with similar Reynolds scores. We performed brain MRI to assess CSVD burden by acquiring 3D T1-MPRAGE, 3D FLAIR, 2D T2-TSE, and mGRE for QSM. We found that signs of CSVD are significantly higher in individuals with HIV-infection compared to controls and that WMH volumes are significantly correlated with age and cardiovascular risk scores. Regional QSM was associated with cardiovascular risk factors, age, sex, and WMH volumes but not HIV status. These results suggest that QSM may be an early imaging marker reflective of alterations in brain microcirculation.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

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.

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.

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 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.

Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT)

PURPOSE

Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems.

THEORY AND METHODS

Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner.

RESULTS

The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data.

CONCLUSION

Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.

Multispectral Imaging for Metallic Biopsy Marker Detection During MRI-Guided Breast Biopsy: A Feasibility Study for Clinical Translation

Purpose

To assess the feasibility and diagnostic accuracy of multispectral MRI (MSI) in the detection and localization of biopsy markers during MRI-guided breast biopsy.

Methods

This prospective study included 20 patients undergoing MR-guided breast biopsy. In 10 patients (Group 1), MSI was acquired following tissue sampling and biopsy marker deployment. In the other 10 patients (Group 2), MSI was acquired following tissue sampling but before biopsy marker deployment (to simulate deployment failure). All patients received post-procedure mammograms. Group 1 and Group 2 designations, in combination with the post-procedure mammogram, served as the reference standard. The diagnostic performance of MSI for biopsy marker identification was independently evaluated by two readers using two-spectral-bin MR and one-spectral-bin MR. The κ statistic was used to assess inter-rater agreement for biopsy marker identification.

Results

The sensitivity, specificity, and accuracy of biopsy marker detection for readers 1 and 2 using 2-bin MSI were 90.0% (9/10) and 90.0% (9/10), 100.0% (10/10) and 100.0% (10/10), 95.0% (19/20) and 95.0% (19/20); and using 1-bin MSI were 70.0% (7/10) and 80.0% (8/10), 100.0% (8/8) and 100.0% (10/10), 85.0% (17/20) and 90.0% (18/20). Positive predictive value was 100% for both readers for all numbers of bins. Inter-rater agreement was excellent: κ was 1.0 for 2-bin MSI and 0.81 for 1-bin MSI.

Conclusion

MSI is a feasible, diagnostically accurate technique for identifying metallic biopsy markers during MRI-guided breast biopsy and may eliminate the need for a post-procedure mammogram.

Integrated quantitative susceptibility and R2 * mapping for evaluation of liver fibrosis: An ex vivo feasibility study

To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R₂ * mapping using corrections for the effects of liver iron. Liver fibrosis is characterized by excessive accumulation of collagen and other extracellular matrix proteins. While collagen increases R₂ * relaxation, measures of R₂ * for fibrosis are confounded by liver iron, which may be present in the liver over a wide range of concentrations. The diamagnetic collagen contribution to susceptibility values measured by QSM is much less than the contribution of highly paramagnetic iron. In 19 ex vivo liver explants with and without fibrosis, QSM (χ), R₂ * and proton density fat fraction (PDFF) maps were constructed from multiecho gradient-recalled echo (mGRE) sequence acquisition at 3 T. Median parameter values were recorded and differences between the MRI parameters in nonfibrotic vs. advanced fibrotic/cirrhotic samples were evaluated using Mann-Whitney U tests and receiver operating characteristic analyses. Logistic regression with stepwise feature selection was employed to evaluate the utility of combined MRI measurements for detection of fibrosis. Median R₂ * increased in fibrotic vs. nonfibrotic liver samples (P = .041), while differences in χ and PDFF were nonsignificant (P = .545 and P = .395, respectively). Logistic regression identified the combination of χ and R₂ * significant for fibrosis detection (logit [prediction] = -8.45 + 0.23 R₂ * - 28.8 χ). For this classifier, a highly significant difference between nonfibrotic vs. advanced fibrotic/cirrhotic samples was observed (P = .002). The model exhibited an AUC of 0.909 (P = .003) for detection of advanced fibrosis/cirrhosis, which was substantially higher compared with AUCs of the individual parameters (AUC 0.591-0.784). An integrated QSM and R₂ * analysis of mGRE 3 T imaging data is promising for noninvasive diagnostic assessment of liver fibrosis.

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

INTRODUCTION

During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood.

METHODS

GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure.

RESULTS

In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively.

CONCLUSIONS

This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.

Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network

Purpose

Proof‐of‐concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer‐labeled MRI data.

Theory and Methods

To determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time‐resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety’s method that uses a global arterial input function. Multiple post‐label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility.

Results

Against the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety’s method (45.7%, 2.5‐fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety’s method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region.

Conclusions

QTM flow velocity mapping is feasible from multi‐delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety’s method with deconvolution in time only.

Initial Experience of Challenge-Free MRI-Based Oxygen Extraction Fraction Mapping of Ischemic Stroke at Various Stages: Comparison With Perfusion and Diffusion Mapping

MRI-based oxygen extraction fraction imaging has a great potential benefit in the selection of clinical strategies for ischemic stroke patients. This study aimed to evaluate the performance of a challenge-free oxygen extraction fraction (OEF) mapping in a cohort of acute and subacute ischemic stroke patients. Consecutive ischemic stroke patients (a total of 30 with 5 in the acute stage, 19 in the early subacute stage, and 6 in the late subacute stage) were recruited. All subjects underwent MRI including multi-echo gradient echo (mGRE), diffusion weighted imaging (DWI), and 3D-arterial spin labeling (ASL). OEF maps were generated from mGRE phase + magnitude data, which were processed using quantitative susceptibility mapping (QSM) + quantitative blood oxygen level-dependent (qBOLD) imaging with cluster analysis of time evolution. Cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) maps were reconstructed from 3D-ASL and DWI, respectively. Further, cerebral metabolic rate of oxygen (CMRO2) was calculated as the product of CBF and OEF. OEF, CMRO2, CBF, and ADC values in the ischemic cores (absolute values) and their contrasts to the contralateral regions (relative values) were evaluated. One-way analysis of variance (ANOVA) was used to compare OEF, CMRO2, CBF, and ADC values and their relative values among different stroke stages. The OEF value of infarct core showed a trend of decrease from acute, to early subacute, and to late subacute stages of ischemic stroke. Significant differences among the three stroke stages were only observed in the absolute OEF (F = 6.046, p = 0.005) and relative OEF (F = 5.699, p = 0.009) values of the ischemic core, but not in other measurements (absolute and relative CMRO2, CBF, ADC values, all values of p > 0.05). In conclusion, the challenge-free QSM + qBOLD-generated OEF mapping can be performed on stroke patients. It can provide more information on tissue viability that was not available with CBF and ADC and, thus, may help to better manage ischemic stroke patients.

Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping

Adaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction.

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TFIR's adaptive regularization obtains magnetic susceptibility from magnetic field

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TFIR has low artifact incidence on both quantitative and clinical scores

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The error for TFIR is low on various numerical and ground truth tests

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Clinical applications for TFIR include hemorrhages and whole head mapping

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.

Quantitative Measurement of Metal Accumulation in Brain of Patients With Wilson's Disease

BACKGROUND

Currently, no study has evaluated metal accumulation in the brains of patients with Wilson's disease by using quantitative susceptibility mapping at 3T MRI. The objectives of this study were to qualitatively and quantitatively evaluate changes in magnetic susceptibility and R2* maps in deep gray matter nuclei to discriminate Wilson's disease patients from healthy controls and to evaluate their sensitivities in diagnosing Wilson's disease.

METHODS

Magnetic susceptibility and R2* maps and conventional T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery images were obtained from 17 Wilson's disease patients and 14 age-matched healthy controls on a 3T MRI scanner. Differences between Wilson's disease and healthy control groups in susceptibility and R2* values in multiple deep nuclei were evaluated using a Mann-Whitney U test and receiver operating characteristic curves. The correlations of susceptibility and R2* values with Unified Wilson's Disease Rating Scale score were also performed.

RESULTS

Magnetic susceptibility and R2* can effectively distinguish different types of signal abnormalities. Magnetic susceptibility and R2* values in multiple deep nuclei of Wilson's disease patients were significantly higher than those in healthy controls. Magnetic susceptibility value in the substantia nigra had the highest area under the curve (0.888). There were positive correlations of the Unified Wilson's Disease Rating Scale score with susceptibility values in the caudate nucleus (r = 0.757, P = 0.011), putamen (r = 0.679, P = 0.031), and red nucleus (r = 0.638, P = 0.047), as well as R2* values in the caudate nucleus (r = 0.754, P = 0.012).

CONCLUSIONS

Quantitative susceptibility mapping at 3T could be a useful tool to evaluate metal accumulation in deep gray matter nuclei of Wilson's disease patients. © 2020 International Parkinson and Movement Disorder Society.

Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction

Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.

Validation of MRI quantitative susceptibility mapping of superparamagnetic iron oxide nanoparticles for hyperthermia applications in live subjects

The use of magnetic fluid hyperthermia (MFH) for cancer therapy has shown promise but lacks suitable methods for quantifying exogenous irons such as superparamagnetic iron oxide (SPIO) nanoparticles as a source of heat generation under an alternating magnetic field (AMF). Application of quantitative susceptibility mapping (QSM) technique to prediction of SPIO in preclinical models has been challenging due to a large variation of susceptibility values, chemical shift from tissue fat, and noisier data arising from the higher resolution required to visualize the anatomy of small animals. In this study, we developed a robust QSM for the SPIO ferumoxytol in live mice to examine its potential application in MFH for cancer therapy. We demonstrated that QSM was able to simultaneously detect high level ferumoxytol accumulation in the liver and low level localization near the periphery of tumors. Detection of ferumoxytol distribution in the body by QSM, however, required imaging prior to and post ferumoxytol injection to discriminate exogenous iron susceptibility from other endogenous sources. Intratumoral injection of ferumoxytol combined with AMF produced a ferumoxytol-dose dependent tumor killing. Histology of tumor sections corroborated QSM visualization of ferumoxytol distribution near the tumor periphery, and confirmed the spatial correlation of cell death with ferumoxytol distribution. Due to the dissipation of SPIOs from the injection site, quantitative mapping of SPIO distribution will aid in estimating a change in temperature in tissues, thereby maximizing MFH effects on tumors and minimizing side-effects by avoiding unwanted tissue heating.

Automated adaptive preconditioner for quantitative susceptibility mapping

PURPOSE

To develop an automated adaptive preconditioner for QSM reconstruction with improved susceptibility quantification accuracy and increased image quality.

THEORY AND METHODS

The total field was used to rapidly produce an approximate susceptibility map, which was then averaged and trended over R 2 ∗ binning to generate a spatially varying distribution of preconditioning values. This automated adaptive preconditioner was used to reconstruct QSM via total field inversion and was compared with its empirical counterparts in a numerical simulation, a brain experiment with 5 healthy subjects and 5 patients with intracerebral hemorrhage, and a cardiac experiment with 3 healthy subjects.

RESULTS

Among evaluated preconditioners, the automated adaptive preconditioner achieved the fastest convergence in reducing the RMSE of the QSM in the simulation, suppressed hemorrhage-associated artifacts while preserving surrounding brain tissue contrasts, and provided cardiac chamber oxygenation values consistent with those reported in the literature.

CONCLUSION

An automated adaptive preconditioner allows high-quality QSM from the total field in imaging various anatomies with dynamic susceptibility ranges.

Quantitative Susceptibility Mapping: MRI at 7T versus 3T

BACKGROUND AND PURPOSE

Ultrahigh-field 7T promises more than doubling the signal-to-noise ratio (SNR) of 3T for magnetic resonance imaging (MRI), particularly for MRI of magnetic susceptibility effects induced by B₀ . Quantitative susceptibility mapping (QSM) is based on deconvolving the induced phase (or field) and would therefore benefit substantially from 7T. The purpose of this work was to compare QSM performance at 7T versus 3T in an intrascanner test-retest experiment with varying echo numbers (5 and 10 echoes).

METHODS

A prospective study in N = 10 healthy subjects was carried out at both 3T and 7T field strengths. Gradient echo data using 5 and 10 echoes were acquired twice in each subject. Test-retest reproducibility was assessed using Bland-Altman and regression analysis of region of interest measurements. Image quality was scored by an experienced neuroradiologist.

RESULTS

Intrascanner bias was below 3.6 parts-per-billion (ppb) with correlation R² > .85. Interscanner bias was below 10.9 ppb with correlation R² > .8. The image quality score for the 3T 10 echo protocol was not different from the 7T 5 echo protocol (P = .65).

CONCLUSION

Excellent image quality and good reproducibility was observed. 7T allows equivalent image quality of 3T in half of the scan time.

Free breathing three-dimensional cardiac quantitative susceptibility mapping for differential cardiac chamber blood oxygenation - initial validation in patients with cardiovascular disease inclusive of direct comparison to invasive catheterization

BACKGROUND

Differential blood oxygenation between left (LV) and right ventricles (RV; ΔSaO2) is a key index of cardiac performance; LV dysfunction yields increased RV blood pool deoxygenation. Deoxyhemoglobin increases blood magnetic susceptibility, which can be measured using an emerging cardiovascular magnetic resonance (CMR) technique, Quantitative Susceptibility Mapping (QSM) - a concept previously demonstrated in healthy subjects using a breath-hold 2D imaging approach (2DBHQSM). This study tested utility of a novel 3D free-breathing QSM approach (3DNAVQSM) in normative controls, and validated 3DNAVQSM for non-invasive ΔSaO2 quantification in patients undergoing invasive cardiac catheterization (cath).

METHODS

Initial control (n = 10) testing compared 2DBHQSM (ECG-triggered 2D gradient echo acquired at end-expiration) and 3DNAVQSM (ECG-triggered navigator gated gradient echo acquired in free breathing using a phase-ordered automatic window selection algorithm to partition data based on diaphragm position). Clinical testing was subsequently performed in patients being considered for cath, including 3DNAVQSM comparison to cine-CMR quantified LV function (n = 39), and invasive-cath quantified ΔSaO2 (n = 15). QSM was acquired using 3 T scanners; analysis was blinded to comparator tests (cine-CMR, cath).

RESULTS

3DNAVQSM generated interpretable QSM in all controls; 2DBHQSM was successful in 6/10. Among controls in whom both pulse sequences were successful, RV/LV susceptibility difference (and ΔSaO2) were not significantly different between 3DNAVQSM and 2DBHQSM (252 ± 39 ppb [17.5 ± 3.1%] vs. 211 ± 29 ppb [14.7 ± 2.0%]; p = 0.39). Acquisition times were 30% lower with 3DNAVQSM (4.7 ± 0.9 vs. 6.7 ± 0.5 min, p = 0.002), paralleling a trend towards lower LV mis-registration on 3DNAVQSM (p = 0.14). Among cardiac patients (63 ± 10y, 56% CAD) 3DNAVQSM was successful in 87% (34/39) and yielded higher ΔSaO2 (24.9 ± 6.1%) than in controls (p < 0.001). QSM-calculated ΔSaO2 was higher among patients with LV dysfunction as measured on cine-CMR based on left ventricular ejection fraction (29.4 ± 5.9% vs. 20.9 ± 5.7%, p < 0.001) or stroke volume (27.9 ± 7.5% vs. 22.4 ± 5.5%, p = 0.013). Cath measurements (n = 15) obtained within a mean interval of 4 ± 3 days from CMR demonstrated 3DNAVQSM to yield high correlation (r = 0.87, p < 0.001), small bias (- 0.1%), and good limits of agreement (±8.6%) with invasively measured ΔSaO2.

CONCLUSION

3DNAVQSM provides a novel means of assessing cardiac performance. Differential susceptibility between the LV and RV is increased in patients with cine-CMR evidence of LV systolic dysfunction; QSM-quantified ΔSaO2 yields high correlation and good agreement with the reference of invasively-quantified ΔSaO2.

Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation

Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi-echo GRE sequence was implemented to acquire out-phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*-IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO-GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi-peak fat model. There was no substantial difference on QSM between the single peak and multi-peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO-GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.

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.

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.