Unambiguous identification of superparamagnetic iron oxide particles through quantitative susceptibility mapping of the nonlinear response to magnetic fields

mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping

PURPOSE

To develop a method for rapid sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO).

METHODS

A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method withR = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard.

RESULTS

The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal.

CONCLUSION

mcLARO enabled fast sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.

Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit

Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed.

Highlights

novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.

Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.

LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO-QSM.git.

Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process?

The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations.

Brain oxygen extraction and neural tissue susceptibility are associated with cognitive impairment in older individuals

BACKGROUND AND PURPOSE

We investigated the effects of aging, white matter hyperintensities (WMH), and cognitive impairment on brain iron levels and cerebral oxygen metabolism, known to be altered in Alzheimer's disease (AD), using quantitative susceptibility mapping and MR-based cerebral oxygen extraction fraction (OEF).

METHODS

In 100 individuals over the age of 50 (68/32 cognitively impaired/intact), OEF and neural tissue susceptibility (χ_n ) were computed retrospectively from MRI multi-echo gradient echo data, obtained on a 3 Tesla MRI scanner. The effects of age and WMH on OEF and χ_n were assessed within groups, and OEF and χ_n were assessed between groups, using multivariate regression analyses.

RESULTS

Cognitively impaired subjects were found to have 19% higher OEF and 34% higher χ_n than cognitively intact subjects in the cortical gray matter and several frontal, temporal, and parietal regions (p < .05). Increased WMH burden was significantly associated with decreased OEF in the cognitively impaired, but not in the cognitively intact. Older age had a stronger association with decreased OEF in the cognitively intact group. Both older age and increased WMH burden were significantly associated with increased χ_n in temporoparietal regions in the cognitively impaired.

CONCLUSIONS

Higher brain OEF and χ_n in cognitively impaired older individuals may reflect altered oxygen metabolism and iron in areas with underlying AD pathology. Both age and WMH have associations with OEF and χ_n but are modified by the presence of cognitive impairment.

Automated Segmentation of Midbrain Structures in High-Resolution Susceptibility Maps Based on Convolutional Neural Network and Transfer Learning

Background

Accurate delineation of the midbrain nuclei, the red nucleus (RN), substantia nigra (SN) and subthalamic nucleus (STN), is important in neuroimaging studies of neurodegenerative and other diseases. This study aims to segment midbrain structures in high-resolution susceptibility maps using a method based on a convolutional neural network (CNN).

Methods

The susceptibility maps of 75 subjects were acquired with a voxel size of 0.83 × 0.83 × 0.80 mm³ on a 3T MRI system to distinguish the RN, SN, and STN. A deeply supervised attention U-net was pre-trained with a dataset of 100 subjects containing susceptibility maps with a voxel size of 0.63 × 0.63 × 2.00 mm³ to provide initial weights for the target network. Five-fold cross-validation over the training cohort was used for all the models’ training and selection. The same test cohort was used for the final evaluation of all the models. Dice coefficients were used to assess spatial overlap agreement between manual delineations (ground truth) and automated segmentation. Volume and magnetic susceptibility values in the nuclei extracted with automated CNN delineation were compared to those extracted by manual tracing. Consistencies of volume and magnetic susceptibility values by different extraction strategies were assessed by Pearson correlation coefficients and Bland-Altman analyses.

Results

The automated CNN segmentation method achieved mean Dice scores of 0.903, 0.864, and 0.777 for the RN, SN, and STN, respectively. There were no significant differences between the achieved Dice scores and the inter-rater Dice scores (p > 0.05 for each nucleus). The overall volume and magnetic susceptibility values of the nuclei extracted by the automatic CNN method were significantly correlated with those by manual delineation (p < 0.01).

Conclusion

Midbrain structures can be precisely segmented in high-resolution susceptibility maps using a CNN-based method.

QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

PURPOSE

To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM.

METHODS

We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady-state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion-weighted imaging phantom data are provided, allowing future investigations of tissue-microstructure effects in phase and QSM algorithms.

RESULTS

The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom's properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial-volume effects or complex distributions of frequency shifts within the voxel.

CONCLUSION

The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

New Developments in Imaging for Sentinel Lymph Node Biopsy in Early-Stage Oral Cavity Squamous Cell Carcinoma

Sentinel lymph node biopsy (SLNB) is a diagnostic staging procedure that aims to identify the first draining lymph node(s) from the primary tumor, the sentinel lymph nodes (SLN), as their histopathological status reflects the histopathological status of the rest of the nodal basin. The routine SLNB procedure consists of peritumoral injections with a technetium-99m [^99mTc]-labelled radiotracer followed by lymphoscintigraphy and SPECT-CT imaging. Based on these imaging results, the identified SLNs are marked for surgical extirpation and are subjected to histopathological assessment. The routine SLNB procedure has proven to reliably stage the clinically negative neck in early-stage oral squamous cell carcinoma (OSCC). However, an infamous limitation arises in situations where SLNs are located in close vicinity of the tracer injection site. In these cases, the hotspot of the injection site can hide adjacent SLNs and hamper the discrimination between tracer injection site and SLNs (shine-through phenomenon). Therefore, technical developments are needed to bring the diagnostic accuracy of SLNB for early-stage OSCC to a higher level. This review evaluates novel SLNB imaging techniques for early-stage OSCC: MR lymphography, CT lymphography, PET lymphoscintigraphy and contrast-enhanced lymphosonography. Furthermore, their reported diagnostic accuracy is described and their relative merits, disadvantages and potential applications are outlined.

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.

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.

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.

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.

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.

Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi-Site Reproducibility and Single-Site Robustness

BACKGROUND AND PURPOSE

Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment.

METHODS

A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm³ reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm³ ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month.

RESULTS

Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients.

CONCLUSION

Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.

Quantitative susceptibility map analysis in preterm neonates with germinal matrix-intraventricular hemorrhage

BACKGROUND

Germinal matrix-intraventricular hemorrhage (GMH-IVH) is a common form of intracranial hemorrhage occurring in preterm neonates that may affect normal brain development. Although the primary lesion is easily identified on MRI by the presence of blood products, its exact extent may not be recognizable with conventional sequences. Quantitative susceptibility mapping (QSM) quantify the spatial distribution of magnetic susceptibility within biological tissues, including blood degradation products.

PURPOSE/HYPOTHESIS

To evaluate magnetic susceptibility of normal-appearing white (WM) and gray matter regions in preterm neonates with and without GMH-IVH.

STUDY TYPE

Retrospective case-control.

POPULATION

A total of 127 preterm neonates studied at term equivalent age: 20 had mild GMH-IVH (average gestational age 28.7 ± 2.1 weeks), 15 had severe GMH-IVH (average gestational age 29.3 ± 1.8 weeks), and 92 had normal brain MRI (average gestational age 29.8 ± 1.8 weeks).

FIELD STRENGTH/SEQUENCE

QSM at 1.5 Tesla.

ASSESSMENT

QSM analysis was performed for each brain hemisphere with a region of interest-based approach including five WM regions (centrum semiovale, frontal, parietal, temporal, and cerebellum), and a subcortical gray matter region (basal ganglia/thalami).

STATISTICAL TESTS

Changes in magnetic susceptibility were explored using a one-way analysis of covariance, according to GMH-IVH severity (P < 0.05).

RESULTS

In preterm neonates with normal brain MRI, all white and subcortical gray matter regions had negative magnetic susceptibility values (diamagnetic). Neonates with severe GMH-IVH showed higher positive magnetic susceptibility values (i.e. paramagnetic) in the centrum semiovale (0.0019 versus -0.0014 ppm; P < 0.001), temporal WM (0.0011 versus -0.0012 ppm; P = 0.037), and parietal WM (0.0005 versus -0.0001 ppm; P = 0.002) compared with controls. No differences in magnetic susceptibility were observed between neonates with mild GMH-IVH and controls (P = 0.236).

DATA CONCLUSION

Paramagnetic susceptibility changes occur in several normal-appearing WM regions of neonates with severe GMH-IVH, likely related to the accumulation of hemosiderin/ferritin iron secondary to diffusion of extracellular hemoglobin from the ventricle into the periventricular WM.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1199-1207.

Quantitative Susceptibility Mapping (QSM) Algorithms: Mathematical Rationale and Computational Implementations

Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.

Significance and In Vivo Detection of Iron-Laden Microglia in White Matter Multiple Sclerosis Lesions

Microglia are resident immune cells that fulfill protective and homeostatic functions in the central nervous system (CNS) but may also promote neurotoxicity in the aged brain and in chronic disease. In multiple sclerosis (MS), an autoimmune demyelinating disease of the CNS, microglia and macrophages contribute to the development of white matter lesions through myelin phagocytosis, and possibly to disease progression through diffuse activation throughout myelinated white matter. In this review, we discuss an additional compartment of myeloid cell activation in MS, i.e., the rim and normal adjacent white matter of chronic active lesions. In chronic active lesions, microglia and macrophages may contain high amounts of iron, express markers of proinflammatory polarization, are activated for an extended period of time (years), and drive chronic tissue damage. Iron-positive myeloid cells can be visualized and quantified with quantitative susceptibility mapping (QSM), a magnetic resonance imaging technique. Thus, QSM has potential as an in vivo biomarker for chronic inflammatory activity in established white matter MS lesions. Reducing chronic inflammation associated with iron accumulation using existing or novel MS therapies may impact disease severity and progression.

MEDI+0: Morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping

PURPOSE

To develop a quantitative susceptibility mapping (QSM) method with a consistent zero reference using minimal variation in cerebrospinal fluid (CSF) susceptibility.

THEORY AND METHODS

The ventricular CSF was automatically segmented on the R2* map. An L₂ -regularization was used to enforce CSF susceptibility homogeneity within the segmented region, with the averaged CSF susceptibility as the zero reference. This regularization for CSF homogeneity was added to the model used in a prior QSM method (morphology enabled dipole inversion [MEDI]). Therefore, the proposed method was referred to as MEDI+0 and compared with MEDI in a numerical simulation, in multiple sclerosis (MS) lesions, and in a reproducibility study in healthy subjects.

RESULTS

In both the numerical simulations and in vivo experiments, MEDI+0 not only decreased the susceptibility variation within the ventricular CSF, but also suppressed the artifact near the lateral ventricles. In the simulation, MEDI+0 also provided more accurate quantification compared to MEDI in the globus pallidus, substantia nigra, corpus callosum, and internal capsule. MEDI+0 measurements of MS lesion susceptibility were in good agreement with those obtained by MEDI. Finally, both MEDI+0 and MEDI showed good and similar intrasubject reproducibility.

CONCLUSION

QSM with a minimal variation in ventricular CSF is viable to provide a consistent zero reference while improving image quality. Magn Reson Med 79:2795-2803, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Perfluorocarbon/Gold Loading for Noninvasive in Vivo Assessment of Bone Fillers Using 19F Magnetic Resonance Imaging and Computed Tomography

Calcium phosphate cement (CPC) is used in bone repair because of its biocompatibility. However, high similarity between CPC and the natural osseous phase results in poor image contrast in most of the available in vivo imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). For accurate identification and localization during and after implantation in vivo, a composition with enhanced image contrast is needed. In this study, we labeled CPC with perfluoro-15-crown-5-ether-loaded (PFCE) poly(latic-co-glycolic acid) nanoparticles (hydrodynamic radius 100 nm) and gold nanoparticles (diameter 40 nm), as 19F MRI and CT contrast agents, respectively. The resulting CPC/PFCE/gold composite is implanted in a rat model for in vivo longitudinal imaging. Our findings show that the incorporation of the two types of different nanoparticles did result in adequate handling properties of the cement. Qualitative and quantitative long-term assessment of CPC/PFCE/gold degradation was achieved in vivo and correlated to the new bone formation. Finally, no adverse biological effects on the bone tissue are observed via histology. In conclusion, an easy and efficient strategy for following CPC implantation and degradation in vivo is developed. As all materials used are biocompatible, this CPC/PFCE/gold composite is clinically applicable.

Establishing the overlap of IONP quantification with echo and echoless MR relaxation mapping

PURPOSE

Iron-oxide nanoparticles (IONPs) have shown tremendous utility for enhancing image contrast and delivering targeted therapies. Quantification of IONPs has been demonstrated at low concentrations with gradient echo (GRE) and spin echo (SE), and at high concentrations with echoless sequences such as swept imaging with Fourier transform (SWIFT). This work examines the overlap of IONP quantification with GRE, SE, and SWIFT.

METHODS

The limit of quantification of GRE, SE, inversion-recovery GRE, and SWIFT sequences was assessed using IONPs at a concentration range of 0.02 to 89.29 mM suspended in 1% agarose. Empirically derived limits of quantification were compared with International Union of Pure and Applied Chemistry definitions. Both commercial and experimental IONPs were used.

RESULTS

All three IONPs assessed demonstrated an overlap of concentration quantification with GRE, SE, and SWIFT sequences. The largest dynamic range observed was 0.004 to 35.7 mM with Feraheme.

CONCLUSIONS

The metrics established allow upper and lower quantitative limitations to be estimated given the relaxivity characteristics of the IONP and the concentration range of the material to be assessed. The methods outlined in this paper are applicable to any pulse sequence, IONP formulation, and field strength. Magn Reson Med 79:1420-1428, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The clinical utility of QSM: disease diagnosis, medical management, and surgical planning

Quantitative susceptibility mapping (QSM) is an MR technique that depicts and quantifies magnetic susceptibility sources. Mapping iron, the dominant susceptibility source in the brain, has many important clinical applications. Herein, we review QSM applications in the diagnosis, medical management, and surgical treatment of disease. To assist in early disease diagnosis, QSM can identify elevated iron levels in the motor cortex of amyotrophic lateral sclerosis patients, in the substantia nigra of Parkinson's disease (PD) patients, in the globus pallidus, putamen, and caudate of Huntington's disease patients, and in the basal ganglia of Wilson's disease patients. Additionally, QSM can distinguish between hemorrhage and calcification, which could prove useful in tumor subclassification, and can measure microbleeds in traumatic brain injury patients. In guiding medical management, QSM can be used to monitor iron chelation therapy in PD patients, to monitor smoldering inflammation of multiple sclerosis (MS) lesions after the blood-brain barrier (BBB) seals, to monitor active inflammation of MS lesions before the BBB seals without using gadolinium, and to monitor hematoma volume in intracerebral hemorrhage. QSM can also guide neurosurgical treatment. Neurosurgeons require accurate depiction of the subthalamic nucleus, a tiny deep gray matter nucleus, prior to inserting deep brain stimulation electrodes into the brains of PD patients. QSM is arguably the best imaging tool for depiction of the subthalamic nucleus. Finally, we discuss future directions, including bone QSM, cardiac QSM, and using QSM to map cerebral metabolic rate of oxygen. Copyright © 2016 John Wiley & Sons, Ltd.

Bone quantitative susceptibility mapping using a chemical species-specific R2* signal model with ultrashort and conventional echo data

PURPOSE

To develop quantitative susceptibility mapping (QSM) of bone using an ultrashort echo time (UTE) gradient echo (GRE) sequence for signal acquisition and a bone-specific effective transverse relaxation rate ( R2*) to model water-fat MR signals for field mapping.

METHODS

Three-dimensional radial UTE data (echo times ≥ 40 μs) was acquired on a 3 Tesla scanner and fitted with a bone-specific signal model to map the chemical species and susceptibility field. Experiments were performed ex vivo on a porcine hoof and in vivo on healthy human subjects (n = 7). For water-fat separation, a bone-specific model assigning R2* decay mostly to water was compared with the standard models that assigned the same decay for both fat and water. In the ex vivo experiment, bone QSM was correlated with CT.

RESULTS

Compared with standard models, the bone-specific R2* method significantly reduced errors in the fat fraction within the cortical bone in all tested data sets, leading to reduced artifacts in QSM. Good correlation was found between bone CT and QSM values in the porcine hoof (R²  = 0.77). Bone QSM was successfully generated in all subjects.

CONCLUSIONS

The QSM of bone is feasible using UTE with a conventional echo time GRE acquisition and a bone-specific R2* signal model. Magn Reson Med 79:121-128, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Magnetic barcode imaging for contrast agents

PURPOSE

To demonstrate a new MR imaging approach that unambiguously identifies and quantitates contrast agents based on intrinsic agent properties such as r1 , r2 , r2*, and magnetic susceptibility. The approach is referred to as magnetic barcode imaging (MBI).

METHODS

Targeted and bioresponsive contrast agents were imaged in agarose phantoms to generate T1 , T2 , T2*, and quantitative susceptibility maps. The parameter maps were processed by a machine learning algorithm that is trained to recognize the contrast agents based on these parameters. The output is a quantitative map of contrast agent concentration, identity, and functional state.

RESULTS

MBI allowed the quantitative interpretation of intensities, removed confounding backgrounds, enabled contrast agent multiplexing, and unambiguously detected the activation and binding states of bioresponsive and targeted contrast agents.

CONCLUSION

MBI has the potential to overcome significant limitations in the interpretation, quantitation, and multiplexing of contrast enhancement by MR imaging probes. Magn Reson Med 77:970-978, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Structure Prior Effects in Bayesian Approaches of Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) has shown its potential for anatomical and functional MRI, as it can quantify, for in vivo tissues, magnetic biomarkers and contrast agents which have differential susceptibilities to the surroundings substances. For reconstructing the QSM with a single orientation, various methods have been proposed to identify a unique solution for the susceptibility map. Bayesian QSM approach is the major type which uses various regularization terms, such as a piece-wise constant, a smooth, a sparse, or a morphological prior. Six QSM algorithms with or without structure prior are systematically discussed to address the structure prior effects. The methods are evaluated using simulations, phantom experiments with the given susceptibility, and human brain data. The accuracy and image quality of QSM were increased when using structure prior in the simulation and phantom compared to same regularization term without it, respectively. The image quality of QSM method using the structure prior is better comparing, respectively, to the method without it by either sharpening the image or reducing streaking artifacts in vivo. The structure priors improve the performance of the various QSMs using regularized minimization including L1, L2, and TV norm.

Quantitative assessment of microvasculopathy in arcAβ mice with USPIO-enhanced gradient echo MRI

Magnetic resonance imaging employing administration of iron oxide-based contrast agents is widely used to visualize cellular and molecular processes in vivo. In this study, we investigated the ability of [Formula: see text] and quantitative susceptibility mapping to quantitatively assess the accumulation of ultrasmall superparamagnetic iron oxide (USPIO) particles in the arcAβ mouse model of cerebral amyloidosis. Gradient-echo data of mouse brains were acquired at 9.4 T after injection of USPIO. Focal areas with increased magnetic susceptibility and [Formula: see text] values were discernible across several brain regions in 12-month-old arcAβ compared to 6-month-old arcAβ mice and to non-transgenic littermates, indicating accumulation of particles after USPIO injection. This was concomitant with higher [Formula: see text] and increased magnetic susceptibility differences relative to cerebrospinal fluid measured in USPIO-injected compared to non-USPIO-injected 12-month-old arcAβ mice. No differences in [Formula: see text] and magnetic susceptibility were detected in USPIO-injected compared to non-injected 12-month-old non-transgenic littermates. Histological analysis confirmed focal uptake of USPIO particles in perivascular macrophages adjacent to small caliber cerebral vessels with radii of 2-8 µm that showed no cerebral amyloid angiopathy. USPIO-enhanced [Formula: see text] and quantitative susceptibility mapping constitute quantitative tools to monitor such functional microvasculopathies.

Echo time-dependent quantitative susceptibility mapping contains information on tissue properties

PURPOSE

Magnetic susceptibility is a physical property of matter that varies depending on chemical composition and abundance of different molecular species. Interest is growing in mapping of magnetic susceptibility in the human brain using magnetic resonance imaging techniques, but the influences affecting the mapped values are not fully understood.

METHODS

We performed quantitative susceptibility mapping on 7 Tesla (T) multiple echo time gradient recalled echo data and evaluated the trend in 10 regions of the human brain. Temporal plots of susceptibility were performed in the caudate, pallidum, putamen, thalamus, insula, red nucleus, substantia nigra, internal capsule, corpus callosum, and fornix. We implemented an existing three compartment signal model and used optimization to fit the experimental result to assess the influences that could be responsible for our findings.

RESULTS

The temporal trend in susceptibility is different for different brain regions, and subsegmentation of specific regions suggests that differences are likely to be attributable to variations in tissue structure and composition. Using a signal model, we verified that a nonlinear temporal behavior in experimentally computed susceptibility within imaging voxels may be the result of the heterogeneous composition of tissue properties.

CONCLUSIONS

Decomposition of voxel constituents into meaningful parameters may lead to informative measures that reflect changes in tissue microstructure. Magn Reson Med 77:1946-1958, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing

The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI) in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM) based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm³, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker

In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a chemical shift for observer protons within the molecule and a magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of MRI signal in the background and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations, including the Bayesian approach, have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination, and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes, and testing data for readers interested in QSM.

Generalized Lorentzian Tensor Approach (GLTA) as a biophysical background for quantitative susceptibility mapping

PURPOSE

Quantitative susceptibility mapping (QSM) is a potentially powerful technique for mapping tissue magnetic susceptibility from gradient recalled echo (GRE) MRI. Herein we aim to derive the relationships between GRE signal phase and the underlying tissue microstructure and magnetic susceptibility at the cellular level.

METHODS

We use Maxwell's equations and a statistical approach to derive the expression for the magnetic-susceptibility-induced MR signal frequency shift of the GRE signal in single- and multicompartment systems, in which inhomogeneous magnetic field is induced by the cellular constituents (proteins, lipids, iron, etc.) distributed in intra- and extracellular spaces.

RESULTS

We introduce the Generalized Lorentzian Tensor Approach (GLTA) that accounts for both types of anisotropy: the anisotropy of magnetic susceptibility and the structural tissue anisotropy. In the GLTA the frequency shift due to the local environment is characterized by the Lorentzian tensor L⁁ which has a substantially different structure than the susceptibility tensor χ⁁. While components of χ⁁ are simply compartmental susceptibilities "weighted" by their relative volumes, the components of L⁁ are weighted by specific numerical factors depending on tissue micro-symmetry and parameters related to the MR pulse sequence. We also provide equations bridging phenomenological and microscopic considerations.

CONCLUSION

The GLTA provides a consistent background for deciphering phase data.

Phase-corrected bipolar gradients in multi-echo gradient-echo sequences for quantitative susceptibility mapping

OBJECTIVE

Large echo spacing of unipolar readout gradients in current multi-echo gradient-echo (GRE) sequences for mapping fields in quantitative susceptibility mapping (QSM) can be reduced using bipolar readout gradients thereby improving acquisition efficiency.

MATERIALS AND METHODS

Phase discrepancies between odd and even echoes in the bipolar readout gradients caused by non-ideal gradient behaviors were measured, modeled as polynomials in space and corrected for accordingly in field mapping. The bipolar approach for multi-echo GRE field mapping was compared with the unipolar approach for QSM.

RESULTS

The odd-even-echo phase discrepancies were approximately constant along the phase encoding direction and linear along the readout and slice-selection directions. A simple linear phase correction in all three spatial directions was shown to enable accurate QSM of the human brain using a bipolar multi-echo GRE sequence. Bipolar multi-echo acquisition provides QSM in good quantitative agreement with unipolar acquisition while also reducing noise.

CONCLUSION

With a linear phase correction between odd-even echoes, bipolar readout gradients can be used in multi-echo GRE sequences for QSM.

Inactivation performance and mechanism of Escherichia coli in aqueous system exposed to iron oxide loaded graphene nanocomposites

The challenge to achieve efficient disinfection and microbial control without harmful disinfection byproducts calls for developing new technologies. Magnetic-graphene oxide (M-GO) with magnetic iron oxide nanoparticles well dispersed on graphene oxide (GO) nanosheets exerted excellent antibacterial activity against Escherichia coli. The antibacterial performance of M-GO was dependent on the concentration and the component mass ratio of M/GO. The synergetic antibacterial effect of M-GO was observed with M/GO mass ratio of 9.09. TEM images illustrated the interaction between E. coli cells and M-GO nanocomposites. M-GO nanomaterials were possible to deposit on or penetrate into cells leading to leakage of intercellular contents and loss of cell integrity. The inactivation mechanism of E. coli by M-GO was supposed to result from both the membrane stress and oxidation stress during the incubation period. M-GO with excellent antibacterial efficiency against E. coli and separation-convenient property from water could be potent bactericidal nanomaterials for water disinfection.

Joint estimation of chemical shift and quantitative susceptibility mapping (chemical QSM)

PURPOSE

The purpose of this work is to address the unsolved problem of quantitative susceptibility mapping (QSM) of tissue with fat where both fat and susceptibility change the MR signal phase.

THEORY AND METHODS

The chemical shift of fat was treated as an additional unknown and was estimated jointly with susceptibility to provide the best data fitting using an automated and iterative algorithm. A simplified susceptibility model was used to calculate an updated value of the chemical shift based on the local magnetic field in each iteration. Numerical simulation, phantom experiments and in vivo imaging were performed. Artifacts were assessed by measuring the susceptibility variance in uniform regions. Accuracy was assessed by comparison with ground truth in simulation, and using a susceptibility matching approach in phantom.

RESULTS

Using the proposed method, artifacts on the QSM image were markedly suppressed in all tested datasets compared with results generated using fixed chemical shifts. Accuracy of the estimated susceptibility was also improved in numerical simulation and phantom experiments.

CONCLUSION

A joint estimation of fat content and magnetic susceptibility using an iterative chemical shift update was shown to improve image quality and accuracy on QSM images.

Background field removal by solving the Laplacian boundary value problem

The removal of the background magnetic field is a critical step in generating phase images and quantitative susceptibility maps, which have recently been receiving increasing attention. Although it is known that the background field satisfies Laplace's equation, the boundary values of the background field for the region of interest have not been explicitly addressed in the existing methods, and they are not directly available from MRI measurements. In this paper, we assume simple boundary conditions and remove the background field by explicitly solving the boundary value problems of Laplace's or Poisson's equation. The proposed Laplacian boundary value (LBV) method for background field removal retains data near the boundary and is computationally efficient. Tests on a numerical phantom and an experimental phantom showed that LBV was more accurate than two existing methods.

On the role of neuronal magnetic susceptibility and structure symmetry on gradient echo MR signal formation

PURPOSE

Phase images obtained by gradient-recalled echo (GRE) MRI provide new contrast in the brain that is distinct from that obtained with conventional T1-weighted and T2-weighted images. The results are especially intriguing in white matter where both signal amplitude and phase display anisotropic properties. However, the biophysical origins of these phenomena are not well understood. The goal of this article is to provide a comprehensive theory of GRE signal formation based on a realistic model of neuronal structure.

METHODS

We use Maxwell equations to find the distribution of magnetic field induced by myelin sheath and axon. We account for both anisotropy of neuronal tissue "magnetic micro-architecture" and anisotropy of myelin sheath magnetic susceptibility.

RESULTS

Model describes GRE signal comprising of three compartments-axonal, myelin, and extracellular. Both axonal and myelin water signals have frequency shifts that are affected by the magnetic susceptibility anisotropy of long molecules forming lipid bilayer membranes. These parts of frequency shifts reach extrema for axon oriented perpendicular to the magnetic field and are zeros in a parallel case. Myelin water signal is substantially non-monoexponential.

CONCLUSIONS

Both, anisotropy of neuronal tissue "magnetic micro-architecture" and anisotropy of myelin sheath magnetic susceptibility, are important for describing GRE signal phase and magnitude.

An iterative spherical mean value method for background field removal in MRI

PURPOSE

The sophisticated harmonic artifact reduction for phase data (SHARP) method has been proposed for the removal of background field in MRI phase data. It relies on the spherical mean value (SMV) property of harmonic functions, and its accuracy depends on the radius of the sphere used for computing the SMV and truncation threshold needed for deconvolution. The goal of this study was to develop an alternative SMV-based background field removal method with reduced dependences on these parameters.

METHODS

The proposed background field removal method, termed iterative SMV (iSMV), consists of applying the SMV operation repeatedly on the field map. It was validated in a phantom and in vivo brain data of five healthy volunteers.

RESULTS

The iSMV method demonstrates accurate background field removal in the phantom. Compared with SHARP, the iSMV method shows a significantly reduced dependence on the SMV radius both in phantom and in human data. Because a smaller radius can be chosen, the iSMV method allows retaining a larger part of the region of interest compared with SHARP.

CONCLUSION

The iSMV method is an effective background field removal method with a reduced dependence on method parameters. Magn Reson Med 72:1065-1071, 2014. © 2013 Wiley Periodicals, Inc.

Toward online reconstruction of quantitative susceptibility maps: superfast dipole inversion

Magnetic susceptibility is an intrinsic tissue property that recently became measureable in vivo by a magnetic-resonance based technique called quantitative susceptibility mapping (QSM). Although QSM may be performed without additional acquisition time, for example, in the course of the well-established susceptibility weighted imaging, the applicability of QSM is currently hampered by the numerical complexity and computational cost associated with the reconstruction procedure. This work introduces a novel QSM framework called superfast dipole inversion which allows rapid online reconstruction of susceptibility maps from wrapped raw gradient-echo phase data. The algorithm relies on the extension and combination of several recent algorithms involving the precalculation of convolution kernels and the correction of inversion artifacts. Reconstruction of three-dimensional high resolution susceptibility maps of the human brain was achieved with superfast dipole inversion in less than 20 s on a conventional workstation computer. Thus, superfast dipole inversion opens the door to an implementation of QSM on MR scanner hardware as well as to the routine reconstruction of large cohorts of datasets.

Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) opens the door for measuring tissue magnetic susceptibility properties that may be important biomarkers, and QSM is becoming an increasingly active area of scientific and clinical investigations. In practical applications, there are sources of errors for QSM including noise, phase unwrapping failures, and signal model inaccuracy. To improve the robustness of QSM quality, we propose a nonlinear data fidelity term for frequency map estimation and dipole inversion to reduce noise and effects of phase unwrapping failures, and a method for model error reduction through iterative tuning. Compared with the previous phase based linear QSM method, this nonlinear QSM method reduced salt and pepper noise or checkerboard pattern in high susceptibility regions in healthy subjects and markedly reduced artifacts in patients with intracerebral hemorrhages.

Gradient echo plural contrast imaging--signal model and derived contrasts: T2*, T1, phase, SWI, T1f, FST2*and T2*-SWI

Gradient Echo Plural Contrast Imaging (GEPCI) is a post processing technique that, based on a widely available multiple gradient echo sequence, allows simultaneous generation of naturally co-registered images with various contrasts: T1 weighted, R2*=1/T2* maps and frequency (f) maps. Herein, we present results demonstrating the capability of GEPCI technique to generate image sets with additional contrast characteristics obtained by combing the information from these three basic contrast maps. Specifically, we report its ability to generate GEPCI-susceptibility weighted images (GEPCI-SWI) with improved SWI contrast that is free of T1 weighting and RF inhomogeneities; GEPCI-SWI-like images with the contrast similar to original SWI; T1f images that offer superior GM/WM matter contrast obtained by combining the GEPCI T1 and frequency map data; Fluid Suppressed T2* (FST2*) images that utilize GEPCI T1 data to suppress CSF signal in T2* maps and provide contrast similar to FLAIR T2 weighted images; and T2*-SWI images that combine SWI contrast with quantitative T2* map and offer advantages of visualizing venous structure with hyperintense T2* lesions (e.g. MS lesions). To analyze GEPCI images we use an improved algorithm for combining data from multi-channel RF coils and a method for unwrapping phase/frequency maps that takes advantage of the information on phase evolution as a function of gradient echo time in GEPCI echo train.

The future of susceptibility contrast for assessment of anatomy and function

The magnetic properties of tissues affect MR images and differences in magnetic susceptibility can be utilized to provide impressive image contrast. Specifically, phase images acquired with gradient echo MRI provide unique and superb contrast which reflects variations in the underlying tissue composition. There is great interest in extracting tissue susceptibility from image data since magnetic susceptibility is an intrinsic tissue property that reflects tissue composition much more closely than MRI phase. Still, this major tissue contrast mechanism is largely unexplored in magnetic resonance imaging because non-conventional reconstruction and dipole deconvolution are required to quantitatively map tissue susceptibility properly. This short review summarizes the current state of susceptibility contrast and susceptibility mapping and aims to identify future directions.

Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping

PURPOSE

To assess quantitative susceptibility mapping (QSM) for reducing the inconsistency of standard magnetic resonance (MR) imaging sequences in measurements of cerebral microbleed burden.

MATERIALS AND METHODS

This retrospective study was HIPAA compliant and institutional review board approved. Ten patients (5.6%) were selected from among 178 consecutive patients suspected of having experienced a stroke who were imaged with a multiecho gradient-echo sequence at 3.0 T and who had cerebral microbleeds on T2*-weighted images. QSM was performed for various ranges of echo time by using both the magnitude and phase components in the morphology-enabled dipole inversion method. Cerebral microbleed size was measured by two neuroradiologists on QSM images, T2*-weighted images, susceptibility-weighted (SW) images, and R2* maps calculated by using different echo times. The sum of susceptibility over a region containing a cerebral microbleed was also estimated on QSM images as its total susceptibility. Measurement differences were assessed by using the Student t test and the F test; P < .05 was considered to indicate a statistically significant difference.

RESULTS

When echo time was increased from approximately 20 to 40 msec, the measured cerebral microbleed volume increased by mean factors of 1.49 ± 0.86 (standard deviation), 1.64 ± 0.84, 2.30 ± 1.20, and 2.30 ± 1.19 for QSM, R2*, T2*-weighted, and SW images, respectively (P < .01). However, the measured total susceptibility with QSM did not show significant change over echo time (P = .31), and the variation was significantly smaller than any of the volume increases (P < .01 for each).

CONCLUSION

The total susceptibility of a cerebral microbleed measured by using QSM is a physical property that is independent of echo time.

Whole brain susceptibility mapping using compressed sensing

The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this article, compressed sensing is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the compressed sensing based method outperforms regularization based methods. The key difference between the regularized inversions and compressed sensing compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the compressed sensing based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings.