Quantitative susceptibility mapping using a superposed dipole inversion method: Application to intracranial hemorrhage

Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a simulated-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms. The PnP OA-LFE module's versatility was further demonstrated by its successful application to xQSM, a distinct DL-QSM network for dipole inversion. In conclusion, this work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32 % accuracy improvement than supervised deep learning methods. It is also 33 % more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 min.

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

Quantitative susceptibility-weighted imaging in presence of strong susceptibility sources: Application to hemorrhage

PURPOSE

To optimize quantitative susceptibility-weighted imaging also known as true susceptibility-weighted imaging (tSWI) for strong susceptibility sources like hemorrhage and compare to standard susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM).

METHODS

Ten patients with known intracerebral hemorrhage were scanned using a 3D SWI sequence. The magnitude and phase images were utilized to compute QSM, tSWI and SWI images. tSWI parameters including the upper threshold for creating susceptibility-weighted masks and the multiplication factor were optimized for hemorrhage depiction. Combined tSWI was also computed with independent optimized parameters for both veins and hemorrhagic regions. tSWI results were compared to SWI and QSM utilizing region-of-interest measurements, Pearson's correlation and Kruskal-Wallis test.

RESULTS

Fifteen hemorrhages were found, with mean susceptibility 0.81 ± 0.37 ppm. Unlike SWI which utilizes a phase mask, tSWI uses a mask computed from QSM. In tSWI, the weighted mask required an extended upper threshold far beyond the standard level for more effective visualization of hemorrhage texture. The upper threshold was set to the mean maximum susceptibility in the hemorrhagic region (3.24 ppm) with a multiplication factor of 2. The blooming effect, seen in SWI, was observed to be larger in hemorrhages with higher susceptibility values (r = 0.78, p < 0.001) with reduced blooming on tSWI. On SWI, 4 out of 15 hemorrhages showed phase wrap artifacts in the hemorrhagic region and all patients showed some phase wraps in the air-tissue interface near the auditory and frontal sinuses. These phase wrap artifacts were absent on tSWI. In hemorrhagic region, a higher correlation was observed between the actual susceptibility values and mean gray value for tSWI (r = -0.93, p < 0.001) than SWI (r = -0.87, p < 0.001).

CONCLUSION

In hemorrhage, tSWI minimizes both blooming effects and phase wrap artifacts observed in SWI. However, unlike SWI, tSWI requires an altered upper threshold for best hemorrhage depiction that greatly differs from the standard value. tSWI can be used as a complementary technique for visualizing hemorrhage along with SWI.

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 seconds using the trained iQFM and iQSM neural networks.

Quantitative Evaluation of Small Intestinal Hemorrhage Using Energy Spectrum CT Iodine-Water Map

The objective of this research is to analyze the quantitative evaluation of human small intestinal bleeding by observing and analyzing animal experiments of small intestinal hemorrhage in rabbit models for the convenience of understanding the role of energy spectrum CT iodine-water diagram in animal experimental research of quantitative evaluation of small intestinal bleeding in rabbit models. Compared with the energy spectrum of iodine-water graph of a rabbit CT model, the present study studied the quantitative evaluation of small intestinal bleeding by using a rabbit model instead of human. According to the method mentioned above and the analysis of experimental data, the role of energy spectrum CT iodine-water map and the quantitative evaluation of human small intestinal bleeding have been understood. It was found that the energy spectrum CT iodine-water map replaces humans in the rabbit model for quantitative evaluation of small intestinal bleeding in animal experiments, which is important in the present study. Besides, based upon the combination of theoretical and experimental data, the ten flow rates set on the base material iodine (water) maps of the arterial phase and the portal phase can be analyzed to detect the leakage of contrast agent. The yield was 100%. The research results showed that the animal experiment of quantitative assessment of small intestinal bleeding by replacing the human body with the rabbit model in the energy spectrum CT iodine-water diagram is critical to humans in the study of small intestinal hemorrhagic diseases. In addition, it can be used to adjust the treatment plan timely according to the amount of bleeding to prevent shock or heavy bleeding that threatens patients’ lives.

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

In studies of the white matter (WM) in aging brains, both quantitative susceptibility mapping (QSM) and direct R₁ measurement offer potentially useful ex vivo MRI tools that allow volumetric characterization of myelin content changes. Despite the technical importance of such MRI methods in numerous age-related diseases, the supposed linear relationship between the estimates of either the QSM or R₁ method and age-affected myelin contents has not been validated. In this study, the absolute myelin volume fraction (MVF) was determined by transmission electron microscopy (TEM) as a gold standard measure for comparison with the values obtained by the aforementioned MR methods. To theoretically evaluate and understand the MR signal characteristics, QSM simulations were performed using the finite perturber method (FPM). Specifically, the simulation geometry modeling was based on TEM-derived structures aligned orthogonally to the main magnetic field, the construct of which was used to estimate the magnetic field shift (ΔB) changes arising from the conjectured myelin structures. Experimentally, ex vivo corpus callosum (CC) samples from rat brains obtained at 6 weeks (n = 3), 4 months (n = 3), and 20 months (n = 3) after birth were used to establish the relationship between changes quantified by either QSM or R₁ with the absolute MVF by TEM. From the ex vivo brain samples, the scatterplot of mean MVF versus R₁ was fitted to a linear equation, where R_1mean  = 0.7948 × MVF_mean  + 0.8118 (Pearson's correlation coefficient r = 0.9138; p < 0.01), while the scatterplot of mean MVF versus MRI-derived magnetic susceptibility (χ) was also fitted to a line where χ_{measured,mean}  = -0.1218 × MVF_mean  - 0.006345 (r = -0.8435; p < 0.01). As a result of the FPM-based QSM simulations, a linearly proportional relationship between the simulated magnetic susceptibility, χ_{simulated,mean} , and MVF (r = -0.9648; p < 0.01) was established. Such a statistically significant linear correlation between MRI-derived values by the QSM (or R₁ ) method and MVF demonstrated that variable myelin contents in the WM (i.e., CC) can be quantified across multiple stages of aging. These findings further support that both techniques based on QSM and R₁ provide an efficient means of studying the brain-aging process with accurate volumetric quantification of the myelin content in WM.

QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping

PURPOSE

Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue magnetic susceptibilities from the phase of a gradient-echo signal. QSM algorithms require a signal mask to delineate regions with reliable phase for subsequent susceptibility estimation. Existing masking techniques used in QSM have limitations that introduce artifacts, exclude anatomical detail, and rely on parameter tuning and anatomical priors that narrow their application. Here, a robust masking and reconstruction procedure is presented to overcome these limitations and enable automated QSM processing. Moreover, this method is integrated within an open-source software framework: QSMxT.

METHODS

A robust masking technique that automatically separates reliable from less reliable phase regions was developed and combined with a two-pass reconstruction procedure that operates on the separated sources before combination, extracting more information and suppressing streaking artifacts.

RESULTS

Compared with standard masking and reconstruction procedures, the two-pass inversion reduces streaking artifacts caused by unreliable phase and high dynamic ranges of susceptibility sources. It is also robust across a range of acquisitions at 3 T in volunteers and phantoms, at 7 T in tumor patients, and in an in silico head phantom, with significant artifact and error reductions, greater anatomical detail, and minimal parameter tuning.

CONCLUSION

The two-pass masking and reconstruction procedure separates reliable from less reliable phase regions, enabling a more accurate QSM reconstruction that mitigates artifacts, operates without anatomical priors, and requires minimal parameter tuning. The technique and its integration within QSMxT makes QSM processing more accessible and robust to streaking artifacts.

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

INTRODUCTION

Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation.

METHOD

A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. The xQSM method is compared with two conventional dipole inversion methods using simulated and in vivo experiments from 4 healthy subjects at 3T. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages, ranging from 100% (∼32mm) to 400% (∼128mm) of the actual DGM physical size.

RESULTS

The proposed xQSM network led to the lowest DGM contrast loss in both simulated and in vivo subjects, with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages, as compared to conventional methods. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48mm axial slabs using the xQSM network, while a minimum of 112mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion.

CONCLUSION

The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.

A role for magnetic susceptibility in synthetic computed tomography

PURPOSE

Radiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping.

METHODS

Volumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans.

RESULTS

The skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU.

CONCLUSION

Susceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.

QSMART: Quantitative susceptibility mapping artifact reduction technique

PURPOSE

Quantitative susceptibility mapping (QSM) is a novel MR technique that allows mapping of tissue susceptibility values from MR phase images. QSM is an ill-conditioned inverse problem, and although several methods have been proposed in the field, in the presence of a wide range of susceptibility sources, streaking artifacts appear around high susceptibility regions and contaminate the whole QSM map. QSMART is a post-processing pipeline that uses two-stage parallel inversion to reduce the streaking artifacts and remove banding artifact at the cortical surface and around the vasculature.

METHOD

Tissue and vein susceptibility values were separately estimated by generating a mask of vasculature driven from the magnitude data using a Frangi filter. Spatially dependent filtering was used for the background field removal step and the two susceptibility estimates were combined in the final QSM map. QSMART was compared to RESHARP/iLSQR and V-SHARP/iLSQR inversion in a numerical phantom, 7T in vivo single and multiple-orientation scans, 9.4T ex vivo mouse data, and 4.7T in vivo rat brain with induced focal ischemia.

RESULTS

Spatially dependent filtering showed better suppression of phase artifacts near cortex compared to RESHARP and V-SHARP, while preserving voxels located within regions of interest without brain edge erosion. QSMART showed successful reduction of streaking artifacts as well as improved contrast between different brain tissues compared to the QSM maps obtained by RESHARP/iLSQR and V-SHARP/iLSQR.

CONCLUSION

QSMART can reduce QSM artifacts to enable more robust estimation of susceptibility values in vivo and ex vivo.

Elevated brain oxygen extraction fraction measured by MRI susceptibility relates to perfusion status in acute ischemic stroke

Recent clinical trials of new revascularization therapies in acute ischemic stroke have highlighted the importance of physiological imaging to identify optimal treatments for patients. Oxygen extraction fraction (OEF) is a hallmark of at-risk tissue in stroke, and can be quantified from the susceptibility effect of deoxyhemoglobin molecules in venous blood on MRI phase scans. We measured OEF within cerebral veins using advanced quantitative susceptibility mapping (QSM) MRI reconstructions in 20 acute stroke patients. Absolute OEF was elevated in the affected (29.3 ± 3.4%) versus the contralateral hemisphere (25.5 ± 3.1%) of patients with large diffusion-perfusion lesion mismatch (P = 0.032). In these patients, OEF negatively correlated with relative CBF measured by dynamic susceptibility contrast MRI (P = 0.004), suggesting compensation for reduced flow. Patients with perfusion-diffusion match or no hypo-perfusion showed less OEF difference between hemispheres. Nine patients received longitudinal assessment and showed OEF ratio (affected to contralateral) of 1.2 ± 0.1 at baseline that normalized (decreased) to 1.0 ± 0.1 at follow-up three days later (P = 0.03). Our feasibility study demonstrates that QSM MRI can non-invasively quantify OEF in stroke patients, relates to perfusion status, and is sensitive to OEF changes over time. Clinical trial registration: Longitudinal MRI examinations of patients with brain ischemia and blood brain barrier permeability; clinicaltrials.org :NCT02077582.

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.

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g., in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications.

Introduction to Quantitative Susceptibility Mapping and Susceptibility Weighted Imaging

Quantitative Susceptibility Mapping (QSM) and Susceptibility Weighted Imaging (SWI) are MRI techniques that measure and display differences in the magnetization that is induced in tissues, i.e. their magnetic susceptibility, when placed in the strong external magnetic field of an MRI system. SWI produces images in which the contrast is heavily weighted by the intrinsic tissue magnetic susceptibility. It has been applied in a wide range of clinical applications. QSM is a further advancement of this technique that requires sophisticated post-processing in order to provide quantitative maps of tissue susceptibility. This review explains the steps involved in both SWI and QSM as well as describing some of their uses in both clinical and research applications.

Visualization of thalamic calcium influx with quantitative susceptibility mapping as a potential imaging biomarker for repeated mild traumatic brain injury

A key event in the pathophysiology of traumatic brain injury (TBI) is the influx of substantial amounts of Ca²⁺ into neurons, particularly in the thalamus. Detection of this calcium influx in vivo would provide a window into the biochemical mechanisms of TBI with potentially significant clinical implications. In the present work, our central hypothesis was that the Ca²⁺ influx could be imaged in vivo with the relatively recent MRI technique of quantitative susceptibility mapping (QSM). Wistar rats were divided into five groups: naive controls, sham-operated experimental controls, single mild TBI, repeated mild TBI, and single severe TBI. We employed the lateral fluid percussion injury (FPI) model, which replicates clinical TBI without skull fracture, performed 9.4 Tesla MRI with a 3D multi-echo gradient-echo sequence at weeks 1 and 4 post-injury, computed susceptibility maps using V-SHARP and the QUASAR-HEIDI technique, and performed histology. Sham, experimental controls animals, and injured animals did not demonstrate calcifications at 1 week after the injury. At week 4, calcifications were found in the ipsilateral thalamus of 25-50% of animals after a single TBI and 83% of animals after repeated mild TBI. The location and appearance of calcifications on stained sections was consistent with the appearance on the in vivo susceptibility maps (correlation of volumes: r = 0.7). Our findings suggest that persistent calcium deposits represent a primary pathology of repeated injury and that FPI-QSM has the potential to become a sensitive tool for studying pathophysiology related to mild TBI in vivo.

Appropriate echo time selection for quantitative susceptibility mapping

The purpose of our study was to clarify the dependence of quantitative susceptibility mapping (QSM) on echo time (TE). We constructed a phantom consisting of six tubes; three tubes were filled with different concentrations (0.5, 1.0, and 2.5 mM) of gadopentetate dimeglumine (Gd-DTPA), and three were filled with different concentrations (100, 200, and 350 mg/mL) of calcium hydroxyapatite. Real and imaginary images from multi-echo spoiled gradient-echo data (12 echoes) were acquired. We then used four datasets with three serial echoes. The QSM procedure consists of four steps: field map estimation, phase unwrapping, background removal, and dipole inversion. For each sample, we compared the measured mean susceptibility value with the theoretical susceptibility value and conducted a linear regression analysis. Accordingly, the relationship between the measured susceptibility and concentration of Gd-DTPA was shown to agree well with the theoretical values (TEs = 16.4, 20.8, and 25.2 ms; slope = 0.24, R² = 1.00). Furthermore, the relationship between the measured susceptibility and concentration of hydroxyapatite also showed good linearity (TEs = 16.4, 20.8, and 25.2 ms; slope = - 0.00121, R² = 1.00). In conclusion, the optimization of the TE in QSM makes it possible to obtain more detailed information regarding the susceptibility of biomaterials.

Clinical feasibility of brain quantitative susceptibility mapping

PURPOSE

To evaluate the quality of brain quantitative susceptibility mapping (QSM) that is fully automatically reconstructed in clinical MRI of various neurological diseases.

METHODS

393 consecutive patients in one month were recruited for this evaluation study. QSM was reconstructed using Morphology Enabled Dipole Inversion without zero reference regularization (MEDI) and using MEDI with cerebrospinal fluid automatic zero-reference regularization to generate susceptibility values (MEDI+0). Two neuroradiologists independently assessed the image quality of MEDI+0 and MEDI and image concordance between them. Lesion susceptibility values were measured in 20 cases of glioma, 21 cases of ischemic stroke and 43 multiple sclerosis (MS) cases on both MEDI+0 and MEDI images.

RESULTS

The two neuroradiologists rated the MEDI+0 image qualities of the 393 cases as 351 (89.3%) and 362 (92.1%) excellent, 29 (7.4%) and 24 (6.1%) diagnostic, and 13 (3.3%) and 7 (1.8%) poor, and scored the concordances between MEDI+0 and MEDI as 364 (92.6%) and 351 (89.3%) excellent, 13 (3.3%) and 31 (7.9%) good, 14 (3.6%) and 9 (2.3%) intermediate, 2 (0.5%) and 2 (0.5%) poor, and 0 (0%) and 0 (0%) none. There was good correlation between MEDI+0 and MEDI in lesion susceptibility contrast of glioma, ischemic stroke, and MS cases (all p < 0.05). The MS lesion susceptibility time course from this patient cohort was found to be similar to the reported pattern: isointense initially for acute enhancing lesions, and hyperintense over the following years for active chronic lesions.

CONCLUSION

Brain QSM images of various neurological diseases have reliable diagnostic quality in clinical MRI, with MEDI+0 providing susceptibility values automatically referenced to CSF in longitudinal and cross-center studies.

Seed prioritized unwrapping (SPUN) for MR phase imaging

BACKGROUND

Region-growing-based phase unwrapping methods have the potential for lossless phase aliasing removal, but generally suffer from unwrapping error propagation associated with discontinuous phase and/or long calculation times. The tradeoff point between robustness and efficiency of phase unwrapping methods in the region-growing category requires improvement.

PURPOSE

To demonstrate an accurate, robust, and efficient region-growing phase unwrapping method for MR phase imaging applications.

STUDY TYPE

Prospective. SUBJECTS, PHANTOM: normal human subjects (10) / brain surgery patients (2) / water phantoms / computer simulation.

FIELD STRENGTH/SEQUENCE

3 T/gradient echo sequences (2D and 3D).

ASSESSMENT

A seed prioritized unwrapping (SPUN) method was developed based on single-region growing, prioritizing only a portion (eg, 100 seeds or 1% seeds) of available seed voxels based on continuity quality during each region-growing iteration. Computer simulation, phantom, and in vivo brain and pelvis scans were performed. The error rates, seed percentages, and calculation times were recorded and reported. SPUN unwrapped phase images were visually evaluated and compared with Laplacian unwrapped results.

STATISTICAL TESTS

Monte Carlo simulation was performed on a 3D dipole phase model with a signal-to-noise ratio (SNR) of 1-9 dB, to obtain the mean and standard deviation of calculation error rates and calculation times.

RESULTS

Simulation revealed a very robust unwrapping performance of SPUN, reaching an error rate of <0.4% even with SNR as low as 1 dB. For all in vivo data, SPUN was able to robustly unwrap the phase images of modest SNR and complex morphology with visually minimal errors and fast calculation speed (eg, <4 min for 368 × 312 × 128 data) when using a proper seed priority number, eg, N_sp = 1 or 10 voxels for 2D and N_sp = 1% for 3D data.

DATA CONCLUSION

SPUN offers very robust and fast region-growing-based phase unwrapping, and does not require any tissue masking or segmentation, nor poses a limitation over imaging parameters.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:62-70.

Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism

BACKGROUND

Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice.

OBJECTIVE

To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community.

METHOD

We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility.

RESULTS

We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase.

CONCLUSION

Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.

Quantitative susceptibility mapping (QSM) as a means to monitor cerebral hematoma treatment

BACKGROUND

Quantitative susceptibility mapping (QSM) offers a consistent hemorrhage volume measurement independent of imaging parameters.

PURPOSE

To investigate the magnetic susceptibility of intracerebral hemorrhage (ICH) as a quantitative measurement for monitoring treatment in hematoma patients.

STUDY TYPE

Prospective.

POPULATION

Twenty-six patients with acute ICH were recruited and enrolled in treatment including surgery or medication (mannitol) for 1 week.

FIELD STRENGTH/SEQUENCE

A 3D gradient echo sequence at 3.0T.

ASSESSMENT

The hematoma volumes on computed tomography (CT) and QSM were calculated and used for correlation analysis. Magnetic susceptibility changes from pre- to posttreatment were calculated and compared to the National Institutes of Health stroke scale (NIHSS) measure of neurological deficit for each patient.

STATISTICAL TESTS

Mean susceptibility values were calculated over each region of interest (ROI). A one-sample t-test was used to assess the changes of total volumes and mean magnetic susceptibility of ICH identified between pre- and posttreatment images (P < 0.05 was considered significant) and the Bland-Altman analysis with 95% limits of agreement (average difference, ±1.96 SD of the difference). Regression of volume measurements on QSM vs. CT and fitted linear regression of mean susceptibility vs. CT signal intensity for hematoma regions were conducted in all patients.

RESULTS

Good correlation was found between hemorrhage volumes calculated from CT and QSM (CT volume = 0.94*QSM volume, r = 0.98). Comparison of QSM pre- and posttreatment showed that the mean ICH volume was reduced by a statistically insignificant amount from 5.74 cm3 to 5.45 cm3 (P = 0.21), while mean magnetic susceptibility was reduced significantly from 0.48 ppm to 0.38 ppm (P = 0.004). A significant positive association was found between changes in magnetic susceptibility values and NIHSS following hematoma treatment (P < 0.01).

DATA CONCLUSIONS

QSM in hematoma assessment, as compared with CT, offers a comparably accurate volume measurement; however, susceptibility measurements may enable improved monitoring of ICH treatment compared to volume measurements alone.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:907-915.

Clinical Integration of Quantitative Susceptibility Mapping Magnetic Resonance Imaging into Neurosurgical Practice

OBJECTIVE

To introduce quantitative susceptibility mapping (QSM), a novel magnetic resonance imaging sequence, to the field of neurosurgery.

METHODS

QSM is introduced both in its historical context and by providing a brief overview of the physics behind the technique tailored to a neurosurgical audience. Its application to clinical neurosurgery is then highlighted using case examples.

RESULTS

QSM offers a quantitative assessment of susceptibility (previously considered as an artifact) via a single, straightforward gradient echo acquisition. QSM differs from standard susceptibility weighted imaging in its ability to both quantify and precisely localize susceptibility effects. Clinical applications of QSM are wide reaching and include precise localization of the deep nuclei for deep brain stimulation electrode placement, differentiation between blood products and calcification within brain lesions, and enhanced sensitivity of cerebral micrometastasis identification.

CONCLUSIONS

We present this diverse range of QSM's clinical applications to neurosurgical care via case examples. QSM can be obtained in all patients able to undergo magnetic resonance imaging and is easily integratable into busy neuroradiology programs because of its short acquisition time and straightforward, automated offline postprocessing workflow. Clinical integration of QSM may help clinicians better identify and characterize neurosurgical lesions, thereby improving patient care.

Mask-Adapted Background Field Removal for Artifact Reduction in Quantitative Susceptibility Mapping of the Prostate

We propose an alternative processing method for quantitative susceptibility mapping of the prostate that reduces artifacts and enables better visibility and quantification of calcifications and other lesions. Three-dimensional gradient-echo magnetic resonance data were obtained from 26 patients at 3 T who previously received a planning computed tomography of the prostate. Phase images were unwrapped using Laplacian-based phase unwrapping. The background field was removed with the V-SHARP method using tissue masks for the entire abdomen (Method 1) and masks that excluded bone and the rectum (Method 2). Susceptibility maps were calculated with the iLSQR method. The quality of susceptibility maps was assessed by one radiologist and two physicists who rated the data for visibility of lesions and data quality on a scale from 1 (poor) to 4 (good). The readers rated susceptibility maps computed with Method 2 to be, on average, better for visibility of lesions with a score of 2.9 ± 1.1 and image quality with a score of 2.8 ± 0.8 compared with maps computed with Method 1 (2.4 ± 1.2/2.3 ± 1.0). Regarding strong artifacts, these could be removed using adapted masks, and the susceptibility values seemed less biased by the artifacts. Thus, using an adapted mask for background field removal when calculating susceptibility maps of the prostate from phase data reduces artifacts and improves visibility of lesions.

Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method

A new dipole field inversion method for whole head quantitative susceptibility mapping (QSM) is proposed. Instead of performing background field removal and local field inversion sequentially, the proposed method performs dipole field inversion directly on the total field map in a single step. To aid this under-determined and ill-posed inversion process and obtain robust QSM images, Tikhonov regularization is implemented to seek the local susceptibility solution with the least-norm (LN) using the L-curve criterion. The proposed LN-QSM does not require brain edge erosion, thereby preserving the cerebral cortex in the final images. This should improve its applicability for QSM-based cortical grey matter measurement, functional imaging and venography of full brain. Furthermore, LN-QSM also enables susceptibility mapping of the entire head without the need for brain extraction, which makes QSM reconstruction more automated and less dependent on intermediate pre-processing methods and their associated parameters. It is shown that the proposed LN-QSM method reduced errors in a numerical phantom simulation, improved accuracy in a gadolinium phantom experiment, and suppressed artefacts in nine subjects, as compared to two-step and other single-step QSM methods. Measurements of deep grey matter and skull susceptibilities from LN-QSM are consistent with established reconstruction methods.

Bone susceptibility mapping with MRI is an alternative and reliable biomarker of osteoporosis in postmenopausal women

OBJECTIVES

To investigate the efficacy of quantitative susceptibility mapping (QSM) in the assessment of osteoporosis for postmenopausal women.

METHODS

Between May and September 2017, a total of 70 postmenopausal women who underwent MRI-based QSM and quantitative computed tomography (QCT) were consecutively enrolled in this prospective study. The measurement of QSM and QCT values was performed on the L3 vertebrae body. On the basis of QCT value, all individuals were divided into three groups (normal, osteopenia and osteoporosis).

RESULTS

On the basis of QCT, 18 individuals were normal (25.7%), 26 osteopenic (37.1%) and 26 osteoporotic (37.1%). The QSM value was age-related (p = 0.04) and significantly higher in the osteoporosis group than in either the normal or osteopenia group (for all, p < 0.001). In addition, the QSM value was highly correlated with QCT value (r = - 0.720, p < 0.001). For QSM, the area under the curve (AUC), sensitivity and specificity for differentiating osteopenia from non-osteopenia were 0.88, 86.5% and 77.8%, respectively, and for differentiating osteoporosis from non-osteoporosis they were 0.86, 80.8% and 77.3%, respectively.

CONCLUSIONS

MRI-based QSM could be used for quantifying susceptibility in vertebrae and has the potential to be a new biomarker in the assessment of osteoporosis for postmenopausal women.

KEY POINTS

• Osteoporosis significantly increases risk of fracture for postmenopausal women. • QSM value was correlated with QCT value (r = - 0.72, p < 0.001). • QSM is feasible in the assessment of osteoporosis for postmenopausal women. • QSM offers the quantification of susceptibility within bone.

Quantitative susceptibility mapping and 23 Na imaging-based in vitro characterization of blood clotting kinetics

Blood clotting is a fundamental biochemical process in post-hemorrhagic hemostasis. Although the varying appearance of coagulating blood in T₁ - and T₂ -weighted images is widely used to qualitatively determine bleeding age, the technique permits only a rough discrimination of coagulation stages, and it remains difficult to distinguish acute and chronic hemorrhagic stages because of low T₁ - and T₂ -weighted signal intensities in both instances. To investigate new biomedical parameters for magnetic resonance imaging-based characterization of blood clotting kinetics, sodium imaging and quantitative susceptibility mapping (QSM) were compared with conventional T₁ - and T₂ -weighted imaging, as well as with biochemical hemolysis parameters. For this purpose, a blood-filled spherical agar phantom was investigated daily for 14 days, as well as after 24 days at 7 T after initial preparation with fresh blood. T₁ - and T₂ -weighted sequences, a three-dimensional (3D) gradient echo sequence and a density-adapted 3D radial projection reconstruction pulse sequence for ²³ Na imaging were applied. For hemolysis estimations, free hemoglobin and free potassium concentrations were measured photometrically and with the direct ion-selective electrode method, respectively, in separate heparinized whole-blood samples along the same timeline. Initial mean susceptibility was low (0.154 ± 0.020 ppm) and increased steadily during the course of coagulation to reach up to 0.570 ± 0.165 ppm. The highest total sodium (NaT) values (1.02 ± 0.06 arbitrary units) in the clot were observed initially, dropped to 0.69 ± 0.13 arbitrary units after one day and increased again to initial values. Compartmentalized sodium (NaS) showed a similar signal evolution, and the NaS/NaT ratio steadily increased over clot evolution. QSM depicts clot evolution in vitro as a process associated with hemoglobin accumulation and transformation, and enables the differentiation of the acute and chronic coagulation stages. Sodium imaging visualizes clotting independent of susceptibility and seems to correspond to clot integrity. A combination of QSM and sodium imaging may enhance the characterization of hemorrhage.

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.

Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels

Background

The quantification of liver iron concentration (LIC) is important for the monitoring of the body iron level in patients with iron overload. Conventionally, LIC is quantified through R2 or R2* mapping using MRI. In this paper, we demonstrate an alternative approach for LIC quantification through measuring the apparent susceptibility of hepatic vessels using quantitative susceptibility mapping (QSM).

Methods

QSM was performed in the liver region with the iterative susceptibility weighted imaging and mapping (iSWIM) algorithm, using the geometry of the vessels extracted from magnitude images as constraints. The susceptibilities of liver tissue were estimated from the apparent susceptibility of the hepatic veins and then converted to LIC. The accuracy of the proposed method was first validated using simulations, and then confirmed using in vivo data collected on 8 healthy controls and 11 patients at 3T. The effects of data acquisition parameters were studied using simulations, and the LICs estimated using QSM were compared with those estimated using R2* mapping.

Results

Simulation results showed that the use of a 3D data acquisition protocol with higher image resolution led to improved accuracy in LIC quantification using QSM. Both simulations and in vivo data results demonstrated that the LICs estimated using the proposed QSM method agreed well with those estimated using R2* mapping. With the shortest echo time being 2.5ms in the multi-echo gradient echo sequence, simulations results showed that LIC up to 12.45 mg iron/g dry tissue can be quantified using the proposed QSM method. For the in vivo data, the highest LIC measured was 11.32 mg iron/g dry tissue.

Conclusions

The proposed method offers a reliable and flexible way to quantify LIC and has the potential to extend the range of LIC that can be accurately measured using R2* and QSM.

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.

Quantitative susceptibility mapping (QSM) and R2* in the human brain at 3T: Evaluation of intra-scanner repeatability

Quantitative susceptibility mapping (QSM) and the effective transverse relaxation rate (R₂*) can be used to monitor iron and myelin content in brain tissue, which are both subject to changes in many neurological diseases but also during healthy aging. In this study, we quantitatively assessed the repeatability of QSM and R₂* by applying four independent scans in eight young healthy, female subjects on a 3T MRI scanner. Since QSM does not yield absolute values for bulk magnetic susceptibilities, we additionally investigated the influence of the choice of a reference brain region for susceptibility by computing susceptibility differences with respect to five different brain structures (whole brain, frontal white matter (fWM), internal capsule (IC), cerebrospinal fluid (CSF) in the lateral ventricle, cortical gray matter (cGM)). The intra-class correlation coefficient (ICC), variance ratio (VR) and repeatability coefficient (RC) were used to evaluate the repeatability of the calculated susceptibility differences and the R₂* values in six different subcortical brain structures. Linear regression was used to analyze the correlation between susceptibility differences and R₂*. We found that the susceptibility differences with respect to each investigated reference region (0.868≤mean ICC≤0.914) and the R₂* values (mean ICC=0.923) were highly repeatable across the four times repeated scans. With consistently higher ICC, higher VR and lower RC, whole brain and cGM appeared to be the two most suitable reference regions for QSM with respect to repeatability.

Joint 2D and 3D phase processing for quantitative susceptibility mapping: application to 2D echo-planar imaging

Quantitative susceptibility mapping (QSM) measures tissue magnetic susceptibility and typically relies on time-consuming three-dimensional (3D) gradient-echo (GRE) MRI. Recent studies have shown that two-dimensional (2D) multi-slice gradient-echo echo-planar imaging (GRE-EPI), which is commonly used in functional MRI (fMRI) and other dynamic imaging techniques, can also be used to produce data suitable for QSM with much shorter scan times. However, the production of high-quality QSM maps is difficult because data obtained by 2D multi-slice scans often have phase inconsistencies across adjacent slices and strong susceptibility field gradients near air-tissue interfaces. To address these challenges in 2D EPI-based QSM studies, we present a new data processing procedure that integrates 2D and 3D phase processing. First, 2D Laplacian-based phase unwrapping and 2D background phase removal are performed to reduce phase inconsistencies between slices and remove in-plane harmonic components of the background phase. This is followed by 3D background phase removal for the through-plane harmonic components. The proposed phase processing was evaluated with 2D EPI data obtained from healthy volunteers, and compared against conventional 3D phase processing using the same 2D EPI datasets. Our QSM results were also compared with QSM values from time-consuming 3D GRE data, which were taken as ground truth. The experimental results show that this new 2D EPI-based QSM technique can produce quantitative susceptibility measures that are comparable with those of 3D GRE-based QSM across different brain regions (e.g. subcortical iron-rich gray matter, cortical gray and white matter). This new 2D EPI QSM reconstruction method is implemented within STI Suite, which is a comprehensive shareware for susceptibility imaging and quantification. Copyright © 2016 John Wiley & Sons, Ltd.

Overview of quantitative susceptibility mapping

Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and represents an important parameter in the field of MRI. With the recently introduced method of quantitative susceptibility mapping (QSM) and its conceptual extension to susceptibility tensor imaging (STI), the non-invasive assessment of this important physical quantity has become possible with MRI. Both methods solve the ill-posed inverse problem to determine the magnetic susceptibility from local magnetic fields. Whilst QSM allows the extraction of the spatial distribution of the bulk magnetic susceptibility from a single measurement, STI enables the quantification of magnetic susceptibility anisotropy, but requires multiple measurements with different orientations of the object relative to the main static magnetic field. In this review, we briefly recapitulate the fundamental theoretical foundation of QSM and STI, as well as computational strategies for the characterization of magnetic susceptibility with MRI phase data. In the second part, we provide an overview of current methodological and clinical applications of QSM with a focus on brain imaging. Copyright © 2016 John Wiley & Sons, Ltd.

Clinical quantitative susceptibility mapping (QSM): Biometal imaging and its emerging roles in patient care

Quantitative susceptibility mapping (QSM) has enabled magnetic resonance imaging (MRI) of tissue magnetic susceptibility to advance from simple qualitative detection of hypointense blooming artifacts to precise quantitative measurement of spatial biodistributions. QSM technology may be regarded to be sufficiently developed and validated to warrant wide dissemination for clinical applications of imaging isotropic susceptibility, which is dominated by metals in tissue, including iron and calcium. These biometals are highly regulated as vital participants in normal cellular biochemistry, and their dysregulations are manifested in a variety of pathologic processes. Therefore, QSM can be used to assess important tissue functions and disease. To facilitate QSM clinical translation, this review aims to organize pertinent information for implementing a robust automated QSM technique in routine MRI practice and to summarize available knowledge on diseases for which QSM can be used to improve patient care. In brief, QSM can be generated with postprocessing whenever gradient echo MRI is performed. QSM can be useful for diseases that involve neurodegeneration, inflammation, hemorrhage, abnormal oxygen consumption, substantial alterations in highly paramagnetic cellular iron, bone mineralization, or pathologic calcification; and for all disorders in which MRI diagnosis or surveillance requires contrast agent injection. Clinicians may consider integrating QSM into their routine imaging practices by including gradient echo sequences in all relevant MRI protocols.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:951-971.

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.

Preconditioned total field inversion (TFI) method for quantitative susceptibility mapping

PURPOSE

To investigate systematic errors in traditional quantitative susceptibility mapping (QSM) where background field removal and local field inversion (LFI) are performed sequentially, to develop a total field inversion (TFI) QSM method to reduce these errors, and to improve QSM quality in the presence of large susceptibility differences.

THEORY AND METHODS

The proposed TFI is a single optimization problem which simultaneously estimates the background and local fields, preventing error propagation from background field removal to QSM. To increase the computational speed, a new preconditioner is introduced and analyzed. TFI is compared with the traditional combination of background field removal and LFI in a numerical simulation and in phantom, 5 healthy subjects, and 18 patients with intracerebral hemorrhage.

RESULTS

Compared with the traditional method projection onto dipole fields+LFI, preconditioned TFI substantially reduced error in QSM along the air-tissue boundaries in simulation, generated high-quality in vivo QSM within similar processing time, and suppressed streaking artifacts in intracerebral hemorrhage QSM. Moreover, preconditioned TFI was capable of generating QSM for the entire head including the brain, air-filled sinus, skull, and fat.

CONCLUSION

Preconditioned total field inversion improves the accuracy of QSM over the traditional method where background and local fields are separately estimated. Magn Reson Med 78:303-315, 2017. © 2016 International Society for Magnetic Resonance in Medicine.