Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition

Multiband accelerated 2D EPI for multi-echo brain QSM at 3 T

PURPOSE

Data for QSM are typically acquired using multi-echo 3D gradient echo (GRE), but EPI can be used to accelerate QSM and provide shorter acquisition times. So far, EPI-QSM has been limited to single-echo acquisitions, which, for 3D GRE, are known to be less accurate than multi-echo sequences. Therefore, we compared single-echo and multi-echo EPI-QSM reconstructions across a range of parallel imaging and multiband acceleration factors.

METHODS

Using 2D single-shot EPI in the brain, we compared QSM from single-echo and multi-echo acquisitions across combined parallel-imaging and multiband acceleration factors ranging from 2 to 16, with volume pulse TRs from 21.7 to 3.2 s, respectively. For single-echo versus multi-echo reconstructions, we investigated the effect of acceleration factors on regional susceptibility values, temporal noise, and image quality. We introduce a novel masking method based on thresholding the magnitude of the local field gradients to improve brain masking in challenging regions.

RESULTS

At 1.6-mm isotropic resolution, high-quality QSM was achieved using multi-echo 2D EPI with a combined acceleration factor of 16 and a TR of 3.2 s, which enables functional applications. With these high acceleration factors, single-echo reconstructions are inaccurate and artefacted, rendering them unusable. Multi-echo acquisitions greatly improve QSM quality, particularly at higher acceleration factors, provide more consistent regional susceptibility values across acceleration factors, and decrease temporal noise compared with single-echo QSM reconstructions.

CONCLUSION

Multi-echo acquisition is more robust for EPI-QSM across parallel imaging and multiband acceleration factors than single-echo acquisition. Multi-echo EPI can be used for highly accelerated acquisition while preserving QSM accuracy and quality relative to gold-standard 3D-GRE QSM.

How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.

MR Susceptibility Separation for Quantifying Lesion Paramagnetic and Diamagnetic Evolution in Relapsing-Remitting Multiple Sclerosis

BACKGROUND

Multiple sclerosis (MS) lesion evolution may involve changes in diamagnetic myelin and paramagnetic iron. Conventional quantitative susceptibility mapping (QSM) can provide net susceptibility distribution, but not the discrete paramagnetic and diamagnetic components.

PURPOSE

To apply susceptibility separation (χ separation) to follow lesion evolution in MS with comparison to R2*/R2 '/QSM.

STUDY TYPE

Longitudinal, prospective.

SUBJECTS

Twenty relapsing-remitting MS subjects (mean age: 42.5 ± 9.4 years, 13 females; mean years of symptoms: 4.3 ± 1.4 years).

FIELD STRENGTH/SEQUENCE

Three-dimensional multiple echo gradient echo (QSM and R2* mapping), two-dimensional dual echo fast spin echo (R2 mapping), T2-weighted fluid attenuated inversion recovery, and T1-weighted magnetization prepared gradient echo sequences at 3 T.

ASSESSMENT

Data were analyzed from two scans separated by a mean interval of 14.4 ± 2.0 months. White matter lesions on fluid-attenuated inversion recovery were defined by an automatic pipeline, then manually refined (by ZZ/AHW, 3/25 years' experience in MRI), and verified by a radiologist (MN, 25 years' experience in MS). Susceptibility separation yielded the paramagnetic and diamagnetic susceptibility content of each voxel. Lesions were classified into four groups based on the variation of QSM/R2* or separated into positive/negative components from χ separation.

STATISTICAL TESTS

Two-sample paired t tests for assessment of longitudinal differences. Spearman correlation coefficients to assess associations between χ separation and R2*/R2 '/QSM. Significant level: P < 0.005.

RESULTS

A total of 183 lesions were quantified. Categorizing lesions into groups based on χ separation demonstrated significant annual changes in QSM//R2*/R2 '. When lesions were grouped based on changes in QSM and R2*, both changing in unison yielded a significant dominant paramagnetic variation and both opposing yielded a dominant diamagnetic variation. Significant Spearman correlation coefficients were found between susceptibility-sensitive MRI indices and χ separation.

DATA CONCLUSION

Susceptibility separation changes in MS lesions may distinguish and quantify paramagnetic and diamagnetic evolution, potentially providing additional insight compared to R2* and QSM alone.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Comparison study of quantitative susceptibility mapping with GRAPPA and wave-CAIPI: reproducibility, consistency, and microbleeds detection

PURPOSE

We compared quantitative susceptibility mapping (QSM) with wave-CAIPI 9 × (QSM_WC9 ×) with reference standard QSM with GRAPPA 2 × (QSM_G2 ×) in two MR scanners. We also compared detectability of microbleeds in both QSMs to demonstrate clinical feasibility of both QSMs.

MATERIALS AND METHODS

This prospective study was approved by the institutional review board and written informed consent was obtained from each subject. Healthy subjects were recruited to evaluate intra-scanner reproducibility, inter-scanner consistency, and inter-sequence consistency of QSM_G2 × and QSM_WC9 × at 2 MR scanners. Susceptibility values measured with volume of interests (VOIs) were evaluated. Patients who were requested for susceptibility weighted imaging were also recruited in this study to measure microbleeds on QSM_G2 × and QSM_WC9 × . The number of microbleeds was compared between two QSMs.

RESULTS

Total 55 healthy subjects (male 34, female 21, 38.3 years [23-79]) were included in this study. We investigated reproducibility and consistency of QSM_WC9 × by comparing reference standard QSM_G2 × in two MR scanners in this study, and high correlation (ρ, 0.93-0.97) and high intraclass correlation coefficient (ICC) (0.97-0.99) were obtained. Sixty patients (male 30, female 30; age, 55.4 years [21-85]) were finally enrolled in this prospective study. The ICC of the detected number of microbleeds between QSM_G2 × and QSM_WC9 × was 0.99 (0.98-0.99).

CONCLUSION

QSM_WC9 × and reference standard QSM_G2 × in two MR scanners showed good reproducibility and consistency in estimating magnetic susceptibilities. QSM_WC9 × and QSM_G2 × were also comparable in terms of microbleeds detection with good agreement of raters and high ICC.

Oxygen extraction fraction mapping based combining quantitative susceptibility mapping and quantitative blood oxygenation level-dependent imaging model using multi-delay PCASL

BACKGROUND AND PURPOSE

The oxygen extraction fraction is an essential biomarker for the assessment of brain metabolism. A recently proposed method combined with quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude enables noninvasive mapping of the oxygen extraction fraction. Our study investigated the oxygen extraction fraction mapping variations of single-delay and multi-delay arterial spin-labeling.

MATERIALS AND METHODS

A total of twenty healthy participants were enrolled. The multi-echo spoiled gradient-echo, multi-delay arterial spin-labeling, and magnetization-prepared rapid gradient echo sequences were acquired at 3.0 T. The mean oxygen extraction fraction was generated under a single delay time of 1780 ms, multi-delay arterial spin-labeling of transit-corrected cerebral blood flow, and multi-delay arterial spin-labeling of arterial cerebral blood volume. The results were compared via paired t tests and the Wilcoxon test. Linear regression analyses were used to investigate the relationships among the oxygen extraction fraction, cerebral blood flow, and venous cerebral blood volume.

RESULTS

The oxygen extraction fraction estimate with multi-delay arterial spin-labeling yielded a significantly lower value than that with single-delay arterial spin-labeling. The average values for the whole brain under single-delay arterial spin-labeling, multi-delay arterial spin-labeling of transit-corrected cerebral blood flow, and multi-delay arterial spin-labeling of arterial cerebral blood volume were 41.5 ± 1.7 % (P < 0.05), 41.3 ± 1.9 % (P < 0.001), and 40.9 ± 1.9 % (N = 20), respectively. The oxygen extraction fraction also showed a significant inverse correlation with the venous cerebral blood volume under steady-state conditions when multi-delay arterial spin-labeling was used (r = 0.5834, p = 0.0069).

CONCLUSION

These findings suggest that the oxygen extraction fraction is significantly impacted by the arterial spin-labeling methods used in the quantitative susceptibility mapping plus the quantitative blood oxygen level-dependent model, indicating that the differences should be accounted for when employing oxygen extraction fraction mapping based on this model in diseases.

Brain Iron in signature regions relating to cognitive aging in older adults: the Taizhou Imaging Study

BACKGROUND

Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer's disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults.

METHODS

In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 2 signature regions where atrophy affected by aging and AD only: Aging (AG) -specific and AD signature meta-ROIs. The QSM and cortical morphometry means of the above ROIs were also computed.

RESULTS

Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse ZMMSE than did those in the lower tertile [β = -0.104, p = 0.026;β = -0.118, p = 0.021, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict decline in ZMoCA and key domains such as attention and visuospatial function (all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific signature and AD signature regions (all p < 0.05).

CONCLUSION

AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.

Combining Quantitative Susceptibility Mapping With the Gray Matter Volume to Predict Neurological Deficits in Patients With Small Artery Occlusion

BACKGROUND

Currently, there is still a lack of valuable neuroimaging markers to assess the clinical severity of stroke patients with small artery occlusion (SAO). Quantitative susceptibility mapping (QSM) is a quantitative processing method for neuroradiological diagnostics. Gray matter (GM) volume changes in stroke patients are also proved to be associated with neurological deficits. This study aims to explore the predictive value of QSM and GM volume in neurological deficits of patients with SAO.

METHODS

As neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. Sixty-six SAO participants within 24 h of first onset were enrolled and divided into mild and moderate groups based on NIHSS. QSM values of infarct area and GM volume were calculated from magnetic resonance imaging (MRI) data. Two-sample t-tests were used to compare differences in QSM value and GM volume between the two groups, and the diagnostic efficacy of the combination of QSM value and GM volume was evaluated.

RESULTS

The results revealed both the QSM value and GM volume within the infarct area of the moderate group were lower compared to the mild group. Moderate group exhibited lower GM volume in some specific gyrus compared with mild group in the case of voxel-wise GM volume on whole-brain voxel level. The support vector machine (SVM) classifier's analysis showed a high power for the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume.

CONCLUSION

Our research first reported the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume could be used to predict neurological impairment of patients with SAO, which provides new insights for further understanding the SAO stroke.

A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance learning. However, performance stability for such methods can be typically undermined by the presence of irrelevant or spuriously-relevant features that mask the intrinsic causal features. Therefore, we propose a QSM-based arising-from-chair assessment method using a causal graph-neural-network framework, where counterfactual and debiasing strategies are developed and integrated into this framework for capturing causal features. Specifically, the counterfactual strategy is proposed to suppress irrelevant features caused by background noise, by producing incorrect predictions when dropping causal parts. The debiasing strategy is proposed to suppress spuriously relevant features caused by the sampling bias and it comprises a resampling guidance scheme for selecting stable instances and a causal invariance constraint for improving stability under various interferences. The results of extensive experiments demonstrated the superiority of the proposed method in detecting arising-from-chair abnormalities. Its clinical feasibility was further confirmed by the coincidence between the selected causal features and those reported in earlier medical studies. Additionally, the proposed method was extensible for another motion task of leg agility. Overall, this study provides a potential tool for automated arising-from-chair assessment in PD patients, and also introduces causal counterfactual thinking in medical image analysis. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CFGNN-PDarising.

Topographical metal burden correlates with brain atrophy and clinical severity in Wilson's disease

BACKGROUND

Quantitative susceptibility mapping (QSM) is a post-processing technique that creates brain susceptibility maps reflecting metal burden through tissue magnetic susceptibility. We assessed topographic differences in magnetic susceptibility between participants with and without Wilson's disease (WD), correlating these findings with clinical severity, brain volume, and biofluid copper and iron indices.

METHODS

A total of 43 patients with WD and 20 unaffected controls, were recruited. QSM images were derived from a 3T MRI scanner. Clinical severity was defined using the minimal Unified Wilson's Disease Rating Scale (M-UWDRS) and Montreal Cognitive Assessment scoring. Differences in magnetic susceptibilities between groups were evaluated using general linear regression models, adjusting for age and sex. Correlations between the susceptibilities and clinical scores were analyzed using Spearman's method.

RESULTS

In age- and sex-adjusted analyses, magnetic susceptibility values were increased in WD patients compared with controls, including caudate nucleus, putamen, globus pallidus, and substantia nigra (all p < 0.01). Putaminal susceptibility was greater with an initial neuropsychiatric presentation (n = 25) than with initial hepatic dysfunction (n = 18; p = 0.04). Susceptibility changes correlated negatively with regional brain volume in almost all topographic regions. Serum ferritin, but not serum copper or ceruloplasmin, correlated positively with magnetic susceptibility level in the caudate nucleus (p = 0.04), putamen (p = 0.04) and the hippocampus (p = 0.03). The dominance of magnetic susceptibility in cortical over subcortical regions correlated with M-UWDRS scores (p < 0.01).

CONCLUSION

The magnetic susceptibility changes could serve as a surrogate marker for patients with WD.

Quantitative mapping of renal oxygen consumption using pseudo-continuous arterial spin labeling and quantitative susceptibility mapping in humans

PURPOSE

To propose a new method for quantitatively mapping the renal metabolic rate of oxygen (RMRO2) and to evaluate the proposed method using a caffeine challenge.

THEORY AND METHODS

Pseudo-continuous arterial spin labeling (pCASL) and QSM sequences were used to obtain MR images in the kidney. Six healthy volunteers were scanned on caffeine and control days. The pCASL and QSM images were registered using DICOM information and rigid translation. The Fick principle was applied to estimate RMRO2. The results on caffeine and control days were compared to evaluate the capability of the proposed method to estimate renal oxygen consumption. A paired t-test was used to assess the statistical significance.

RESULTS

Estimated renal blood flow (RBF), QSM, and RMRO2 maps were consistent with those reported in the literature. RMRO2 values were higher than the cerebral metabolic rate of oxygen (CMRO2) and were significantly reduced on the caffeine days compared to the control days, consistent with findings from non-MRI literature.

CONCLUSION

The feasibility of measuring renal oxygen consumption using pCASL and QSM images was demonstrated. To the best of our knowledge, this work provides quantitative maps of renal oxygen consumption in humans for the first time. The results were consistent with the literature, including the statistically significant reduction in renal oxygen consumption with caffeine challenge. These findings suggest the potential utility of our technique in measuring renal oxygen consumption noninvasively, especially for patients with complications associated with contrast agents.

Three-dimensional EPI with shot-selective CAIPIRIHANA for rapid high-resolution quantitative susceptibility mapping at 3 T

PURPOSE

QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging (3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring.

METHODS

To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition.

RESULTS

The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated.

CONCLUSION

We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.

Comparative analysis of quantitative susceptibility mapping in preclinical dementia detection

PURPOSE

This review aims to explore the role of Quantitative Susceptibility Mapping (QSM) in the early detection of neurodegenerative diseases, particularly Alzheimer's disease (AD) and Lewy body dementia (LBD). By examining QSM's ability to map brain iron deposition, we seek to highlight its potential as a diagnostic tool for preclinical dementia.

METHODOLOGY

QSM techniques involve the advanced processing of MRI phase images to reconstruct tissue susceptibility, employing methods such as spherical mean value filtering and Tikhonov regularization for accurate background field removal. This review discusses how these methodologies enable the precise quantification of iron and other elements within the brain.

RESULTS

QSM has demonstrated effectiveness in identifying early pathological changes in key brain regions, including the hippocampus, basal ganglia, and substantia nigra. These regions are significantly impacted in the early stages of AD and LBD. Studies reviewed indicate that QSM can detect subtle neurodegenerative changes, providing valuable insights into disease progression. However, challenges remain in standardizing QSM processing algorithms to ensure consistent results across different studies.

CONCLUSION

QSM emerges as a promising tool for early dementia detection, offering precise measurements of brain iron deposition and other critical biomarkers. The review underscores the importance of refining QSM methodologies and integrating them with other imaging modalities to improve early diagnosis and management of neurodegenerative diseases. Future research should focus on standardizing QSM techniques and exploring their synergistic use with other neuroimaging methods to enhance its clinical utility.

Exploring cognitive related microstructural alterations in normal appearing white matter and deep grey matter for small vessel disease: A quantitative susceptibility mapping study

Brain microstructural alterations possibly occur in the normal-appearing white matter (NAWM) and grey matter of small vessel disease (SVD) patients, and may contribute to cognitive impairment. The aim of this study was to explore cognitive related microstructural alterations in white matter and deep grey matter nuclei in SVD patients using magnetic resonance (MR) quantitative susceptibility mapping (QSM). 170 SVD patients, including 103 vascular mild cognitive impairment (VaMCI) and 67 no cognitive impairment (NCI), and 21 healthy control (HC) subjects were included, all underwent a whole-brain QSM scanning. Using a white matter and a deep grey matter atlas, subregion-based QSM analysis was conducted to identify and characterize microstructural alterations occurring within white matter and subcortical nuclei. Significantly different susceptibility values were revealed in NAWM and in several specific white matter tracts including anterior limb of internal capsule, corticospinal tract, medial lemniscus, middle frontal blade, superior corona radiata and tapetum among VaMCI, NCI and HC groups. However, no difference was found in white matter hyperintensities between VaMCI and NCI. A trend toward higher susceptibility in the caudate nucleus and globus pallidus of VaMCI patients compared to HC, indicating elevated iron deposition in these areas. Interestingly, some of these QSM parameters were closely correlated with both global and specific cognitive function scores, controlling age, gender and education level. Our study suggested that QSM may serve as a useful imaging tool for monitoring cognitive related microstructural alterations in brain. This is especially meaningful for white matter which previously lacks of attention.

In vivo demonstration of globotriaosylceramide brain accumulation in Fabry Disease using MR Relaxometry

PURPOSE

How to measure brain globotriaosylceramide (Gb3) accumulation in Fabry Disease (FD) patients in-vivo is still an open challenge. The objective of this study is to provide a quantitative, non-invasive demonstration of this phenomenon using quantitative MRI (qMRI).

METHODS

In this retrospective, monocentric cross-sectional study conducted from November 2015 to July 2018, FD patients and healthy controls (HC) underwent an MRI scan with a relaxometry protocol to compute longitudinal relaxation rate (R1) maps to evaluate gray (GM) and white matter (WM) lipid accumulation. In a subgroup of 22 FD patients, clinical (FAbry STabilization indEX -FASTEX- score) and biochemical (residual α-galactosidase activity) variables were correlated with MRI data. Quantitative maps were analyzed at both global ("bulk" analysis) and regional ("voxel-wise" analysis) levels.

RESULTS

Data were obtained from 42 FD patients (mean age = 42.4 ± 12.9, M/F = 16/26) and 49 HC (mean age = 42.3 ± 16.3, M/F = 28/21). Compared to HC, FD patients showed a widespread increase in R1 values encompassing both GM (pFWE = 0.02) and WM (pFWE = 0.02) structures. While no correlations were found between increased R1 values and FASTEX score, a significant negative correlation emerged between residual enzymatic activity levels and R1 values in GM (r = -0.57, p = 0.008) and WM (r = -0.49, p = 0.03).

CONCLUSIONS

We demonstrated the feasibility and clinical relevance of non-invasively assessing cerebral Gb3 accumulation in FD using MRI. R1 mapping might be used as an in-vivo quantitative neuroimaging biomarker in FD patients.

Assessing brain iron and its relationship to cognition and comorbidity in children with ADHD with quantitative susceptibility mapping (QSM)

BACKGROUND

Quantitative susceptibility mapping (QSM) is a neuroimaging technique that detects local changes in magnetic susceptibility induced by brain iron. Brain iron and the dopaminergic system are linked since iron is an important cofactor for dopamine synthesis. ADHD is associated with dysregulation of dopaminergic transmission. Therefore, we applied QSM on subcortical structures, to study potential alterations in brain iron and its impact on cognition and mental health in children with ADHD.

METHODS

3 Tesla QSM-data of 111 participants (nADHD= 58, mean age: 13.2 (0.63); nControls=53, mean age: 13.2 (0.51)) were analyzed. Subcortical regional brain iron values were extracted. ANOVAs examined group differences for each region of interest. For dimensional approaches, Pearson correlation analysis was performed across the cohort examining the association with symptoms, mental health, and cognition.

RESULTS

No significant differences were found in iron susceptibility between ADHD and control, between persistent and remitted ADHD, or between medication use. An unexpected finding was that children with internalising disorder had significantly higher iron susceptibility, but the result did not survive multiple comparison corrections. Higher brain iron was associated with sustained attention, but not on inhibition, IQ, and working memory.

CONCLUSION

This is the first study addressing brain iron susceptibility and its association with comorbidities and cognition in ADHD. Alterations in brain iron may not account for the full diagnosis of ADHD but may be an indicator of internalising problems in children. Alterations in brain iron content in children were linked to detrimental sustained attention and may represent developmental variation in cognition.

SIPAS: A comprehensive susceptibility imaging process and analysis studio

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.

Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo

T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored. Detection sensitivity was evaluated against standard multiecho gradient echo (MEGE) QSM using 3-T in vivo data of 15 subjects with a combined total of 108 confirmed microbleeds. The two methods were compared based on the microbleed size and susceptibility measurements. In addition, simulations explored the detection sensitivity of MPRAGE-QSM at different representative magnetic field strengths and echo times using microbleeds of different size, susceptibility, and location. Results showed that in vivo microbleeds appeared to be smaller (× 0.54) and of higher mean susceptibility (× 1.9) on MPRAGE-QSM than on MEGE-QSM, but total susceptibility estimates were in closer agreement (slope: 0.97, r2: 0.94), and detection sensitivity was comparable. In simulations, QSM at 1.5 T had a low contrast-to-noise ratio that obscured the detection of many microbleeds. Signal-to-noise ratio (SNR) levels at 3 T and above resulted in better contrast and increased detection. The detection rates for microbleeds of minimum one-voxel diameter and 0.4-ppm susceptibility were 0.55, 0.80, and 0.88 at SNR levels of 1.5, 3, and 7 T, respectively. Size and total susceptibility estimates were more consistent than mean susceptibility estimates, which showed size-dependent underestimation. MPRAGE-QSM provides an opportunity to detect and quantify the size and susceptibility of microbleeds of at least one-voxel diameter at B0 of 3 T or higher with no additional time cost, when standard T2*-weighted images are not available or have inadequate spatial resolution. The total susceptibility measure is more robust against sequence variations and might allow combining data from different protocols.

Quantitative susceptibility mapping in amyotrophic lateral sclerosis: automatic quantification of the magnetic susceptibility in the subcortical nuclei

Objective: Previous studies have suggested a link between dysregulation of cortical iron levels and neuronal loss in amyotrophic lateral sclerosis (ALS) patients. However, few studies have reported differences in quantitative susceptibility mapping (QSM) values in subcortical nuclei between patients with ALS and healthy controls (HCs). Methods: MRI was performed using a 3 Tesla Prisma scanner (64-channel head coil), including 3D T1-MPRAGE and multi-echo 3D GRE for QSM reconstruction. Automated QSM segmentation was used to measure susceptibility values in the subcortical nuclei, which were compared between the groups. Correlations with clinical scales were analyzed. Group comparisons were performed using independent t-tests, with p < 0.05 considered significant. Correlations were assessed using Pearson's correlation, with p < 0.05 considered significant. Cohen's d was reported to compare the standardized mean difference (SMD) of QSM. Results: Twelve patients with limb-onset ALS (mean age 48.7 years, 75% male) and 13 age-, sex-, and handedness-matched HCs (mean age 44.6 years, 69% male) were included. Compared to HCs, ALS patients demonstrated significantly lower susceptibility in the left caudate nucleus (CN) (SMD = -0.845), right CN (SMD = -0.851), whole CN (SMD = -1.016), and left subthalamic nucleus (STN) (SMD = -1.000). Susceptibility in the left putamen (SMD = -0.857), left thalamus (SMD = -1.081), and whole thalamus (SMD = -0.968) was significantly higher in the patients. The susceptibility of the substantia nigra (SN), CN, and pulvinar was positively correlated with disease duration. Conclusions: QSM detects abnormal iron accumulation patterns in the subcortical gray matter of ALS patients, which correlates with disease characteristics, supporting its potential as a neuroimaging biomarker.

Quantitative susceptibility mapping based basal ganglia segmentation via AGSeg: leveraging active gradient guiding mechanism in deep learning

Background

With better visual contrast and the ability for magnetic susceptibility quantification analysis, quantitative susceptibility mapping (QSM) has emerged as an important magnetic resonance imaging (MRI) method for basal ganglia studies. Precise segmentation of basal ganglia is a prerequisite for quantification analysis of tissue magnetic susceptibility, which is crucial for subsequent disease diagnosis and surgical planning. The conventional method of localizing and segmenting basal ganglia heavily relies on layer-by-layer manual annotation by experts, resulting in a tedious amount of workload. Although several morphology registration and deep learning based methods have been developed to automate segmentation, the voxels around the nuclei boundary remain a challenge to distinguish due to insufficient tissue contrast. This paper proposes AGSeg, an active gradient guidance-based susceptibility and magnitude information complete (MIC) network for real-time and accurate basal ganglia segmentation.

Methods

Various datasets, including clinical scans and data from healthy volunteers, were collected across multiple centers with different magnetic field strengths (3T/5T/7T), with a total of 210 three-dimensional (3D) susceptibility measurements. Manual segmentations following fixed rules for anatomical borders annotated by experts were used as ground truth labels. The proposed network took QSM maps and Magnitude images as two individual inputs, of which the features are selectively enhanced in the proposed magnitude information complete (MIC) module. AGSeg utilized a dual-branch architecture, with Seg-branch aiming to generate a proper segmentation map and Grad-branch to reconstruct the gradient map of regions of interest (ROIs). With the support of the newly designed active gradient module (AGM) and gradient guiding module (GGM), the Grad-branch provided attention guidance for the Seg-branch, facilitating it to focus on the boundary of target nuclei.

Results

Ablation studies were conducted to assess the functionality of the proposed modules. Significant performance decrement was observed after ablating relative modules. AGSeg was evaluated against several existing methods on both healthy and clinical data, achieving an average Dice similarity coefficient (DSC) =0.874 and average 95% Hausdorff distance (HD95) =2.009. Comparison experiments indicated that our model had superior performance on basal ganglia segmentation and better generalization ability over existing methods. The AGSeg outperformed all implemented comparison deep learning algorithms with average DSC enhancement ranging from 0.036 to 0.074.

Conclusions

The current work integrates a deep learning-based method into automated basal ganglia segmentation. The high processing speed and segmentation robustness of AGSeg contribute to the feasibility of future surgery planning and intraoperative navigation. Experiments show that leveraging active gradient guidance mechanisms and magnitude information completion can facilitate the segmentation process. Moreover, this approach also offers a portable solution for other multi-modality medical image segmentation tasks.

Examining iron-related off-target binding effects of 18F-AV1451 PET in the cortex of Aβ+ individuals

The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (18F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in 18F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aβ+) individuals, 20 amyloid beta negative (Aβ-) with MCI and 31 Aβ- control participants. After controlling for iron, increased 18F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aβ+ participants compared to Aβ- MCI and control participants. Within the Aβ+ group, significant correlations were seen between 18F-AV1451 PET uptake and tissue microstructure and these correlations remained significant after controlling for iron. These findings indicate that off-target binding of iron to the 18F-AV1451 ligand may not affect its sensitivity to Aβ status or cognition in early-stage AD.

A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation

Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems. Recent model-incorporated DL approaches also overlook the non-local effect of the tissue phase in applying the source-to-field forward model due to patch-based training constraint, resulting in a discrepancy between the prediction and measurement and subsequently suboptimal QSM reconstruction. This study proposes an unsupervised and subject-specific DL method for QSM reconstruction based on implicit neural representation (INR), referred to as INR-QSM. INR has emerged as a powerful framework for learning a high-quality continuous representation of the signal (image) by exploiting its internal information without training labels. In INR-QSM, the desired susceptibility map is represented as a continuous function of the spatial coordinates, parameterized by a fully-connected neural network. The weights are learned by minimizing a loss function that includes a data fidelity term incorporated by the physical model and regularization terms. Additionally, a novel phase compensation strategy is proposed for the first time to account for the non-local effect of tissue phase in data consistency calculation to make the physical model more accurate. Our experiments show that INR-QSM outperforms traditional established QSM reconstruction methods and the compared unsupervised DL method both qualitatively and quantitatively, and is competitive against supervised DL methods under data perturbations.

Chaos and COSMOS-Considerations on QSM methods with multiple and single orientations and effects from local anisotropy

PURPOSE

Field-to-susceptibility inversion in quantitative susceptibility mapping (QSM) is ill-posed and needs numerical stabilization through either regularization or oversampling by acquiring data at three or more object orientations. Calculation Of Susceptibility through Multiple Orientations Sampling (COSMOS) is an established oversampling approach and regarded as QSM gold standard. It achieves a well-conditioned inverse problem, requiring rotations by 0°, 60° and 120° in the yz-plane. However, this is impractical in vivo, where head rotations are typically restricted to a range of ±25°. Non-ideal sampling degrades the conditioning with residual streaking artifacts whose mitigation needs further regularization. Moreover, susceptibility anisotropy in white matter is not considered in the COSMOS model, which may introduce additional bias. The current work presents a thorough investigation of these effects in primate brain.

METHODS

Gradient-recalled echo (GRE) data of an entire fixed chimpanzee brain were acquired at 7 T (350 μm resolution, 10 orientations) including ideal COSMOS sampling and realistic rotations in vivo. Comparisons of the results included ideal COSMOS, in-vivo feasible acquisitions with 3-8 orientations and single-orientation iLSQR QSM.

RESULTS

In-vivo feasible and optimal COSMOS yielded high-quality susceptibility maps with increased SNR resulting from averaging multiple acquisitions. COSMOS reconstructions from non-ideal rotations about a single axis required additional L2-regularization to mitigate residual streaking artifacts.

CONCLUSION

In view of unconsidered anisotropy effects, added complexity of the reconstruction, and the general challenge of multi-orientation acquisitions, advantages of sub-optimal COSMOS schemes over regularized single-orientation QSM appear limited in in-vivo settings.

Localized Changes in Dentate Nucleus Shape and Magnetic Susceptibility in Friedreich Ataxia

BACKGROUND

The dentate nuclei of the cerebellum are key sites of neuropathology in Friedreich ataxia (FRDA). Reduced dentate nucleus volume and increased mean magnetic susceptibility, a proxy of iron concentration, have been reported by magnetic resonance imaging studies in people with FRDA. Here, we investigate whether these changes are regionally heterogeneous.

METHODS

Quantitative susceptibility mapping data were acquired from 49 people with FRDA and 46 healthy controls. The dentate nuclei were manually segmented and analyzed using three dimensional vertex-based shape modeling and voxel-based assessments to identify regional changes in morphometry and susceptibility, respectively.

RESULTS

Individuals with FRDA, relative to healthy controls, showed significant bilateral surface contraction most strongly at the rostral and caudal boundaries of the dentate nuclei. The magnitude of this surface contraction correlated with disease duration, and to a lesser extent, ataxia severity. Significantly greater susceptibility was also evident in the FRDA cohort relative to controls, but was instead localized to bilateral dorsomedial areas, and also correlated with disease duration and ataxia severity.

CONCLUSIONS

Changes in the structure of the dentate nuclei in FRDA are not spatially uniform. Atrophy is greatest in areas with high gray matter density, whereas increases in susceptibility-reflecting iron concentration, demyelination, and/or gliosis-predominate in the medial white matter. These findings converge with established histological reports and indicate that regional measures of dentate nucleus substructure are more sensitive measures of disease expression than full-structure averages. Biomarker development and therapeutic strategies that directly target the dentate nuclei, such as gene therapies, may be optimized by targeting these areas of maximal pathology. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale

To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.

Quantitative susceptibility mapping in the brain reflects spatial expression of genes involved in iron homeostasis and myelination

Quantitative susceptibility mapping (QSM) is an MRI modality used to non-invasively measure iron content in the brain. Iron exhibits a specific anatomically varying pattern of accumulation in the brain across individuals. The highest regions of accumulation are the deep grey nuclei, where iron is stored in paramagnetic molecule ferritin. This form of iron is considered to be what largely contributes to the signal measured by QSM in the deep grey nuclei. It is also known that QSM is affected by diamagnetic myelin contents. Here, we investigate spatial gene expression of iron and myelin related genes, as measured by the Allen Human Brain Atlas, in relation to QSM images of age-matched subjects. We performed multiple linear regressions between gene expression and the average QSM signal within 34 distinct deep grey nuclei regions. Our results show a positive correlation (p < .05, corrected) between expression of ferritin and the QSM signal in deep grey nuclei regions. We repeated the analysis for other genes that encode proteins thought to be involved in the transport and storage of iron in the brain, as well as myelination. In addition to ferritin, our findings demonstrate a positive correlation (p < .05, corrected) between the expression of ferroportin, transferrin, divalent metal transporter 1, several gene markers of myelinating oligodendrocytes, and the QSM signal in deep grey nuclei regions. Our results suggest that the QSM signal reflects both the storage and active transport of iron in the deep grey nuclei regions of the brain.

Enlarged perivascular spaces in the basal ganglia are associated with arteries not veins

Enlarged perivascular spaces (EPVS) are common in cerebral small vessel disease (CSVD) and have been identified as a marker of dysfunctional brain clearance. However, it remains unknown if the enlargement occurs predominantly around arteries or veins. We combined in vivo ultra-high-resolution MRI and histopathology to investigate the spatial relationship of veins and arteries with EPVS within the basal ganglia (BG). Furthermore, we assessed the relationship between the EPVS and measures of blood-flow (blood-flow velocity, pulsatility index) in the small arteries of the BG. Twenty-four healthy controls, twelve non-CAA CSVD patients, and five probable CAA patients underwent a 3 tesla [T] and 7T MRI-scan, and EPVS, arteries, and veins within the BG were manually segmented. Furthermore, the scans were co-registered. Six autopsy-cases were also assessed. In the BG, EPVS were significantly closer to and overlapped more frequently with arteries than with veins. Histological analysis showed a higher proportion of BG EPVS surrounding arteries than veins. Finally, the pulsatility index of BG arteries correlated with EPVS volume. Our results are in line with previous works and establish a pathophysiological relationship between arteries and EPVS, contributing to elucidating perivascular clearance routes in the human brain.

Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis

Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.

Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology

Quantitative susceptibility mapping (QSM) is a unique technique for providing quantitative information on tissue magnetic susceptibility using phase image data. QSM can provide valuable information regarding physiological and pathological processes such as iron deposition, hemorrhage, calcification, and myelin. QSM has been considered for use as an imaging biomarker to investigate physiological status and pathological changes. Although various studies have investigated the clinical applications of QSM, particularly regarding the use of QSM in clinical practice, have not been examined well. This review provides on an overview of the basics of QSM and its clinical applications in neuroradiology. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

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.

Distinguishing microgliosis and tau deposition in the mouse brain using paramagnetic and diamagnetic susceptibility source separation

Tauopathies, including Alzheimer's disease (AD), are neurodegenerative disorders characterized by hyperphosphorylated tau protein aggregates in the brain. In addition to protein aggregates, microglia-mediated inflammation and iron dyshomeostasis are other pathological features observed in AD and other tauopathies. It is known that these alterations at the subcellular level occur much before the onset of macroscopic tissue atrophy or cognitive deficits. The ability to detect these microstructural changes with MRI therefore has substantive importance for improved characterization of disease pathogenesis. In this study, we demonstrate that quantitative susceptibility mapping (QSM) with paramagnetic and diamagnetic susceptibility source separation has the potential to distinguish neuropathological alterations in a transgenic mouse model of tauopathy. 3D multi-echo gradient echo data were acquired from fixed brains of PS19 (Tau) transgenic mice and age-matched wild-type (WT) mice (n = 5 each) at 11.7 T. The multi-echo data were fit to a 3-pool complex signal model to derive maps of paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS). Group-averaged signal fraction and composite susceptibility maps showed significant region-specific differences between the WT and Tau mouse brains. Significant bilateral increases in PCS and |DCS| were observed in specific hippocampal and cortical sub-regions of the Tau mice relative to WT controls. Comparison with immunohistological staining for microglia (Iba1) and phosphorylated-tau (AT8) further indicated that the PCS and DCS differences corresponded to regional microgliosis and tau deposition in the PS19 mouse brains, respectively. The results demonstrate that quantitative susceptibility source separation may provide sensitive imaging markers to detect distinct pathological alterations in tauopathies.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Maximum spherical mean value filtering for whole-brain QSM

PURPOSE

To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion.

THEORY AND METHODS

Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients.

RESULTS

Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis.

CONCLUSION

The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.

Simultaneous time-of-flight MR angiography and quantitative susceptibility mapping with key time-of-flight features

A technique for combined time-of-flight (TOF) MR angiography (MRA) and quantitative susceptibility mapping (QSM) was developed with key features of standard three-dimensional (3D) TOF acquisitions, including multiple overlapping thin slab acquisition (MOTSA), ramped RF excitation, and venous saturation. The developed triple-echo 3D TOF-QSM sequence enabled TOF-MRA, susceptibility-weighted imaging (SWI), QSM, and R2* mapping. The effects of ramped RF, resolution, flip angle, venous saturation, and MOTSA were studied on QSM. Six volunteers were scanned at 3 T with the developed sequence, conventional TOF-MRA, and conventional SWI. Quantitative comparison of susceptibility values on QSM and normalized arterial and venous vessel-to-background contrasts on TOF and SWI were performed. The ramped RF excitation created an inherent phase variation in the raw phase. A generic correction factor was computed to remove the phase variation to obtain QSM without artifacts from the TOF-QSM sequence. No statistically significant difference was observed between the developed and standard QSM sequence for susceptibility values. However, maintaining standard TOF features led to compromises in signal-to-noise ratio for QSM and SWI, arising from the use of MOTSA rather than one large 3D slab, higher TOF spatial resolution, increased TOF background suppression due to larger flip angles, and reduced venous signal from venous saturation. In terms of vessel contrast, veins showed higher normalized contrast on SWI derived from TOF-QSM than the standard SWI sequence. While fast flowing arteries had reduced contrast compared with standard TOF-MRA, no statistical difference was observed for slow flowing arteries. Arterial contrast differences largely arise from the longer TR used in TOF-QSM over standard TOF-MRA to accommodate additional later echoes for SWI. In conclusion, although the sequence has a longer TR and slightly lower arterial contrast, provided an adequate correction is made for ramped RF excitation effects on phase, QSM may be performed from a multiecho sequence that includes all key TOF features, thus enabling simultaneous TOF-MRA, SWI, QSM, and R2* map computation.

Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution

Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pre-training on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data.

Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network

Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.

Model resolution-based deconvolution for improved quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution-based deconvolution for improved estimation of the susceptibility map obtained using the thresholded k-space division (TKD). A two-step approach has been proposed, wherein the first step involves the TKD susceptibility map computation and the second step involves the correction of this susceptibility map using the model-resolution matrix. The TKD-estimated susceptibility map can be expressed as the weighted average of the true susceptibility map, where the weights are determined by the rows of the model-resolution matrix, and hence a deconvolution of the TKD susceptibility map using the model-resolution matrix yields a better approximation to the true susceptibility map. The model resolution-based deconvolution is realized using closed-form, iterative, and sparsity-regularized implementations. The proposed approach was compared with L2 regularization, TKD, rescaled TKD in superfast dipole inversion, the modulated closed-form method, and iterative dipole inversion, as well as sparsity-regularized dipole inversion. It was observed that the proposed approach showed a substantial reduction in the streaking artifacts across 94 test volumes considered in this study. The proposed approach also showed better error reduction and edge preservation compared with other approaches. The proposed model resolution-based deconvolution compensates for the truncation of zero coefficients in the dipole kernel at the magic angle and hence provides a closer approximation to the true susceptibility map compared with other direct methods.

Increased plasma lipocalin-2 levels are associated with nonmotor symptoms and neuroimaging features in patients with Parkinson's disease

Lipocalin-2 (LCN2) is essential for the regulation of neuroinflammation and cellular uptake of iron. This study aimed to evaluate plasma LCN2 levels and explore their correlation with clinical and neuroimaging features in Parkinson's disease (PD) patients. Enzyme-linked immunosorbent assay (ELISA) was used to measure plasma LCN2 levels in 120 subjects. Evaluation of motor symptoms and nonmotor symptoms in PD patients was assessed by the associated scales. Voxel-based morphometry (VBM) was used to evaluate brain volume alterations, and quantitative susceptibility mapping (QSM) was used to quantitatively analyze brain iron deposition in 46 PD patients. Plasma LCN2 levels were significantly higher in PD patients than those in healthy controls. LCN2 levels were negatively correlated with Montreal Cognitive Assessment (MoCA) scores, total brain gray matter volume (GMV), and GMV/total intracranial volume (TIV) ratio, but positively correlated with Hamilton Anxiety Rating Scale (HAMD) scores and mean QSM values of the bilateral substantial nigra (SN). Receiver operating characteristic (ROC) curves confirmed that plasma LCN2 levels had good predictive accuracy for PD. The results suggest that plasma LCN2 levels have potential as a biomarker for the diagnosis of PD. LCN2 may be a therapeutic target for neuroinflammation and brain iron deposition.

Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease

In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.

Iron content affects age group differences in associative learning-related fMRI activity

Brain regions accumulate different amounts of iron with age, with older adults having higher iron in the basal ganglia (globus pallidus, putamen, caudate) relative to the hippocampus. This has important implications for functional magnetic resonance imaging (fMRI) studies in aging as the presence of iron may influence both neuronal functioning as well as the measured fMRI (BOLD) signal, and these effects will vary across age groups and brain regions. To test this hypothesis, the current study examined the effect of iron on age group differences in task-related activity within each basal nuclei and the hippocampus. Twenty-eight younger and 22 older adults completed an associative learning task during fMRI acquisition. Iron content (QSM, R2*) was estimated from a multi-echo gradient echo sequence. As previously reported, older adults learned significantly less than younger adults and age group differences in iron content were largest in the basal ganglia (putamen, caudate). In the hippocampus (early task stage) and globus pallidus (late task stage), older adults had significantly higher learning-related activity than younger adults both before and after controlling for iron. In the putamen (late task stage), however, younger adults had significantly higher learning-related activity than older adults that was only seen after controlling for iron. These findings support the notion that age-related differences in iron influence both neuronal functioning and the measured fMRI signal in select basal nuclei. Moreover, previous fMRI studies in aging populations may have under-reported age group differences in task-related activity by not accounting for iron within these regions.

A Causality-Driven Graph Convolutional Network for Postural Abnormality Diagnosis in Parkinsonians

Abnormal posture is a common movement disorder in the progress of Parkinson's disease (PD), and this abnormality can increase the risk of falls or even disabilities. The conventional assessment approach depends on the judgment of well-trained experts via canonical scales. However, this approach requires extensive clinical expertise and is highly subjective. Considering the potential of quantitative susceptibility mapping (QSM) in PD diagnosis, this study explored the QSM-based method for the automated classification between PD patients with and without postural abnormalities. Nevertheless, a major challenge is that unstable non-causal features typically lead to less reliable performance. Therefore, we propose a causality-driven graph-convolutional-network framework based on multi-instance learning, where performance stability is enhanced through the invariant prediction principle and causal interventions. Specifically, we adopt an intervention strategy that combines a non-causal intervenor with causal prediction. A stability constraint is proposed to ensure robust integrated prediction under different interventions. Moreover, an intra-class homogeneity constraint is enforced for each individually-learned causality scoring module to promote the extraction of group-level general features, and hence achieve a balance between subject-specific and group-level features. The proposed method demonstrated promising performance through extensive experiments on a real clinical dataset. Also, the features extracted by our method coincide with those reported in previous medical studies on PD posture abnormalities. In general, our work provides a clinically-valuable approach for automated, objective, and reliable diagnosis of postural abnormalities in Parkinsonians. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CausalGCN-PDPA.

Sub-voxel quantitative susceptibility mapping for assessing whole-brain magnetic susceptibility from ages 4 to 80

The evolution of magnetic susceptibility of the brain is mainly determined by myelin in white matter (WM) and iron deposition in deep gray matter (DGM). However, existing imaging techniques have limited abilities to simultaneously quantify the myelination and iron deposition within a voxel throughout brain development and aging. For instance, the temporal trajectories of iron in the brain WM and myelination in DGM have not been investigated during the aging process. This study aimed to map the age-related iron and myelin changes in the whole brain, encompassing myelin in DGM and iron deposition in WM, using a novel sub-voxel quantitative susceptibility mapping (QSM) method. To achieve this, a cohort of 494 healthy adults (18-80 years old) was studied. The sub-voxel QSM method was employed to obtain the paramagnetic and diamagnetic susceptibility based on the approximatedR 2 ' map from acquiredR 2 * map. The linear relationship betweenR 2 * andR 2 ' maps was established from the regression coefficients on a small cohort data acquired with both 3D gradient recalled echo data andR 2 mapping. Large cohort sub-voxel susceptibility maps were used to create longitudinal and age-specific atlases via group-wise registration. To explore the differential developmental trajectories in the DGM and WM, we employed nonlinear models including exponential and Poisson functions, along with generalized additive models. The constructed atlases reveal the iron accumulation in the posterior part of the putamen and the gradual myelination process in the globus pallidus with aging. Interestingly, the developmental trajectories show that the rate of myelination differs among various DGM regions. Furthermore, the process of myelin synthesis is paralleled by an associated pattern of iron accumulation in the primary WM fiber bundles. In summary, our study offers significant insights into the distinctive developmental trajectories of iron in the brain's WM and myelination/demyelination in the DGM in vivo. These findings highlight the potential of using sub-voxel QSM to uncover new perspectives in neuroscience and improve our understanding of whole-brain myelination and iron deposit processes across the lifespan.

Susceptibility-based imaging aids accurate distinction of pediatric-onset MS from myelin oligodendrocyte glycoprotein antibody-associated disease

BACKGROUND

Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) and pediatric-onset multiple sclerosis (POMS) share clinical and magnetic resonance imaging (MRI) features but differ in prognosis and management. Early POMS diagnosis is essential to avoid disability accumulation. Central vein sign (CVS), paramagnetic rim lesions (PRLs), and central core lesions (CCLs) are susceptibility-based imaging (SbI)-related signs understudied in pediatric populations that may help discerning POMS from MOGAD.

METHODS

T2-FLAIR and SbI (three-dimensional echoplanar imaging (3D-EPI)/susceptibility-weighted imaging (SWI) or similar) were acquired on 1.5T/3T scanners. Two readers assessed CVS-positive rate (%CVS+), and their average score was used to build a receiver operator curve (ROC) assessing the ability to discriminate disease type. PRLs and CCLs were identified using a consensual approach.

RESULTS

The %CVS+ distinguished 26 POMS cases (mean age 13.7 years, 63% females, median EDSS 1.5) from 14 MOGAD cases (10.8 years, 35% females, EDSS 1.0) with ROC = 1, p < 0.0001, (cutoff 41%). PRLs were only detectable in POMS participants (mean 2.1±2.3, range 1-10), discriminating the two conditions with a sensitivity of 69% and a specificity of 100%. CCLs were more sensitive (81%) but less specific (71.43%).

CONCLUSION

The %CVS+ and PRLs are highly specific markers of POMS. After proper validation on larger multicenter cohorts, consideration should be given to including such imaging markers for diagnosing POMS at disease onset.

Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas

Background

Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC).

Methods

This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance.

Results

This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively.

Conclusions

QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.

Thin slab quantitative susceptibility mapping

PURPOSE

Susceptibility maps reconstructed from thin slabs may suffer underestimation due to background-field removal imperfections near slab boundaries and the increased difficulty of solving a 3D-inversion problem with reduced support, particularly in the direction of the main magnetic field. Reliable QSM reconstruction from thin slabs would enable focal acquisitions in a much-reduced scan time.

METHODS

This work proposes using additional rapid low-resolution data of extended spatial coverage to improve background-field estimation and regularize the inversion-to-susceptibility process for high resolution, thin slab data. The new method was tested using simulated and in-vivo brain data of high resolution (0.33 × 0.33 × 0.33 mm3 and 0.54 × 0.54 × 0.65 mm3 , respectively) at 3T, and compared to the standard large volume approach.

RESULTS

Using the proposed method, in-vivo high-resolution QSM at 3T was obtained from slabs of width as small as 10.4 mm, aided by a lower-resolution dataset of 24 times coarser voxels. Simulations showed that the proposed method produced more consistent measurements from slabs of at least eight slices. Reducing the mean ROI error to 5% required the low-resolution data to cover ˜60 mm in the direction of the main field, have at least 2-mm isotropic resolution that is not coarser than the high-resolution data by more than four-fold in any direction.

CONCLUSION

Applying the proposed method enabled focal QSM acquisitions at sub-millimeter resolution within reasonable acquisition time.

Aberrant dentato-rubro-thalamic pathway in action tremor but not rest tremor: A multi-modality magnetic resonance imaging study

AIMS

The purpose of this study was to clarify the dentato-rubro-thalamic (DRT) pathway in action tremor in comparison to normal controls (NC) and disease controls (i.e., rest tremor) by using multi-modality magnetic resonance imaging (MRI).

METHODS

This study included 40 essential tremor (ET) patients, 57 Parkinson's disease (PD) patients (29 with rest tremor, 28 without rest tremor), and 41 NC. We used multi-modality MRI to comprehensively assess major nuclei and fiber tracts of the DRT pathway, which included decussating DRT tract (d-DRTT) and non-decussating DRT tract (nd-DRTT), and compared the differences in DRT pathway components between action and rest tremor.

RESULTS

Bilateral dentate nucleus (DN) in the ET group had excessive iron deposition compared with the NC group. Compared with the NC group, significantly decreased mean diffusivity and radial diffusivity were observed in the left nd-DRTT in the ET group, which were negatively correlated with tremor severity. No significant difference in each component of the DRT pathway was observed between the PD subgroup or the PD and NC.

CONCLUSION

Aberrant changes in the DRT pathway may be specific to action tremor and were indicating that action tremor may be related to pathological overactivation of the DRT pathway.

Brainstem anatomy with 7-T MRI: in vivo assessment and ex vivo comparison

BACKGROUND

The brainstem contains grey matter nuclei and white matter tracts to be identified in clinical practice. The small size and the low contrast among them make their in vivo visualisation challenging using conventional magnetic resonance imaging (MRI) sequences at high magnetic field strengths. Combining higher spatial resolution, signal- and contrast-to-noise ratio and sensitivity to magnetic susceptibility (χ), susceptibility-weighted 7-T imaging could improve the assessment of brainstem anatomy.

METHODS

We acquired high-resolution 7-T MRI of the brainstem in a 46-year-old female healthy volunteer (using a three-dimensional multi-echo gradient-recalled-echo sequence; spatial resolution 0.3 × 0.3 × 1.2 mm3) and in a brainstem sample from a 48-year-old female body donor that was sectioned and stained. Images were visually assessed; nuclei and tracts were labelled and named according to the official nomenclature.

RESULTS

This in vivo imaging revealed structures usually evaluated through light microscopy, such as the accessory olivary nuclei, oculomotor nucleus and the medial longitudinal fasciculus. Some fibre tracts, such as the medial lemniscus, were visible for most of their course. Overall, in in vivo acquisitions, χ and frequency maps performed better than T2*-weighted imaging and allowed for the evaluation of a greater number of anatomical structures. All the structures identified in vivo were confirmed by the ex vivo imaging and histology.

CONCLUSIONS

The use of multi-echo GRE sequences at 7 T allowed the visualisation of brainstem structures that are not visible in detail at conventional magnetic field and opens new perspectives in the diagnostic and therapeutical approach to brain disorders.

RELEVANCE STATEMENT

In vivo MR imaging at UHF provides detailed anatomy of CNS substructures comparable to that obtained with histology. Anatomical details are fundamentals for diagnostic purposes but also to plan a direct targeting for a minimally invasive brain stimulation or ablation.

KEY POINTS

• The in vivo brainstem anatomy was explored with ultrahigh field MRI (7 T). • In vivo T2*-weighted magnitude, χ, and frequency images revealed many brainstem structures. • Ex vivo imaging and histology confirmed all the structures identified in vivo. • χ and frequency imaging revealed more brainstem structures than magnitude imaging.

Quantitative susceptibility mapping of brain iron in healthy aging and cognition

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can assess the magnetic properties of cerebral iron in vivo. Although brain iron is necessary for basic neurobiological functions, excess iron content disrupts homeostasis, leads to oxidative stress, and ultimately contributes to neurodegenerative disease. However, some degree of elevated brain iron is present even among healthy older adults. To better understand the topographical pattern of iron accumulation and its relation to cognitive aging, we conducted an integrative review of 47 QSM studies of healthy aging, with a focus on five distinct themes. The first two themes focused on age-related increases in iron accumulation in deep gray matter nuclei versus the cortex. The overall level of iron is higher in deep gray matter nuclei than in cortical regions. Deep gray matter nuclei vary with regard to age-related effects, which are most prominent in the putamen, and age-related deposition of iron is also observed in frontal, temporal, and parietal cortical regions during healthy aging. The third theme focused on the behavioral relevance of iron content and indicated that higher iron in both deep gray matter and cortical regions was related to decline in fluid (speed-dependent) cognition. A handful of multimodal studies, reviewed in the fourth theme, suggest that iron interacts with imaging measures of brain function, white matter degradation, and the accumulation of neuropathologies. The final theme concerning modifiers of brain iron pointed to potential roles of cardiovascular, dietary, and genetic factors. Although QSM is a relatively recent tool for assessing cerebral iron accumulation, it has significant promise for contributing new insights into healthy neurocognitive aging.

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

INTRODUCTION

Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial hemorrhages, is challenging due to the relatively large scale of the field induced by these pathological susceptibility sources.

METHOD

This study proposes a new deep learning-based method, BFRnet, to remove the background field in healthy and hemorrhagic subjects. The network is built with the dual-frequency octave convolutions on the U-net architecture, trained with synthetic field maps containing significant susceptibility sources. The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects. Robustness against acquisition field-of-view (FOV) orientation and brain masking are also investigated.

RESULTS

For both simulation and in vivo experiments, BFRnet led to the best visually appealing results in the local field and QSM results with the minimum contrast loss and the most accurate hemorrhage susceptibility measurements among all five methods. In addition, BFRnet produced the most consistent local field and susceptibility maps between different sizes of brain masks, while conventional methods depend drastically on precise brain extraction and further brain edge erosions. It is also observed that BFRnet performed the best among all BFR methods for acquisition FOVs oblique to the main magnetic field.

CONCLUSION

The proposed BFRnet improved the accuracy of local field reconstruction in the hemorrhagic subjects compared with conventional BFR algorithms. The BFRnet method was effective for acquisitions of tilted orientations and retained whole brains without edge erosion as often required by traditional BFR methods.

Cortical Iron Accumulation as an Imaging Marker for Neurodegeneration in Clinical Cognitive Impairment Spectrum: A Quantitative Susceptibility Mapping Study

OBJECTIVE

Cortical iron deposition has recently been shown to occur in Alzheimer's disease (AD). In this study, we aimed to evaluate how cortical gray matter iron, measured using quantitative susceptibility mapping (QSM), differs in the clinical cognitive impairment spectrum.

MATERIALS AND METHODS

This retrospective study evaluated 73 participants (mean age ± standard deviation, 66.7 ± 7.6 years; 52 females and 21 males) with normal cognition (NC), 158 patients with mild cognitive impairment (MCI), and 48 patients with AD dementia. The participants underwent brain magnetic resonance imaging using a three-dimensional multi-dynamic multi-echo sequence on a 3-T scanner. We employed a deep neural network (QSMnet+) and used automatic segmentation software based on FreeSurfer v6.0 to extract anatomical labels and volumes of interest in the cortex. We used analysis of covariance to investigate the differences in susceptibility among the clinical diagnostic groups in each brain region. Multivariable linear regression analysis was performed to study the association between susceptibility values and cognitive scores including the Mini-Mental State Examination (MMSE).

RESULTS

Among the three groups, the frontal (P < 0.001), temporal (P = 0.004), parietal (P = 0.001), occipital (P < 0.001), and cingulate cortices (P < 0.001) showed a higher mean susceptibility in patients with MCI and AD than in NC subjects. In the combined MCI and AD group, the mean susceptibility in the cingulate cortex (β = -216.21, P = 0.019) and insular cortex (β = -276.65, P = 0.001) were significant independent predictors of MMSE scores after correcting for age, sex, education, regional volume, and APOE4 carrier status.

CONCLUSION

Iron deposition in the cortex, as measured by QSMnet+, was higher in patients with AD and MCI than in NC participants. Iron deposition in the cingulate and insular cortices may be an early imaging marker of cognitive impairment related neurodegeneration.