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

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

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.

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.

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.

Repurposing of the PET Probe Prototype PiB for Label and Radiation-Free CEST MRI Molecular Imaging of Amyloid

Positron emission tomography (PET) probes are specific and sensitive while suffering from radiation risk. It is worthwhile to explore the chemical emission saturation transfer (CEST) effects of the probe prototypes and repurpose them for CEST imaging to avoid radiation. In this study, we used 11C-PiB as an example of a PET probe for detecting amyloid and tested the feasibility of repurposing this PET probe prototype, PiB, for CEST imaging. After optimizing the parameters through preliminary phantom experiments, we used APP/PS1 transgenic mice and age-matched C57 mice for in vivo CEST magnetic resonance imaging (MRI) of amyloid. Furthermore, the pathological assessment was conducted on the same brain slices to evaluate the correlation between the CEST MRI signal abnormality and β-amyloid deposition detected by immunohistochemical staining. In our results, the Z-spectra revealed an apparent CEST effect that peaked at approximately 6 ppm. APP/PS1 mice as young as 9 months injected with PiB showed a significantly higher CEST effect compared to the control groups. The hyperintense region was correlated with the Aβ deposition shown by pathological staining. In conclusion, repurposing the PET probe prototype for CEST MRI imaging is feasible and enables label- and radiation-free detection of the amyloid while maintaining the sensitivity and specificity of the ligand. This study opens the door to developing CEST probes based on PET probe prototypes.

Quantitative Susceptibility Mapping Changes Relate to Gait Issues in Parkinson's Disease

BACKGROUND

Quantitative susceptibility mapping (QSM) demonstrates elevated iron content in Parkinson's disease (PD) patients within the basal ganglia, though it has infrequently been studied in relation to gait difficulties including freezing of gait (FOG). Our purpose was to relate QSM of basal ganglia and extra-basal ganglia structures with qualitative and quantitative gait measures in PD.

METHODS

This case-control study included PD and cognitively unimpaired (CU) participants from the Comprehensive Assessment of Neurodegeneration and Dementia study. Whole brain QSM was acquired at 3T. Region of interests (ROIs) were drawn blinded manually in the caudate nucleus, putamen, globus pallidus, pulvinar nucleus of the thalamus, red nucleus, substantia nigra, and dentate nucleus. Susceptibilities of ROIs were compared between PD and CU. Items from the FOG questionnaire and quantitative gait measures from PD participants were compared to susceptibilities.

RESULTS

Twenty-nine participants with PD and 27 CU participants were included. There was no difference in susceptibility values in any ROI when comparing CU versus PD (p > 0.05 for all). PD participants with gait impairment (n = 23) had significantly higher susceptibility in the putamen (p = 0.008), red nucleus (p = 0.01), and caudate nucleus (p = 0.03) compared to those without gait impairment (n = 6). PD participants with FOG (n = 12) had significantly higher susceptibility in the globus pallidus (p = 0.03) compared to those without FOG (n = 17). Among quantitative gait measures, only stride time variability was significantly different between those with and without FOG (p = 0.04).

CONCLUSION

Susceptibilities in basal ganglia and extra-basal ganglia structures are related to qualitative measures of gait impairment and FOG in PD.

Effects of MRI scanner manufacturers in classification tasks with deep learning models

Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language processing to medical image analysis. In medical imaging, researchers have progressively turned towards multi-center neuroimaging studies to address complex questions in neuroscience, leveraging larger sample sizes and aiming to enhance the accuracy of deep learning models. However, variations in image pixel/voxel characteristics can arise between centers due to factors including differences in magnetic resonance imaging scanners. Such variations create challenges, particularly inconsistent performance in machine learning-based approaches, often referred to as domain shift, where the trained models fail to achieve satisfactory or improved results when confronted with dissimilar test data. This study analyzes the performance of multiple disease classification tasks using multi-center MRI data obtained from three widely used scanner manufacturers (GE, Philips, and Siemens) across several deep learning-based networks. Furthermore, we investigate the efficacy of mitigating scanner vendor effects using ComBat-based harmonization techniques when applied to multi-center datasets of 3D structural MR images. Our experimental results reveal a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Moreover, despite applying ComBat-based harmonization, the harmonized images do not demonstrate any noticeable performance enhancement for disease classification tasks.

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.

SF2Former: Amyotrophic lateral sclerosis identification from multi-center MRI data using spatial and frequency fusion transformer

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called SF2Former, which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques.

Signal-to-noise ratio penalties from a loss of stimulated echoes when using slab-selective excitation in three-dimensional fast spin echo imaging with long echo trains

Three-dimensional fast spin echo imaging with long echo trains combines high resolution with reasonable acquisition times and reduced specific absorption rate due to low refocusing flip angles. Typically, an entire volume is encoded (nonselective excitation) or localization can be performed with slab select excitation, which uses a long 90° pulse for precise localization, followed by a preliminary nonselective 180° pulse bounded by spoiler gradients to destroy signal outside of the volume of interest. Subsequent flip angles in the train are nonselective and identical between the two methods. The inclusion of the initial selective pulse and spoiler gradients results in a signal-to-noise ratio (SNR) penalty for slab selection, beyond the slice-averaging dependence, arising from a loss of stimulated echoes. SNR differences are explored using Bloch equation simulations of a T2-weighted 96 echo train sequence with varying parameters including T2, T1, and B1⁺ and compared with phantom and in vivo brain, neck, and knee experiments. In vivo SNR measurements in the three regions showed a maximum decrease of selective SNR by 29% (gastrocnemius muscle), 25% (pons), and 22% (globus pallidus), despite similar experimental parameters to nonselective experiments. Decreased SNR was compounded by B1⁺ variation affecting prescribed flip angles with further smaller reductions with T2 and T1 times. In conclusion, the elimination of coherences via the preliminary nominal 180° pulse and spoiler gradients in addition to the extended echo timing from the long excitation pulse resulted in a reduction in SNR compared with the nonselective case. Consideration of the required SNR and chosen anatomy as well as sequence restrictions should be weighed before choosing slab-selective excitation.

Longitudinal hippocampal diffusion-weighted imaging and T2 relaxometry demonstrate regional abnormalities which are stable and predict subfield pathology in temporal lobe epilepsy

OBJECTIVE

High-resolution (1 mm isotropic) diffusion tensor imaging (DTI) of the hippocampus in temporal lobe epilepsy (TLE) patients has shown patterns of hippocampal subfield diffusion abnormalities, which were consistent with hippocampal sclerosis (HS) subtype on surgical histology. The objectives of this longitudinal imaging study were to determine the stability of focal hippocampus diffusion changes over time in TLE patients, compare diffusion and quantitative T2 abnormalities of the sclerotic hippocampus, and correlate presurgical mean diffusivity (MD) and T2 maps with postsurgical histology.

METHODS

Nineteen TLE patients and 19 controls underwent two high-resolution (1 mm isotropic) DTI and 1.1 × 1.1 × 1 mm³  T2 relaxometry scans (in a subset of 16 TLE patients and 9 controls) of the hippocampus at 3T, with a 2.6 ± 0.8 year inter-scan interval. Within-participant hippocampal volume, MD and T2 were compared between the scans. Contralateral hippocampal changes 2.3 ± 1.0 years after surgery and ipsilateral preoperative MD maps versus postoperative subfield histopathology were evaluated in eight patients who underwent surgical resection of the hippocampus.

RESULTS

Reduced volume and elevated MD and T2 of sclerotic hippocampi remained unchanged between longitudinal scans. Focal regions of elevated MD and T2 in bilateral hippocampi of HS TLE were detected consistently at both scans. Regions of high MD and T2 correlated and remained consistent over time. Volume, MD, and T2 remained unchanged in postoperative contralateral hippocampus. Regional elevations of MD identified subfield neuron loss on postsurgical histology with 88% sensitivity and 88% specificity. Focal T2 elevations identified subfield neuron loss with 75% sensitivity and 88% specificity.

SIGNIFICANCE

Diffusion and T2 abnormalities in ipsilateral and contralateral hippocampi remained unchanged between the scans suggesting permanent microstructural alterations. MD and T2 demonstrated good sensitivity and specificity to detect hippocampal subfield neuron loss on postsurgical histology, supporting the potential that high-resolution hippocampal DTI and T2 could be used to diagnose HS subtype before surgery.

Inline dual-echo T2 quantification in brain using a fast mapping reconstruction technique

T2 mapping from 2D proton density and T2-weighted images (PD-T2) using Bloch equation simulations can be time consuming and introduces a latency between image acquisition and T2 map production. A fast T2 mapping reconstruction method is investigated and compared with a previous modeling approach to reduce computation time and allow inline T2 maps on the MRI console. Brain PD-T2 images from five multiple sclerosis patients were used to compare T2 map reconstruction times between the new subtraction method and the Euclidean norm minimization technique. Bloch equation simulations were used to create the lookup table for decay curve matching in both cases. Agreement of the two techniques used Bland-Altman analysis for investigating individual subsets of data and all image points in the five volumes (meta-analysis). The subtraction method resulted in an average reduction of computation time for single slices from 134 s (minimization method) to 0.44 s. Comparing T2 values between the subtraction and minimization methods resulted in a confidence interval ranging from -0.06 to 0.06 ms (95% of values were within ± 0.06 ms between the techniques). Using identical reconstruction code based on the subtraction method, inline T2 maps were produced from PD-T2 images directly on the scanner console. The excellent agreement between the two methods permits the subtraction technique to be interchanged with the previous method, reducing computation time and allowing inline T2 map reconstruction based on Bloch simulations directly on the scanner.

Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning

BACKGROUND AND OBJECTIVES

Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography.

METHODS

In a retrospective study, magnetic resonance images with radiological reports of intracranial arterial stenosis and occlusion were extracted. The images were randomly divided into a training set and a test set. The manual annotation of lesions with a bounding box labeled "moderate stenosis," "severe stenosis," "occlusion," and "absence of signal" was considered as ground truth. A deep learning algorithm based on you only look once version 5 (YOLOv5) detection model was developed with the training set, and its sensitivity and positive predictive values to detect lesions were evaluated in the test set.

RESULTS

A dataset of 200 examinations consisted of a total of 411 lesions-242 moderate stenoses, 84 severe stenoses, 70 occlusions, and 15 absence of signal. The magnetic resonance images contained 291 lesions in the training set and 120 lesions in the test set. The sensitivity and positive predictive values were 64.2 and 83.7%, respectively. The detection sensitivity in relation to the location was greatest in the internal carotid artery (86.2%).

CONCLUSIONS

Applying deep learning algorithms in the automated detection of intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography is feasible and has great potential.

Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R₂ *) mapping from multiecho T₂ *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R₂ * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R₂ * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R₂ * were highly correlated (R²  ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R₂ *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s^-1 for R₂ *. For within-site measurements of QSM and R₂ *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s^-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Myelin water fraction mapping from multiple echo spin echoes and an independent B1 + map

Purpose

Myelin water fraction (MWF) is often obtained from a multiple echo spin echo (MESE) sequence using multi‐component T₂ fitting with non‐negative least squares. This process fits many unknowns including B₁⁺ to produce a T₂ spectrum for each voxel. Presented is an alternative using a rapid B₁⁺ mapping sequence to supply B₁⁺ for the MWF fitting procedure.

Methods

Effects of B₁⁺ errors on MWF calculations were modeled for 2D and 3D MESE using Bloch and extended phase graph simulations, respectively. Variations in SNR and relative refocusing widths were tested. Human brain experiments at 3 T used 2D MESE and an independent B₁⁺ map. MWF maps were produced with the standard approach and with the use of the independent B₁⁺ map. Differences in B₁⁺ and mean MWF in specific brain regions were compared.

Results

For 2D MESE, MWF with the standard method was strongly affected by B₁⁺ misestimations arising from limited SNR and response asymmetry around 180°, which decreased with increasing relative refocusing width. Using an independent B₁⁺ map increased mean MWF and decreased coefficient of variation. Notable differences in vivo in 2D MESE were in areas of high B₁⁺ such as thalamus and splenium where mean MWF increased by 88% and 31%, respectively (P < 0.001). Simulations also demonstrated the advantages of this approach for 3D MESE when SNR is <500.

Conclusion

For 2D MESE, because of increased complexity of decay curves and limited SNR, supplying B₁⁺ improves MWF results in peripheral and central brain regions where flip angles differ substantially from 180°.

T2 quantification in brain using 3D fast spin-echo imaging with long echo trains

Purpose

Three‐dimensional fast spin‐echo (FSE) sequences commonly use very long echo trains (>64 echoes) and severely reduced refocusing angles. They are increasingly used in brain exams due to high, isotropic resolution and reasonable scan time when using long trains and short interecho spacing. In this study, T₂ quantification in 3D FSE is investigated to achieve increased resolution when comparing with established 2D (proton‐density dual‐echo and multi‐echo spin‐echo) methods.

Methods

The FSE sequence design was explored to use long echo trains while minimizing T₂ fitting error and maintaining typical proton density and T₂‐weighted contrasts. Constant and variable flip angle trains were investigated using extended phase graph and Bloch equation simulations. Optimized parameters were analyzed in phantom experiments and validated in vivo in comparison to 2D methods for eight regions of interest in brain, including deep gray‐matter structures and white‐matter tracts.

Results

Phantom and healthy in vivo brain T₂ measurements showed that optimized variable echo‐train 3D FSE performs similarly to previous 2D methods, while achieving three‐fold‐higher slice resolution, evident visually in the 3D T₂ maps. Optimization resulted in better T₂ fitting and compared well with standard multi‐echo spin echo (within the 8‐ms confidence limits defined based on Bland‐Altman analysis).

Conclusion

T₂ mapping using 3D FSE with long echo trains and variable refocusing angles provides T₂ accuracy in agreement with 2D methods with additional high‐resolution benefits, allowing isotropic views while avoiding incidental magnetization transfer effects. Consequently, optimized 3D sequences should be considered when choosing T₂ mapping methods for high anatomic detail.

Characterization of B1 + field variation in brain at 3 T using 385 healthy individuals across the lifespan

PURPOSE

The transmit field B 1 + at 3 T in brain affects the spatial uniformity and contrast of most image acquisitions. Here, B 1 + spatial variation in brain at 3 T is characterized in a large healthy population.

METHODS

Bloch-Siegert B 1 + maps were acquired at 3 T from 385 healthy subjects aged 5-90 years on a single MRI system. After transforming all B 1 + maps to a standard brain atlas space, region-of-interest analysis was performed, and intersubject voxel-wise coefficient of variation was calculated across the whole brain. The B 1 + variability due to age and brain size was studied separately in males and females, along with B 1 + variability due to nonideal transmit calibration.

RESULTS

The voxel-based mean coefficient of variation was 4.0% across all subjects, and the difference in B 1 + between central (left thalamus) and outer regions (left frontal gray matter) was 24.2% ± 2.3%. The least intersubject variability occurred in central regions, whereas regions toward brain edges increased markedly in variation. The B 1 + variability with age was mostly attributed to lifespan changes in CSF volume (which alters brain conductivity) and head orientation. Larger brain size correlated with more B 1 + inhomogeneity (p < .001). Varying head position and anatomy resulted in an inaccurate transmit calibration.

CONCLUSION

In standard atlas space, intersubject B 1 + variability at 3 T was relatively small in a large population aged 5-90 years. The B 1 + varied with age-related changes of CSF volume and head orientation, as well as differences in brain size and transmit calibration.

R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years

Putative MRI markers of iron in deep gray matter have demonstrated age related changes during discrete periods of healthy childhood or adulthood, but few studies have included subjects across the lifespan. This study reports both transverse relaxation rate (R2*) and quantitative susceptibility mapping (QSM) of four primary deep gray matter regions (thalamus, putamen, caudate, and globus pallidus) in 498 healthy individuals aged 5–90 years. In the caudate, putamen, and globus pallidus, increases of QSM and R2* were steepest during childhood continuing gradually throughout adulthood, except caudate susceptibility which reached a plateau in the late 30s. The thalamus had a unique profile with steeper changes of R2* (reflecting additive effects of myelin and iron) than QSM during childhood, both reaching a plateau in the mid‐30s to early 40s and decreasing thereafter. There were no hemispheric or sex differences for any region. Notably, both R2* and QSM values showed more inter‐subject variability with increasing age from 5 to 90 years, potentially reflecting a common starting point in iron/myelination during childhood that diverges as a result of lifestyle and genetic factors that accumulate with age.

Prevalence of High-risk Plaques and Risk of Stroke in Patients With Asymptomatic Carotid Stenosis: A Meta-analysis

Importance

There is an ongoing debate regarding the management of asymptomatic carotid stenosis. Previous studies have reported imaging features of high-risk plaques that could help to optimize the risk-benefit ratio of revascularization. However, such studies have not provided an accurate estimate of the prevalence of high-risk plaques and the associated annual incidence of ipsilateral ischemic cerebrovascular events to inform the design of clinical trials using a risk-oriented selection of patients before randomization.

Objective

To assess the relevance and feasibility of risk-oriented selection of patients for revascularization.

Data Sources

A systematic search of PubMed and Ovid Embase from database inception to July 31, 2019, was performed.

Study Selection

Prospective observational studies that reported prevalence of high-risk plaques and incidence of ipsilateral ischemic cerebrovascular events were included.

Data Extraction and Synthesis

Aggregated data were pooled using random-effects meta-analysis. Data were analyzed from December 16, 2019, to January 15, 2020.

Main Outcomes and Measures

Prevalence of high-risk plaques and annual incidence of ipsilateral ischemic events.

Results

Overall, 64 studies enrolling 20 751 participants aged 29 to 95 years (mean age range, 55.0-76.5 years; proportion of men, 45%-87%) were included in the meta-analysis. Among all participants, the pooled prevalence of high-risk plaques was 26.5% (95% CI, 22.9%-30.3%). The most prevalent high-risk plaque features were neovascularization (43.4%; 95% CI, 31.4%-55.8%) in 785 participants, echolucency (42.3%; 95% CI, 32.2%-52.8%) in 12 364 participants, and lipid-rich necrotic core (36.3%; 95% CI, 27.7%-45.2%) in 3728 participants. The overall incidence of ipsilateral ischemic cerebrovascular events was 3.2 events per 100 person-years (22 cohorts with 10 381 participants; mean follow-up period, 2.8 years; range, 0.7-6.5 years). The incidence of ipsilateral ischemic cerebrovascular events was higher in patients with high-risk plaques (4.3 events per 100 person-years; 95% CI, 2.5-6.5 events per 100 person-years) than in those without high-risk plaques (1.2 events per 100 person-years; 95% CI, 0.6-1.8 events per 100 person-years), with an odds ratio of 3.0 (95% CI, 2.1-4.3; I2 = 48.8%). In studies focusing on severe stenosis (9 cohorts with 2128 participants; mean follow-up period, 2.8 years; range, 1.4-6.5 years), the incidence of ipsilateral ischemic cerebrovascular events was 3.7 events per 100 person-years (95% CI, 1.9-6.0 events per 100 person-years). The incidence of ipsilateral ischemic cerebrovascular events was also higher in patients with high-risk plaques (7.3 events per 100 person-years; 95% CI, 2.0-15.0 events per 100 person-years) than in those without high-risk plaques (1.7 events per 100 person-years; 95% CI, 0.6-3.3 events per 100 person-years), with an odds ratio of 3.2 (95% CI, 1.7-5.9; I2 = 39.6%).

Conclusions and Relevance

High-risk plaques are common in patients with asymptomatic carotid stenosis, and the associated risk of an ipsilateral ischemic cerebrovascular event is higher than the currently accepted estimates. Extension of routine assessment of asymptomatic carotid stenosis beyond the grade of stenosis may help improve risk stratification and optimize therapy.

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks

Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing noise regularization and modified octave convolutional layers into a U-net backbone and trained with synthetic and in vivo datasets, respectively. The xQSM method was compared with two recent deep learning (QSMnet⁺ and DeepQSM) and two conventional dipole inversion (MEDI and iLSQR) methods, using both digital simulations and in vivo experiments. Reconstruction error metrics, including peak signal-to-noise ratio, structural similarity, normalized root mean squared error and deep gray matter susceptibility measurements, were evaluated for comparison of the different methods. The results showed that the proposed xQSM network trained with in vivo datasets achieved the best reconstructions of all the deep learning methods. In particular, it led to, on average, 32.3%, 25.4% and 11.7% improvement in the accuracy of globus pallidus susceptibility estimation for digital simulations and 39.3%, 21.8% and 6.3% improvements for in vivo acquisitions compared with DeepQSM, QSMnet⁺ and iLSQR, respectively. It also exhibited the highest linearity against different susceptibility intensity scales and demonstrated the most robust generalization capability to various spatial resolutions of all the deep learning methods. In addition, the xQSM method also substantially shortened the reconstruction time from minutes using MEDI to only a few seconds.

Amide signal intensities may be reduced in the motor cortex and the corticospinal tract of ALS patients

OBJECTIVES

The aim of the study is to assess amide concentration changes in ALS patients compared with healthy controls by using quantitative amide proton transfer (APT) and multiparameter magnetic resonance imaging, and testing its correlation with clinical scores.

METHODS

Sixteen ALS patients and sixteen healthy controls were recruited as part of the Canadian ALS Neuroimaging Consortium, and multimodal magnetic resonance imaging was performed at 3 T, including APT and diffusion imaging. Lorentz fitting was used to quantify the amide effect. Clinical disability was evaluated using the revised ALS functional rating scale (ALSFRS-R), and its correlation with image characteristics was assessed. The diagnostic performance of different imaging parameters was evaluated with receiver operating characteristic analysis.

RESULTS

Our results showed that the amide peak was significantly different between the motor cortex and other gray matter territories within the brain of ALS patients (p < 0.001). Compared with controls, amide signal intensities in ALS were significantly reduced in the motor cortex (p < 0.001) and corticospinal tract (p = 0.046), while abnormalities were not detected using routine imaging methods. There was no significant correlation between amide and ALSFRS-R score. The diagnostic accuracy of the amide peak was superior to that of diffusion imaging.

CONCLUSIONS

This study demonstrated changes of amide signal intensities in the motor cortex and corticospinal tract of ALS patients.

KEY POINTS

• The neurodegenerative disease amyotrophic lateral sclerosis (ALS) has a lack of objective imaging indicators for diagnosis and assessment. • Analysis of amide proton transfer imaging revealed changes in the motor cortex and corticospinal tract of ALS patients that were not visible on standard magnetic resonance imaging. • The diagnostic accuracy of the amide peak was superior to that of diffusion imaging.

Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain

Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters-R2* and quantitative susceptibility (QS)-to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L ₁ (lasso), total variation (TV), and L ₂ group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.

Lower Blood Pressure Is Not Associated With Decreased Arterial Spin Labeling Estimates of Perfusion in Intracerebral Hemorrhage

Background

Subacute ischemic lesions in intracerebral hemorrhage (ICH) have been hypothesized to result from hypoperfusion. Although studies of cerebral blood flow (CBF) indicate modest hypoperfusion in ICH, these investigations have been limited to early time points. Arterial spin labeling (ASL), a magnetic resonance imaging technique, can be used to measure CBF without a contrast agent. We assessed CBF in patients with ICH using ASL and tested the hypothesis that CBF is related to systolic blood pressure (SBP).

Methods and Results

In this cross‐sectional study, patients with ICH were assessed with ASL at 48 hours, 7 days, and/or 30 days after onset. Relative CBF (rCBF; ratio of ipsilateral/contralateral perfusion) was measured in the perihematomal regions, hemispheres, border zones, and the perilesional area in patients with diffusion‐weighted imaging hyperintensities. Twenty‐patients (65% men; mean±SD age, 68.5±12.7 years) underwent imaging with ASL at 48 hours (N=12), day 7 (N=6), and day 30 (N=11). Median (interquartile range) hematoma volume was 13.1 (6.3–19.3) mL. Mean±SD baseline SBP was 185.4±25.5 mm Hg. Mean perihematomal rCBF was 0.9±0.2 at 48 hours at all time points. Baseline SBP and other SBP measurements were not associated with a decrease in rCBF in any of the regions of interest (P≥0.111). rCBF did not differ among time points in any of the regions of interest (P≥0.097). Mean perilesional rCBF was 1.04±0.65 and was unrelated to baseline SBP (P=0.105).

Conclusions

ASL can be used to measure rCBF in patients with acute and subacute ICH. Perihematomal CBF was not associated with SBP changes at any time point.

Clinical Trial Registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00963976.

Reducing MRI susceptibility artefacts in implants using additively manufactured porous Ti-6Al-4V structures

Magnetic Resonance Imaging (MRI) is critical in diagnosing post-operative complications following implant surgery and imaging anatomy adjacent to implants. Increasing field strengths and use of gradient-echo sequences have highlighted difficulties from susceptibility artefacts in scan data. Artefacts manifest around metal implants, including those made from titanium alloys, making detection of complications (e.g. bleeding, infection) difficult and hindering imaging of surrounding structures such as the brain or inner ear. Existing research focusses on post-processing and unorthodox scan sequences to better capture data around these devices. This study proposes a complementary up-stream design approach using lightweight structures produced via additive manufacturing (AM). Strategic implant mass reduction presents a potential tool in managing artefacts. Uniform specimens of Ti-6Al-4V structures, including lattices, were produced using the AM process, selective laser melting, with various unit cell designs and relative densities (3.1%-96.7%). Samples, submerged in water, were imaged in a 3T MRI system using clinically relevant sequences. Artefacts were quantified by image analysis revealing a strong linear relationship (RR² = 0.99) between severity and relative sample density. Likewise, distortion due to slice selection errors showed a squared relationship (RR² = 0.92) with sample density. Unique artefact features were identified surrounding honeycomb samples suggesting a complex relationship exists for larger unit cells. To demonstrate clinical utility, a honeycomb design was applied to a representative cranioplasty. Analysis revealed 10% artefact reduction compared to traditional solid material illustrating the feasibility of this approach. This study provides a basis to strategically design implants to reduce MRI artefacts and improve post-operative diagnosis capability. STATEMENT OF SIGNIFICANCE: MRI susceptibility artefacts surrounding metal implants present a clinical challenge for the diagnosis of post-operative complications relating to the implant itself or underlying anatomy. In this study for the first time we demonstrate that additive manufacturing may be exploited to create lattice structures that predictably reduce MRI image artefact severity surrounding titanium alloy implants. Specifically, a direct correlation of artefact severity, both total signal loss and distortion, with the relative material density of these functionalised materials has been demonstrated within clinically relevant MRI sequences. This approach opens the door for strategic implant design, utilising this structurally functionalised material, that may improve post-operative patient outcomes and compliments existing efforts in this area which focus on data acquisition and post-processing methods.

Curved multiplanar reformatting provides improved visualization of hippocampal anatomy

There is a growing body of literature studying changes in hippocampal subfields in a variety of different neurological conditions, but this work has mainly focused on the hippocampal body given challenges in visualization of hippocampal anatomy in the head and tail when sectioned in the typical coronal image plane. Curved multiplanar reformatting (CMPR) is an image reconstruction method that can improve visualization of complex three‐dimensional structures. The objective of this study was to determine whether CMPR could facilitate visualization of the human hippocampal anatomy along the entire caudal–rostral axis. CMPR was applied to high‐resolution magnetic resonance imaging acquired ex vivo on four cadaveric hippocampal specimens at 4.7 T (T2‐weighted, 0.2 × 0.2 × 0.5 mm³). CMPR provided clear visualization of the classic “interlocking C” appearance of the dentate gyrus and cornu ammonis along the entire caudal–rostral axis including the head and tail, which otherwise show complex anatomy on the standard coronal slices. CMPR facilitated visualization of hippocampal anatomy providing the impetus to develop simplified approaches to delineate subfields along the entire hippocampus including the usually neglected head and tail.

Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis

Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls.

Hematocrit Measurement with R2* and Quantitative Susceptibility Mapping in Postmortem Brain

BACKGROUND AND PURPOSE

Noninvasive venous oxygenation quantification with MR imaging will improve the neurophysiologic investigation and the understanding of the pathophysiology in neurologic diseases. Available MR imaging methods are limited by sensitivity to flow and often require assumptions of the hematocrit level. In situ postmortem imaging enables evaluation of methods in a fully deoxygenated environment without flow artifacts, allowing direct calculation of hematocrit. This study compares 2 venous oxygenation quantification methods in in situ postmortem subjects.

MATERIALS AND METHODS

Transverse relaxation (R2*) mapping and quantitative susceptibility mapping were performed on a whole-body 4.7T MR imaging system. Intravenous measurements in major draining intracranial veins were compared between the 2 methods in 3 postmortem subjects. The quantitative susceptibility mapping technique was also applied in 10 healthy control subjects and compared with reference venous oxygenation values.

RESULTS

In 2 early postmortem subjects, R2* mapping and quantitative susceptibility mapping measurements within intracranial veins had a significant and strong correlation (R² = 0.805, P = .004 and R² = 0.836, P = .02). Higher R2* and susceptibility values were consistently demonstrated within gravitationally dependent venous segments during the early postmortem period. Hematocrit ranged from 0.102 to 0.580 in postmortem subjects, with R2* and susceptibility as large as 291 seconds^-1 and 1.75 ppm, respectively.

CONCLUSIONS

Measurements of R2* and quantitative susceptibility mapping within large intracranial draining veins have a high correlation in early postmortem subjects. This study supports the use of quantitative susceptibility mapping for evaluation of in vivo venous oxygenation and postmortem hematocrit concentrations.

Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects

BACKGROUND

Combined R2* and quantitative susceptibility (QS) has been previously used in cross-sectional multiple sclerosis (MS) studies to distinguish deep gray matter (DGM) iron accumulation and demyelination.

PURPOSE

We propose and apply discriminative analysis of regional evolution (DARE) to define specific changes in MS and healthy DGM.

STUDY TYPE

Longitudinal (baseline and 2-year follow-up) retrospective study.

SUBJECTS

Twenty-seven relapsing-remitting MS (RRMS), 17 progressive MS (PMS), and corresponding age-matched healthy subjects.

FIELD STRENGTH/SEQUENCE

4.7T 10-echo gradient-echo acquisition.

ASSESSMENT

Automatically segmented caudate nucleus (CN), thalamus (TH), putamen (PU), globus pallidus, red nucleus (RN), substantia nigra, and dentate nucleus were retrospectively analyzed to quantify regional volumes, bulk mean R2*, and bulk mean QS. DARE utilized combined R2* and QS localized changes to compute spatial extent, mean intensity, and total changes of DGM iron and myelin/calcium over 2 years.

STATISTICAL TESTS

We used mixed factorial analysis for bulk analysis, nonparametric tests for DARE (α = 0.05), and multiple regression analysis using backward elimination of DGM structures (α = 0.05, P = 0.1) to regress bulk and DARE measures with the follow-up Multiple Sclerosis Severity Score (MSSS). False detection rate correction was applied to all tests.

RESULTS

Bulk analysis only detected significant (Q ≤ 0.05) interaction effects in RRMS CN QS (η = 0.45; Q = 0.004) and PU volume (η = 0.38; Q = 0.034). DARE demonstrated significant group differences in all RRMS structures, and in all PMS structures except the RN. The largest RRMS effect size was CN total R2* iron decrease (r = 0.74; Q = 0.00002), and TH mean QS myelin/calcium decrease for PMS (r = 0.70; Q = 0.002). DARE iron increase using total QS demonstrated the highest correlation with MSSS (r = 0.68; Q = 0.0005).

DATA CONCLUSION

DARE enabled discriminative assessment of specific DGM changes over 2 years, where iron and myelin/calcium changes were the primary drivers in RRMS and PMS compared to age-matched controls, respectively. Specific DARE measures of MS DGM correlated with follow-up MSSS, and may reflect complex disease pathology.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Significant Anatomy Detection Through Sparse Classification: A Comparative Study

We present a comparative study for discriminative anatomy detection in high dimensional neuroimaging data. While most studies solve this problem using mass univariate approaches, recent works show better accuracy and variable selection using a sparse classification model. Two types of image-based regularization methods have been proposed in the literature based on either a Graph Net (GN) model or a total variation (TV) model. These studies showed increased classification accuracy and interpretability of results when using image-based regularization, but did not look at the accuracy and quality of the recovered significant regions. In this paper, we theoretically prove bounds on the recovered sparse coefficients and the corresponding selected image regions in four models (two based on GN penalty and two based on TV penalty). Practically, we confirm the theoretical findings by measuring the accuracy of selected regions compared with ground truth on simulated data. We also evaluate the stability of recovered regions over cross-validation folds using real MRI data. Our findings show that the TV penalty is superior to the GN model. In addition, we showed that adding an l₂ penalty improves the accuracy of estimated coefficients and selected significant regions for the both types of models.

Structural and functional quantitative susceptibility mapping from standard fMRI studies

Standard functional MRI (fMRI), which includes resting-state or paradigm-driven designs, is widely used in studies of brain function, aging, and disease. These fMRI studies typically use two-dimensional gradient echo-planar imaging, which inherently contains phase data that enables quantitative susceptibility mapping (QSM). This work focuses on the dual value of QSM within fMRI studies, by providing both a localized analysis of functional changes in activated tissue, and iron-sensitive structural maps in deep grey matter (DGM). Using a visual paradigm fMRI study on healthy volunteers at clinical (1.5 T) and high field strength (4.7 T), we perform functional analysis of magnitude and QSM time series, and at the same time harness structural QSM of iron-rich DGM, including globus pallidus, putamen, caudate head, substantia nigra, and red nucleus. The effects of fMRI spatial resolution and time series variation on structural DGM QSM are investigated. Our results indicate that structural DGM QSM is feasible within existing fMRI studies, provided that the voxel dimensions are equal to or less than 3 mm, with higher resolutions preferred. The mean DGM QSM values were about 40 to 220 ppb, while the interquartile ranges of the DGM QSM time series varied from about 3 to 9 ppb, depending on structure and resolution. In contrast, the peak voxel functional QSM (fQSM) changes in activated visual cortex ranged from about -10 to -30 ppb, and functional clusters were consistently smaller on QSM than magnitude fMRI. Mean-level DGM QSM of the time series was successfully extracted in all cases, while fQSM results were more prone to residual background fields and showed less functional change compared with standard magnitude fMRI. Under the conditions prescribed, standard fMRI studies may be used for robust mean-level DGM QSM, enabling study of DGM iron accumulation, in addition to functional analysis. Copyright © 2016 John Wiley & Sons, Ltd.

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

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

LEVEL OF EVIDENCE

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

Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis

BACKGROUND AND PURPOSE

Deep gray matter iron accumulation is increasingly recognized in association with multiple sclerosis and can be measured in vivo with MR imaging. The cognitive implications of this pathology are not well-understood, especially vis-à-vis deep gray matter atrophy. Our aim was to investigate the relationships between cognition and deep gray matter iron in MS by using 2 MR imaging-based iron-susceptibility measures.

MATERIALS AND METHODS

Forty patients with multiple sclerosis (relapsing-remitting, n = 16; progressive, n = 24) and 27 healthy controls were imaged at 4.7T by using the transverse relaxation rate and quantitative susceptibility mapping. The transverse relaxation rate and quantitative susceptibility mapping values and volumes (atrophy) of the caudate, putamen, globus pallidus, and thalamus were determined by multiatlas segmentation. Cognition was assessed with the Brief Repeatable Battery of Neuropsychological Tests. Relationships between cognition and deep gray matter iron were examined by hierarchic regressions.

RESULTS

Compared with controls, patients showed reduced memory (P < .001) and processing speed (P = .02) and smaller putamen (P < .001), globus pallidus (P = .002), and thalamic volumes (P < .001). Quantitative susceptibility mapping values were increased in patients compared with controls in the putamen (P = .003) and globus pallidus (P = .003). In patients only, thalamus (P < .001) and putamen (P = .04) volumes were related to cognitive performance. After we controlled for volume effects, quantitative susceptibility mapping values in the globus pallidus (P = .03; trend for transverse relaxation rate, P = .10) were still related to cognition.

CONCLUSIONS

Quantitative susceptibility mapping was more sensitive compared with the transverse relaxation rate in detecting deep gray matter iron accumulation in the current multiple sclerosis cohort. Atrophy and iron accumulation in deep gray matter both have negative but separable relationships to cognition in multiple sclerosis.

Deep grey matter iron accumulation in alcohol use disorder

PURPOSE

Evaluate brain iron accumulation in alcohol use disorder (AUD) patients compared to controls using quantitative susceptibility mapping (QSM).

METHODS

QSM was performed retrospectively by using phase images from resting state functional magnetic resonance imaging (fMRI). 20 male AUD patients and 15 matched healthy controls were examined. Susceptibility values were manually traced in deep grey matter regions including caudate nucleus, combined putamen and globus pallidus, combined substantia nigra and red nucleus, dentate nucleus, and a reference white matter region in the internal capsule. Average susceptibility values from each region were compared between the patients and controls. The relationship between age and susceptibility was also explored.

RESULTS

The AUD group exhibited increased susceptibility in caudate nucleus (+8.5%, p=0.034), combined putamen and globus pallidus (+10.8%, p=0.006), and dentate nucleus (+14.9%, p=0.022). Susceptibility increased with age in two of the four measured regions - combined putamen and globus pallidus (p=0.013) and combined substantia nigra and red nucleus (p=0.041). AUD did not significantly modulate the rate of susceptibility increase with age in our data.

CONCLUSION

Retrospective QSM computed from standard fMRI datasets provides new opportunities for brain iron studies in psychiatry. Substantially elevated brain iron was found in AUD subjects in the basal ganglia and dentate nucleus. This was the first human AUD brain iron study and the first retrospective clinical fMRI QSM study.

Spin echo transverse relaxation and atrophy in multiple sclerosis deep gray matter: A two-year longitudinal study

Background:

Deep gray matter (DGM) is affected in relapsing–remitting multiple sclerosis (RRMS) and may be studied using short-term longitudinal MRI.

Objective:

To investigate two-year changes in spin-echo transverse relaxation rate (R2) and atrophy in DGM, and its relationship with disease severity in RRMS patients.

Methods:

Twenty six RRMS patients and 26 matched controls were imaged at 4.7 T. Multiecho spin-echo R2 maps and atrophy measurements were obtained in DGM at baseline and two-year follow-up. Differences between MRI measures and correlations to disease severity were examined.

Results:

After two years, mean R2 values in the globus pallidus and pulvinar increased by ~4% ( p<0.001) in patients and <1.7% in controls. Two-year changes in R2 showed significant correlation to disease severity in the globus pallidus, pulvinar, substantia nigra, and thalamus. Multiple regression of the two-year R2 difference using these four DGM structures as variables, yielded high correlation with disease severity ( r=0.83, p<0.001). Two-year changes in volume and R2 showed significant correlation only for the globus pallidus in multiple sclerosis (MS) ( p<0.05).

Conclusions:

Two-year difference R2 measurements in DGM correlate to disease severity in MS. R2 mapping and atrophy measurements over two years can be used to identify changes in DGM in MS.

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

PURPOSE

To investigate gradient-echo phase errors caused by intracranial hemorrhage (ICH) of low signal magnitude, and propose methods to reduce artifacts from phase errors in quantitative susceptibility mapping (QSM) of ICH.

METHODS

Two QSM methods are proposed: (1) mask-inversion that masks the phase of low signal magnitude regions, and (2) ICH magnetic dipole field isolation followed by susceptibility superposition using multiple boundaries for background field removal. The reconstruction methods were tested in eight subjects with ICH using standard single-echo susceptibility-weighted imaging at 1.5 Tesla with 40 ms echo time. Different phase unwrapping algorithms were also compared.

RESULTS

Significant phase errors were evident inside ICHs with low signal magnitude. The mask-inversion method recovered susceptibility of ICH in numerical simulation and minimized phase error propagation in patients with ICH. The additional superposed dipole inversion process substantially suppressed and constrained streaking artifacts in all subjects. Using the proposed superposition method, ICH susceptibilities measured from long and short echo times were similar. Laplacian based phase unwrapping substantially underestimated the ICH dipole field as compared to a path-based method.

CONCLUSION

The proposed methods of mask-inversion as well as ICH isolation and superposition can substantially reduce artifacts in QSM of ICH. Magn Reson Med 76:781-791, 2016. © 2015 Wiley Periodicals, Inc.