Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis.

Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.

Compartmental anisotropy of filtered exchange imaging (FEXI) in human white matter: What is happening in FEXI?

PURPOSE: To investigate the effects of compartmental anisotropy on filtered exchange imaging (FEXI) in white matter (WM). THEORY AND METHODS: FEXI signals were measured using multiple combinations of diffusion filter and detection directions in five healthy volunteers. Additional filters, including a trace-weighted diffusion filter with trapezoidal gradients, a spherical b-tensor encoded diffusion filter, and a T2 filter, were tested with trace-weighted diffusion detection. RESULTS: A large range of apparent exchange rates (AXR) and both positive and negative filter efficiencies (σ) were found depending on the mutual orientation of the filter and detection gradients relative to WM fiber orientation. The data demonstrated that the fast-diffusion compartment suppressed by diffusional filtering is not exclusively extra-cellular, but also intra-cellular. While not comprehensive, a simple two-compartment diffusion tensor model with water exchange was able to account qualitatively for the trends in positive and negative filtering efficiencies, while standard model imaging (SMI) without exchange could not. This two-compartment diffusion tensor model also demonstrated smaller AXR variances across subjects. When employing trace-weighted diffusion detection, AXR values were on the order of the R1 (=1/T1) of water at 3T for crossing fibers, while being less than R1 for parallel fibers. CONCLUSION: Orientation-dependent AXR and σ values were observed when using multi-orientation filter and detection gradients in FEXI, indicating that WM FEXI models need to account for compartmental anisotropy. When using trace-weighted detection, AXR values were on the order of or less than R1, complicating the interpretation of FEXI results in WM in terms of biological exchange properties. These findings may contribute toward better understanding of FEXI results in WM.

Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mapping.

PURPOSE: This work is to investigate the microstructure-induced frequency shift in white matter (WM) with crossing fibers and to separate the microstructure-related frequency shift from the bulk susceptibility-induced frequency shift by model fitting the gradient-echo (GRE) frequency evolution for potentially more accurate quantitative susceptibility mapping (QSM). METHODS: A hollow-cylinder fiber model (HCFM) with two fiber populations was developed to investigate GRE frequency evolutions in WM voxels with microstructural orientation dispersion. The simulated and experimentally measured TE-dependent local frequency shift was then fitted to a simplified frequency evolution model to obtain a microstructure-related frequency difference parameter ( ∆ f $$ \Delta f $$ ) and a TE-independent bulk susceptibility-induced frequency shift ( C f $$ {C}_f $$ ). The obtained C f $$ {C}_f $$ was then used for QSM reconstruction. Reconstruction performances were evaluated using a numerical head phantom and in vivo data and then compared to other multi-echo combination methods. RESULTS: GRE frequency evolutions and ∆ f $$ \Delta f $$ -based tissue parameters in both parallel and crossing fibers determined from our simulations were comparable to those observed in vivo. The TE-dependent frequency fitting method outperformed other multi-echo combination methods in estimating C f $$ {C}_f $$ in simulations. The fitted ∆ f $$ \Delta f $$ , C f $$ {C}_f $$ , and QSM could be improved further by navigator-based B0 fluctuation correction. CONCLUSION: A HCFM with two fiber populations can be used to characterize microstructure-induced frequency shifts in WM regions with crossing fibers. HCFM-based TE-dependent frequency fitting provides tissue contrast related to microstructure ( ∆ f $$ \Delta f $$ ) and in addition may help improve the quantification accuracy of C f $$ {C}_f $$ and the corresponding QSM.

Depth-wise profiles of iron and myelin in the cortex and white matter using χ-separation: A preliminary study.

The in-vivo profiling of iron and myelin across cortical depths and underlying white matter has important implications for advancing knowledge about their roles in brain development and degeneration. Here, we utilize χ-separation, a recently-proposed advanced susceptibility mapping that creates positive (χpos) and negative (χneg) susceptibility maps, to generate the depth-wise profiles of χpos and χneg as surrogate biomarkers for iron and myelin, respectively. Two regional sulcal fundi of precentral and middle frontal areas are profiled and compared with findings from previous studies. The results show that the χpos profiles peak at superificial white matter (SWM), which is an area beneath cortical gray matter known to have the highest accumulation of iron within the cortex and white matter. On the other hand, the χneg profiles increase in SWM toward deeper white matter. These characteristics in the two profiles are in agreement with histological findings of iron and myelin. Furthermore, the χneg profiles report regional differences that agree with well-known distributions of myelin concentration. When the two profiles are compared with those of QSM and R2*, different shapes and peak locations are observed. This preliminary study offers an insight into one of the possible applications of χ-separation for exploring microstructural information of the human brain, as well as clinical applications in monitoring changes of iron and myelin in related diseases.

χ-Separation Imaging for Diagnosis of Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disorder.

Background Use of χ-separation imaging can provide surrogates for iron and myelin that relate closely to abnormal changes in multiple sclerosis (MS) lesions. Purpose To evaluate the appearances of MS and neuromyelitis optica spectrum disorder (NMOSD) brain lesions on χ-separation maps and explore their diagnostic value in differentiating the two diseases in comparison with previously reported diagnostic criteria. Materials and Methods This prospective study included individuals with MS or NMOSD who underwent χ-separation imaging from October 2017 to October 2020. Positive (χpos) and negative (χneg) susceptibility were estimated separately by using local frequency shifts and calculating R2' (R2' = R2* - R2). R2 mapping was performed with a machine learning approach. For each lesion, presence of the central vein sign (CVS) and paramagnetic rim sign (PRS) and signal characteristics on χneg and χpos maps were assessed and compared. For each participant, the proportion of lesions with CVS, PRS, and hypodiamagnetism was calculated. Diagnostic performances were assessed using receiver operating characteristic (ROC) curve analysis. Results A total of 32 participants with MS (mean age, 34 years ± 10 [SD]; 25 women, seven men) and 15 with NMOSD (mean age, 52 years ± 17; 14 women, one man) were evaluated, with a total of 611 MS and 225 NMOSD brain lesions. On the χneg maps, 80.2% (490 of 611) of MS lesions were categorized as hypodiamagnetic versus 13.8% (31 of 225) of NMOSD lesions (P < .001). Lesion appearances on the χpos maps showed no evidence of a difference between the two diseases. In per-participant analysis, participants with MS showed a higher proportion of hypodiamagnetic lesions (83%; IQR, 72-93) than those with NMOSD (6%; IQR, 0-14; P < .001). The proportion of hypodiamagnetic lesions achieved excellent diagnostic performance (area under the ROC curve, 0.96; 95% CI: 0.91, 1.00). Conclusion On χ-separation maps, multiple sclerosis (MS) lesions tend to be hypodiamagnetic, which can serve as an important hallmark to differentiate MS from neuromyelitis optica spectrum disorder. © RSNA, 2022 Supplemental material is available for this article.

Blip up-down acquisition for spin- and gradient-echo imaging (BUDA-SAGE) with self-supervised denoising enables efficient T2 , T2 *, para- and dia-magnetic susceptibility mapping.

PURPOSE: To rapidly obtain high resolution T2 , T2 *, and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. METHODS: We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient EPI sequence for quantitative mapping. The acquisition includes multiple T2 *-, T2 '-, and T2 -weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate distortion. A self-supervised neural network (NN), MR-Self2Self (MR-S2S), was used to perform denoising to boost SNR. Using Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on volumes acquired with 2 mm slice thickness. Quantitative T2 (=1/R2 ) and T2 * (=1/R2 *) maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion on the gradient echoes. Starting from the estimated R2 /R2 * maps, R2 ' information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps. RESULTS: In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution, multi-contrast images and quantitative T2 /T2 * maps, as well as yielding para- and dia-magnetic susceptibility maps. Estimated quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements. CONCLUSION: BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enables rapid, distortion-free, whole-brain T2 /T2 * mapping at 1 mm isotropic resolution under 90 s.

χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain.

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.

Quad-Contrast Imaging: Simultaneous Acquisition of Four Contrast-Weighted Images (PD-Weighted, T2-Weighted, PD-FLAIR and T2-FLAIR Images) With Synthetic T1-Weighted Image, T1- and T2-Maps.

Magnetic resonance imaging (MRI) can provide multiple contrast-weighted images using different pulse sequences and protocols. However, a long acquisition time of the images is a major challenge. To address this limitation, a new pulse sequence referred to as quad-contrast imaging is presented. The quad-contrast sequence enables the simultaneous acquisition of four contrast-weighted images (proton density (PD)-weighted, T2-weighted, PD-fluid attenuated inversion recovery (FLAIR), and T2-FLAIR), and the synthesis of T1-weighted images and T1- and T2-maps in a single scan. The scan time is less than 6 min and is further reduced to 2 min 50 s using a deep learning-based parallel imaging reconstruction. The natively acquired quad contrasts demonstrate high quality images, comparable to those from the conventional scans. The deep learning-based reconstruction successfully reconstructed highly accelerated data (acceleration factor 6), reporting smaller normalized root mean squared errors (NRMSEs) and higher structural similarities (SSIMs) than those from conventional generalized autocalibrating partially parallel acquisitions (GRAPPA)-reconstruction (mean NRMSE of 4.36% vs. 10.54% and mean SSIM of 0.990 vs. 0.953). In particular, the FLAIR contrast is natively acquired and does not suffer from lesion-like artifacts at the boundary of tissue and cerebrospinal fluid, differentiating the proposed method from synthetic imaging methods. The quad-contrast imaging method may have the potentials to be used in a clinical routine as a rapid diagnostic tool.

DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE.

Respiration-induced B0 fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8 ± 5.2% to 1.3 ± 0.6%; structural similarity (SSIM) from 0.88 ± 0.08 to 0.99 ± 0.01; ghost-to-signal-ratio (GSR) from 7.9 ± 7.2% to 0.6 ± 0.6%; deep breathing: NRMSE from 13.9 ± 4.6% to 5.8 ± 1.4%; SSIM from 0.86 ± 0.03 to 0.95 ± 0.01; GSR 20.2 ± 10.2% to 5.7 ± 2.3%; natural breathing: NRMSE from 5.2 ± 3.3% to 4.0 ± 2.5%; SSIM from 0.94 ± 0.04 to 0.97 ± 0.02; GSR 5.7 ± 5.0% to 2.8 ± 1.1%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks.

A geometric approach to separate the effects of magnetic susceptibility and chemical shift/exchange in a phantom with isotropic magnetic susceptibility.

PURPOSE: To separate the effects of magnetic susceptibility and chemical shift/exchange in a phantom with isotropic magnetic susceptibility, and to generate a chemical shift/exchange-corrected QSM result. METHODS: Magnetic susceptibility and chemical shift/exchange are the properties of a material. Both are known to induce the resonance frequency shift in MRI. In current QSM, the susceptibility is reconstructed from the frequency shift, ignoring the contribution of the chemical shift/exchange. In this work, a simple geometric approach, which averages the frequency shift maps from three orthogonal B0 directions to generate a chemical shift/exchange map, is developed using the fact that the average nullifies the (isotropic) susceptibility effects. The resulting chemical shift/exchange map is subtracted from the total frequency shift, producing a frequency shift map solely from susceptibility. Finally, this frequency shift map is reconstructed to a susceptibility map using a QSM algorithm. The proposed method is validated in numerical simulations and applied to phantom experiments with olive oil, bovine serum albumin, ferritin, and iron oxide solutions. RESULTS: Both simulations and experiments confirm that the method successfully separates the contributions of the susceptibility and chemical shift/exchange, reporting the susceptibility and chemical shift/exchange of olive oil (susceptibility: 0.62 ppm, chemical shift: -3.60 ppm), bovine serum albumin (susceptibility: -0.059 ppm, chemical shift: 0.008 ppm), ferritin (susceptibility: 0.125 ppm, chemical shift: -0.005 ppm), and iron oxide (susceptibility: 0.30 ppm, chemical shift: -0.039 ppm) solutions. CONCLUSION: The proposed method successfully separates the susceptibility and chemical shift/exchange in phantoms with isotropic magnetic susceptibility.

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging.

Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2 -, T2 *-, T1 -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.

Artificial neural network for myelin water imaging.

PURPOSE: To demonstrate the application of artificial neural network (ANN) for real-time processing of myelin water imaging (MWI). METHODS: Three neural networks, ANN-IMWF , ANN-IGMT2 , and ANN-II, were developed to generate MWI. ANN-IMWF and ANN-IGMT2 were designed to output myelin water fraction (MWF) and geometric mean T2 of intra- and extra-cellular water signal (GMT2,IEW ), respectively, whereas ANN-II generates a T2 distribution. For the networks, gradient and spin echo data from 18 healthy controls (HC) and 26 multiple sclerosis patients (MS) were utilized. Among them, 10 HC and 12 MS had the same scan parameters and were used for training (6 HC and 6 MS), validation (1 HC and 1 MS), and test sets (3 HC and 5 MS). The remaining data had different scan parameters and were applied to exam effects of the scan parameters. The network results were compared with those of conventional MWI in the white matter mask and regions of interest. RESULTS: The networks produced highly accurate results, showing averaged normalized root-mean-squared error under 3% for MWF and 0.4% for GMT2,IEW in the white matter mask of the test set. In the region of interest analysis, the differences between ANNs and conventional MWI were less than 0.1% in MWF and 0.1 ms in GMT2,IEW (no statistical difference and R2 > 0.97). Datasets with different scan parameters showed increased errors. The average processing time was 0.68 s in ANNs, gaining 11,702 times acceleration in the computational speed (conventional MWI: 7,958 s). CONCLUSION: The proposed neural networks demonstrate the feasibility of real-time processing for MWI with high accuracy.

Advances in gradient echo myelin water imaging at 3T and 7T.

Gradient echo myelin water imaging (GRE-MWI) is an MRI technique to measure myelin concentration and involves the analysis of signal decay characteristics of the multi-echo gradient echo data. The method provides a myelin water fraction as a quantitative biomarker for myelin. In this work, a new sequence and post-processing methods were proposed to generate high quality GRE-MWI images at 3T and 7T. In order to capture the rapidly decaying myelin water signals, a bipolar readout GRE sequence was designed with "gradient pairing," compensating for the eddy current effects. The flip angle dependency from the multi-compartmental T1 effects was explored and avoided using a 2D multi-slice acquisition with a long TR. Additionally, the sequence was tested for the effects of inflow and magnetization transfer and demonstrated robustness to these error sources. Lastly, the temporal and spatial B0 inhomogeneity effects were mitigated by using the B0 navigator and field inhomogeneity corrections. Using the method, high-quality myelin water images were successfully generated for the in-vivo human brain at both field strengths. When the myelin water fraction at 3T and 7T were compared, they showed a good correlation (R2≥ 0.88; p < 0.001) with a larger myelin water fraction at 7T. The proposed method also opens the possibility of high resolution (isotropic 1.5 mm resolution) myelin water mapping at 7T.

Initial diagnostic workup of parkinsonism: Dopamine transporter positron emission tomography versus susceptibility map-weighted imaging at 3T.

BACKGROUND AND PURPOSE: Evaluation of dorsal nigral hyperintensity on MRI can help detect nigrostriatal degeneration. We aimed to compare the diagnostic performance between susceptibility map-weighted imaging (SMWI) and N-3-fluoropropyl-2-ß-carbomethoxy-3-ß-(4-iodophenyl) nortropane (18F-FP-CIT) positron emission tomography (PET) as an initial diagnostic tool of parkinsonism. MATERIALS AND METHODS: This local ethics committee-approved retrospective study enrolled 223 patients with parkinsonism and 15 healthy subjects (mean age, 69.7 years; 135 females) who underwent both SMWI at 3T and 18F-FP-CIT PET. The diagnostic performances of the two tests for nigrostriatal degeneration were compared by evaluating whether the 90% confidence interval (CI) of the difference between the two tests was within the equivalence margin by using the DTComPair package of R. The concordance rate was tested by Cohen's kappa. RESULTS: The diagnostic sensitivities of SMWI and 18F-FP-CIT PET were 94.5% and 100% per SN and 100% and 100% per participant, respectively; their specificities were 95.3% and 86.7% per SN and 94.4% and 84.0% per participant, respectively. While the diagnostic sensitivity was comparable between the two tests for each SN and participant, the lower 90% CI of the differences in the specificity were -0.086 per SN and -0.104 per participant, indicating a higher diagnostic specificity of SMWI than that of 18F-FP-CIT PET. When excluding 20 participants with basal ganglia lesions, the two tests exhibited similar diagnostic performance and had excellent agreement (k = 0.899 per SN; k = 0.945 per participant). CONCLUSION: For patients with parkinsonism, SMWI and 18F-FP-CIT PET exhibit similar diagnostic performance.

Diagnosis of Early-Stage Idiopathic Parkinson's Disease Using High-Resolution Quantitative Susceptibility Mapping Combined with Histogram Analysis in the Substantia Nigra at 3 T.

BACKGROUND AND PURPOSE: To test whether nigrosome-1 imaging using high-resolution quantitative susceptibility mapping (QSM) combined with histogram analysis can improve the diagnostic accuracy in early-stage idiopathic Parkinson's disease (IPD) patients. METHODS: Three-dimensional multiecho gradient-recalled echo images (0.5×0.5×1.0 mm³) were obtained at 3 T for QSM in 38 patients with IPD and 25 healthy subjects. To segment the substantia nigra (SN), regions of interest (ROIs) were semiautomatically drawn at the location below the red nucleus, and the normal-appearing nigrosome-1 was determined by manual correction. QSM histograms were obtained within the ROI. The segmented SN regions on the right and left that had higher mean susceptibility values and fewer voxels with susceptibility values lower than 60, 65, 70, 75, and 80 ppb were chosen for comparisons between the IPD patients and healthy subjects. These results were compared with those of the visual assessments of nigrosome-1 in susceptibility map-weighted imaging (SMWI) by analyzing receiver operating characteristics curves. RESULTS: The proportion of voxels with susceptibility values lower than 70 ppb showed the best diagnostic performance, with its value differing significantly between the IPD patients (median=0, interquartile range=0-0.23) and healthy subjects (median=10.67, interquartile range=5.98-21.57) (p<0.0001). The number of voxels with susceptibility values lower than 60, 65, 70, 75, and 80 ppb showed worse diagnostic performances but were still significantly better than that of the mean susceptibility value (p=0.0249, 0.0192, 0.0183, 0.0191, and 0.0186, respectively), which also differed significantly between the two groups: 125.81±16.27 ppb (mean±standard deviation) in IPD versus 98.41±11.70 ppb in healthy subjects (p<0.0001). Additionally, using the proportion of voxels with susceptibility values lower than 70 ppb provided significantly better diagnostic performance than did visual assessments of SMWI (p=0.0143). CONCLUSIONS: High-spatial-resolution QSM combined with histogram analysis at 3 T can improve the diagnostic accuracy of early-stage IPD.

Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI).

MR g-ratio, which measures the ratio of the aggregate volume of axons to that of fibers in a voxel, is a potential biomarker for white matter microstructures. In this study, a new approach for acquiring an in-vivo whole human brain g-ratio map is proposed. To estimate the g-ratio, myelin volume fraction and axonal volume fraction are acquired using multi-echo gradient echo myelin water imaging (GRE-MWI) and neurite orientation dispersion and density imaging (NODDI), respectively. In order to translate myelin water fraction measured in GRE-MWI into myelin volume fraction, a new scaling procedure is proposed and validated. This scaling approach utilizes geometric measures of myelin structure and, therefore, provides robustness over previous methods. The resulting g-ratio map reveals an expected range of g-ratios (0.71-0.85 in major fiber bundles) with a small inter-subject coefficient of variance (less than 2%). Additionally, a few fiber bundles (e.g. cortico-spinal tract and optic radiation) show different constituents of myelin volume fraction and axonal volume fraction, indicating potentials to utilize the measures for deciphering fiber tracking.

Differential involvement of nigral subregions in idiopathic parkinson's disease.

In this study, the prevalence of abnormality in putative nigrosome 1 and nigrosome 4 (N1 and N4, respectively) was investigated in early versus late-stage idiopathic Parkinson's disease (IPD) patients. A total of 128 IPD patients (early stage[n = 89]; late stage[n = 39]) and 15 healthy subjects were scanned for high-resolution (0.5 × 0.5 × 1.0 mm3 ) multiecho gradient-recalled echo MRI and dopamine transporter PET imaging. The MRI data were processed for susceptibility map-weighted imaging (SMWI) to improve a contrast-to-noise ratio, and the images were resliced at 0.5 mm to define N1 and N4. When each side of N1 and N4 was assessed separately for the loss of hyperintensity by two independent reviewers, the consensus review results showed that in early-stage IPD (178 substantia nigras [SNs]), the loss of hyperintensity was observed more often in only the N1 region (65.2%) when compared to in both N1 and N4 regions (34.8%). In late-stage IPD (78 SNs), on the other hand, the loss in only the N1 region (25.6%) was less prevalent than in both N1 and N4 (74.4%) (P < 0.0001). Additionally, intact SNs (both in N1 and N4) were observed 17 SNs (9.6%) of the early-stage IPD patients, whereas it was not found in any SNs of the late-stage IPD patients (P = 0.005). Moreover, involvement of both N1 and N4 on both sides was found in 19.1% of the early-stage IPD patients, whereas its incidence was higher (61.5%) in the late-stage IPD patients (P < 0.0001), suggesting that the loss of hyperintensity in IPD progresses from N1 to N4 as the disease advances. Hum Brain Mapp 39:542-553, 2018. © 2017 Wiley Periodicals, Inc.

An R2* model of white matter for fiber orientation and myelin concentration.

Myelin, which consists of lipid bilayers, is one of the major constituents of white matter in the brain and has been suggested as a primary source of magnetic susceptibility contrasts. In this study, a new R2* model that simultaneously explains the effects of fiber orientation and myelin concentration is proposed and tested. In addition, a new approach that produces R2* maps without the effects of myelin is suggested. Experimental results demonstrate that the model reveals a high goodness of fit for the R2* distribution of white matter compared to a model that explains either fiber orientation or myelin concentration. The proposed R2* map shows a relatively uniform spatial distribution of R2* compared to the uncorrected R2* map and the fiber orientation or myelin concentration corrected R2* map.