Seed prioritized unwrapping (SPUN) for MR phase imaging

Validation of deep-learning accelerated quantitative susceptibility mapping for deep brain nuclei

Purpose

To test the feasibility and consistency of a deep-learning (DL) accelerated QSM method for deep brain nuclei evaluation.

Methods

Participants were scanned with both parallel imaging (PI)-QSM and DL-QSM methods. The PI- and DL-QSM scans had identical imaging parameters other than acceleration factors (AF). The DL-QSM employed Poisson disk style under-sampling scheme and a previously developed cascaded CNN based reconstruction model, with acquisition time of 4:35, 3:15, and 2:11 for AF of 3, 4, and 5, respectively. For PI-QSM acquisition, the AF was 2 and the acquisition time was 6:46. The overall image similarity was assessed between PI- and DL-QSM images using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). QSM values from 7 deep brain nuclei were extracted and agreements between images with different Afs were assessed. Finally, the correlations between age and QSM values in the selected deep brain nuclei were evaluated.

Results

59 participants were recruited. Compared to PI-QSM images, the mean SSIM of DL images were 0.87, 0.86, and 0.85 for AF of 3, 4, and 5. The mean PSNR were 44.56, 44.53, and 44.23. Susceptibility values from DL-QSM were highly consistent with routine PI-QSM images, with differences of less than 5% at the group level. Furthermore, the associations between age and QSM values could be consistently revealed.

Conclusion

DL-QSM could be used for measuring susceptibility values of deep brain nucleus. An AF up to 5 did not significantly impact the correlation between age and susceptibility in deep brain nuclei.

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

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

MULTI-parametric MR imaging with fLEXible design (MULTIPLEX)

PURPOSE

To introduce a gradient echo (GRE) -based method, namely MULTIPLEX, for single-scan 3D multi-parametric MRI with high resolution, signal-to-noise ratio (SNR), accuracy, efficiency, and acquisition flexibility.

THEORY

With a comprehensive design with dual-repetition time (TR), dual flip angle (FA), multi-echo, and optional flow modulation features, the MULTIPLEX signals contain information on radiofrequency (RF) B_1t fields, proton density, T₁ , susceptibility and blood flows, facilitating multiple qualitative images and parametric maps.

METHODS

MULTIPLEX was evaluated on system phantom and human brains, via visual inspection for image contrasts and quality or quantitative evaluation via simulation, phantom scans and literature comparison. Region-of-interest (ROI) analysis was performed on parametric maps of the system phantom and brain scans, extracting the mean and SD of the T₁ , T2∗ , proton density (PD), and/or quantitative susceptibility mapping (QSM) values for comparison with reference values or literature.

RESULTS

One MULTIPLEX scan offers multiple sets of images, including but not limited to: composited PDW/T₁ W/ T2∗ W, aT₁ W, SWI, MRA (optional), B_1t map, T₁ map, T2∗ / R2∗ maps, PD map, and QSM. The quantitative error of phantom T₁ , T2∗ and PD mapping were <5%, and those in brain scans were in good agreement with literature. MULTIPLEX scan times for high resolution (0.68 × 0.68 × 2 mm³ ) whole brain coverage were about 7.5 min, while processing times were <1 min. With flow modulation, additional MRA images can be obtained without affecting the quality or accuracy of other images.

CONCLUSION

The proposed MUTLIPLEX method possesses great potential for multi-parametric MR imaging.

The Influence of Demographics and Vascular Risk Factors on Glymphatic Function Measured by Diffusion Along Perivascular Space

Assessing glymphatic function using in-vivo imaging method is of great value for understanding its contribution to major brain diseases. In the present study, we aim to validate the association between a variety of risk factors and a potential index of glymphatic function-Diffusion Tensor Image Analysis Along the Perivascular Space (ALPS index). We enrolled 142 subjects from communities and performed multi-modality magnetic resonance imaging scans. The ALPS index was calculated from diffusion tensor imaging data, and its associations with demographic factors, vascular factors were investigated using regression analyses. We found that the ALPS index was negatively associated with age (β = -0.284, p < 0.001). Compared to males, females had significantly higher ALPS index (β = -0.243, p = 0.001). Hypertensive subjects had significantly lower ALPS index compared to non-hypertensive subjects (β = -0.189, p = 0.013). Furthermore, venous disruption could decrease ALPS index (β = -0.215, p = 0.003). In general, our results are in consistent with previous conceptions and results from animal studies about the pathophysiology of glymphatic dysfunction. Future studies utilizing this method should consider introducing the above-mentioned factors as important covariates.

Direct Targeting of the Anterior Nucleus of the Thalamus via 3 T Quantitative Susceptibility Mapping

Objective: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is a potentially effective, minimally invasive, and reversible method for treating epilepsy. The goal of this study was to explore whether 3 T quantitative susceptibility mapping (QSM) could delineate the ANT from surrounding structures, which is important for the direct targeting of DBS surgery.

Methods: We obtained 3 T QSM, T1-weighted (T1w), and T2-weighted (T2w) images from 11 patients with Parkinson’s disease or dystonia who received subthalamic nucleus (STN) or globus pallidus interna (GPi) DBS surgery in our center. The ANT and its surrounding white matter structures on QSM were compared with available atlases. The contrast-to-noise ratios (CNRs) of ANT relative to the external medullary lamina (eml) were compared across the three imaging modalities. Additionally, the morphology and location of the ANT were depicted in the anterior commissure (AC)-posterior commissure (PC)-based system.

Results: ANT can be clearly distinguished from the surrounding white matter laminas and appeared hyperintense on QSM. The CNRs of the ANT-eml on QSM, T1w, and T2w images were 10.20 ± 4.23, 1.71 ± 1.03, and 1.35 ± 0.70, respectively. One-way analysis of variance (ANOVA) indicated significant differences in CNRs among QSM, T1w, and T2w imaging modalities [F(2) = 85.28, p < 0.0001]. In addition, both the morphology and location of the ANT were highly variable between patients in the AC–PC-based system.

Conclusion: The potential utility of QSM for the visualization of ANTs in clinical imaging is promising and may be suitable for targeting the ANT for DBS to treat epilepsy.

Nigral Iron Deposition Is Associated With Levodopa-Induced Dyskinesia in Parkinson's Disease

Objective

To investigate iron deposition in the substantia nigra (SN) of Parkinson’s disease (PD) patients associated with levodopa-induced dyskinesia (LID).

Methods

Seventeen PD patients with LID, 17 PD patients without LID, and 16 healthy controls were recruited for this study. The mean QSM values of the whole, left, and right SN were compared among the three groups. A multivariate logistic regression model was constructed to determine the factors associated with increased risk of LID. The receiver operating characteristic curve of the QSM value of SN in discriminating PD with and without LID was evaluated.

Results

The mean QSM values of the whole and right SN in the PD with LID were higher than those in the PD without LID (^∗P = 0.03, ^∗P = 0.03). Multivariate logistic regression analysis revealed that the QSM value of whole, left, or right SN was a predictor of the development of LID (^∗P = 0.03, ^∗P = 0.04, and ^∗P = 0.04). The predictive accuracy of LID in adding the QSM value of the whole, left, and right SN to LID-related clinical risk factors was 70.6, 64.7, and 67.6%, respectively. The QSM cutoff values between PD with and without LID of the whole, left, and right SN were 148.3, 165.4, and 152.7 ppb, respectively.

Conclusion

This study provides the evidence of higher iron deposition in the SN of PD patients with LID than those without LID, suggesting that the QSM value of the SN may be a potential early diagnostic neuroimaging biomarker for LID.