Seven Tesla MRI in Alzheimer's disease research: State of the art and future directions: A narrative review

Seven tesla magnetic resonance imaging (7T MRI) is known to offer a superior spatial resolution and a signal-to-noise ratio relative to any other non-invasive imaging technique and provides the possibility for neuroimaging researchers to observe disease-related structural changes, which were previously only apparent on post-mortem tissue analyses. Alzheimer's disease is a natural and widely used subject for this technology since the 7T MRI allows for the anticipation of disease progression, the evaluation of secondary prevention measures thought to modify the disease trajectory, and the identification of surrogate markers for treatment outcome. In this editorial, we discuss the various neuroimaging biomarkers for Alzheimer's disease that have been studied using 7T MRI, which include morphological alterations, molecular characterization of cerebral T2*-weighted hypointensities, the evaluation of cerebral microbleeds and microinfarcts, biochemical changes studied with MR spectroscopy, as well as some other approaches. Finally, we discuss the limitations of the 7T MRI regarding imaging Alzheimer's disease and we provide our outlook for the future.

Evaluation of High Intracranial Plaque Prevalence in Type 2 Diabetes Using Vessel Wall Imaging on 7 T Magnetic Resonance Imaging

BACKGROUND

While type 2 diabetes (T2D) is a major risk for ischemic stroke, the associated vessel wall characteristics remain essentially unknown. This study aimed to clarify intracranial vascular changes on three-dimensional vessel wall imaging (3D-VWI) using fast spin echo by employing 7Tesla (7T) magnetic resonance imaging (MRI) in T2D patients without advanced atherosclerosis as compared to healthy controls.

METHODS

In 48 T2D patients and 35 healthy controls, the prevalence of cerebral small vessel diseases and intracranial plaques were evaluated by 3D-VWI with 7T MRI.

RESULTS

The prevalence rate of lacunar infarction was significantly higher in T2D than in controls (n = 8 in T2D vs. n = 0 in control, p = 0.011). The mean number of intracranial plaques in both anterior and posterior circulation of each subject was significantly larger in T2D than in controls (2.23 in T2D vs. 0.94 in control, p < 0.01). In T2D patients, gender was associated with the presence of intracranial plaques.

CONCLUSION

This is the first study to demonstrate the high prevalence of intracranial plaque in T2D patients with neither confirmed atherosclerotic disease nor symptoms by performing intracranial 3D-VWI employing 7TMRI. Investigation of intracranial VWI with 7T MRI is expected to provide novel insights allowing early intensive risk management for prevention of ischemic stroke in T2D patients.

Effect of magnetic resonance imaging pre-processing on the performance of model-based prostate tumor probability mapping

Objective. Multi-parametric magnetic resonance imaging (mpMRI) has become an important tool for the detection of prostate cancer in the past two decades. Despite the high sensitivity of MRI for tissue characterization, it often suffers from a lack of specificity. Several well-established pre-processing tools are publicly available for improving image quality and removing both intra- and inter-patient variability in order to increase the diagnostic accuracy of MRI. To date, most of these pre-processing tools have largely been assessed individually. In this study we present a systematic evaluation of a multi-step mpMRI pre-processing pipeline to automate tumor localization within the prostate using a previously trained model.Approach. The study was conducted on 31 treatment-naïve prostate cancer patients with a PI-RADS-v2 compliant mpMRI examination. Multiple methods were compared for each pre-processing step: (1) bias field correction, (2) normalization, and (3) deformable multi-modal registration. Optimal parameter values were estimated for each step on the basis of relevant individual metrics. Tumor localization was then carried out via a model-based approach that takes both mpMRI and prior clinical knowledge features as input. A sequential optimization approach was adopted for determining the optimal parameters and techniques in each step of the pipeline.Main results. The application of bias field correction alone increased the accuracy of tumor localization (area under the curve (AUC) = 0.77;p-value = 0.004) over unprocessed data (AUC = 0.74). Adding normalization to the pre-processing pipeline further improved diagnostic accuracy of the model to an AUC of 0.85 (p-value = 0.000 12). Multi-modal registration of apparent diffusion coefficient images to T2-weighted images improved the alignment of tumor locations in all but one patient, resulting in a slight decrease in accuracy (AUC = 0.84;p-value = 0.30).Significance. Overall, our findings suggest that the combined effect of multiple pre-processing steps with optimal values has the ability to improve the quantitative classification of prostate cancer using mpMRI. Clinical trials: NCT03378856 and NCT03367702.

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.

Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients

Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.

High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)

PURPOSE

The goal of this study is to leverage an advanced fast imaging technique, wave-controlled aliasing in parallel imaging (Wave-CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high-quality high-signal-to-noise-ratio (SNR) volumetric MRI.

METHODS

Three-dimensional (3D) T₂ -weighted fluid-attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave-CAIPI sequence (acceleration factor R = 3×2, 2.75 minutes) and a standard T₂ -SPACE FLAIR sequence (R = 2, 7.25 minutes). A hybrid denoising GAN entitled "HDnGAN" consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave-CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from 8 MS patients not seen during training. HDnGAN was compared to other denoising methods including AONLM, BM4D, MU-Net, and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and VGG perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise.

RESULTS

HDnGAN effectively denoised low-SNR Wave-CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN (λ = 10^-3 ) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN (λ = 10^-3 ) significantly improved the SNR of Wave-CAIPI images (P<0.001), outperformed AONLM (P = 0.015), BM4D (P<0.001), MU-Net (P<0.001) and 3D GAN (λ = 10^-3 ) (P<0.001) regarding image sharpness, and outperformed MU-Net (P<0.001) and 3D GAN (λ = 10^-3 ) (P = 0.001) regarding lesion conspicuity. The overall quality score of HDnGAN (λ = 10^-3 ) (4.25±0.43) was significantly higher than those from Wave-CAIPI (3.69±0.46, P = 0.003), BM4D (3.50±0.71, P = 0.001), MU-Net (3.25±0.75, P<0.001), and 3D GAN (λ = 10^-3 ) (3.50±0.50, P<0.001), with no significant difference compared to standard FLAIR images (4.38±0.48, P = 0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels.

CONCLUSION

HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave-CAIPI to achieve high-fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs. This article is protected by copyright. All rights reserved.

White matter plasticity in healthy older adults: The effects of aerobic exercise

White matter deterioration is associated with cognitive impairment in healthy aging and Alzheimer's disease. It is critical to identify interventions that can slow down white matter deterioration. So far, clinical trials have failed to demonstrate the benefits of aerobic exercise on the adult white matter using diffusion Magnetic Resonance Imaging. Here, we report the effects of a 6-month aerobic walking and dance interventions (clinical trial NCT01472744) on white matter integrity in healthy older adults (n = 180, 60-79 years) measured by changes in the ratio of calibrated T1- to T2-weighted images (T1w/T2w). Specifically, the aerobic walking and social dance interventions resulted in positive changes in the T1w/T2w signal in late-myelinating regions, as compared to widespread decreases in the T1w/T2w signal in the active control. Notably, in the aerobic walking group, positive change in the T1w/T2w signal correlated with improved episodic memory performance. Lastly, intervention-induced increases in cardiorespiratory fitness did not correlate with change in the T1w/T2w signal. Together, our findings suggest that white matter regions that are vulnerable to aging retain some degree of plasticity that can be induced by aerobic exercise training. In addition, we provided evidence that the T1w/T2w signal may be a useful and broadly accessible measure for studying short-term within-person plasticity and deterioration in the adult human white matter.

Spatial and temporal variations of flip-angle distributions in the human brain using an eight-channel parallel transmission system at 7T: comparison of three radiofrequency excitation methods

We investigated the spatial and temporal variations of flip-angle (FA) distributions in the human brain from multiple scans, using an eight-channel parallel transmission (pTx) system at 7T. Nine healthy volunteers were scanned in five sessions using three radiofrequency excitation techniques each time: circular polarization (CP), static pTx, and dynamic pTx. We calculated the coefficients of variation of the FA values within the brain area to evaluate the variations, and the maximum intersession differences in the FA values (D_max), comparing them between the three methods. The coefficients of variation decreased in the following order: CP, static pTx, and dynamic pTx (median: 20.1%, 13.6%, and 5.7%, respectively; p < 0.001). The average D_max values were significantly higher for the static pTx (5.4°) than for the dynamic pTx (2.8°) and CP (1.7°) methods (p = 0.004 and 0.001, respectively). Compared to the CP method, the dynamic pTx method at 7T can efficiently minimize spatial variations in the FA distribution with a mild increase in temporal variations. The static pTx method exhibited a remarkably wide temporal variation.

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T₁-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T₁-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.

7-T MRI for brain virtual autopsy: a proof of concept in comparison to 3-T MRI and CT

The detection and assessment of cerebral lesions and traumatic brain injuries are of particular interest in forensic investigations in order to differentiate between natural and traumatic deaths and to reconstruct the course of events in case of traumatic deaths. For this purpose, computed tomography (CT) and magnetic resonance imaging (MRI) are applied to supplement autopsy (traumatic death) or to supplant autopsy (natural deaths). This approach is termed “virtual autopsy.” The value of this approach increases as more microlesions and traumatic brain injuries are detected and assessed. Focusing on these findings, this article describes the examination of two decedents using CT, 3-T, and 7-T MRI. The main question asked was whether there is a benefit in using 7-T over 3-T MRI. To answer this question, the 3-T and 7-T images were graded regarding the detectability and the assessability of coup/contrecoup injuries and microlesions using 3-point Likert scales. While CT missed these findings, they were detectable on 3-T and 7-T MRI. However, the 3-T images appeared blurry in direct comparison with the 7-T images; thus, the detectability and assessability of small findings were hampered on 3-T MRI. The potential benefit of 7-T over 3-T MRI is discussed.

Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution

Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.

Convolutional Neural Network-based MR Image Analysis for Alzheimer’s Disease Classification

Background:

In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain.

Materials and Methods:

The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images.

Results:

The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC.

Conclusion:

The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

SUITer: An Automated Method for Improving Segmentation of Infratentorial Structures at Ultra-High-Field MRI

BACKGROUND AND PURPOSE

The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet.

METHODS

We present the implementation of an automated algorithm-SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs).

RESULTS

The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R² = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R² = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively).

CONCLUSIONS

SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields.

High Prevalence of Cerebral Small Vessel Disease on 7T Magnetic Resonance Imaging in Familial Hypercholesterolemia

Aim: It remains unclear whether elevated low-density lipoprotein cholesterol (LDL-C) is a risk factor for cerebral vascular disease. Familial hypercholesterolemia (FH) is the most appropriate model for understanding the effects of excess LDL-C because affected individuals have inherently high levels of circulating LDL-C. To clarify the effects of hypercholesterolemia on cerebral small vessel disease (SVD), we investigated cerebrovascular damage in detail due to elevated LDL-C using high resolution brain magnetic resonance imaging (MRI) in patients with FH.

Methods: Twenty-eight patients with FH and 35 healthy controls underwent 7T brain MRI. The prevalence of SVD and arterial structural changes were determined in each group.

Results: The prevalence of periventricular hyperintensity (PVH) was significantly higher (control, 0% vs. FH, 14.2%, p = 0.021) and deep white matter intensity tended to be more frequent in FH patients than in controls. The prevalence of SVD in patients with forms of cerebral damage, such as lacunar infarction, PVH, deep white matter hyperintensities (DWMH), microbleeding, and brain atrophy, was significantly higher among FH patients (control, n = 2, 5.7% vs. FH, n = 7, 25.0%, p < 0.001, chi-square test). The tortuosity of major intracranial arteries and the signal intensity of lenticulostriate arteries were similar in the two groups. In FH patients, as the grade of PVH progressed, several atherosclerosis risk factors, such as body mass index, blood pressure, and triglyceride level, showed ever worsening values.

Conclusion: These results obtained from FH patients revealed that persistently elevated LDL-C leads to cerebral PVH. It is necessary in the management of FH to pay attention not only to the development of coronary heart disease but also to the presence of cerebral SVD.

Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan

BACKGROUND

MR image intensity nonuniformity is often observed at 7T. Reference scans from the body coil used for uniformity correction at lower field strengths are typically not available at 7T.

PURPOSE

To evaluate the efficacy of a novel algorithm, Uniform Combined Reconstruction (UNICORN), to correct receive coil-induced nonuniformity in musculoskeletal 7T MRI without the use of a reference scan.

STUDY TYPE

Retrospective image analysis study.

SUBJECTS

MRI data of 20 subjects was retrospectively processed offline. Field Strength/Sequence: Knees of 20 subjects were imaged at 7T with a single-channel transmit, 28-channel phased-array receive knee coil. A turbo-spin-echo sequence was used to acquire 33 series of images.

ASSESSMENT

Three fellowship-trained musculoskeletal radiologists with cumulative experience of 42 years reviewed the images. The uniformity, contrast, signal-to-noise ratio (SNR), and overall image quality were evaluated for images with no postprocessing, images processed with N4 bias field correction algorithm, and the UNICORN algorithm.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) was used for measuring the interrater reliability. ICC and 95% confidence intervals (CIs) were calculated using the R statistical package employing a two-way mixed-effects model based on a mean rating (k = 3) for absolute agreement. The Wilcoxon signed-rank test with continuity correction was used for analyzing the overall image quality scores.

RESULTS

UNICORN was preferred among the three methods evaluated for uniformity in 97.9% of the pooled ratings, with excellent interrater agreement (ICC of 0.98, CI 0.97-0.99). UNICORN was also rated better than N4 for contrast and equivalent to N4 in SNR with ICCs of 0.80 (CI 0.72-0.86) and 0.67 (CI 0.54-0.77), respectively. The overall image quality scores for UNICORN were significantly higher than N4 (P < 6 × 10^-13 ), with good to excellent interrater agreement (ICC 0.90, CI 0.86-0.93).

DATA CONCLUSION

Without the use of a reference scan, UNICORN provides better image uniformity, contrast, and overall image quality at 7T compared with the N4 bias field-correction algorithm.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1534-1544.

Evaluation of Lenticulostriate Arteries Changes by 7 T Magnetic Resonance Angiography in Type 2 Diabetes

AIM

Progress in neuroimaging techniques allows us to investigate the microvasculature characteristics including lenticulostriate arteries (LSA), which are closely associated with lacunar infarction. Because ischemic stroke is a more critical health problem in East Asian than in other populations, in order to clarify pathological changes underlying cerebral small vessel disease (SVD), we projected an imaging analysis of LSA using high-resolution brain magnetic resonance imaging (MRI) in middle-aged Japanese subjects with type 2 diabetes.

METHODS

Twenty-five subjects with type 2 diabetes and 25 non-diabetic control subjects underwent 7 Tesla (7 T) brain MRI. The prevalences of SVD and LSA structural changes were determined in each group.

RESULTS

SVD prevalence did not differ significantly between the type 2 diabetes and control groups. The average numbers of stems, as well as numbers of branches, of LSA were significantly smaller in diabetic subjects than non-diabetic control subjects. The signal intensity of LSA was markedly decreased, indicating reduced blood flow in type 2 diabetes.

CONCLUSION

In spite of the prevalence of SVD being similar, structural changes and decreased signal intensity of LSA were highly detected in diabetic subjects compared with non-diabetic controls, suggesting that 7 T MRA enables us to determine LSA impairment prior to the development of SVD. Early detection of LSA impairment allows us earlier interventions aimed at the prevention of atherosclerotic events.

Analysis strategies for high-resolution UHF-fMRI data

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.

Improvement of the repeatability of parallel transmission at 7T using interleaved acquisition in the calibration scan

BACKGROUND

Respiration-induced phase shift affects B₀ /B₁⁺ mapping repeatability in parallel transmission (pTx) calibration for 7T brain MRI, but is improved by breath-holding (BH). However, BH cannot be applied during long scans.

PURPOSE

To examine whether interleaved acquisition during calibration scanning could improve pTx repeatability and image homogeneity.

STUDY TYPE

Prospective.

SUBJECTS

Nine healthy subjects.

FIELD STRENGTH/SEQUENCE

7T MRI with a two-channel RF transmission system was used.

ASSESSMENT

Calibration scanning for B₀ /B₁⁺ mapping was performed under sequential acquisition/free-breathing (Seq-FB), Seq-BH, and interleaved acquisition/FB (Int-FB) conditions. The B₀ map was calculated with two echo times, and the B₁⁺ map was obtained using the Bloch-Siegert method. Actual flip-angle imaging (AFI) and gradient echo (GRE) imaging were performed using pTx and quadrature-Tx (qTx). All scans were acquired in five sessions. Repeatability was evaluated using intersession standard deviation (SD) or coefficient of variance (CV), and in-plane homogeneity was evaluated using in-plane CV.

STATISTICAL TESTS

A paired t-test with Bonferroni correction for multiple comparisons was used.

RESULTS

The intersession CV/SDs for the B₀ /B₁⁺ maps were significantly smaller in Int-FB than in Seq-FB (Bonferroni-corrected P < 0.05 for all). The intersession CVs for the AFI and GRE images were also significantly smaller in Int-FB, Seq-BH, and qTx than in Seq-FB (Bonferroni-corrected P < 0.05 for all). The in-plane CVs for the AFI and GRE images in Seq-FB, Int-FB, and Seq-BH were significantly smaller than in qTx (Bonferroni-corrected P < 0.01 for all).

DATA CONCLUSION

Using interleaved acquisition during calibration scans of pTx for 7T brain MRI improved the repeatability of B₀ /B₁⁺ mapping, AFI, and GRE images, without BH.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2017.

Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE

Recent advances in MR technology have enabled increased spatial resolution for routine functional and anatomical imaging, which has created demand for software tools that are able to process these data. The availability of high-resolution data also raises the question of whether higher resolution leads to substantial gains in accuracy of quantitative morphometric neuroimaging procedures, in particular the cortical surface reconstruction and cortical thickness estimation. In this study we adapted the FreeSurfer cortical surface reconstruction pipeline to process structural data at native submillimeter resolution. We then quantified the differences in surface placement between meshes generated from (0.75 mm)³ isotropic resolution data acquired in 39 volunteers and the same data downsampled to the conventional 1 mm³ voxel size. We find that when processed at native resolution, cortex is estimated to be thinner in most areas, but thicker around the Cingulate and the Calcarine sulci as well as in the posterior bank of the Central sulcus. Thickness differences are driven by two kinds of effects. First, the gray-white surface is found closer to the white matter, especially in cortical areas with high myelin content, and thus low contrast, such as the Calcarine and the Central sulci, causing local increases in thickness estimates. Second, the gray-CSF surface is placed more interiorly, especially in the deep sulci, contributing to local decreases in thickness estimates. We suggest that both effects are due to reduced partial volume effects at higher spatial resolution. Submillimeter voxel sizes can therefore provide improved accuracy for measuring cortical thickness.

Breath-holding during the Calibration Scan Improves the Reproducibility of Parallel Transmission at 7T for Human Brain

Purpose:

The B0 and B1+ maps required for calculation of the radiofrequency (RF) pulse of parallel transmission (pTx) are obtained in calibration scans; however, they may be affected by respiratory motion. We aimed to compare the reproducibility of B0 and B1+ maps and gradient echo (GRE) images of the brain scanned with pTx at 7T between free-breathing (FB) and breath-holding (BH) conditions during the calibration scan.

Methods:

Nine healthy volunteers were scanned by 7T MRI using a two-channel quadrature head coil. In the pTx calibration scans performed with FB and BH, the B0 map was obtained from two different TE images and the B1+ map was calculated by the Bloch-Siegert method. A GRE image (gradient-recalled-acquisition in steady state) was also obtained with RF shimming and RF design of pTx with spoke method, as well as quadrature transmission (qTx). All the scans were repeated over five sessions. The reproducibility of the B0 and B1+ maps and GRE image was evaluated with region-of-interest measurements using inter-session standard deviation (SD) and coefficient of variation (CV) values. Intensity homogeneity of GRE images was also assessed with in-plane CV.

Results:

Inter-session SDs of B0 and B1+ maps were significantly smaller in BH (P < 0.01). Inter-session CVs of GRE images were significantly smaller in qTx than BH and FB (P < 0.01, both); however, the CVs of BH were significantly smaller (P < 0.01). In-plane CVs of FB and BH with RF shimming were not significantly different with qTx; however, CVs of FB and BH with RF design were significantly smaller than those of qTx (P < 0.05 and P < 0.01, respectively).

Conclusion:

BH could improve the reproducibility of B0 and B1+ maps in pTx calibration scans and GRE images. These results might facilitate the development of pTx in human brain at 7T.

An Algorithm of Image Heterogeneity with Contrast-Enhanced Ultrasound in Differential Diagnosis of Solid Thyroid Nodules

Enhancement heterogeneity on contrast-enhanced ultrasonography (CEUS) is used to differentiate between benign and malignant thyroid nodules. In this study, we used an algorithm to quantify enhancement heterogeneity of solid thyroid nodules on CEUS. The heterogeneity value (HV) is calculated as standard deviation/mean intensity × 100 (using Adobe Photoshop). The heterogeneity ratio (HR) is calculated as the ratio of the HV of the nodule to that of the surrounding parenchyma. Three phases-ascending, peak and descending phases-were studied. HV values at ascending (HV_a) and peak (HV_p) phases were significantly higher in malignant nodules than in benign nodules (95.57 ± 43.87 vs. 73.06 ± 44.04, p = 0.009, and 32.53 ± 10.73 vs. 26.44 ± 8.25, p = 0.002, respectively). HR_a, HR_p and HR_d were significantly higher in malignant nodules than in benign nodules (1.93 ± 1.03 vs. 1.00 ± 0.47, p = 0.000, 1.43 ± 0.51 vs. 1.09 ± 0.28, p = 0.000, and 1.33 ± 0.40 vs. 1.08 ± 0.33, p = 0.001, respectively). HR_a achieved optimal diagnostic performance on receiver operating characteristic curve analysis. The algorithm used for assessment of image heterogeneity on CEUS examination may be a useful adjunct to conventional ultrasound for differential diagnosis of solid thyroid nodules.

Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.

Recent applications of UHF-MRI in the study of human brain function and structure: a review

The increased availability of ultra-high-field (UHF) MRI has led to its application in a wide range of neuroimaging studies, which are showing promise in transforming fundamental approaches to human neuroscience. This review presents recent work on structural and functional brain imaging, at 7 T and higher field strengths. After a short outline of the effects of high field strength on MR images, the rapidly expanding literature on UHF applications of blood-oxygenation-level-dependent-based functional MRI is reviewed. Structural imaging is then discussed, divided into sections on imaging weighted by relaxation time, including quantitative relaxation time mapping, phase imaging and quantitative susceptibility mapping, angiography, diffusion-weighted imaging, and finally magnetization-transfer imaging. The final section discusses studies using the high spatial resolution available at UHF to identify explicit links between structure and function. Copyright © 2015 John Wiley & Sons, Ltd.

Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters

Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.