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High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiological and hardware limitations. In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. The public IXI dataset (only structural images) was used. Data were artificially downsampled to obtain lower-resolution spatial MRIs (downsampling factor varying from 8 to 64). When assessing performance using the SSIM metric in the test set, all models performed well. In particular, regardless of the downsampling factor, the UNet consistently obtained the top results. On the other hand, the SPSR model consistently performed worse. In conclusion, UNet and UNet-MSS achieved overall top performances while RRDB performed relatively poorly compared to the other models.

The primary somatosensory cortex (SI) contains fine-grained tactile representations of the body, arranged in an orderly fashion. The use of ultra-high resolution fMRI data to detect group differences, for example between younger and older adults’ SI maps, is challenging, because group alignment often does not preserve the high spatial detail of the data. Here, we use robust-shared response modeling (rSRM) that allows group analyses by mapping individual stimulus-driven responses to a lower dimensional shared feature space, to detect age-related differences in tactile representations between younger and older adults using 7T-fMRI data. Using this method, we show that finger representations are more precise in Brodmann-Area (BA) 3b and BA1 compared to BA2 and motor areas, and that this hierarchical processing is preserved across age groups. By combining rSRM with column-based decoding (C-SRM), we further show that the number of columns that optimally describes finger maps in SI is higher in younger compared to older adults in BA1, indicating a greater columnar size in older adults’ SI. Taken together, we conclude that rSRM is suitable for finding fine-grained group differences in ultra-high resolution fMRI data, and we provide first evidence that the columnar architecture in SI changes with increasing age.

This study investigates the experimental conditions required for magnetic resonance imaging (MRI) of thermally polarized hydrocarbon gas, focusing on ethane. The nuclear magnetic resonance (NMR) spectra and relaxation properties of ethane were analysed at different pressures in the range from 1.5 to 6 bar at 7 T using 1H NMR spectroscopy. The spin-lattice relaxation time (T1) and spin-spin relaxation time (T2) were measured, and their dependence on the pressure was determined, showing that both relaxation times increase with pressure. Using the estimated relaxation times, we adjusted parameters for imaging of static ethane using rapid acquisition with relaxation enhancement (RARE) and fast low-angle shot (FLASH). The signal-to-noise ratio (SNR) of ethane images was evaluated and compared to the calculation for the given range of pressures. Then, we imaged flowing gas using a 2D velocity-encoded pulse sequence, which is usually used for liquid flow studies. The MRI-measured flow rates are compared to those pre-set with a pump, showing good agreement in the slow flow range. Overall, the results provide insights into the feasibility of 1H MRI for imaging and flow measurements of thermally polarized ethane.

Commercial availability, ease of printing and cost effectiveness have rendered 3D printing an essential part of magnetic resonance (MR) experimental design. However, the magnetic properties of several materials contemporarily used for 3D printing are lacking in literature to some extent. A database of the magnetic susceptibilities of several commonly used 3D printing materials is provided, which may aid MR experiment design. Here, we exploit the capability of magnetic resonance imaging (MRI) to map the local magnetic field variations caused by these materials when placed in the scanner's B0 field. Exact analytical solutions of the magnetic flux density distribution for a cylindrical geometry are utilized to fit experimentally obtained data with theory in order to quantify the magnetic susceptibilities. A detailed explanation of the data processing and fitting procedure is presented and validated by measuring the susceptibility of air along with high resolution MR measurements. Furthermore, an initiative is taken to address the need for a comprehensive database comprising of not only the magnetic susceptibilities of 3D printing materials, but also information on the 3D printing parameters, the printers used, and other information available for the materials that may also influence the measured magnetic properties. An open platform with the magnetic susceptibilities of materials reported in this work besides existing literature values is provided here, with the aim to invite researchers to enable further extension and development towards an open database to characterize commonly used 3D printing materials based on their magnetic properties.

Abstract Ketamine shows rapid antidepressant effects peaking 24 h after administration. The antidepressant effects may occur through changes in glutamatergic metabolite levels and resting-state functional connectivity (rsFC) within the default mode network (DMN). A multistage drug effect of ketamine has been suggested, inducing acute effects on dysfunctional network configuration and delayed effects on homeostatic synaptic plasticity. Whether the DMN-centered delayed antidepressant-related changes are associated with the immediate changes remains unknown. Thirty-five healthy male participants (25.1 ± 4.2 years) underwent 7 T magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (rsfMRI) before, during, and 24 h after a single S-ketamine or placebo infusion. Changes in glutamatergic measures and rsFC in the DMN node pregenual anterior cingulate cortex (pgACC) were examined. A delayed rsFC decrease of the pgACC to inferior parietal lobe (family-wise error corrected p (p FWEc) = 0.018) and dorsolateral prefrontal cortex (PFC; p FWEc = 0.002) was detected that was preceded by an immediate rsFC increase of the pgACC to medial PFC (p FWEc

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their ensemble, using majority voting, have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods—occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT—and using a global technique—neuron activation profiles. The mean micro F1 score of the models for COVID-19 classifications ranged from 0.66 to 0.875, and was 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model.

The association between cerebral blood supply and cognition has been widely discussed in the recent literature. One focus of this discussion has been the anatomical variability of the circle of Willis, with morphological differences being present in more than half of the general population. While previous studies have attempted to classify these differences and explore their contribution to hippocampal blood supply and cognition, results have been controversial. To disentangle these previously inconsistent findings, we introduce Vessel Distance Mapping (VDM) as a novel methodology for evaluating blood supply, which allows for obtaining vessel pattern metrics with respect to the surrounding structures, extending the previously established binary classification into a continuous spectrum. To accomplish this, we manually segmented hippocampal vessels obtained from high-resolution 7T time-of-flight MR angiographic imaging in older adults with and without cerebral small vessel disease, generating vessel distance maps by computing the distances of each voxel to its nearest vessel. Greater values of VDM-metrics, which reflected higher vessel distances, were associated with poorer cognitive outcomes in subjects affected by vascular pathology, while this relation was not observed in healthy controls. Therefore, a mixed contribution of vessel pattern and vessel density is proposed to confer cognitive resilience, consistent with previous research findings. In conclusion, VDM provides a novel platform, based on a statistically robust and quantitative method of vascular mapping, for addressing a variety of clinical research questions.

High childhood emotional maltreatment (CM-EMO) is reported in mood and anxiety disorders. The associations with an increased risk for psychopathology are not fully understood. One potential factor may be through alterations in gamma-Aminobutyric acid (GABA). The pregenual anterior cingulate cortex (pgACC) is an important brain region for emotion processing and its’ GABA levels were previously implicated in mood and anxiety disorders pathophysiology. We examined the association between the self-reported CM-EMO in adulthood and GABA + levels in the pgACC and in a control region, anterior mid cingulate cortex. GABA+ and total creatine (tCr) were measured in the pgACC and aMCC voxels in seventy-four healthy volunteers (32 (43%) women, ages 19–54, age [standard deviation] = 27.1 [6.5]) using proton magnetic resonance spectroscopy at 7 T. Childhood Trauma Questionnaire was completed by adult participants to measure retrospective self-reported experience of emotional neglect (CM-EMO-NEG) and emotional abuse (CM-EMO-AB) during childhood. Linear mixed models tested the interaction between the region and the two subscales, and GABA+/tCr ratios, with an adjusted alpha = 0.025. Following, linear models, including with covariates were tested. There was an interaction effect between region and CM-EMO-NEG (B = −0.007, p = 0.009), driven by a negative relationship between CM-EMO-NEG and GABA+/tCr in the pgACC (B = −0.004, p = 0.013). Results for CM-EMO-NEG were robust to inclusion of different covariates (ps

Abstract A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging is the principal imaging modality for diagnosing brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models, and the improvements in the model architectures yield better approximations in a supervised setting. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models can be employed as “spatiospatial” models for this task. These models have the capabilities of learning specific spatial and temporal relationships while reducing computational costs. This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0.9345 and a test accuracy of 96.98%, while at the same time being the model with the least computational cost.

In order to test whether there is a cortical depth compartmentalization in the processing of external and internally-generated visual contents, Iamshchinina et al use high-resolution fMRI at 7 T in participants performing a mental rotation task. They demonstrate that feedforward and feedback representations during mental rotation manifest at differentiable grey matter depth in early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.

Abstract Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis.

Measurement(s) Diffusion Weighted Imaging • Magnetization-Prepared Rapid Gradient Echo MRI • Functional Magnetic Resonance Imaging • T2 (Observed)-Weighted Imaging • Gradient Echo MRI • MR Imaging Acquisition Protocol Element Time of Flight Contrast Indicator Technology Type(s) 7 Tesla Magnetic Resonance Imaging Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14095997

Here, the authors show that the balance between excitation and inhibition in two cortical areas is differentially related to maximizing immediate rewards, and to weighting the cost against long-term gains of moving to a new environment.

The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50–300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight magnetic resonance angiography (TOF-MRA)—which is well suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140 µm isotropic resolution using a 7 Tesla (T) magnetic resonance imaging (MRI) scanner and prospective motion correction, and show that pial arteries one voxel width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.

Abstract Introduction The goal of European Ultrahigh‐Field Imaging Network in Neurodegenerative Diseases (EUFIND) is to identify opportunities and challenges of 7 Tesla (7T) MRI for clinical and research applications in neurodegeneration. EUFIND comprises 22 European and one US site, including over 50 MRI and dementia experts as well as neuroscientists. Methods EUFIND combined consensus workshops and data sharing for multisite analysis, focusing on 7 core topics: clinical applications/clinical research, highest resolution anatomy, functional imaging, vascular systems/vascular pathology, iron mapping and neuropathology detection, spectroscopy, and quality assurance. Across these topics, EUFIND considered standard operating procedures, safety, and multivendor harmonization. Results The clinical and research opportunities and challenges of 7T MRI in each subtopic are set out as a roadmap. Specific MRI sequences for each subtopic were implemented in a pilot study presented in this report. Results show that a large multisite 7T imaging network with highly advanced and harmonized imaging sequences is feasible and may enable future multicentre ultrahigh‐field MRI studies and clinical trials. Discussion The EUFIND network can be a major driver for advancing clinical neuroimaging research using 7T and for identifying use‐cases for clinical applications in neurodegeneration.

Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi’s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.

Object: This study evaluates inter-site and intra-site reproducibility at ten different 7 T sites for quantitative brain imaging. Material and Methods: Two subjects – termed the “traveling heads” – were imaged at ten different 7 T sites with a harmonized quantitative brain MR imaging protocol. In conjunction with the system calibration, MP2RAGE, QSM, CEST and multi-parametric mapping/relaxometry were examined. Results: Quantitative measurements with MP2RAGE showed very high reproducibility across sites and subjects, and errors were in concordance with previous results and other field strengths. QSM had high inter-site reproducibility for relevant subcortical volumes. CEST imaging revealed systematic differences between the sites, but reproducibility was comparable to results in the literature. Relaxometry had also very high agreement between sites, but due to the high sensitivity, differences caused by different applications of the B1 calibration of the two RF coil types used were observed. Conclusion: Our results show that quantitative brain imaging can be performed with high reproducibility at 7 T and with similar reliability as found at 3 T for multicenter studies of the supratentorial brain.