Laminar analysis of the cortical T1/T2-weighted ratio at 7T

Objective: In this observational study, we explored cortical structure as function of cortical depth through a laminar analysis of the T1/T2-weighted (T1w/T2w) ratio, which has been related to dendrite density in ex vivo brain tissue specimens of patients with MS. Methods: In 39 patients (22 relapsing-remitting, 13 female, age 41.1 ± 10.6 years; 17 progressive, 11 female, age 54.1 ± 9.9 years) and 21 healthy controls (8 female, , age 41.6 ± 10.6 years), we performed a voxel-wise analysis of T1w/T2w ratio maps from high-resolution 7T images from the subpial surface to the gray matter/white matter boundary. Six layers were sampled to ensure accuracy based on mean cortical thickness and image resolution. Results: At the voxel-wise comparison (p < 0.05, family wise error rate corrected), the whole MS group showed lower T1w/T2w ratio values than controls, both when considering the entire cortex and each individual layer, with peaks occurring in the fusiform, temporo-occipital, and superior and middle frontal cortex. In relapsing-remitting patients, differences in the T1w/T2w ratio were only identified in the subpial layer, with the peak occurring in the fusiform cortex, whereas results obtained in progressive patients mirrored the widespread damage found in the whole group. Conclusions: Laminar analysis of T1w/T2w ratio mapping confirms the presence of cortical damage in MS and shows a variable expression of intracortical damage according to the disease phenotype. Although in the relapsing-remitting stage, only the subpial layer appears susceptible to damage, in progressive patients, widespread cortical abnormalities can be observed, not only, as described before, with regard to myelin/iron concentration but, possibly, to other microstructural features

Combining navigator and optical prospective motion correction for high-quality 500 μm resolution quantitative multi-parameter mapping at 7T

Purpose: High-resolution quantitative multi-parameter mapping shows promise for non-invasively characterizing human brain microstructure but is limited by physiological artifacts. We implemented corrections for rigid head movement and respiration-related B0-fluctuations and evaluated them in healthy volunteers and dementia patients. Methods: Camera-based optical prospective motion correction (PMC) and FID navigator correction were implemented in a gradient and RF-spoiled multi-echo 3D gradient echo sequence for mapping proton density (PD), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*). We studied their effectiveness separately and in concert in young volunteers and then evaluated the navigator correction (NAVcor) with PMC in a group of elderly volunteers and dementia patients. We used spatial homogeneity within white matter (WM) and gray matter (GM) and scan-rescan measures as quality metrics. Results: NAVcor and PMC reduced artifacts and improved the homogeneity and reproducibility of parameter maps. In elderly participants, NAVcor improved scan-rescan reproducibility of parameter maps (coefficient of variation decreased by 14.7% and 11.9% within WM and GM respectively). Spurious inhomogeneities within WM were reduced more in the elderly than in the young cohort (by 9% vs. 2%). PMC increased regional GM/WM contrast and was especially important in the elderly cohort, which moved twice as much as the young cohort. We did not find a significant interaction between the two corrections. Conclusion: Navigator correction and PMC significantly improved the quality of PD, R1, and R2* maps, particularly in less compliant elderly volunteers and dementia patients. <br

Evaluation of deep decoder for image reconstruction of multi-parametric mapping acquisitions

Image reconstruction is the first post-processing step in magnetic resonance (MR) imaging and determines the base quality for further processing. Modern techniques driven by sparsity constraints lead to better image quality and more options to accelerate the acquisition compared to methods strictly based on analytical models of the imaging process. Deep Decoder (DD) [1], a promising approach of this kind, tunes a convolutional neural network (CNN) as an adaptive function transforming a fixed noise vector to the image of interest. The CNN represents an implicit prior on the structure of clean images leading the network to create essentially a denoised or artifact reduced version of the target image. DD has been successfully applied to MR data with Poisson-sampling [2]. Looking for improved image reconstruction of our own multi-parametric mapping (MPM) MR data [3] acquired in sub-sampled cartesian grid pattern, we want to evaluate the applicability of DD. Following [2] the original DD was modified to process axial slices of MPM data of a human head. The subsampled k-space was zero filled, Fourier transformed, sliced in readout direction and zero padded to 256x256 voxels before processing. Final magnitude images were generated by root-sum-of-squares combination across channels. For comparison different number of filters per layer (128 to 512) and iterations (10k to 100k) were tested. Qualitative inspection showed that models using less than 384 filters reconstructed only very blurry images. Output quality generally increased with number of iterations. All reconstructed images retained a significant amount of SENSE ghosting, specifically for high contrast regions like the skull, rendering the results unusable. Since the DD relies on the affinity of CNNs for structures over random noise, this is not surprising and difficult to counteract. With the cartesian sampling pattern given, DD can in consequence only be used to target data without structured artifacts. In conclusion the DD as used in [2] is not directly applicable to data sampled in cartesian fashion. Future research could instead try to include DD for optimized sensitivity estimation in mSENSE or recovering k-space directly in GRAPPA like methods. [1] R. Heckel and P. Hand, arXiv:1810.03982, Feb. 2019. [2] S. Arora, V. Roeloffs, and M. Lustig, in ISMRM 2020. [3] N. Weiskopf et al., Front Neurosci, vol. 7, p. 95, Jun. 2013

Correction of temporal B0-fluctuations in ultra high resolution quantitative multi-parametric mapping (MPM) at 7T

Multi-parametric quantitative MRI enables simultaneous measurement of MR parameters and offers the potential to characterise human brain microstructure. An efficient implementation of this concept, the multi-parameter mapping (MPM) protocol, uses 3 multi-echo 3D FLASH volumes to simultaneously quantify R1, R2*, PD and MT. Ultra high spatial resolution is needed to resolve small structures playing an important role in brain function, e.g. cortical laminae, but means low SNR and high sensitivity to respiration-related variation of the B0 field, particularly at 7T. We addressed B0 field fluctuations by implementing free induction decay (FID) navigators into the MPM protocol, and tested their performance for 500 µm quantitative parameter mapping

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

Distinct roles for the cerebellum, angular gyrus, and middle temporal gyrus in action-feedback monitoring

Action-feedback monitoring is essential to ensure meaningful interactions with the external world. This process involves generating efference copy-based sensory predictions and comparing these with the actual action-feedback. As neural correlates of comparator processes, previous fMRI studies have provided heterogeneous results, including the cerebellum, angular and middle temporal gyrus. However, these studies usually comprised only self-generated actions. Therefore, they might have induced not only action-based prediction errors, but also general sensory mismatch errors. Here, we aimed to disentangle these processes using a custom-made fMRI-compatible movement device, generating active and passive hand movements with identical sensory feedback. Online visual feedback of the hand was presented with a variable delay. Participants had to judge whether the feedback was delayed. Activity in the right cerebellum correlated more positively with delay in active than in passive trials. Interestingly, we also observed activation in the angular and middle temporal gyri, but across both active and passive conditions. This suggests that the cerebellum is a comparator area specific to voluntary action, whereas angular and middle temporal gyri seem to detect more general intersensory conflict. Correlations with behavior and cerebellar activity nevertheless suggest involvement of these temporoparietal areas in processing and awareness of temporal discrepancies in action-feedback monitoring

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

none6siBACKGROUND 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.mixedEl Mendili M.M.; Petracca M.; Podranski K.; Fleysher L.; Cocozza S.; Inglese M.El Mendili, M. M.; Petracca, M.; Podranski, K.; Fleysher, L.; Cocozza, S.; Inglese, M

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 (R 2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R 2 = .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