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Purpose: The aim was to estimate T1 relaxivity of gadobutrol and gadoteric acid in cerebrospinal fluid (CSF) at 3T, to support research on CSF-flow and the glymphatic system in humans utilizing T1 mapping after intrathecal injection.

Methods: Using a phantom, relaxivity was estimated for gadobutrol and gadoteric acid in lumbar CSF and an isotonic solution. All samples were scanned simultaneously using the variable flip angle method with B1 correction, repeated six times on one 3T scanner, and once on a second 3T scanner. Difference in relaxivity between CSF and the isotonic solution were evaluated from the repeated measurements.

Results: There was a significant difference in relaxivity between CSF and the isotonic solution for both gadobutrol and gadoteric acid. The relaxivity for gadobutrol for the respective scanners was estimated to 3.02 ± 0.09 vs. 3.63 L mmol⁻¹ s⁻¹ in CSF and 2.35 ± 0.05 vs. 2.74 L mmol⁻¹ s⁻¹ in isotonic solution. For gadoteric acid, corresponding results were 2.47 ± 0.02 vs. 2.91 L mmol⁻¹ s⁻¹ in CSF and 2.37 ± 0.03 vs. 2.8 L mmol⁻¹ s⁻¹ in isotonic solution. Between the scanners, there was a high correlation (R2 0.998) but an 18% scaling difference in the T1 relaxation rates and corresponding relaxivities.

Conclusions: The relaxivity was higher in CSF than in the isotonic solution, particularly for gadobutrol. Systematic differences in relaxivity between scanners may potentially be corrected using a scaling factor derived from the T1 time of baseline CSF. For CSF studies using T1 mapping with a gadolinium-based contrast agent, we recommend using a CSF-specific relaxivity constant.

This work proposes a structured prior integrated within the Bayesian framework for variable flip angle T1 mapping. The proposed structured prior combines total variation (TV) and L1 norm functions, and is proven to be a proper prior. The TV–L1 prior promotes sparsity in the spatial gradients of the parametric maps, resulting in smooth and coherent image reconstructions. Embedding the prior within the Bayesian framework enables uncertainty quantification for both T1 and M0 estimates. Posterior inference was performed using the No-U-Turn Sampler (NUTS). The proposed method is compared to maximum likelihood estimation and to alternative Bayesian models that employ uniform, Laplace, and bounded TV priors. The results show that the proposed method yields narrower probability density functions, indicating reduced uncertainty. The proposed method also achieves lower variance and exhibits a smaller negative bias, reflecting more stable estimates. Overall, the integration of TV and L1 functions in a prior within the Bayesian framework enhances spatial coherence in T1 mapping and delivers improved uncertainty quantification, making it a promising tool for robust quantitative MRI parameter estimation.

Purpose: To make Deep Image Prior (DIP)-based parameter mapping faster, more accurate, and suitable for clinical applications, with added support for multislice and 3D datasets.

Methods: DIP leverages the inherent structure of an untrained image generator to address various inverse imaging tasks, including denoising. In this study, we enhance DIP-based denoising for parameter mapping with warm-start across neighboring image slices and different patient subjects. This approach leverages spatial similarity to reduce computation time. Additionally, we introduce an early-stopping criterion that selects the denoising level based on MRI signal noise. We further investigate uncertainty calibration through dropout probability tuning to address issues with miscalibrated uncertainty estimates from Monte Carlo dropout. Furthermore, we explore reducing computation time by tuning learning rates and network complexity.

Results: We show that reusing image generator weights with warm-start significantly accelerates the denoising of large datasets, reducing computation time by 78% to 95% across various tasks. The early stopping approach proved effective, eliminating the need to manually select the number of optimization steps. Dropout probability tuning helps mitigate the issue of miscalibrated uncertainty, though further refinements are necessary, particularly to achieve better calibration on a per-pixel level. Additionally, tuning learning rates and network complexity provided valuable insights into optimizing the model for different tasks.

Conclusion: The proposed developments enable DIP-based parameter mapping to become faster, more accurate, and, consequently, more practical and scalable for clinical applications involving larger datasets.

Background: White matter lesions (WML) and dilated perivascular spaces (PVS) are features of small vessel disease (SVD), commonly observed in aging and dementia, with unknown pathophysiology. Human studies have documented contrast accumulation within and in proximity of SVD-lesions. However, whether such observations mainly reflect excessive blood-brain barrier (BBB) leakage, or altered microvascular density in the investigated regions, remains unclear.

Methods: To evaluate the roles of BBB leakage and vascular density in aging and SVD, dynamic contrast enhanced (DCE) MRI was used to estimate the permeability-surface area product (PS) and fractional plasma volume () in normal-appearing brain tissue and in proximity of and within WML and PVS in a population-based cohort (N = 56; 34/22 m/f; age 64 to 84 years). Analysis of variance (ANOVA) was used to assess regional differences in PS and and analysis of covariance (ANCOVA) was used to assess regional differences in PS with and vascular risk as covariates.

Results: Pronounced increases in PS and were observed from normal-appearing white matter (NAWM) to WML peripheries to WMLs. Similar PS and increases were observed from basal ganglia (BG) to BG-PVS. Further, PS in NAWM and white matter (WM) PVS were found to increase with cortex-to-ventricular depth. However, ANCOVA models with as a covariate showed that variance in PS was mainly explained by vp (η2=0.17 to η2=0.35; all p < 10− 3), whereas the effect of region was only borderline-significant when comparing NAWM, WML peripheries and WML (p = 0.03) and non-significant for the other comparisons (p > 0.29).

Conclusions: Our findings support the notion that contrast leakage across the BBB accumulates within and in proximity of SVD-related lesions. However, high contrast accumulation may mainly reflect high vascularization, and to a lesser degree than previously recognized BBB dysfunction.

Blood-brain barrier (BBB) disruption may contribute to cognitive decline, but questions remain whether this association is more pronounced for certain brain regions, such as the hippocampus, or represents a whole-brain mechanism. Further, whether human BBB leakage is triggered by excessive vascular pulsatility, as suggested by animal studies, remains unknown. In a prospective cohort (N = 50; 68-84 years), we used contrast-enhanced MRI to estimate the permeability-surface area product (PS) and fractional plasma volume ( formula presented ), and 4D flow MRI to assess cerebral arterial pulsatility. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) score. We hypothesized that high PS would be associated with high arterial pulsatility, and that links to cognition would be specific to hippocampal PS. For 15 brain regions, PS ranged from 0.38 to 0.85 (·10-3 min-1) and formula presented from 0.79 to 1.78%. Cognition was related to PS (·10-3 min-1) in hippocampus (β = - 2.9; p = 0.006), basal ganglia (β = - 2.3; p = 0.04), white matter (β = - 2.6; p = 0.04), whole-brain (β = - 2.7; p = 0.04) and borderline-related for cortex (β = - 2.7; p = 0.076). Pulsatility was unrelated to PS for all regions (p > 0.19). Our findings suggest PS-cognition links mainly reflect a whole-brain phenomenon with only slightly more pronounced links for the hippocampus, and provide no evidence of excessive pulsatility as a trigger of BBB disruption.

White matter hyperintensities (WMH), perivascular spaces (PVS) and lacunes are common MRI features of small vessel disease (SVD). However, no shared underlying pathological mechanism has been identified. We investigated whether SVD burden, in terms of WMH, PVS and lacune status, was related to changes in the cerebral arterial wall by applying global cerebral pulse wave velocity (gcPWV) measurements, a newly described marker of cerebral vascular stiffness. In a population-based cohort of 190 individuals, 66–85 years old, SVD features were estimated from T1-weighted and FLAIR images while gcPWV was estimated from 4D flow MRI data. Additionally, the gcPWV’s stability to variations in field-of-view was analyzed. The gcPWV was 10.82 (3.94) m/s and displayed a significant correlation to WMH and white matter PVS volume (r = 0.29, p < 0.001; r = 0.21, p = 0.004 respectively from nonparametric tests) that persisted after adjusting for age, blood pressure variables, body mass index, ApoB/A1 ratio, smoking as well as cerebral pulsatility index, a previously suggested early marker of SVD. The gcPWV displayed satisfactory stability to field-of-view variations. Our results suggest that SVD is accompanied by changes in the cerebral arterial wall that can be captured by considering the velocity of the pulse wave transmission through the cerebral arterial network.

The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.

To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.

Purpose: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

Methods: We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping.

Results: We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior.

Conclusion: DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.

PURPOSE: During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored.

METHODS: In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test.

RESULTS: Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality.

CONCLUSION: We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.

Concomitant fields are the unwanted transverse components that arise when spatial encoding gradients are applied in MRI. We measured the changing gradient magnetic field at multiple locations inside the scanner and examined the internal distribution and linearity of the three vector components of the field. Our results illustrate some not-so-obvious spatial characteristics of the gradient field, which can seem unintuitive at first glance, but are quite reasonable when considering electromagnetic theory and MRI-scanner physics constraints.