Juan Antonio Hernandez Tamames

Perfusion MRI, especially dynamic susceptibility contrast (DSC), is vital for glioma diagnosis and monitoring. While gradient‐echo DSC is the current recommended implementation, combined spin‐ and gradient‐echo (SAGE) DSC additionally allows for vessel size imaging (VSI) and has shown feasibility for microvascular characterization at 3 T. Higher field strength MRI offers increased contrast, resolution and signal‐to‐noise ratio, providing potential to improve microvascular characterization. This feasibility study aimed to implement a protocol for SAGE‐DSC at 7 T and study its potential for microvascular characterization. SAGE‐DSC was acquired in eight patients undergoing treatment for glioma (four females, median age 57 years) at 3 and 7 T. Data were analyzed using the temporal signal‐to‐noise ratio (tSNR) and contrast‐to‐noise ratio (CNR) and by comparing perfusion maps, VSI and vascular architectural imaging (VAI). Simulations were performed to study the relationship between vessel size and for SAGE‐DSC at 3 and 7 T. Contrast agent dosage could be reduced while maintaining higher CNR at 7 T (but lower tSNR). Assessment by an experienced neuroradiologist showed similar rCBV map quality at 3 and 7 T. VSI maps showed the same normal‐appearing gray‐to‐white matter ratios while in‐plane resolution improved at least 52% at 7 T. VAI hysteresis loops at both field strengths traversed the same counter‐clockwise direction, with increased loop areas reflecting increased susceptibility effects at 7 T. Simulations confirmed the higher sensitivity of gradient‐echo and the higher specificity of spin‐echo to the microvasculature, with peak spin‐echo specificity moving from 3 μm at 3 T towards 1.5 μm at 7 T. This study confirmed the feasibility of using SAGE‐DSC at 7 T for microvascular characterization. rCBV and VSI maps show similar behavior while allowing for reduced contrast agent dosage and improved resolution. The spin‐echo specificity shifted towards smaller vessel radii for increasing field strengths. These findings form the basis for exploration and validation in larger patient populations.

High-resolution (HR) quantitative MRI (qMRI) relaxometry provides objective tissue characterization but remains clinically underutilized due to lengthy acquisition times. We propose a physics-informed, self-supervised framework for qMRI super-resolution that uses routinely acquired HR weighted MRI (wMRI) scans as guidance, thus, removing the necessity for HR qMRI ground truth during training. We formulate super-resolution as Bayesian maximum a posteriori inference, minimizing two discrepancies: (1) between HR images synthesized from super-resolved qMRI maps and acquired wMRI guides via forward signal models, and (2) between acquired LR qMRI and downsampled predictions. This physics-informed objective allows the models to learn from clinical wMRI without HR qMRI supervision. To validate the concept, we generate training data by synthesizing wMRI guides from HR qMRI using signal equations, then degrading qMRI resolution via k-space truncation. A deep neural network learns the super-resolution mapping. Ablation experiments demonstrate that T1-weighted images primarily enhance T1 maps, T2-weighted images improve T2 maps, and combined guidance optimally enhances all parameters simultaneously. Validation on independently acquired in-vivo data from a different qMRI sequence confirms cross-qMRI sequence generalizability. Models trained on synthetic data can produce super-resolved maps from a 1-minute acquisition with quality comparable to a 5-minute reference scan, leveraging the scanner-independent nature of relaxometry parameters. By decoupling training from HR qMRI requirement, our framework enables fast qMRI acquisitions enhanced via routine clinical images, offering a practical pathway for integrating quantitative relaxometry into clinical workflows with acceptable additional scan time.

Dynamic susceptibility contrast (DSC) MRI is commonly part of brain tumor imaging. For quantitative analysis, measurement of the arterial input function and tissue concentration time curve is required. Usually, a linear relationship between the MR signal changes and contrast agent concentration ([Gd]) is assumed, even though this is a known simplification. The aim of this study was to develop a realistic 3D simulation model as an efficient method to assess the relationship between ΔR₂^(*) and [Gd] both in whole blood and brain tissue. We modified an open‐source 3D simulation model to study different red blood cell configurations for assessing whole‐blood ΔR₂^(*) versus [Gd]. The results were validated against previously obtained 2D data and in vitro data. Furthermore, hematocrit levels (30%–50%) and field strengths (1.5–3.0–7.0 T) were varied. Subsequently, realistic tumor vascular networks were derived from intraoperative high framerate Doppler ultrasound data to study the influence of vascular structure and orientation with respect to the main magnetic field (1.5–3.0–7.0 T) for the calculation of ΔR₂^(*) versus [Gd] in brain tissue. For whole blood, good agreement of the 3D model was found with in vitro and 2D simulation data when red blood cells were aligned with the blood flow. For brain tissue, minor differences were found between the vascular networks. The effect of vessel direction with respect to B₀ was apparent in case of clear directionality of the main vessels. The dependency on field strength agreed with previous reports. In conclusion, we have shown that the relationship between ΔR₂^(*) and [Gd] is affected by the organization of red blood cells and orientation of blood vessels with respect to the main magnetic field, as well as the field strength. These findings are important for further optimization of the realistic 3D model that could eventually be used to improve the estimation of hemodynamic parameters from DSC‐MRI.

Purpose: Dynamic susceptibility contrast (DSC) MRI is commonly part of the clinical brain tumor imaging protocol. Usually, a preload of contrast agent is administered to minimize contrast‐leakage T ₁ effects. However, recent studies have indicated that with adaptation of scan parameters (in particular, low flip angle), a preload is not required. For advanced approaches, like MR vascular fingerprinting (MRVF), which is based on combining gradient‐echo and spin‐echo readouts, excitation flip angles cannot easily be reduced, but postprocessing might alleviate leakage effects. In this study, the aim was to verify whether DSC‐MRVF can be performed without preload. Methods: A previously established simulation model was used to create a dictionary of DSC‐MRVF signals resulting from a double‐bolus injection with varying input values for permeability, vessel radius, and relative cerebral blood volume (rCBV). A sensitivity analysis based on a Cramer Rao Lower Bound approach was performed, from which the logarithmic ratio of standard deviations for parameter estimation was derived. A ratio above zero corresponded with a better determination without preload. Two brain tumor patients were included for validation. Results: The results show that the standard deviation for determining permeability was lower without preload, whereas for determining rCBV, the opposite result was found. For vessel radius, the standard deviation was similar with and without preload. The in vivo data reasonably agreed with the simulation results. Conclusion: This work has shown the potential of DSC‐MRVF with a single bolus injection for determining vessel radius and permeability, although for rCBV, a preload is still advisable.

Diffusion‐weighted magnetic resonance imaging (DW‐MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW‐MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state‐of‐the‐art of DW‐MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW‐MRI method is essential. In this article, we review the current landscape of DW‐MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b‐tensor diffusion encoding or joint diffusion‐relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter‐/intra‐scanner robustness, and on assessing histological validity in broader contexts.Level of Evidence: NATechnical Efficacy: Stage 2

BACKGROUND AND PURPOSE: DTI is prone to susceptibility artifacts. Air in the paranasal sinuses can cause field inhomogeneity, thus affecting measurements. Children often have mucus in their sinuses or no pneumatization of them. This study investigated the influence of lack of air in the paranasal sinuses on measurements of WM diffusion characteristics. MATERIALS AND METHODS: The study was embedded in the Generation R Study, a prospective population-based birth cohort in Rotterdam (the Netherlands). Brain MR imaging studies (1070 children, 6–9 years of age) were evaluated for mucosal thickening of the paranasal sinuses. Nonaeration of the paranasal sinuses (modified Lund-Mackay score) was compared with that in a randomly selected control group. The relationship between nonaerated paranasal sinuses and fractional anisotropy and mean diffusivity in the DTI fiber tracts was evaluated using ANCOVA and independent t tests. RESULTS: The prevalence of mucosal thickening was 10.2% (109/1070). The mean modified Lund-Mackay score was 6.87 (SD, 3.76). In 52.3% (57/109), ≥ 1 paranasal sinus was not pneumatized. The results are reported in effect sizes (Cohen’s d). Lower mean fractional anisotropy values were found in the uncinate fasciculus (right uncinate fasciculus/right frontal sinus, d = –0.60), superior longitudinal fasciculus (right superior longitudinal fasciculus/right ethmoid sinus, d = –0.56; right superior longitudinal fasciculus/right sphenoid sinus, d = –2.09), and cingulate bundle (right cingulum bundle/right sphenoid sinus, d = –1.28; left cingulum bundle/left sphenoid sinus, d = –1.49). Higher mean diffusivity values were found in the forceps major/right and left sphenoid sinuses, d = 0.78. CONCLUSIONS: Nonaeration of the paranasal sinuses is a common incidental finding on pediatric MR imaging brain scans. The amount of air in the paranasal sinuses can influence fractional anisotropy and, to a lesser degree, mean diffusivity values of WM tracts and should be considered in DTI studies in pediatric populations.