Imaging Blood-Brain Barrier Permeability Through MRI in Pediatric Sickle Cell Disease: A Feasibility Study

Blood-brain barrier breakdown in COVID-19 ICU survivors: an MRI pilot study

Objectives

Coronavirus disease 2019 (COVID-19) results in severe inflammation at the acute stage. Chronic neuroinflammation and abnormal immunological response have been suggested to be the contributors to neuro-long-COVID, but direct evidence has been scarce. This study aims to determine the integrity of the blood-brain barrier (BBB) in COVID-19 intensive care unit (ICU) survivors using a novel MRI technique.

Methods

COVID-19 ICU survivors (n=7) and age and sex-matched control participants (n=17) were recruited from June 2021 to March 2023. None of the control participants were hospitalized due to COVID-19 infection. The COVID-19 ICU survivors were studied at 98.6 ± 14.9 days after their discharge from ICU. A non-invasive MRI technique was used to assess the BBB permeability to water molecules, in terms of permeability surface area-product (PS) in the units of mL/100 g/min.

Results

PS was significantly higher in COVID-19 ICU survivors (p=0.038) when compared to the controls, with values of 153.1 ± 20.9 mL/100 g/min and 132.5 ± 20.7 mL/100 g/min, respectively. In contrast, there were no significant differences in whole-brain cerebral blood flow (p=0.649) or brain volume (p=0.471) between the groups.

Conclusions

There is preliminary evidence of a chronic BBB breakdown in COVID-19 survivors who had a severe acute infection, suggesting a plausible contributor to neurological long-COVID symptoms.

Blood-brain barrier water exchange measurements using contrast-enhanced ASL

A technique for quantifying regional blood-brain barrier (BBB) water exchange rates using contrast-enhanced arterial spin labelling (CE-ASL) is presented and evaluated in simulations and in vivo. The two-compartment ASL model describes the water exchange rate from blood to tissue,k b , but to estimatek b in practice it is necessary to separate the intra- and extravascular signals. This is challenging in standard ASL data owing to the small difference inT 1 values. Here, a gadolinium-based contrast agent is used to increase thisT 1 difference and enable the signal components to be disentangled. The optimal post-contrast bloodT 1 (T 1 , b post ) at 3 T was determined in a sensitivity analysis, and the accuracy and precision of the method quantified using Monte Carlo simulations. Proof-of-concept data were acquired in six healthy volunteers (five female, age range 24-46 years). The sensitivity analysis identified the optimalT 1 , b post at 3 T as 0.8 s. Simulations showed thatk b could be estimated in individual cortical regions with a relative error ϵ < 1 % and coefficient of variation CoV = 30 %; however, a high dependence on bloodT 1 was also observed. In volunteer data, mean parameter values in grey matter were: arterial transit timet A = 1 . 15 ± 0 . 49  s, cerebral blood flow f = 58 . 0 ± 14 . 3  mL blood/min/100 mL tissue and water exchange ratek b = 2 . 32 ± 2 . 49 s-1 . CE-ASL can provide regional BBB water exchange rate estimates; however, the clinical utility of the technique is dependent on the achievable accuracy of measuredT 1 values.

Blood-brain barrier water exchange measurements using FEXI: Impact of modeling paradigm and relaxation time effects

PURPOSE

To evaluate potential modeling paradigms and the impact of relaxation time effects on human blood-brain barrier (BBB) water exchange measurements using FEXI (BBB-FEXI), and to quantify the accuracy, precision, and repeatability of BBB-FEXI exchange rate estimates at 3 T $$ \mathrm{T} $$ .

METHODS

Three modeling paradigms were evaluated: (i) the apparent exchange rate (AXR) model; (ii) a two-compartment model (2 CM $$ 2\mathrm{CM} $$ ) explicitly representing intra- and extravascular signal components, and (iii) a two-compartment model additionally accounting for finite compartmentalT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ relaxation times (2 CM r $$ 2{\mathrm{CM}}_r $$ ). Each model had three free parameters. Simulations quantified biases introduced by the assumption of infinite relaxation times in the AXR and2 CM $$ 2\mathrm{CM} $$ models, as well as the accuracy and precision of all three models. The scan-rescan repeatability of all paradigms was quantified for the first time in vivo in 10 healthy volunteers (age range 23-52 years; five female).

RESULTS

The assumption of infinite relaxation times yielded exchange rate errors in simulations up to 42%/14% in the AXR/2 CM $$ 2\mathrm{CM} $$ models, respectively. Accuracy was highest in the compartmental models; precision was best in the AXR model. Scan-rescan repeatability in vivo was good for all models, with negligible bias and repeatability coefficients in grey matter ofRC AXR = 0 . 43 $$ {\mathrm{RC}}_{\mathrm{AXR}}=0.43 $$  s - 1 $$ {\mathrm{s}}^{-1} $$ ,RC 2 CM = 0 . 51 $$ {\mathrm{RC}}_{2\mathrm{CM}}=0.51 $$  s - 1 $$ {\mathrm{s}}^{-1} $$ , andRC 2 CM r = 0 . 61 $$ {\mathrm{RC}}_{2{\mathrm{CM}}_r}=0.61 $$  s - 1 $$ {\mathrm{s}}^{-1} $$ .

CONCLUSION

Compartmental modelling of BBB-FEXI signals can provide accurate and repeatable measurements of BBB water exchange; however, relaxation time and partial volume effects may cause model-dependent biases.

Red cell exchange transfusions increase cerebral capillary transit times and may alter oxygen extraction in sickle cell disease

Persons with sickle cell disease (SCD) suffer from chronic hemolytic anemia, reduced blood oxygen content, and lifelong risk of silent and overt stroke. Major conventional stroke risk factors are absent in most individuals with SCD, yet nearly 50% have evidence of brain infarcts by the age of 30 years, indicating alternative etiologies for ischemia. We investigated whether radiological evidence of accelerated blood water transit through capillaries, visible on arterial spin labeling (ASL) magnetic resonance imaging, reduces following transfusion-induced increases in hemoglobin and relates to oxygen extraction fraction (OEF). Neurological evaluation along with anatomical and hemodynamic imaging with cerebral blood flow (CBF)-weighted pseudocontinuous ASL and OEF imaging with T2 -relaxation-under-spin-tagging were applied in sequence before and after blood transfusion therapy (n = 32) and in a comparator cohort of nontransfused SCD participants on hydroxyurea therapy scanned at two time points to assess stability without interim intervention (n = 13). OEF was calculated separately using models derived from human hemoglobin-F, hemoglobin-A, and hemoglobin-S. Gray matter CBF and dural sinus signal, indicative of rapid blood transit, were evaluated at each time point and compared with OEF using paired statistical tests (significance: two-sided p < 0.05). No significant change in sinus signal was observed in nontransfused participants (p = 0.650), but a reduction was observed in transfused participants (p = 0.034), consistent with slower red cell transit following transfusion. The dural sinus signal intensity was inversely associated with OEF pretransfusion (p = 0.011), but not posttransfusion. Study findings suggest that transfusion-induced increases in total hemoglobin may lengthen blood transit times through cerebral capillaries and alter cerebral OEF in SCD.

Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence

Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.