A General Model to Calculate the Spin-Lattice Relaxation Rate (R1) of Blood, Accounting for Hematocrit, Oxygen Saturation, Oxygen Partial Pressure, and Magnetic Field Strength Under Hyperoxic Conditions

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T

PURPOSE

Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI.

METHODS

Dynamic OE-MRI was performed on healthy participants using a dual-echo multi-slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with nonsmokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors were assessed.

RESULTS

ICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed the opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and nonsmokers. The scan-rescan repeatability and the ICA pipeline repeatability were good.

CONCLUSION

The developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and nonsmokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies.

Determination of oxygen relaxivity in oxygen nanobubbles at 3 and 7 Tesla

OBJECTIVE

Oxygen-loaded nanobubbles have shown potential for reducing tumour hypoxia and improving treatment outcomes, however, it remains difficult to noninvasively measure the changes in partial pressure of oxygen (PO₂) in vivo. The linear relationship between PO₂ and longitudinal relaxation rate (R₁) has been used to noninvasively infer PO₂ in vitreous and cerebrospinal fluid, and therefore, this experiment aimed to investigate whether R₁ is a suitable measurement to study oxygen delivery from such oxygen carriers.

METHODS

T₁ mapping was used to measure R₁ in phantoms containing nanobubbles with varied PO₂ to measure the relaxivity of oxygen (r_1Ox) in the phantoms at 7 and 3 T. These measurements were used to estimate the limit of detection (LOD) in two experimental settings: preclinical 7 T and clinical 3 T MRI.

RESULTS

The r_1Ox in the nanobubble solution was 0.00057 and 0.000235 s^-1/mmHg, corresponding to a LOD of 111 and 103 mmHg with 95% confidence at 7 and 3 T, respectively.

CONCLUSION

This suggests that T₁ mapping could provide a noninvasive method of measuring a > 100 mmHg oxygen delivery from therapeutic nanobubbles.

Modeling the Effect of Hyperoxia on the Spin-Lattice Relaxation Rate R1 of Tissues

PURPOSE

Inducing hyperoxia in tissues is common practice in several areas of research, including oxygen-enhanced MRI (OE-MRI), which attempts to use the resulting signal changes to detect regions of tumor hypoxia or pulmonary disease. The linear relationship between PO₂ and R1 has been reproduced in phantom solutions and body fluids such as vitreous fluid; however, in tissue and blood experiments, factors such as changes in deoxyhemoglobin levels can also affect the ΔR1.

THEORY AND METHODS

This manuscript proposes a three-compartment model for estimating the hyperoxia-induced changes in R1 of tissues depending on B0, SO₂ , blood volume, hematocrit, oxygen extraction fraction, and changes in blood and tissue PO₂ . The model contains two blood compartments (arterial and venous) and a tissue compartment. This model has been designed to be easy for researchers to tailor to their tissue of interest by substituting their preferred model for tissue oxygen diffusion and consumption. A specific application of the model is demonstrated by calculating the resulting ΔR1 expected in healthy, hypoxic and necrotic tumor tissues. In addition, the effect of sex-based hematocrit differences on ΔR1 is assessed.

RESULTS

The ΔR1 values predicted by the model are consistent with reported literature OE-MRI results: with larger positive changes in the vascular periphery than hypoxic and necrotic regions. The observed sex-based differences in ΔR1 agree with findings by Kindvall et al. suggesting that differences in hematocrit levels may sometimes be a confounding factor in ΔR1.

CONCLUSION

This model can be used to estimate the expected tissue ΔR1 in oxygen-enhanced MRI experiments.

Using Variable Flip Angle (VFA) and Modified Look-Locker Inversion Recovery (MOLLI) T1 mapping in clinical OE-MRI

BACKGROUND AND PURPOSE

The imaging technique known as Oxygen-Enhanced MRI is under development as a noninvasive technique for imaging hypoxia in tumours and pulmonary diseases. While promising results have been shown in preclinical experiments, clinical studies have mentioned experiencing difficulties with patient motion, image registration, and the limitations of single-slice images compared to 3D volumes. As clinical studies begin to assess feasibility of using OE-MRI in patients, it is important for researchers to communicate about the practical challenges experienced when using OE-MRI on patients to help the technique advance.

MATERIALS AND METHODS

We report on our experience with using two types of T1 mapping (MOLLI and VFA) for a recently completed OE-MRI clinical study on oropharyngeal squamous cell carcinoma.

RESULTS

We report: (1) the artefacts and practical difficulties encountered in this study; (2) the difference in estimated T1 from each method used - the VFA T1 estimation was higher than the MOLLI estimation by 27% on average; (3) the standard deviation within the tumour ROIs - there was no significant difference in the standard deviation seen within the tumour ROIs from the VFA versus MOLLI; and (4) the OE-MRI response collected from either method. Lastly, we collated the MRI acquisition details from over 45 relevant manuscripts as a convenient reference for researchers planning future studies.

CONCLUSION

We have reported our practical experience from an OE-MRI clinical study, with the aim that sharing this is helpful to researchers planning future studies. In this study, VFA was a more useful technique for using OE-MRI in tumours than MOLLI T1 mapping.