Recently, the FDA-approved iron oxide nanoparticle, ferumoxytol, has been found to enhance the efficacy of pharmacological ascorbate (AscH⁻) in treating glioblastoma, as AscH⁻ reduces the Fe³⁺ sites in the nanoparticle core. Given the iron oxidation state specificity of T2* relaxation mapping, this study aims to investigate the ability of T2* relaxation to monitor the reduction of ferumoxytol by AscH⁻ with respect to its in vitro therapeutic enhancement. This study employed an in vitro glioblastoma MRI model system to investigate the chemical interaction of ferumoxytol with T₂* mapping. Lipofectamine was utilized to facilitate ferumoxytol internalization and assess intracellular versus extracellular chemistry. In vitro T₂* mapping successfully detected an AscH⁻-mediated reduction of ferumoxytol (25.6 ms versus 2.8 ms for FMX alone). The T₂* relaxation technique identified the release of Fe²⁺ from ferumoxytol by AscH⁻ in glioblastoma cells. However, the high iron content of ferumoxytol limited T2* ability to differentiate between the external and internal reduction of ferumoxytol by AscH⁻ (ΔT₂* = +839% for external FMX and +1112% for internal FMX reduction). Notably, the internalization of ferumoxytol significantly enhances its ability to promote AscH⁻ toxicity (dose enhancement ratio for extracellular FMX = 1.16 versus 1.54 for intracellular FMX). These data provide valuable insights into the MR-based nanotheranostic application of ferumoxytol and AscH⁻ therapy for glioblastoma management. Future developmental efforts, such as FMX surface modifications, may be warranted to enhance this approach further.

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.

The MR transverse relaxation rate, $R_2^{*}$, has been widely used to detect iron and myelin content in tissue. However, it is also sensitive to macroscopic B₀ inhomogeneities. One approach to correct for the B₀ effect is to fit gradient-echo signals with the three-parameter model, a sinc function-weighted monoexponential decay. However, such three-parameter models are subject to increased noise sensitivity. To address this issue, this study presents a two-stage fitting procedure based on the three-parameter model to mitigate the B₀ effect and reduce the noise sensitivity of $R_2^{*}$ measurement in the mouse brain at 7T. MRI scans were performed on eight healthy mice. The gradient-echo signals were fitted with the two-stage fitting procedure to generate $R_{2corr\_t}^{*}$. The signals were also fitted with the monoexponential and three-parameter models to generate $R_{2nocorr}^{*}$ and $R_{2corr}^{*}$, respectively. Regions of interest (ROIs), including the corpus callosum, internal capsule, somatosensory cortex, caudo-putamen, thalamus, and lateral ventricle, were selected to evaluate the within-ROI mean and standard deviation (SD) of the $R_2^{*}$ measurements. The results showed that the Akaike information criterion of the monoexponential model was significantly reduced by using the three-parameter model in the selected ROIs (p = 0.0039–0.0078). However, the within-ROI SD of $R_{2corr}^{*}$ using the three-parameter model was significantly higher than that of the $R_{2nocorr}^{*}$ in the internal capsule, caudo-putamen, and thalamus regions (p = 0.0039), a consequence partially due to the increased noise sensitivity of the three-parameter model. With the two-stage fitting procedure, the within-ROI SD of $R_{2corr}^{*}$ was significantly reduced by 7.7–30.2% in all ROIs, except for the somatosensory cortex region with a fast in-plane variation of the B₀ gradient field (p = 0.0039–0.0078). These results support the utilization of the two-stage fitting procedure to mitigate the B₀ effect and reduce noise sensitivity for $R_2^{*}$ measurement in the mouse brain.