O₂‐sensitive MRI distinguishes brain tumor versus radiation necrosis in murine models

Abstract

Purpose To quantify the relationship between 1H longitudinal relaxation rate constant, R1, and O2 concentration (relaxivity, r1) in tissue and to quantify O2-driven changes in R1 (ΔR1) during a breathing gas challenge in normal brain, radiation-induced lesions, and tumor lesions. Methods Relaxivity determination. R1 data were collected in control-state mice (n=4) during three different breathing gas (and thus tissue O2) conditions. In parallel experiments, pO2 was measured in the thalamus of control-state mice (n=4) under the same breathing gas conditions using an O2-sensitive micro-probe. The relaxivity of tissue O2 was calculated using the R1 and pO2 data. Pathology determination. R1 data were collected in control-state (n=4) mice, a glioma model (n=7), and a radiation necrosis model (n=6) during two breathing gas (thus tissue O2) conditions. R1 and ΔR1 were calculated for each cohort. Results O2 r1 in the brain was 9×10−4±3×10−4 mmHg−1s−1 at 4.7T. R1 and ΔR1 measurements distinguished radiation necrosis from tumor (p<0.03 and p<0.01, respectively). Conclusion The relaxivity of O2 in brain is determined. R1 and ΔR1 measurements differentiate tumor lesions from radiation necrosis lesions in the mouse models. These pathologies are difficult to distinguish by traditional imaging techniques; O2-driven changes in R1 holds promise in this regard.

Developing oxygen-enhanced magnetic resonance imaging as a prognostic biomarker of radiation response

“…1) are not dissimilar from previous reports for this tumor type at 4.7 T [33, 34]. Present results (ΔR 1 = −0.006 to 0.121 s −1 ) are also comparable to reports of orthotopic gliomas implanted in mice [52], though overall population mean response was somewhat higher (0.04 vs. 0.01 s −1 with oxygen or carbogen) than reported for the orthotopic gliomas or squamous cell carcinomas with respect to hyperbaric oxygen [31]. …”

“…However, for the Air–breathing group those with improved oxygenation (positive ΔΔR 1 ), did as well as those with oxygen breathing, whereas those with negative ΔΔR 1 did significantly less well. Noting the reported relaxivity of oxygen in tissue at 4.7 T (r 1 = 9 *10–4 Torr −1 .s −1 [52]) the observed changes (ΔR 1 = −0.006 to 0.121) would correspond with ΔpO 2 = −6 to +134 Torr in response to oxygen breathing challenge and ΔΔR 1 ± 0.05 s −1 implies ΔΔpO 2 = 55 Torr suggesting that the observed changes in oxygenation would cause distinct differences in radiation response.…”

In vivoimaging of prodromal hippocampus CA1 subfield oxidative stress in models of Alzheimer disease and Angelman syndrome

“…The results of the present xanthine/xanthine oxidase experiment validate a key assumption of Quest MRI: sustained production of free radicals has a net detectable impact as an effective paramagnetic 1/T 1 contrast mechanism in the absence of a spin trap contrast agent and potentially confounding variables that are present in vivo (15,19,20,52,53). It is commonly thought that because free radicals have very short lifetimes and low concentrations in vivo, they would have a negligible impact on water 1/T 1 (20,52,(54)(55)(56)(57)(58). However, oxidative stress is a condition that is defined as a sustained, net production of free radicals, and the available data clearly demonstrate robust detection via Quest MRI under these conditions (15,19,20,53).…”

In Vivo Chemiluminescent Imaging Agents for Nitroreductase and Tissue Oxygenation

“…These targeting strategies have been cross-validated in comparison to other methods for direct measurement of tissue oxygenation including pO 2 electrodes, 13 magnetic resonance imaging (MRI) of 19 F-labeled perfluorocarbons, 14 dynamic contrast enhanced MRI, 15 tissue 16 or blood 17 oxygen level dependent (TOLD or BOLD) MRI, MRI-based oxygen imaging (MOXI), 18 and multispectral optoacoustic (MSOT) imaging of hemoglobin and oxygenated hemoglobin. 19 These techniques offer significant clinical opportunity, but require expensive instrumentation and highly trained experts that increase the cost and lower the throughput of these technologies.…”