<i>In Vivo</i>NMR Spectroscopy – Static Aspects

doi:10.1002/9781119382461.ch2

Cited by 5 publications

(1 citation statement)

References 395 publications

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“…Endogenous magnetic resonance biomarkers are immediately translatable between in-vitro and in-vivo settings. Magnetic Resonance experiments on cells reflect the time scale on which molecular response builds up-on the order of hours -following treatment using drugs or radiation [5]. This enables early evaluation of the efficacy and the toxicity of specific therapies.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance biomarkers for timely diagnostic of radiation dose-rate effects

Zagrean-Tuza

Suditu²,

Popescu

et al. 2023

Preprint

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Diagnostic of radiation effects can be obtained within hours from delivery relying on spectroscopic detection of cell metabolite concentrations. Clinical and pre-clinical studies show that radiation delivery with elevated dose-rates can achieve tumor suppression while minimizing toxicity to surrounding areas. Diagnostic biomarkers detected on short timescales are needed to orient high dose-rate radiation delivery. We have designed an 1H magnetic resonance approach to observe metabolite concentrations, in particular Choline, Creatine, and Lactate, in order to detect radiation dose and dose-rate effects within hours from radiation delivery. The results of our metabolic profiling method in glioblastoma cells are consistent with observations from clinical studies guided by magnetic resonance spectroscopy for radiotherapy of head tumors. At 5 Gy/min we have observed increases in lactate concentrations and decreases in [Cho]/[Cr] ratios at increasing radiation doses. An increase of the radiation dose-rate to 35 Gy/min is correlated with an increase of [Cho]/[Cr] consistent with a reduction in radiation-induced oxidative effects at high dose-rates. The observed biomarkers can be translated for radiation pulse sequences optimization.

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Section: Introductionmentioning

confidence: 99%

Magnetic resonance biomarkers for timely diagnostic of radiation dose-rate effects

Zagrean-Tuza

Suditu²,

Popescu

et al. 2023

Preprint

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Selectively Probing the Magnetic Resonance Signals of γ‐Aminobutyric Acid in Human Brains In Vivo

Yang

Liu

et al. 2023

Magnetic Resonance Imaging

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BackgroundGamma‐aminobutyric acid (GABA) is an inhibitory neurotransmitter in human brains, playing a role in the pathogenesis of various psychiatric disorders. Current methods have some non‐neglectable shortcomings and noninvasive and accurate detection of GABA in human brains is long‐term challenge.PurposeTo develop a pulse sequence capable of selectively detecting and quantifying the 1H signal of GABA in human brains based on optimal controlled spin singlet order.Study TypeProspective.Subjects/PhantomA phantom of GABA (pH = 7.3 ± 0.1) and 11 healthy subjects (5 females and 6 males, body mass index: 21 ± 3 kg/m2, age: 25 ± 4 years).Field Strength/Sequence7 Tesla, 3 Tesla, GABA‐targeted magnetic resonance spectroscopy (GABA‐MRS‐7 T, GABA‐MRS‐3 T), magnetization prepared two rapid acquisition gradient echoes sequence.AssessmentBy using the developed pulse sequences applied on the phantom and healthy subjects, the signals of GABA were successfully selectively probed. Quantification of the signals yields the concentration of GABA in the dorsal anterior cingulate cortex (dACC) in human brains.Statistical TestsFrequency.ResultsThe 1H signals of GABA in the phantom and in the human brains of healthy subjects were successfully detected. The concentration of GABA in the dACC of human brains was 3.3 ± 1.5 mM.Data ConclusionThe developed pulse sequences can be used to selectively probe the 1H MR signals of GABA in human brains in vivo.Evidence Level1Technical Efficacy Stage1

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MRSCloud: A cloud‐basedMRStool for basis set simulation

Hui

Saleh

Zöllner

et al. 2022

Magnetic Resonance in Med

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Purpose The purpose of this study is to present a cloud‐based spectral simulation tool “MRSCloud,” which allows MRS users to simulate a vendor‐specific and sequence‐specific basis set online in a convenient and time‐efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi‐LASER localization at 3 T. Methods The MRSCloud tool was built on the spectral simulation functionality in the FID‐A software package. We added three extensions to accelerate computation (ie, one‐dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID‐A and MARSS, and a phantom‐acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio. Results Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID‐A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N‐acetylaspartate at 0.96. Conclusions Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.

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In VivoNMR Spectroscopy – Static Aspects

Cited by 5 publications

References 395 publications

Magnetic resonance biomarkers for timely diagnostic of radiation dose-rate effects

Magnetic resonance biomarkers for timely diagnostic of radiation dose-rate effects

Selectively Probing the Magnetic Resonance Signals of γ‐Aminobutyric Acid in Human Brains In Vivo

MRSCloud: A cloud‐basedMRStool for basis set simulation

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