511 research outputs found
Relevance of electron spin dissipative processes to dynamic nuclear polarization via thermal mixing
The available theoretical approaches aiming at describing Dynamic Nuclear
spin Polarization (DNP) in solutions containing molecules of biomedical
interest and paramagnetic centers are not able to model the behaviour observed
upon varying the concentration of trityl radicals or the polarization
enhancement caused by moderate addition of gadolinium complexes. In this
manuscript, we first show experimentally that the nuclear steady state
polarization reached in solutions of pyruvic acid with 15 mM trityl radicals is
substantially independent from the average internuclear distance. This
evidences a leading role of electron (over nuclear) spin relaxation processes
in determining the ultimate performances of DNP. Accordingly, we have devised a
variant of the Thermal Mixing model for inhomogenously broadened electron
resonance lines which includes a relaxation term describing the exchange of
magnetic anisotropy energy of the electron spin system with the lattice. Thanks
to this additional term, the dependence of the nuclear polarization on the
electron concentration can be properly accounted for. Moreover, the model
predicts a strong increase of the final polarization on shortening the electron
spin-lattice relaxation time, providing a possible explanation for the effect
of gadolinium doping.Comment: 13 pages, 12 figure
Role of the glassy dynamics and thermal mixing in the dynamic nuclear polarization and relaxation mechanisms of pyruvic acid
The temperature dependence of H and C nuclear spin-lattice
relaxation rate has been studied in the 1.6 K - 4.2 K temperature range
in pure pyruvic acid and in pyruvic acid containing trityl radicals at a
concentration of 15 mM. The temperature dependence of is found to
follow a quadratic power law for both nuclei in the two samples. Remarkably the
same temperature dependence is displayed also by the electron spin-lattice
relaxation rate in the sample containing radicals. These results are
explained by considering the effect of the structural dynamics on the
relaxation rates in pyruvic acid. Dynamic nuclear polarization experiments show
that below 4 K the C build up rate scales with , in
analogy to C and consistently with a thermal mixing scenario
where all the electrons are collectively involved in the dynamic nuclear
polarization process and the nuclear spin reservoir is in good thermal contact
with the electron spin system.Comment: 14 pages, 13 figure
Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data
Electron and nuclear spin dynamics in the thermal mixing model of dynamic nuclear polarization
A novel mathematical treatment is proposed for computing the time evolution
of dynamic nuclear polarization processes in the low temperature thermal mixing
regime. Without assuming any a priori analytical form for the electron
polarization, our approach provides a quantitative picture of the steady state
that recovers the well known Borghini prediction based on thermodynamics
arguments, as long as the electrons-nuclei transition rates are fast compared
to the other relevant time scales. Substantially different final polarization
levels are achieved instead when the latter assumption is relaxed in the
presence of a nuclear leakage term, even though very weak, suggesting a
possible explanation for the deviation between the measured steady state
polarizations and the Borghini prediction. The proposed methodology also allows
to calculate nuclear polarization and relaxation times, once specified the
electrons/nuclei concentration ratio and the typical rates of the microscopic
processes involving the two spin species. Numerical results are shown to
account for the manifold dynamical behaviours of typical DNP samples.Comment: 11 pages, 11 figure
Chemical exchange saturation transfer MRI shows low cerebral 2-deoxy-D-glucose uptake in a model of Alzheimer's Disease
Glucose is the central nervous system's only energy source. Imaging techniques capable to detect pathological alterations of the brain metabolism are useful in different diagnostic processes. Such techniques are also beneficial for assessing the evaluation efficacy of therapies in pre-clinical and clinical stages of diseases. Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a possible alternative to positron emission tomography (PET) imaging that has been widely explored in cancer research in humans and animal models. We propose that pathological alterations in brain 2-deoxy-D-glucose (2DG) uptake, typical of neurodegenerative diseases, can be detected with CEST MRI. Transgenic mice overexpressing a mutated form of amyloid precusrsor protein (APP23), a model of Alzheimer's disease, analyzed with CEST MRI showed a clear reduction of 2DG uptake in different brain regions. This was reminiscent of the cerebral condition observed in Alzheimer's patients. The results indicate the feasibility of CEST for analyzing the brain metabolic state, with better image resolution than PET in experimental models
Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data
OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.</p
Enhanced Relaxivity of Gd III-Complexes with HP-DO3A-Like Ligands Upon the Activation of the Intramolecular Catalysis of the Prototropic Exchange
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