Direct observation of NMR transverse relaxation in nanopatterned clusters of iron oxide particles

We aim to verify predictions showing T relaxation rate of nanoparticle clusters and its dependence on spacing, size, geometry, and pulse sequence. We performed a laboratory validation study using nanopatterned arrays of iron oxide nanoparticles to precisely control cluster geometry and image diverse...

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Veröffentlicht in:Magnetic resonance in medicine 2024-02, Vol.91 (2), p.687-698
Hauptverfasser: Bok, Ilhan, Rauch, Beth, Ashtiani, Alireza, Hai, Aviad
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Sprache:eng
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Zusammenfassung:We aim to verify predictions showing T relaxation rate of nanoparticle clusters and its dependence on spacing, size, geometry, and pulse sequence. We performed a laboratory validation study using nanopatterned arrays of iron oxide nanoparticles to precisely control cluster geometry and image diverse samples using a 4.7T MRI scanner with a T -weighted fast spin-echo multislice sequence. We applied denoising and normalization to regions of interest and estimated relative R for each relevant nanoparticle array or nanocluster array. We determined significance using an unpaired two-tailed t-test or one-way analysis of variance and performed curve fitting. We measured a density-dependent T effect (p = 8.9976 × 10 , one-way analysis of variance) and insignificant effect of cluster anisotropy (p = 0.5924, unpaired t-test) on T relaxation. We found negative quadratic relationships (-0.0045[log τ ] -0.0655[log τ ]-2.7800) for single nanoparticles of varying sizes and for clusters (-0.0045[log τ ] -0.0827[log τ ]-2.3249) for diffusional correlation time τ  = r /D. Clusters show positive quadratic relationships for large (3.8615 × 10 [d /r ] -9.3853 × 10 [d /r ]-2.0393) and exponential relationships for small (-2.0050[d /r ] ) clusters. Calculated R peak values also align well with in silico predictions (7.85 × 10  ms compared with 1.47 × 10 , 4.23 × 10 , and 5.02 × 10  ms for single iron oxide nanoparticles, 7.88 × 10  ms compared with 5.24 × 10  ms for nanoparticle clusters). Our verification affirms longstanding in silico predictions and demonstrates aggregation-dependent behavior in agreement with previous Monte Carlo simulation studies.
ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.29898