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      Anatomical measurements correlate with individual magnetostimulation thresholds for kHz‐range homogeneous magnetic fields

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      Embargo Lift Date: 2022-04-01
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      Author(s)
      Demirel, Ömer Burak
      Kılıç, Toygan
      Çukur, Tolga
      Sarıtaş, Emine Ülkü
      Date
      2020
      Source Title
      Medical Physics
      Print ISSN
      0094-2405
      Publisher
      Wiley
      Volume
      47
      Issue
      4
      Pages
      1836 - 1844
      Language
      English
      Type
      Article
      Item Usage Stats
      103
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      53
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      Abstract
      Purpose: Magnetostimulation, also known as peripheral nerve stimulation (PNS), is the dominant safety constraint in magnetic resonance imaging (MRI) for the gradient magnetic fields that operate around 0.1–1 kHz, and for the homogeneous drive field in magnetic particle imaging (MPI) that operates around 10–150 kHz. Previous studies did not report correlations between anatomical measures and magnetostimulation thresholds for the gradient magnetic fields in MRI. In contrast, a strong linear correlation was shown between the thresholds and the inverse of body part size in MPI. Yet, the effects of other anatomical measures on the thresholds for the drive field remain unexplored. Here, we investigate the effects of fat percentage on magnetostimulation thresholds for kHz‐range homogeneous magnetic fields such as the drive field in MPI, with the ultimate goal of predicting subject‐specific thresholds based on simple anatomical measures. Methods: Human subject experiments were performed on the upper arms of 10 healthy male subjects (age: 26 ± 2 yr) to determine magnetostimulation thresholds. Experiments were repeated three times for each subject, with brief resting periods between repetitions. Using a solenoidal magnetostimulation coil, a homogeneous magnetic field at 25 kHz with 100 ms pulse duration was applied at 4‐s intervals, while the subject reported stimulation via a mouse click. To determine the thresholds, individual subject responses were fitted to a cumulative distribution function modeled by a sigmoid curve. Next, anatomical images of the upper arms of the subjects were acquired on a 3 T MRI scanner. A two‐point Dixon method was used to obtain separate images of water and fat tissues, from which several anatomical measures were derived: the effective outer radius of the upper arm, the effective inner radius (i.e., the muscle radius), and fat percentage. Pearson’s correlation coefficient was used to assess the relationship between the threshold and anatomical measures. This statistical analysis was repeated after factoring out the expected effects of body part size. An updated model for threshold prediction is provided, where in addition to scaling in proportion with the inverse of the outer radius, the threshold has an affine dependence on fat percentage. Results: A strong linear correlation (r = 0.783, P < 0.008) was found between magnetostimulation threshold and fat percentage, and the correlation became stronger after factoring out the effects of outer radius (r = 0.839, P < 0.003). While considering body part size alone did not explain any significant variance in measured thresholds (P > 0.398), the updated model that also incorporates fat percentage yielded substantially improved threshold predictions with = 0.654 (P < 0.001). Conclusions: This work shows for the first time that fat percentage strongly correlates with magnetostimulation thresholds for kHz‐range homogenous magnetic fields such as the drive field in MPI, and that the correlations get even stronger after factoring out the effects of body part size. These results have important practical implications for predicting subject‐specific thresholds, which in turn can increase the performance of the drive field and improve image quality while remaining within the safety limits.
      Keywords
      Anatomical measurements
      Magnetic particle imaging
      Magnetostimulation
      Peripheral nerve stimulation (PNS)
      Threshold prediction
      Permalink
      http://hdl.handle.net/11693/75839
      Published Version (Please cite this version)
      https://dx.doi.org/10.1002/mp.14032
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 228
      • Department of Electrical and Electronics Engineering 3868
      • National Magnetic Resonance Research Center (UMRAM) 250
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