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Improved T*₂ determination in 23 Na, 35 Cl, and 17 O MRI using iterative partial volume correction based on 1 H MRI segmentation.
Magma 2017 December
OBJECTIVE: Functional parameters can be measured with the help of quantitative non-proton MRI where exact relaxometry parameters are needed. Investigation of [Formula: see text] is often biased by strong partial volume (PV) effects. Hence, in this work a PV correction algorithm approach was evaluated that uses iteratively adapted [Formula: see text]-values and high-resolution structural 1 H data to determine transverse relaxation in non-proton MRI more accurately.
MATERIALS AND METHODS: Simulations, a phantom study and in vivo 23 Na, 17 O and 35 Cl MRI measurements of five healthy volunteers were performed to evaluate the algorithm. [Formula: see text] values of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) were obtained. Data were acquired at B 0 = 7T with nominal spatial resolutions of (4-7 mm)3 using a density-adapted radial sequence. The resulting transverse relaxation times were used for quantification of 17 O data.
RESULTS: The conducted simulations and phantom study verified the correction performance of the algorithm. For in vivo measured [Formula: see text] values, the correction of PV effects leads to an increase in CSF and to a decrease in GM/WM (23 Na MRI: long/short GM, WM [Formula: see text]: 36.4 ± 3.1/5.4 ± 0.2, 23.3 ± 2.6/3.5 ± 0.1 ms; 35 Cl MRI: 8.9 ± 1.4/1.0 ± 0.4, 5.9 ± 0.3/0.4 ± 0.1 ms; 17 O MRI: 2.5 ± 0.1, 2.8 ± 0.1 ms). Iteratively corrected in vivo [Formula: see text] values of the 17 O study resulted in improved water content quantification.
CONCLUSION: The proposed iterative algorithm for PV correction leads to more accurate [Formula: see text] values and, thus, can improve accuracy in quantitative non-proton MRI.
MATERIALS AND METHODS: Simulations, a phantom study and in vivo 23 Na, 17 O and 35 Cl MRI measurements of five healthy volunteers were performed to evaluate the algorithm. [Formula: see text] values of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) were obtained. Data were acquired at B 0 = 7T with nominal spatial resolutions of (4-7 mm)3 using a density-adapted radial sequence. The resulting transverse relaxation times were used for quantification of 17 O data.
RESULTS: The conducted simulations and phantom study verified the correction performance of the algorithm. For in vivo measured [Formula: see text] values, the correction of PV effects leads to an increase in CSF and to a decrease in GM/WM (23 Na MRI: long/short GM, WM [Formula: see text]: 36.4 ± 3.1/5.4 ± 0.2, 23.3 ± 2.6/3.5 ± 0.1 ms; 35 Cl MRI: 8.9 ± 1.4/1.0 ± 0.4, 5.9 ± 0.3/0.4 ± 0.1 ms; 17 O MRI: 2.5 ± 0.1, 2.8 ± 0.1 ms). Iteratively corrected in vivo [Formula: see text] values of the 17 O study resulted in improved water content quantification.
CONCLUSION: The proposed iterative algorithm for PV correction leads to more accurate [Formula: see text] values and, thus, can improve accuracy in quantitative non-proton MRI.
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