A T Newton1, S Pruthi2, A M Stokes3, J T Skinner4, C C Quarles3. 1. From the Department of Radiology and Radiological Sciences (A.T.N., S.P.), Vanderbilt University Medical Center, Nashville, Tennessee Institute of Imaging Science (A.T.N.), Vanderbilt University, Nashville, Tennessee allen.t.newton@vanderbilt.edu. 2. From the Department of Radiology and Radiological Sciences (A.T.N., S.P.), Vanderbilt University Medical Center, Nashville, Tennessee Monroe Carell Jr. Children's Hospital at Vanderbilt (S.P.), Nashville, Tennessee. 3. Barrow Neurological Institute (A.M.S., C.C.Q.), Phoenix, Arizona Saint Joseph's Hospital and Medical Center (A.M.S., C.C.Q.), Phoenix, Arizona. 4. National Comprehensive Cancer Network (J.T.S.), Fort Washington, Pennsylvania.
Abstract
BACKGROUND AND PURPOSE: Clinical measurements of cerebral perfusion have been increasingly performed with multiecho dynamic susceptibility contrast-MR imaging techniques due to their ability to remove confounding T1 effects of contrast agent extravasation from perfusion quantification. However, to this point, the extra information provided by multiecho techniques has not been used to improve the process of estimating the arterial input function, which is critical to accurate perfusion quantification. The purpose of this study is to investigate methods by which multiecho DSC-MRI data can be used to automatically avoid voxels whose signal decreases to the level of noise when calculating the arterial input function. MATERIALS AND METHODS: Here we compare postprocessing strategies for clinical multiecho DSC-MR imaging data to test whether arterial input function measures could be improved by automatically identifying and removing voxels exhibiting signal attenuation (truncation) artifacts. RESULTS: In a clinical pediatric population, we found that the Pearson correlation coefficient between ΔR2* time-series calculated from each TE individually was a valuable criterion for automated estimation of the arterial input function, resulting in higher peak arterial input function values while maintaining smooth and reliable arterial input function shapes. CONCLUSIONS: This work is the first to demonstrate that multiecho information may be useful in clinically important automatic arterial input function estimation because it can be used to improve automatic selection of voxels from which the arterial input function should be measured.
BACKGROUND AND PURPOSE: Clinical measurements of cerebral perfusion have been increasingly performed with multiecho dynamic susceptibility contrast-MR imaging techniques due to their ability to remove confounding T1 effects of contrast agent extravasation from perfusion quantification. However, to this point, the extra information provided by multiecho techniques has not been used to improve the process of estimating the arterial input function, which is critical to accurate perfusion quantification. The purpose of this study is to investigate methods by which multiecho DSC-MRI data can be used to automatically avoid voxels whose signal decreases to the level of noise when calculating the arterial input function. MATERIALS AND METHODS: Here we compare postprocessing strategies for clinical multiecho DSC-MR imaging data to test whether arterial input function measures could be improved by automatically identifying and removing voxels exhibiting signal attenuation (truncation) artifacts. RESULTS: In a clinical pediatric population, we found that the Pearson correlation coefficient between ΔR2* time-series calculated from each TE individually was a valuable criterion for automated estimation of the arterial input function, resulting in higher peak arterial input function values while maintaining smooth and reliable arterial input function shapes. CONCLUSIONS: This work is the first to demonstrate that multiecho information may be useful in clinically important automatic arterial input function estimation because it can be used to improve automatic selection of voxels from which the arterial input function should be measured.
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