Characterization and correlation of signal drift in diffusion weighted MRI.

Autor: Hansen CB; Computer Science, Vanderbilt University, Nashville, TN, USA. Electronic address: colin.b.hansen@vanderbilt.edu., Nath V; Computer Science, Vanderbilt University, Nashville, TN, USA., Hainline AE; Biostatistics, Vanderbilt University, Nashville, TN, USA., Schilling KG; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Parvathaneni P; Electrical Engineering, Vanderbilt University, Nashville, TN, USA., Bayrak RG; Computer Science, Vanderbilt University, Nashville, TN, USA., Blaber JA; Electrical Engineering, Vanderbilt University, Nashville, TN, USA., Irfanoglu O; National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA., Pierpaoli C; National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA., Anderson AW; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA., Rogers BP; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA., Landman BA; Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Electrical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
Jazyk: angličtina
Zdroj: Magnetic resonance imaging [Magn Reson Imaging] 2019 Apr; Vol. 57, pp. 133-142. Date of Electronic Publication: 2018 Nov 22.
DOI: 10.1016/j.mri.2018.11.009
Abstrakt: Diffusion weighted MRI (DWMRI) and the myriad of analysis approaches (from tensors to spherical harmonics and brain tractography to body multi-compartment models) depend on accurate quantification of the apparent diffusion coefficient (ADC). Signal drift during imaging (e.g., due to b0 drift associated with heating) can cause systematic non-linearities that manifest as ADC changes if not corrected. Herein, we present a case study on two phantoms on one scanner. Different scan protocols exhibit different degrees of drift during similar scans and may be sensitive to the order of scans within an exam. Vos et al. recently reviewed the effects of signal drift in DWMRI acquisitions and proposed a temporal model for correction. We propose a novel spatial-temporal model to correct for higher order aspects of the signal drift and derive a statistically robust variant. We evaluate the Vos model and propose a method using two phantoms that mimic the ADC of the relevant brain tissue (0.36-2.2 × 10-3 mm 2 /s) on a single 3 T scanner. The phantoms are (1) a spherical isotropic sphere consisting of a single concentration of polyvinylpyrrolidone (PVP) and (2) an ice-water phantom with 13 vials of varying PVP concentrations. To characterize the impact of interspersed minimally weighted volumes ("b0's"), image volumes with b-value equal to 0.1 s/mm 2 are interspersed every 8, 16, 32, 48, and 96 diffusion weighted volumes in different trials. Signal drift is found to have spatially varying effects that are not accounted for with temporal-only models. The novel model captures drift more accurately (i.e., reduces the overall change per-voxel over the course of a scan) and results in more consistent ADC metrics.
(Copyright © 2018 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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