Noise in MR image data increases the mean signal intensity of image regions due to the usually performed magnitude reconstruction. Diffusion-weighted imaging (DWI) is especially affected by high noise levels for several reasons, and a decreasing SNR at increasing diffusion weighting causes systematic errors when calculating apparent diffusion coefficients (ADCs). Two different methods are presented to correct biased signal intensities due to the presence of complex noise: 1) with Gaussian intensity distribution, and 2) with arbitrary intensity distribution. The performance of the correction schemes is demonstrated by numerical simulations and DWI measurements on two different MR systems with different noise characteristics. These experiments show that noise significantly influences the determination of ADCs. Applying the proposed correction schemes reduced the bias of the determined ADC to less than 10% of the bias without correction. Magn Reson Med 45:448-453, 2001.
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http://dx.doi.org/10.1002/1522-2594(200103)45:3<448::aid-mrm1059>3.0.co;2-w | DOI Listing |
Endovascular thrombectomy (EVT) dramatically improves clinical outcomes, but the final infarct volume (FIV) on MRI only accounts for a minority of the treatment effect. An imaging biomarker that more strongly correlates with post-EVT functional outcome would be helpful for clinical prognosis and serve as a surrogate outcome measure in trials of EVT-adjuvant therapies. Here, we aimed to validate a novel MRI-based metric, infarct density, which leverages post-EVT apparent diffusion coefficient (ADC) as a marker of infarct severity.
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