A simple method for rectified noise floor suppression: Phase‐corrected real data reconstruction with application to diffusion‐weighted imaging

Prah, D.E.; Paulson, E.S.; Nencka, Andrew S.; Schmainda, Kathleen M.

doi:10.1002/mrm.22407

Cited by 38 publications

(55 citation statements)

References 26 publications

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“…ADC was then calculated as -ln(S/S 0 )/b. A noise floor rectification scheme was used in the ADC calculation (28), which was performed on a voxel-by-voxel basis, generating an ADC map as well as averaged values for the ROIs. In order to suppress the technical factors influencing the ADC values, a normalized ADC value (nADC) was calculated (the ratio of ADC value of PTCs to the ADC value of normal thyroid tissue).…”

Section: Mri Studymentioning

confidence: 99%

Using Diffusion-Weighted MRI to Predict Aggressive Histological Features in Papillary Thyroid Carcinoma: A Novel Tool for Pre-Operative Risk Stratification in Thyroid Cancer

Moreira

Hatzoglou

et al. 2015

Thyroid

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Background: Initial management recommendations of papillary thyroid carcinoma (PTC) are very dependent on preoperative studies designed to evaluate the presence of PTC with aggressive features. The purpose of this study was to evaluate whether diffusion-weighted magnetic resonance imaging (DW-MRI) before surgery can be used as a tool to stratify tumor aggressiveness in patients with PTC. Methods: In this prospective study, 28 patients with PTC underwent DW-MRI studies on a three Tesla MR scanner prior to thyroidectomy. Due to image quality, 21 patients were finally suitable for further analysis. Apparent diffusion coefficients (ADCs) of normal thyroid tissues and PTCs for 21 patients were calculated. Tumor aggressiveness was defined by surgical histopathology. The Mann-Whitney U test was used to compare the difference in ADCs among groups of normal thyroid tissues and PTCs with and without features of tumor aggressiveness. Receiver operating characteristic (ROC) analysis was performed to assess the discriminative specificity, sensitivity, and accuracy of and determine the cutoff value for the ADC in stratifying PTCs with tumor aggressiveness. Results: There was no significant difference in ADC values between normal thyroid tissues and PTCs. However, ADC values of PTCs with extrathyroidal extension (ETE; 1.53 -0.25 · 10 -3 mm 2 /s) were significantly lower than corresponding values from PTCs without ETE (2.37 -0.67 · 10 -3 mm 2 /s; p < 0.005). ADC values identified 3 papillary carcinoma patients with extrathyroidal extension that would have otherwise been candidates for observation based on ultrasound evaluations. The cutoff value of ADC to discriminate PTCs with and without ETE was determined at 1.85 · 10 -3 mm 2 /s with a sensitivity of 85%, specificity of 85%, and ROC curve area of 0.85. Conclusion: ADC value derived from DW-MRI before surgery has the potential to stratify ETE in patients with PTCs.

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Section: Mri Studymentioning

confidence: 99%

Using Diffusion-Weighted MRI to Predict Aggressive Histological Features in Papillary Thyroid Carcinoma: A Novel Tool for Pre-Operative Risk Stratification in Thyroid Cancer

Moreira

Hatzoglou

et al. 2015

Thyroid

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“…In body DWI, each b ‐value image is typically calculated from 12‐24 signal averages (3 diffusion directions and 4‐8 signal excitations [NeX]), applying the magnitude operation before averaging to avoid phase‐interference artifacts. However, while magnitude averaging does reduce apparent noise, it does not reduce the upward bias of the noise floor …”

Section: Introductionmentioning

confidence: 99%

“…Several methods have been proposed to remove noise‐floor bias from DWI . A common approach is to use a correction‐scheme to compensate for biasing effects in noisy magnitude images .…”

Section: Introductionmentioning

confidence: 99%

“…A common approach is to use a correction‐scheme to compensate for biasing effects in noisy magnitude images . However, these techniques require knowledge of the noise statistics in a specific region of interest (ROI) and this is not easily determined in body DWI, especially on a voxelwise basis . Alternatively, NC methods see r ician statistics taken into account in fitting the diffusion model, although this requires the acquisition of additional noise‐only images at the expense of longer scanning times.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, NC methods see r ician statistics taken into account in fitting the diffusion model, although this requires the acquisition of additional noise‐only images at the expense of longer scanning times. A third approach is to reconstruct phase‐corrected real data in an attempt to maintain the original noise statistics with no rectified noise floor . The purpose of this study is to investigate the effects of phase‐corrected real data for DKI in the prostate compared to standard magnitude data, with and without NC.…”

Section: Introductionmentioning

confidence: 99%

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Removing rician bias in diffusional kurtosis of the prostate using real‐data reconstruction

Goodburn

Barrett

Patterson

et al. 2019

Magnetic Resonance in Med

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Purpose To compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase‐corrected real data with those generated using magnitude data with and without noise compensation (NC). Methods Diffusion‐weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b‐values (0‐1500 s/mm2), each acquired with 6 signal averages along 3 diffusion directions, with noise‐only images acquired to allow NC. In addition to conventional magnitude averaging, phase‐corrected real data were averaged in an attempt to reduce rician noise‐bias, with a range of phase‐correction low‐pass filter (LPF) sizes (8‐128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase‐corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ). Results Simulations indicated LPF size can strongly affect K metrics, where 64‐pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64‐LPF real‐data K were lower (P < 0.0001) by 4/10/7%, respectively. Conclusion Compared with magnitude data with NC, phase‐corrected real data can produce similar K, although the choice of phase‐correction LPF should be chosen carefully.

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Estimating non‐gaussian diffusion model parameters in the presence of physiological noise and rician signal bias

Kristoffersen

2011

Magnetic Resonance Imaging

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Purpose: To assess the effects of Rician bias and physiological noise on parameter estimation for non-Gaussian diffusion models. Materials and Methods:At high b-values, there are deviations from monoexponential signal decay known as nonGaussian diffusion. Magnitude images have a Rician distribution, which introduces a bias that appears as non-Gaussian diffusion. A second factor that complicates parameter estimation is physiological noise. It has an intensity that depends on the b-value in a complicated manner. Hence, the signal distribution is unknown a priori. By measuring a large number of averages, however, the variance at each b-value can be estimated. Using Monte Carlo simulations, we compared uncorrected estimation to a corrected scheme that involves fitting to the mean value of the Rician distribution. We also evaluated effects of weighting with the inverse of the estimated variance in least-squares fitting. A human brain experiment illustrates parameter estimation effects and identifies brain regions affected by physiological noise. Results:The simulations show that the corrected estimator is very accurate. The uncorrected estimator is heavily biased. In the human brain experiment, the magnitude of the relative bias ranges from 6%-31%, depending on the diffusion model. Weighting has negligible effects on accuracy, but improves precision in the presence of physiological noise. At low b-values, physiological noise is prominent in cerebrospinal fluid. At high b-values there is physiological noise in white matter structures near the ventricles.Conclusion: Bias correction is essential and weighting may be beneficial. Physiological noise has significant effects.

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A simple method for rectified noise floor suppression: Phase‐corrected real data reconstruction with application to diffusion‐weighted imaging

Cited by 38 publications

References 26 publications

Using Diffusion-Weighted MRI to Predict Aggressive Histological Features in Papillary Thyroid Carcinoma: A Novel Tool for Pre-Operative Risk Stratification in Thyroid Cancer

Using Diffusion-Weighted MRI to Predict Aggressive Histological Features in Papillary Thyroid Carcinoma: A Novel Tool for Pre-Operative Risk Stratification in Thyroid Cancer

Removing rician bias in diffusional kurtosis of the prostate using real‐data reconstruction

Estimating non‐gaussian diffusion model parameters in the presence of physiological noise and rician signal bias

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