Dataset Information

SNR-weighted regularization of ADC estimates from double-echo in steady-state (DESS).

ABSTRACT: PURPOSE:To improve the homogeneity and consistency of apparent diffusion coefficient (ADC) estimates in cartilage from the double-echo in steady-state (DESS) sequence by applying SNR-weighted regularization during post-processing. METHODS:An estimation method that linearizes ADC estimates from DESS is used in conjunction with a smoothness constraint to suppress noise-induced variation in ADC estimates. Simulations, phantom scans, and in vivo scans are used to demonstrate how the method reduces ADC variability. Conventional diffusion-weighted echo-planar imaging (DW EPI) maps are acquired for comparison of mean and standard deviation (SD) of the ADC estimate. RESULTS:Simulations and phantom scans demonstrated that the SNR-weighted regularization can produce homogenous ADC maps at varying levels of SNR, whereas non-regularized maps only estimate ADC accurately at high SNR levels. The in vivo maps showed that the SNR-weighted regularization produced ADC maps with similar heterogeneity to maps produced with standard DW EPI, but without the distortion of such reference scans. CONCLUSION:A linear approximation of a simplified model of the relationship between DESS signals allows for fast SNR-weighted regularization of ADC maps that reduces estimation error in relatively short T2 tissue such as cartilage.

SUBMITTER: Sveinsson B

PROVIDER: S-EPMC6258287 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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SNR-weighted regularization of ADC estimates from double-echo in steady-state (DESS).

Sveinsson Bragi B Gold Garry E GE Hargreaves Brian A BA Yoon Daehyun D

Magnetic resonance in medicine 20180819 1

<h4>Purpose</h4>To improve the homogeneity and consistency of apparent diffusion coefficient (ADC) estimates in cartilage from the double-echo in steady-state (DESS) sequence by applying SNR-weighted regularization during post-processing.<h4>Methods</h4>An estimation method that linearizes ADC estimates from DESS is used in conjunction with a smoothness constraint to suppress noise-induced variation in ADC estimates. Simulations, phantom scans, and in vivo scans are used to demonstrate how the m ...[more]

PMID: 30125389

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Project description:BACKGROUND:Biomarkers for assessing osteoarthritis activity necessitate multiple MRI sequences with long acquisition times. PURPOSE:To perform 5-minute simultaneous morphometry (thickness/volume measurements) and T2 relaxometry of both cartilage and meniscus, and semiquantitative MRI Osteoarthritis Knee Scoring (MOAKS). STUDY TYPE:Prospective. SUBJECTS:Fifteen healthy volunteers for morphometry and T2 measurements, and 15 patients (five each Kellgren-Lawrence grades 0/2/3) for MOAKS assessment. FIELD STRENGTH/SEQUENCE:A 5-minute double-echo steady-state (DESS) sequence was evaluated for generating quantitative and semiquantitative osteoarthritis biomarkers at 3T. ASSESSMENT:Flip angle simulations evaluated tissue signals and sensitivity of T2 measurements. Morphometry and T2 reproducibility was compared against morphometry-optimized and relaxometry-optimized sequences. Repeatability was assessed by scanning five volunteers twice. MOAKS reproducibility was compared to MOAKS derived from a clinical knee MRI protocol by two readers. STATISTICAL TESTS:Coefficients of variation (CVs), concordance confidence intervals (CCI), and Wilcoxon signed-rank tests compared morphometry and relaxometry measurements with their reference standards. DESS MOAKS positive percent agreement (PPA), negative percentage agreement (NPA), and interreader agreement was calculated using the clinical protocol as a reference. Biomarker variations between Kellgren-Lawrence groups were evaluated using Wilcoxon rank-sum tests. RESULTS:Cartilage thickness (P?=?0.65), cartilage T2 (P?=?0.69), and meniscus T2 (P?=?0.06) did not significantly differ from their reference standard (with a 20° DESS flip angle). DESS slightly overestimated meniscus volume (P < 0.001). Accuracy and repeatability CVs were <3.3%, except the meniscus T2 accuracy (7.6%). DESS MOAKS had substantial interreader agreement and high PPA/NPA values of 87%/90%. Bone marrow lesions and menisci had slightly lower PPAs. Cartilage and meniscus T2 , and MOAKS (cartilage surface area, osteophytes, cysts, and total score) was higher in Kellgren-Lawrence groups 2 and 3 than group 0 (P < 0.05). DATA CONCLUSION:The 5-minute DESS sequence permits MOAKS assessment for a majority of tissues, along with repeatable and reproducible simultaneous cartilage and meniscus T2 relaxometry and morphometry measurements. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1328-1341.

Project description:Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p's < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p's < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p's < 0.001). These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments.

Dataset Information

SNR-weighted regularization of ADC estimates from double-echo in steady-state (DESS).

Publications

SNR-weighted regularization of ADC estimates from double-echo in steady-state (DESS).

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