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首页> 美国卫生研究院文献>The Yale Journal of Biology and Medicine >Focus: Medical Technology: Improving the Accuracy Quality and Signal-To-Noise Ratio of MRI Parametric Mapping Using Rician Bias Correction and Parametric-Contrast-Matched Principal Component Analysis (PCM-PCA)
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Focus: Medical Technology: Improving the Accuracy Quality and Signal-To-Noise Ratio of MRI Parametric Mapping Using Rician Bias Correction and Parametric-Contrast-Matched Principal Component Analysis (PCM-PCA)

机译：重点：医疗技术：使用Rician偏差校正和参数对比度匹配的主成分分析（PCM-PCA）提高MRI参数映射的准确性质量和信噪比

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MRI parametric mapping, including T2 mapping, can quantitatively characterize tissue properties and is an important MRI procedure in biomedical research and studies of diseases [-]. However, the accuracy, quality, and signal-to-noise ratio (SNR) of MRI parametric mapping may be negatively impacted by Rician noise in multi-contrast MRI data []. As such, it is important to develop a post-processing method to minimize the negative impact of Rician noise. In this study, we report a new parametric-contrast-matched principal component analysis (PCM-PCA) denoising method that involves 1) identifying voxels with similar T2 decay characteristics and 2) using the principal component analysis (PCA) to denoise multi-contrast MRI data along the echo time (TE) dimension. We additionally evaluated the effects of integrating Rician bias correction and the new PCM-PCA method. In this study, we mathematically added Rician noise at various levels to human brain MRI data and performed different combinations of denoising and Rician bias correction on the magnitude-valued images. We found that MRI denoising using the PCM-PCA method resulted in improved image quality, SNR, and accuracy of the measured T2 relaxation time constants. Additionally, we found that for data with low SNR (e.g., 1.5 or lower), Rician bias correction further improved image quality and T2 mapping accuracy. In summary, our experimental results demonstrated that the new PCM-PCA denoising method and Rician bias correction adequately improve multi-contrast MRI quality and T2 parametric mapping accuracy.
机译：MRI参数映射（包括T2映射）可以定量地表征组织特性，并且是生物医学研究和疾病研究中的重要MRI程序[-]。但是，多参数MRI数据中的Rician噪声可能会对MRI参数映射的准确性，质量和信噪比（SNR）产生负面影响[]。因此，开发一种后处理方法以使Rician噪声的负面影响最小化非常重要。在这项研究中，我们报告了一种新的参数对比度匹配主成分分析（PCM-PCA）去噪方法，该方法包括1）识别具有类似T2衰减特性的体素，以及2）使用主成分分析（PCA）去噪多对比度沿回波时间（TE）维的MRI数据。我们还评估了集成Rician偏差校正和新PCM-PCA方法的效果。在这项研究中，我们在数学上向人脑MRI数据添加了各种水平的Rician噪声，并对幅值图像进行了不同的降噪和Rician偏差校正组合。我们发现，使用PCM-PCA方法进行MRI去噪可提高图像质量，SNR和所测得的T2弛豫时间常数的准确性。另外，我们发现，对于具有低SNR（例如1.5或更低）的数据，Rician偏差校正可进一步提高图像质量和T2映射精度。总而言之，我们的实验结果表明，新的PCM-PCA去噪方法和Rician偏差校正可以充分提高多对比度MRI质量和T2参数映射的准确性。

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