Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK.
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Magn Reson Med. 2022 Nov;88(5):2157-2166. doi: 10.1002/mrm.29349. Epub 2022 Jul 25.
To develop a robust reconstruction pipeline for EPI data that enables 2D Nyquist phase error correction using sensitivity encoding without incurring major noise artifacts in low SNR data.
SENSE with 2D phase error correction (PEC-SENSE) was combined with channel-wise noise removal using Marcenko-Pastur principal component analysis (MPPCA) to simultaneously eliminate Nyquist ghost artifacts in EPI data and mitigate the noise amplification associated with phase correction using parallel imaging. The proposed pipeline (coined SPECTRE) was validated in phantom DW-EPI data using the accuracy and precision of diffusion metrics; ground truth values were obtained from data acquired with a spin echo readout. Results from the SPECTRE pipeline were compared against PEC-SENSE reconstructions with three alternate denoising strategies: (i) no denoising; (ii) denoising of magnitude data after image formation; (iii) denoising of complex data after image formation. SPECTRE was then tested using high -value (i.e., low SNR) diffusion data (up to s/mm ) in four healthy subjects.
Noise amplification associated with phase error correction incurred a 23% bias in phantom mean diffusivity (MD) measurements. Phantom MD estimates using the SPECTRE pipeline were within 8% of the ground truth value. In healthy volunteers, the SPECTRE pipeline visibly corrected Nyquist ghost artifacts and reduced associated noise amplification in high -value data.
The proposed reconstruction pipeline is effective in correcting low SNR data, and improves the accuracy and precision of derived diffusion metrics.
开发一种稳健的 EPI 数据重建管道，该管道能够在不引入低 SNR 数据中主要噪声伪影的情况下，使用灵敏度编码进行 2D 奈奎斯特相位误差校正。
将具有 2D 相位误差校正（PEC-SENSE）的灵敏度编码与通道噪声去除相结合，使用 Marcenko-Pastur 主成分分析（MPPCA），同时消除 EPI 数据中的奈奎斯特鬼影伪影，并减轻使用并行成像进行相位校正时的噪声放大。该方法（称为 SPECTRE）在体模 DW-EPI 数据中进行了验证，使用扩散指标的准确性和精密度进行评估；从使用自旋回波读出方式采集的数据中获得真实值。SPECTRE 管道的结果与使用三种替代去噪策略的 PEC-SENSE 重建进行了比较：（i）无去噪；（ii）图像形成后对幅度数据进行去噪；（iii）图像形成后对复数数据进行去噪。然后，在四名健康志愿者中使用高值（即低 SNR）扩散数据（高达 s/mm ）测试了 SPECTRE。
相位误差校正引起的噪声放大导致体模平均扩散系数（MD）测量值出现 23%的偏差。使用 SPECTRE 管道的体模 MD 估计值与真实值相差在 8%以内。在健康志愿者中，SPECTRE 管道明显校正了奈奎斯特鬼影伪影，并降低了高值数据中的相关噪声放大。
所提出的重建管道能够有效地校正低 SNR 数据，并提高衍生扩散指标的准确性和精密度。
