Akasaka Thai, Fujimoto Koji, Yamamoto Takayuki, Okada Tomohisa, Fushimi Yasutaka, Yamamoto Akira, Tanaka Toshiyuki, Togashi Kaori
Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Japan.
PLoS One. 2016 Jan 8;11(1):e0146548. doi: 10.1371/journal.pone.0146548. eCollection 2016.
In Compressed Sensing (CS) of MRI, optimization of the regularization parameters is not a trivial task. We aimed to establish a method that could determine the optimal weights for regularization parameters in CS of time-of-flight MR angiography (TOF-MRA) by comparing various image metrics with radiologists' visual evaluation. TOF-MRA of a healthy volunteer was scanned using a 3T-MR system. Images were reconstructed by CS from retrospectively under-sampled data by varying the weights for the L1 norm of wavelet coefficients and that of total variation. The reconstructed images were evaluated both quantitatively by statistical image metrics including structural similarity (SSIM), scale invariant feature transform (SIFT) and contrast-to-noise ratio (CNR), and qualitatively by radiologists' scoring. The results of quantitative metrics and qualitative scorings were compared. SSIM and SIFT in conjunction with brain masks and CNR of artery-to-parenchyma correlated very well with radiologists' visual evaluation. By carefully selecting a region to measure, we have shown that statistical image metrics can reflect radiologists' visual evaluation, thus enabling an appropriate optimization of regularization parameters for CS.
在磁共振成像（MRI）的压缩感知（CS）中，正则化参数的优化并非易事。我们旨在建立一种方法，通过将各种图像指标与放射科医生的视觉评估进行比较，来确定飞行时间磁共振血管造影（TOF-MRA）的CS中正则化参数的最佳权重。使用3T-MR系统对一名健康志愿者进行TOF-MRA扫描。通过改变小波系数的L1范数和总变差的权重，从回顾性欠采样数据中通过CS重建图像。通过包括结构相似性（SSIM）、尺度不变特征变换（SIFT）和对比度噪声比（CNR）在内的统计图像指标对重建图像进行定量评估，并由放射科医生进行定性评分。比较定量指标和定性评分的结果。结合脑掩码的SSIM和SIFT以及动脉与实质的CNR与放射科医生的视觉评估相关性非常好。通过仔细选择测量区域，我们表明统计图像指标可以反映放射科医生的视觉评估，从而能够对CS的正则化参数进行适当优化。
