High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study.

Yuya Hirano, Noriyuki Fujima, Hiroyuki Kameda, Kinya Ishizaka, Jihun Kwon, Masami Yoneyama, Kohsuke Kudo
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Abstract

Purpose: To investigate the visibility of the lenticulostriate arteries (LSAs) in time-of-flight (TOF)-MR angiography (MRA) using compressed sensing (CS)-based deep learning (DL) image reconstruction by comparing its image quality with that obtained by the conventional CS algorithm.

Methods: Five healthy volunteers were included. High-resolution TOF-MRA images with the reduction (R)-factor of 1 were acquired as full-sampling data. Images with R-factors of 2, 4, and 6 were then reconstructed using CS-DL and conventional CS (the combination of CS and sensitivity conceding; CS-SENSE) reconstruction, respectively. In the quantitative assessment, the number of visible LSAs (identified by two radiologists), length of each depicted LSA (evaluated by one radiological technologist), and normalized mean squared error (NMSE) value were assessed. In the qualitative assessment, the overall image quality and the visibility of the peripheral LSA were visually evaluated by two radiologists.

Results: In the quantitative assessment of the DL-CS images, the number of visible LSAs was significantly higher than those obtained with CS-SENSE in the R-factors of 4 and 6 (Reader 1) and in the R-factor of 6 (Reader 2). The length of the depicted LSAs in the DL-CS images was significantly longer in the R-factor 6 compared to the CS-SENSE result. The NMSE value in CS-DL was significantly lower than in CS-SENSE for R-factors of 4 and 6. In the qualitative assessment of DL-CS images, the overall image quality was significantly higher than that obtained with CS-SENSE in the R-factors 4 and 6 (Reader 1) and in the R-factor 4 (Reader 2). The visibility of the peripheral LSA was significantly higher than that shown by CS-SENSE in all R-factors (Reader 1) and in the R-factors 2 and 4 (Reader 2).

Conclusion: CS-DL reconstruction demonstrated preserved image quality for the depiction of LSAs compared to the conventional CS-SENSE when the R-factor is elevated.

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利用基于压缩传感的深度学习图像重建技术实现睑动脉可视化的高分辨率 TOF-MRA:初步研究。
目的:通过比较基于压缩传感(CS)的深度学习(DL)图像重建与传统 CS 算法获得的图像质量,研究飞行时间(TOF)-MR 血管造影(MRA)中睑板动脉(LSA)的可见性:方法:纳入五名健康志愿者。方法:纳入五名健康志愿者,以全采样数据的形式获取还原(R)因子为 1 的高分辨率 TOF-MRA 图像。然后分别使用 CS-DL 和传统 CS(CS 和灵敏度调节的组合;CS-SENSE)重建 R 因子为 2、4 和 6 的图像。在定量评估中,对可见 LSA 的数量(由两名放射科医生识别)、每个描述的 LSA 的长度(由一名放射科技术人员评估)和归一化均方误差(NMSE)值进行了评估。在定性评估中,由两名放射科医生对整体图像质量和外周 LSA 的可见度进行目测评估:结果:在 DL-CS 图像的定量评估中,在 R 因子为 4 和 6 时(阅读器 1),可见 LSA 的数量明显高于 CS-SENSE 图像;在 R 因子为 6 时(阅读器 2),可见 LSA 的数量明显高于 CS-SENSE 图像。与 CS-SENSE 结果相比,DL-CS 图像在 R 因子 6 中描绘的 LSA 长度明显更长。在 R 因子为 4 和 6 时,CS-DL 的 NMSE 值明显低于 CS-SENSE。在 DL-CS 图像的定性评估中,R 因子 4 和 6(读者 1)以及 R 因子 4(读者 2)的整体图像质量明显高于 CS-SENSE。在所有 R 因子(读者 1)和 R 因子 2 和 4(读者 2)中,外周 LSA 的可见度明显高于 CS-SENSE 所显示的可见度:结论:与传统的 CS-SENSE 相比,当 R 因子升高时,CS-DL 重建在描绘 LSA 方面表现出更高的图像质量。
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