Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation
Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation
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DOI：
10.1007/s00330-022-09103-9
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发表时间：
2022-08
影响因子：
5.9
通讯作者：
K. Wicaksono;Koji Fujimoto;Y. Fushimi;A. Sakata;S. Okuchi;Takuya Hinoda;S. Nakajima;Y. Yamao
中科院分区：
文献类型：
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作者：
K. Wicaksono;Koji Fujimoto;Y. Fushimi;A. Sakata;S. Okuchi;Takuya Hinoda;S. Nakajima;Y. Yamao
目的建立一种生成性对抗性网络(GAN)模型，以提高脑部飞行时间磁共振血管成像(TOF-MRA)的图像分辨率，并评价重建图像的图像质量和诊断价值。我们使用了50名患者的数据集进行训练，12名患者用于定量图像质量评估，其余的用于诊断验证。我们修改了一个Pix2pix GaN以适应TOF-MRA数据集和微调GaN相关参数，包括损失函数。利用多尺度结构相似性(MS-SSIM)和基于信息论的统计相似性度量(ISSM)指标生成最大强度投影图像并进行比较。两位放射科医生使用5分的Likert分级对血管的能见度进行了评分。最后，我们评估了GaN-MRA在显示动脉瘤、狭窄和闭塞方面的敏感性和特异性。结果获得了波长为1e5、L1+MS-SSIM丢失的最佳模型。GaN-MRA的图像质量指标高于LR-MRA(MS-SSIM，0.87vs.0.73；ISSM，0.60vs.0.35；p.adjusted<；0.001)。GAN-MRA的血管可见度优于LR-MRA(A组4.18vs.2.53；B组4.61vs.2.65；P调整后为0.001)。在描述血管异常方面，GAN-MRA显示出类似的灵敏度和特异度，一个评价者检测动脉瘤的灵敏度更高(93%比84%，p<；结论优化的GaN模型可以显著提高低分辨率脑TOF-MRA的图像质量和血管可见性，在检测动脉瘤、狭窄和闭塞方面具有同等的灵敏度和特异度。关键点·GaN可以显著提高低分辨率脑MR血管成像(MRA)的图像质量和血管显示。·通过优化训练参数，GAN模型不会因为产生大量的假阳性或假阴性而降低诊断性能。·GaN可能是一种从短时间内扫描的图像获得更高分辨率TOF-MRA的有前景的方法。
ObjectivesTo develop a generative adversarial network (GAN) model to improve image resolution of brain time-of-flight MR angiography (TOF-MRA) and to evaluate the image quality and diagnostic utility of the reconstructed images.MethodsWe included 180 patients who underwent 1-min low-resolution (LR) and 4-min high-resolution (routine) brain TOF-MRA scans. We used 50 patients’ datasets for training, 12 for quantitative image quality evaluation, and the rest for diagnostic validation. We modified a pix2pix GAN to suit TOF-MRA datasets and fine-tuned GAN-related parameters, including loss functions. Maximum intensity projection images were generated and compared using multi-scale structural similarity (MS-SSIM) and information theoretic-based statistic similarity measure (ISSM) index. Two radiologists scored vessels’ visibilities using a 5-point Likert scale. Finally, we evaluated sensitivities and specificities of GAN-MRA in depicting aneurysms, stenoses, and occlusions.ResultsThe optimal model was achieved with a lambda of 1e5 and L1 + MS-SSIM loss. Image quality metrics for GAN-MRA were higher than those for LR-MRA (MS-SSIM, 0.87 vs. 0.73; ISSM, 0.60 vs. 0.35;p.adjusted < 0.001). Vessels’ visibility of GAN-MRA was superior to LR-MRA (rater A, 4.18 vs. 2.53; rater B, 4.61 vs. 2.65;p.adjusted < 0.001). In depicting vascular abnormalities, GAN-MRA showed comparable sensitivities and specificities, with greater sensitivity for aneurysm detection by one rater (93% vs. 84%,p< 0.05).ConclusionsAn optimized GAN could significantly improve the image quality and vessel visibility of low-resolution brain TOF-MRA with equivalent sensitivity and specificity in detecting aneurysms, stenoses, and occlusions.Key Points•GAN could significantly improve the image quality and vessel visualization of low-resolution brain MR angiography (MRA).•With optimally adjusted training parameters, the GAN model did not degrade diagnostic performance by generating substantial false positives or false negatives.•GAN could be a promising approach for obtaining higher resolution TOF-MRA from images scanned in a fraction of time.