基于深度学习的降噪技术可保留脑肿瘤患者的定量MRI生物标志物。
Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors.
作者信息
Pouliquen Geoffroy, Debacker Clément, Charron Sylvain, Roux Alexandre, Provost Corentin, Benzakoun Joseph, de Graaf Wolter, Prevost Valentin, Pallud Johan, Oppenheim Catherine
机构信息
Imaging department, GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014, Paris, France; Université Paris Cité, Institute of Psychiatry and Neuroscience (IPNP), INSERM U1266, 75014, Paris, France.
Université Paris Cité, Institute of Psychiatry and Neuroscience (IPNP), INSERM U1266, 75014, Paris, France; Neurosurgery department, GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014, Paris, France.
出版信息
J Neuroradiol. 2023 Oct 28. doi: 10.1016/j.neurad.2023.10.008.
The use of relaxometry and Diffusion-Tensor Imaging sequences for brain tumor assessment is limited by their long acquisition time. We aim to test the effect of a denoising algorithm based on a Deep Learning Reconstruction (DLR) technique on quantitative MRI parameters while reducing scan time. In 22 consecutive patients with brain tumors, DLR applied to fast and noisy MR sequences preserves the mean values of quantitative parameters (Fractional anisotropy, mean Diffusivity, T1 and T2-relaxation time) and produces maps with higher structural similarity compared to long duration sequences. This could promote wider use of these biomarkers in clinical setting.
弛豫测量法和扩散张量成像序列在脑肿瘤评估中的应用受到其较长采集时间的限制。我们旨在测试一种基于深度学习重建（DLR）技术的去噪算法对定量MRI参数的影响，同时缩短扫描时间。在连续22例脑肿瘤患者中，将DLR应用于快速且有噪声的MR序列可保留定量参数（分数各向异性、平均扩散率、T1和T2弛豫时间）的平均值，并生成与长时间序列相比具有更高结构相似性的图像。这可能会促进这些生物标志物在临床环境中的更广泛应用。
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