Ramanarayanan Sriprabha, Murugesan Balamurali, Kalyanasundaram Ananth, Prabhakaran Surya, Ram Keerthi, Patil Shantanu, Sivaprakasam Mohanasankar
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1584-1587. doi: 10.1109/EMBC44109.2020.9176100.
High spatial resolution of Magnetic Resonance images (MRI) provide rich structural details to facilitate accurate diagnosis and quantitative image analysis. However the long acquisition time of MRI leads to patient discomfort and possible motion artifacts in the reconstructed image. Single Image Super-Resolution (SISR) using Convolutional Neural networks (CNN) is an emerging trend in biomedical imaging especially Magnetic Resonance (MR) image analysis for image post processing. An efficient choice of SISR architecture is required to achieve better quality reconstruction. In addition, a robust choice of loss function together with the domain in which these loss functions operate play an important role in enhancing the fine structural details as well as removing the blurring effects to form a high resolution image. In this work, we propose a novel combined loss function consisting of an L1 Charbonnier loss function in the image domain and a wavelet domain loss function called the Isotropic Undecimated Wavelet loss (IUW loss) to train the existing Laplacian Pyramid Super-Resolution CNN. The proposed loss function was evaluated on three MRI datasets - privately collected Knee MRI dataset and the publicly available Kirby21 brain and iSeg infant brain datasets and on benchmark SISR datasets for natural images. Experimental analysis shows promising results with better recovery of structure and improvements in qualitative metrics.
磁共振成像（MRI）的高空间分辨率提供了丰富的结构细节，便于进行准确的诊断和定量图像分析。然而，MRI较长的采集时间会导致患者不适，并可能在重建图像中产生运动伪影。使用卷积神经网络（CNN）的单图像超分辨率（SISR）是生物医学成像领域，尤其是磁共振（MR）图像分析用于图像后处理的一个新兴趋势。为了实现更好质量的重建，需要高效选择SISR架构。此外，损失函数的稳健选择以及这些损失函数所作用的域，在增强精细结构细节以及消除模糊效应以形成高分辨率图像方面起着重要作用。在这项工作中，我们提出了一种新颖的组合损失函数，它由图像域中的L1 Charbonnier损失函数和小波域损失函数（称为各向同性非下采样小波损失（IUW损失））组成，用于训练现有的拉普拉斯金字塔超分辨率CNN。所提出的损失函数在三个MRI数据集上进行了评估——私人收集的膝关节MRI数据集以及公开可用的Kirby21脑部和iSeg婴儿脑部数据集，以及自然图像的基准SISR数据集。实验分析显示了有前景的结果，结构恢复更好，定性指标也有所改善。
