In medical imaging, the lack of high-quality images is present in many areas such as magnetic resonance (MR). Due to many acquisition impediments, the generated images have not enough resolution to carry out an adequate diagnosis. Image super-resolution (SR) is an ill-posed problem that tries to infer information from the image to enhance its resolution. Nowadays, deep learning techniques have become a powerful tool to extract features from images and infer new information. In MR, most of the recent works are based on the minimization of the errors between the input and the output images based on the Euclidean norm. This work presents a new methodology to perform three-dimensional SR based on the combination of Lp-norms in the loss layer. Two multiobjective optimization techniques are used to combine two cost functions. The proposed loss layers were trained with the SRCNN3D and DCSRN networks and tested with two MR structural T1-weighted datasets, and then compared with the traditional Euclidean loss. Experimental results show significant differences in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Bhattacharyya Coefficient (BC), while the residual images show refined details.

中文翻译：

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结合Lp范数成本函数的3D医学图像超分辨率深度神经网络的多目标优化

在医学成像中，许多领域（例如磁共振（MR））都缺乏高质量的图像。由于许多采集障碍，生成的图像没有足够的分辨率来执行适当的诊断。图像超分辨率（SR）是一个不适定的问题，试图从图像中推断信息以增强其分辨率。如今，深度学习技术已成为从图像中提取特征并推断新信息的强大工具。在MR中，最近的大多数工作都基于最小化基于欧几里得范数的输入和输出图像之间的误差。这项工作提出了一种新的方法，可以基于损耗层中Lp范数的组合执行三维SR。两种多目标优化技术用于组合两个成本函数。所建议的损耗层使用SRCNN3D和DCSRN网络进行了训练，并使用两个MR结构T1加权数据集进行了测试，然后与传统的欧几里得损耗进行了比较。实验结果表明，在峰值信噪比（PSNR），结构相似性指数（SSIM）和Bhattacharyya系数（BC）方面存在显着差异，而残留图像则显示出精细的细节。