Molina-Maza Juan Manuel, Galiana-Bordera Adrian, Jimenez Mar, Malpica Norberto, Torrado-Carvajal Angel
Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Madrid, Spain.
Department of Radiology, Hospital Universitario Quirónsalud, Madrid, Spain.
Front Neurosci. 2022 Oct 25;16:830143. doi: 10.3389/fnins.2022.830143. eCollection 2022.
Pediatric medical imaging represents a real challenge for physicians, as children who are patients often move during the examination, and it causes the appearance of different artifacts in the images. Thus, it is not possible to obtain good quality images for this target population limiting the possibility of evaluation and diagnosis in certain pathological conditions. Specifically, magnetic resonance imaging (MRI) is a technique that requires long acquisition times and, therefore, demands the use of sedation or general anesthesia to avoid the movement of the patient, which is really damaging in this specific population. Because ALARA (as low as reasonably achievable) principles should be considered for all imaging studies, one of the most important reasons for establishing novel MRI imaging protocols is to avoid the harmful effects of anesthesia/sedation. In this context, ground-breaking concepts and novel technologies, such as artificial intelligence, can help to find a solution to these challenges while helping in the search for underlying disease mechanisms. The use of new MRI protocols and new image acquisition and/or pre-processing techniques can aid in the development of neuroimaging studies for children evaluation, and their translation to pediatric populations. In this paper, a novel super-resolution method based on a convolutional neural network (CNN) in two and three dimensions to automatically increase the resolution of pediatric brain MRI acquired in a reduced time scheme is proposed. Low resolution images have been generated from an original high resolution dataset and used as the input of the CNN, while several scaling factors have been assessed separately. Apart from a healthy dataset, we also tested our model with pathological pediatric MRI, and it successfully recovers the original image quality in both visual and quantitative ways, even for available examples of dysplasia lesions. We hope then to establish the basis for developing an innovative free-sedation protocol in pediatric anatomical MRI acquisition.
儿科医学影像对医生来说是一项真正的挑战，因为作为患者的儿童在检查过程中经常会移动，这会导致图像中出现不同的伪影。因此，无法为这一目标人群获取高质量图像，限制了在某些病理状况下进行评估和诊断的可能性。具体而言，磁共振成像（MRI）是一种需要较长采集时间的技术，因此需要使用镇静剂或全身麻醉来避免患者移动，而这对这一特定人群具有极大危害。由于所有成像研究都应考虑“尽可能合理达到低剂量”（ALARA）原则，制定新型MRI成像方案的最重要原因之一就是避免麻醉/镇静的有害影响。在这种背景下，诸如人工智能等开创性概念和新技术有助于找到应对这些挑战的解决方案，同时有助于探寻潜在的疾病机制。使用新的MRI方案以及新的图像采集和/或预处理技术有助于开展用于儿童评估的神经影像学研究，并将其应用于儿科人群。本文提出了一种基于卷积神经网络（CNN）的二维和三维新型超分辨率方法，以自动提高在缩短时间方案下采集的儿科脑MRI的分辨率。从原始高分辨率数据集中生成低分辨率图像，并将其用作CNN的输入，同时分别评估了几个缩放因子。除了健康数据集外，我们还用儿科病理MRI对我们的模型进行了测试，即使对于发育异常病变的现有示例，该模型也能在视觉和定量方面成功恢复原始图像质量。我们希望以此为基础，在儿科解剖MRI采集中开发一种创新的无镇静方案。
