Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus.

As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use.

随着低场磁共振成像技术在全球临床环境中的推广，评估正确诊断和治疗特定疾病所需的图像质量以及评估机器学习算法（如深度学习）在提高低质量图像方面的作用至关重要。在对一项正在进行的随机临床试验的事后分析中，我们评估了质量降低的图像以及经深度学习增强的图像在脑积水治疗规划中的诊断效用。感染后脑积水婴儿的计算机断层扫描图像在空间分辨率、噪声以及脑与脑脊液之间的对比度方面有所降低，并使用深度学习算法进行了增强。质量降低的图像和增强后的图像都呈现给了三位习惯在中低收入国家（LMIC）工作的经验丰富的小儿神经外科医生，以评估其在脑积水治疗规划中的临床效用。此外，增强后的图像与其原始的计算机断层扫描图像一同呈现，以评估深度学习增强程序导致的重建误差是否能被评估者接受。结果表明，图像分辨率以及脑与脑脊液之间的对比度噪声比可预测图像被认定对脑积水治疗规划有用的可能性。深度学习增强显著提高了对比度噪声比，提高了图像有用的表观可能性；然而，深度学习增强会引入结构误差，这会带来误导临床解读的重大风险。我们发现，质量低于通常可接受水平的图像可能对脑积水治疗规划有用。此外，低质量图像可能比经深度学习增强的图像更可取，因为它们不会引入可能误导治疗决策的错误信息的风险。这些发现倡导在评估临床可接受的图像质量方面制定新的标准。