Mathematics Research Center, Academy of Athens, Athens, Greece.
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK.
NMR Biomed. 2021 Apr;34(4):e4479. doi: 10.1002/nbm.4479. Epub 2021 Jan 14.
Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the proton signal of different molecules present in the tissue. However, its performance is severely hindered by low SNR, field inhomogeneities and overlapping spectra of metabolites, which affect the quantification of metabolites. Consequently, MRS is rarely included in routine clinical protocols and has not been proven in multi-institutional trials. This work proposes an alternative approach, where instead of quantifying metabolites' concentration, deep learning (DL) is used to model the complex nonlinear relationship between diseases and their spectroscopic metabolic fingerprint (pattern). DL requires large training datasets, acquired (ideally) with the same protocol/scanner, which are very rarely available. To overcome this limitation, a novel method is proposed that can quantum mechanically synthesise MRS data for any scanner/acquisition protocol. The proposed methodology is applied to the challenging clinical problem of differentiating metastasis from glioblastoma brain tumours on data acquired across multiple institutions. DL algorithms were trained on the augmented synthetic spectra and tested on two independent datasets acquired by different scanners, achieving a receiver operating characteristic area under the curve of up to 0.96 and 0.97, respectively.
代谢指纹是与代谢紊乱相关疾病的有价值的生物标志物。1H 磁共振波谱（MRS）是一种独特的非侵入性诊断工具，仅基于组织中存在的不同分子的质子信号就能描绘代谢指纹。然而，其性能受到低信噪比、磁场不均匀性和代谢物光谱重叠的严重限制，这会影响代谢物的定量。因此，MRS 很少包含在常规临床方案中，也没有在多机构试验中得到证实。这项工作提出了一种替代方法，即用深度学习（DL）代替量化代谢物浓度，来对疾病与其光谱代谢指纹（模式）之间的复杂非线性关系进行建模。DL 需要大型训练数据集，最好使用相同的协议/扫描仪获取，但这种数据集非常罕见。为了克服这一限制，提出了一种新方法，可以对任何扫描仪/采集协议的 MRS 数据进行量子合成。所提出的方法应用于具有挑战性的临床问题，即区分来自多个机构的数据的脑转移瘤和胶质母细胞瘤。DL 算法在经过扩充的合成光谱上进行训练，并在由不同扫描仪采集的两个独立数据集上进行测试，分别达到了 0.96 和 0.97 的接收器操作特征曲线下面积。
