论文标题
多通道质谱成像的动态抽样的深度学习方法
Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging
论文作者
论文摘要
使用传统的直线扫描，质谱成像（MSI）需要数小时到几天才能获得高空间分辨率的采集。鉴于样本视野中的大多数像素通常与潜在的生物结构既不相关，也不是化学信息丰富的，因此MSI作为与稀疏和动态采样算法进行整合的主要候选者。在扫描过程中，随机模型确定哪些位置概率包含对低误差重建至关重要的信息。减少所需物理测量的数量，从而最大程度地减少了总体采集时间。使用卷积神经网络（CNN）的动态抽样（DLADS）的深度学习方法，并将分子质量强度分布封装在第三维内，证明了纳米喷雾阿力吸收电化电离（Nano-Desi）MSI组织的模拟70％的吞吐量改善。在DLADS和动态采样的监督学习方法之间进行评估，具有最小二乘回归（SLADS-LS）和多层感知器（MLP）网络（SLADS-NET）。与SLADS-LS相比，仅限于单个M/Z通道，以及多通道SLADS-LS和SLADS-NET，DLADS分别将回归性能提高了36.7％，7.0％和6.2％，从而增加了重建质量6.0％，2.1％，2.1％，2.1％，以及3.4％的目标，以及目标M/Z的收购3.4％。
Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions. Given that most pixels within a sample's field of view are often neither relevant to underlying biological structures nor chemically informative, MSI presents as a prime candidate for integration with sparse and dynamic sampling algorithms. During a scan, stochastic models determine which locations probabilistically contain information critical to the generation of low-error reconstructions. Decreasing the number of required physical measurements thereby minimizes overall acquisition times. A Deep Learning Approach for Dynamic Sampling (DLADS), utilizing a Convolutional Neural Network (CNN) and encapsulating molecular mass intensity distributions within a third dimension, demonstrates a simulated 70% throughput improvement for Nanospray Desorption Electrospray Ionization (nano-DESI) MSI tissues. Evaluations are conducted between DLADS and a Supervised Learning Approach for Dynamic Sampling, with Least-Squares regression (SLADS-LS) and a Multi-Layer Perceptron (MLP) network (SLADS-Net). When compared with SLADS-LS, limited to a single m/z channel, as well as multichannel SLADS-LS and SLADS-Net, DLADS respectively improves regression performance by 36.7%, 7.0%, and 6.2%, resulting in gains to reconstruction quality of 6.0%, 2.1%, and 3.4% for acquisition of targeted m/z.
