Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging.
Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging.
复制标题
多通道质谱成像动态采样的深度学习方法。
DOI：
10.1109/tci.2023.3248947
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发表时间：
2023
影响因子：
5.4
通讯作者：
HyeYe,Dong
中科院分区：
文献类型：
--
作者：
Helminiak,David;Hu,Hang;Laskin,Julia;HyeYe,Dong
使用传统直线扫描的质谱成像（MSI）需要数小时到数天的时间才能进行高空间分辨率采集。考虑到样本视场内的大多数像素通常既不与底层生物结构相关，也不提供化学信息，MSI作为与稀疏和动态采样算法集成的主要候选者。在扫描过程中，随机模型确定哪些位置可能包含对生成低误差重建至关重要的信息。减少所需的物理测量的数量，从而最大限度地减少总的采集时间。一种用于动态采样的深度学习方法（DLADS），利用卷积神经网络（CNN）并将分子质量强度分布封装在第三维内，展示了Nanospray解吸电喷雾电离（nano-DESI）MSI组织的模拟70%的吞吐量改进。DLADS，动态采样的监督学习方法，最小二乘回归（SLADS-LS）和多层感知器（MLP）网络（SLADS-Net）之间进行评估。与SLADS-LS相比，仅限于单个lem/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%，从而使采集目标dm/z的重建质量提高了6.0%、2.1%和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, 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 singlem/zchannel, 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 targetedm/z.