This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Current approaches in experimental design focus on model-parameter estimation and require specification of a particular model, whereas in imaging, other tasks may drive the design. Furthermore, such approaches often lead to intractable optimization problems in real-world imaging applications. Here we present a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task. The approach obtains data densely-sampled over the measurement space (many image channels) for a small number of acquisitions, then identifies a subset of channels of prespecified size that best supports the task. We propose a method: TADRED for TAsk-DRiven Experimental Design in imaging, to identify the most informative channel-subset whilst simultaneously training a network to execute the task given the subset. Experiments demonstrate the potential of TADRED in diverse imaging applications: several clinically-relevant tasks in magnetic resonance imaging; and remote sensing and physiological applications of hyperspectral imaging. Results show substantial improvement over classical experimental design, two recent application-specific methods within the new paradigm, and state-of-the-art approaches in supervised feature selection. We anticipate further applications of our approach. Code is available: https://github.com/sbb-gh/experimental-design-multichannel

翻译：本文提出一种数据驱动、任务导向的实验设计范式，旨在缩短采集时间、降低成本并加速成像设备的部署。当前实验设计方法侧重于模型参数估计，需指定特定模型，而在成像领域，其他任务可能主导设计过程。此外，此类方法在真实成像应用中常导致难以求解的优化问题。本文提出一种新的实验设计范式，可同步优化设计方案（图像通道集合）并训练机器学习模型执行用户指定的图像分析任务。该方法对少量采集样本在测量空间（多图像通道）进行密集采样，然后识别出预设大小的最优支持任务通道子集。我们提出名为TADRED（成像中任务驱动实验设计）的方法，可在识别最具信息量的通道子集的同时，训练网络基于该子集执行任务。实验验证了TADRED在多种成像应用中的潜力：磁共振成像中的多项临床相关任务，以及高光谱成像在遥感和生理学领域的应用。结果表明，相较于经典实验设计方法、新范式下两种近期专用方法及监督式特征选择领域的先进技术，本方法取得显著提升。我们预期该方法具有更广泛的应用前景。代码开源于：https://github.com/sbb-gh/experimental-design-multichannel