Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of information indispensable for understanding, modelling, diagnosis, and treatment of diseases. Reconstruction algorithms entail transforming signals collected by acquisition hardware into interpretable images. Reconstruction is a challenging task given the ill-posedness of the problem and the absence of exact analytic inverse transforms in practical cases. While the last decades witnessed impressive advancements in terms of new modalities, improved temporal and spatial resolution, reduced cost, and wider applicability, several improvements can still be envisioned such as reducing acquisition and reconstruction time to reduce patient’s exposure to radiation and discomfort while increasing clinics throughput and reconstruction accuracy. Furthermore, the deployment of biomedical imaging in handheld devices with small power requires a fine balance between accuracy and latency. The design of fast, robust, and accurate reconstruction algorithms is a desirable, yet challenging, research goal. While the classical image reconstruction algorithms approximate the inverse function relying on expert-tuned parameters to ensure reconstruction performance, deep learning (DL) allows automatic feature extraction and real-time inference. Hence, DL presents a promising approach to image reconstruction with artifact reduction and reconstruction speed-up reported in recent works as part of a rapidly growing field. We review state-of-the-art image reconstruction algorithms with a focus on DL-based methods. First, we examine common reconstruction algorithm designs, applied metrics, and datasets used in the literature. Then, key challenges are discussed as potentially promising strategic directions for future research.

中文翻译：

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用于生物医学图像重建的深度学习：一项调查

医学影像是医学中的宝贵资源，因为它能够窥视人体内部，并为科学家和医生提供理解、建模、诊断和治疗疾病必不可少的丰富信息。重建算法需要将采集硬件收集的信号转换为可解释的图像。考虑到问题的不适定性以及在实际案例中缺乏精确的解析逆变换，重建是一项具有挑战性的任务。虽然过去几十年在新模式、改进的时间和空间分辨率、降低的成本和更广泛的适用性方面取得了令人瞩目的进步，仍然可以设想一些改进，例如减少采集和重建时间以减少患者暴露于辐射和不适，同时增加诊所的吞吐量和重建准确性。此外，在小功率手持设备中部署生物医学成像需要精确度和延迟之间的良好平衡。设计快速、稳健和准确的重建算法是一个理想但具有挑战性的研究目标。经典图像重建算法依赖于专家调整参数来近似反函数以确保重建性能，而深度学习 (DL) 允许自动特征提取和实时推理。因此，DL 提供了一种很有前景的图像重建方法，在最近的工作中报道了伪影减少和重建加速，这是一个快速发展的领域的一部分。我们回顾了最先进的图像重建算法，重点是基于 DL 的方法。首先，我们检查文献中使用的常见重建算法设计、应用指标和数据集。然后，关键挑战被讨论为未来研究的潜在有希望的战略方向。