Deep learning for biomedical image reconstruction: a survey
Deep learning for biomedical image reconstruction: a survey
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DOI：
10.1007/s10462-020-09861-2
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发表时间：
2020-08-05
影响因子：
12
通讯作者：
Hamarneh, Ghassan
中科院分区：
文献类型：
--
作者：
Ben Yedder, Hanene;Cardoen, Ben;Hamarneh, Ghassan
医学成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，诊断和治疗疾病所不可或缺的丰富信息。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。重建是一个具有挑战性的任务，给定的不适定性的问题，并在实际情况下没有精确的解析逆变换。虽然在过去的几十年里，在新的模式、提高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印象深刻的进步，但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精度。此外，在具有小功率的手持设备中部署生物医学成像需要在准确性和延迟之间保持良好的平衡。快速、鲁棒和精确的重建算法的设计是一个理想的，但具有挑战性的研究目标。虽然经典的图像重建算法依赖于专家调整的参数来近似逆函数以确保重建性能，但深度学习（DL）允许自动特征提取和实时推理。因此，DL提出了一种很有前途的方法，图像重建与伪影减少和重建速度在最近的作品报道的一部分，一个快速增长的领域。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，我们研究常见的重建算法设计，应用的指标，和文献中使用的数据集。然后，关键的挑战进行了讨论，为未来的研究潜在的有前途的战略方向。
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.