Deep-Learning-Based Microwave-Induced Thermoacoustic Tomography Applying Realistic Properties of Ultrasound Transducer

IF 4.1 1区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Microwave Theory and Techniques Pub Date : 2024-08-16 DOI:10.1109/TMTT.2024.3439551
Lejia Zhang;Qizhi Wang;Simin Zhao;Dantong Liu;Chenzhe Li;Baosheng Wang;Xiong Wang
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Abstract

Microwave-induced thermoacoustic tomography (MITAT) is a noninvasive hybrid modality that has been widely applied in a bunch of biomedical applications. MITAT integrated with cutting-edge deep learning (DL-MITAT) technique is highly promising for tackling many challenging problems that cannot be efficiently solved by traditional methods. However, the previous MITAT or DL-MITAT works rarely consider the realistic properties of the utilized ultrasound transducer, which can significantly degrade the image quality in some application scenarios. To address this issue, we propose a DL-MITAT technique applying realistic properties of ultrasound transducers (referred to as DL-MITAT-PoT), which is very effective for imaging large or long samples. To be specific, the previous related works simply assume an omnidirectional receiving pattern of the transducer. Instead, we take into account the limited receiving angle of a realistic transducer in the DL-MITAT-PoT technique to improve the imaging performance. To implement this technique, we incorporate the receiving properties of a realistic transducer into the training process of the ResU-Net network. We test the trained network by imaging several 2-mm-diameter long blood-vessel-mimicking samples via both simulations and experiments. The obtained imaging results demonstrate that the proposed DL-MITAT-PoT yields much better imaging quality than its counterpart without considering the property of the transducer. We show that the network trained using complicated vessel sample data is endowed with downward compatibility for samples having less complexity. This technique greatly improves the image quality for dealing with large or long samples and has a promising prospect in many biomedical applications.
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基于深度学习的微波诱导热声断层成像技术,应用超声换能器的真实特性
微波诱导热声层析成像(MITAT)是一种无创的混合模式,已广泛应用于多种生物医学领域。MITAT 与最前沿的深度学习(DL-MITAT)技术相结合,在解决许多传统方法无法有效解决的挑战性问题方面大有可为。然而,以往的 MITAT 或 DL-MITAT 作品很少考虑所使用的超声换能器的实际属性,这可能会在某些应用场景中显著降低图像质量。为了解决这个问题,我们提出了一种应用超声换能器现实特性的 DL-MITAT 技术(简称为 DL-MITAT-PoT),它对大型或长样本成像非常有效。具体来说,之前的相关研究只是简单地假设了换能器的全向接收模式。而在 DL-MITAT-PoT 技术中,我们考虑到了实际传感器的有限接收角度,从而提高了成像性能。为了实现这一技术,我们在 ResU-Net 网络的训练过程中加入了现实传感器的接收特性。我们通过模拟和实验对几个直径为 2 毫米的长血管模拟样本进行成像,以测试训练后的网络。获得的成像结果表明,在不考虑换能器特性的情况下,所提出的 DL-MITAT-PoT 的成像质量远远优于同类产品。我们发现,使用复杂血管样本数据训练的网络对复杂度较低的样本具有向下兼容性。这项技术大大提高了处理大型或较长样本的成像质量,在许多生物医学应用中具有广阔的前景。
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来源期刊
IEEE Transactions on Microwave Theory and Techniques
IEEE Transactions on Microwave Theory and Techniques 工程技术-工程:电子与电气
CiteScore
8.60
自引率
18.60%
发文量
486
审稿时长
6 months
期刊介绍: The IEEE Transactions on Microwave Theory and Techniques focuses on that part of engineering and theory associated with microwave/millimeter-wave components, devices, circuits, and systems involving the generation, modulation, demodulation, control, transmission, and detection of microwave signals. This includes scientific, technical, and industrial, activities. Microwave theory and techniques relates to electromagnetic waves usually in the frequency region between a few MHz and a THz; other spectral regions and wave types are included within the scope of the Society whenever basic microwave theory and techniques can yield useful results. Generally, this occurs in the theory of wave propagation in structures with dimensions comparable to a wavelength, and in the related techniques for analysis and design.
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