通过机器学习改进有限视角超声断层成像。

IF 3 2区 工程技术 Q1 ACOUSTICS
Mikolaj Mroszczak, Stefano Mariani, Peter Huthwaite
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引用次数: 0

摘要

断层摄影重建被广泛应用于医学、无损检测和地质学领域。在理想情况下,从物体周围的所有角度进行测量(即全视图配置),就能得到物体的完整重建图。层析成像面临的一个主要问题是无法围绕被检测物体自由进行测量。造成这种情况的原因可能是物体的尺寸和几何形状,也可能是难以从特定方向进行测量。由此产生的有限视角传感器配置会导致图像质量大幅下降,因此采用补偿算法是非常有益的。目前,最有效的补偿算法需要大量的计算能力或定制的个案方法,通常需要针对特定应用调整大量任意常数。这项工作提出了一种基于机器学习的方法来执行有限视角补偿。该模型基于自动编码器架构。它在人工数据集上进行训练,利用了在全视角输入的情况下生成任意有限视角图像的能力。该方法在十张激光扫描腐蚀图上进行了评估,并将评估结果与正则化进行了比较,正则化是一种有限视图补偿算法,在执行速度和泛化潜力方面与有限视图补偿算法相似。比较了两种算法在整个图像上的均方根误差 (RMSE) 和最大绝对误差 (MAE)。此外,还对它们的主观质量进行了直观比较。与传统算法相比,基于 ML 的方法在十种情况中有八种的 MAE 有所改进。传统方法在平均均方根误差(RMSE)方面表现更好,这是因为 ML 输出的背景水平不准确,而这并不是关键能力。最重要的是,对输出结果的目测表明,ML 方法能更好地重建图像,尤其是在不规则腐蚀斑块的情况下。与有限视角图像相比,ML 方法的 RMSE 和 MAE 均提高了 41%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved limited view ultrasound tomography via machine learning.

Tomographic reconstruction is used extensively in medicine, non-destructive testing and geology. In an ideal situation where measurements are taken at all angles around an object, known as full view configuration, a full reconstruction of the object can be produced. One of the major issues faced in tomographic imaging is when measurements cannot be taken freely around the object under inspection. This may be caused by the size and geometry of the object or difficulty accessing from particular directions. The resulting limited view transducer configuration leads to a large deterioration in image quality, thus it is very beneficial to employ a compensation algorithm. At present, the most effective compensation algorithms require a large amount of computing power or a bespoke case-by case approach, often with numerous arbitrary constants which must be tuned for a specific application. This work proposes a machine learning based approach to perform the limited view compensation. The model is based around an autoencoder architecture. It is trained on an artificial dataset, taking advantage of the ability to generate arbitrary limited view images given a full view input. The approach is evaluated on ten laser-scanned corrosion maps and the results compared to positivity regularisation - a limited view compensation algorithm similar in the speed of execution and generalisation potential. The algorithms are compared for root mean squared error (RMSE) across the image, and maximum absolute error (MAE). Furthermore, they are visually compared for subjective quality. Compared to the conventional algorithm, the ML-based approach improves on the MAE in eight out of the ten cases. The conventional approach performs better on mean RMSE, which is explained by the ML outputting inaccurate background level, which is not a critical ability. Most importantly, the visual inspection of outputs shows the ML approach reconstructs the images better, especially in the case of irregular corrosion patches. Compared to limited view images, the ML method improves both the RMSE and MAE by 41%.

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来源期刊
CiteScore
7.70
自引率
16.70%
发文量
583
审稿时长
4.5 months
期刊介绍: IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control includes the theory, technology, materials, and applications relating to: (1) the generation, transmission, and detection of ultrasonic waves and related phenomena; (2) medical ultrasound, including hyperthermia, bioeffects, tissue characterization and imaging; (3) ferroelectric, piezoelectric, and piezomagnetic materials, including crystals, polycrystalline solids, films, polymers, and composites; (4) frequency control, timing and time distribution, including crystal oscillators and other means of classical frequency control, and atomic, molecular and laser frequency control standards. Areas of interest range from fundamental studies to the design and/or applications of devices and systems.
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