Carotenuto Riccardo, Sabbi Gabriele, Pappalardo Massimo
Dipartimento di Ingegneria Elettronica, Università degli Studi Roma Tre, Italy.
IEEE Trans Ultrason Ferroelectr Freq Control. 2002 Aug;49(8):1039-49. doi: 10.1109/tuffc.2002.1026016.
Spatial resolution in modern ultrasound imaging systems is limited by the high cost of large aperture transducer arrays, which require a large number of transducer elements and electronic channels. A new technique to enhance the spatial resolution of pulse-echo imaging systems is presented. The method attempts to build an image that could be obtained with a transducer array aperture larger than that physically available. We consider two images of the same object obtained with two different apertures, the full aperture and a subaperture, of the same transducer. A suitable artificial neural network (ANN) is trained to reproduce the relationship between the image obtained with the transducer full aperture and the image obtained with a subaperture. The inputs of the neural network are portions of the image obtained with the subaperture (low resolution image), and the target outputs are the corresponding portions of the image produced by the full aperture (high resolution image). After the network is trained, it can produce images with almost the same resolution of the full aperture transducer, but using a reduced number of real transducer elements. All computations are carried out on envelope-detected decimated images; for this reason, the computational cost is low and the method is suitable for real-time applications. The proposed method was applied to experimental data obtained with the ultrasound synthetic aperture focusing technique (SAFT), giving quite promising results. Real-time implementation on a modern, full-digital echographic system is currently being developed.
现代超声成像系统中的空间分辨率受到大孔径换能器阵列高成本的限制，这种阵列需要大量的换能器元件和电子通道。本文提出了一种提高脉冲回波成像系统空间分辨率的新技术。该方法试图构建一幅使用比实际可用孔径更大的换能器阵列孔径所获得的图像。我们考虑用同一个换能器的两个不同孔径（全孔径和子孔径）获得的同一物体的两幅图像。训练一个合适的人工神经网络（ANN）来再现用换能器全孔径获得的图像与用子孔径获得的图像之间的关系。神经网络的输入是用子孔径获得的图像部分（低分辨率图像），目标输出是由全孔径产生的图像的相应部分（高分辨率图像）。网络训练完成后，它可以使用较少数量的实际换能器元件生成几乎与全孔径换能器具有相同分辨率的图像。所有计算都在包络检测抽取图像上进行；因此，计算成本低，该方法适用于实时应用。所提出的方法应用于通过超声合成孔径聚焦技术（SAFT）获得的实验数据，取得了相当有前景的结果。目前正在现代全数字超声成像系统上进行实时实现。
