Halder Rajat Suvra, Sahani Ashish
Department of Biomedical Engineering, Indian Institute of Technology, Ropar, Punjab, India.
Phys Eng Sci Med. 2022 Dec;45(4):1139-1151. doi: 10.1007/s13246-022-01181-9. Epub 2022 Sep 29.
Ultrasound modalities are cost-effective and radiation-free technology for real-time medical imaging. These modalities require image reconstruction to obtain the actual ultrasound images from ultrasound raw data. The ultrasound raw data is obtained in the form of echo after scanning an imaging plane through ultrasound waves. The most commonly used image reconstruction beamforming technique is Delay and Sum (DAS). Other sophisticated beamforming techniques are Delay Multiply and Sum (DMAS) and Minimum Variance Distortionless Response (MVDR). DAS has limited image quality, and the employment of sophisticated techniques increases the computational complexity and computational time with improvement in image quality. To overcome these problems, various DNN (Deep Neural Networks) based techniques have been proposed which can reconstruct ultrasound images directly from ultrasound raw data. But DNN implementation has two limitations: accuracy of reconstruction and generalizability of the model. To overcome these limitations, we are proposing methodologies with a DNN model which was able to reduce these limitations. Firstly, we generated the datasets which include multiple shapes such as line, circle, ellipse, and parabola. After that, we have implemented a CNN-DNN (Convolution Neural Network and Deep Neural Network) hybrid model which has significantly improved computational time as well as image quality. We have trained our model with different sets of data to validate the reconstruction of the image matrix. We achieved a significant improvement in computational time of around 100 times (from around 0.6 s to 0.0059 s) as compared to DAS beamforming technique. At the same time, we also achieved a significant improvement in image quality with 37.19 dB average and 41.37 dB maximum improved Peak Signal to Noise Ratio (PSNR), and 87.41% average and 95% maximum Structural Similarity Index Matrix (SSIM) value. We also achieved generalizability and precise image reconstruction by using the proposed model.
超声模态是用于实时医学成像的经济高效且无辐射的技术。这些模态需要图像重建，以便从超声原始数据中获取实际的超声图像。超声原始数据是在通过超声波扫描成像平面后以回波的形式获得的。最常用的图像重建波束形成技术是延迟求和（DAS）。其他复杂的波束形成技术是延迟相乘求和（DMAS）和最小方差无失真响应（MVDR）。DAS的图像质量有限，而采用复杂技术会增加计算复杂度和计算时间，同时图像质量也会提高。为了克服这些问题，已经提出了各种基于深度神经网络（DNN）的技术，这些技术可以直接从超声原始数据重建超声图像。但是DNN的实现有两个局限性：重建的准确性和模型的通用性。为了克服这些局限性，我们提出了一种使用DNN模型的方法，该模型能够减少这些局限性。首先，我们生成了包含多种形状（如直线、圆形、椭圆形和抛物线）的数据集。之后，我们实现了一个卷积神经网络-深度神经网络（CNN-DNN）混合模型，该模型显著提高了计算时间以及图像质量。我们用不同的数据集训练了我们的模型，以验证图像矩阵的重建。与DAS波束形成技术相比，我们在计算时间上实现了约100倍的显著改进（从约0.6秒到0.0059秒）。同时，我们在图像质量方面也取得了显著改进，平均峰值信噪比（PSNR）提高了37.19dB，最大提高了41.37dB，平均结构相似性指数矩阵（SSIM）值提高了87.41%，最大提高了95%。我们还通过使用所提出的模型实现了通用性和精确的图像重建。
