Deep Learning-based Self-high-resolution for 3D Ultrasound Imaging

For 3D ultrasound (US) images with large slice thickness, high frequency information in the slice direction is missing and cannot be resolved through interpolation. As an ill-posed problem, current high-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution images to high resolution images. In this study, we aim to propose a self-supervised learning method, which does not use any external atlas images, yet can still resolve high resolution images only reliant on the acquired image with a large slice thickness. To circumvent the lack of training data, the simulated training data were obtained from the input image. To do this, each 2D sagittal slice is regarded as a high-resolution image, while each coronal and axial slice is regarded as low-resolution images. By training a deep learning-based model on sagittal slices and using this model to infer high-resolution coronal and axial slices, we can apply the mapping to low-resolution images with large slice thickness to estimate the high-resolution images with thin slice thickness. The proposed algorithm was evaluated using 30 sets of US breast data. The US image downsampled in z-axis was used as low-resolution image, the original US image was used as ground truth. The normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) indices were used to quantify the accuracy of the proposed algorithm. The NMAE, PSNR and NCC were 0.011 +/- 0.02, 34.6 +/- 2.14 dB and 0.98 +/- 0.01. The proposed method showed similar image quality as compared to ground truth.