Deep learning-based reconstruction of ultrasound images from raw channel data

• Published in: International journal for computer assisted radiology and surgery Vol. 15; no. 9; pp. 1487 - 1490
• Main Authors: Strohm, Hannah, Rothlübbers, Sven, Eickel, Klaus, Günther, Matthias
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2020; Springer Nature B.V
• Subjects: Algorithms; Computer Imaging; Computer Science; Contrast Media; Deep Learning; Diagnosis, Computer-Assisted - methods; Feasibility; Ground truth; Health Informatics; Humans; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; In vivo methods and tests; Information retrieval; Machine learning; Medicine; Medicine & Public Health; Models, Theoretical; Optimization; Pattern Recognition and Graphics; Phantoms, Imaging; Plane waves; Radiology; Reference Values; Reproducibility of Results; Short Communication; Signal-To-Noise Ratio; Surgery; Target recognition; Ultrasonic imaging; Ultrasonic testing; Ultrasonography; Ultrasound; Vision; Deep learning; Plane wave ultrasound imaging
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-020-02197-w

Purpose We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions. Methods We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data. Results The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is 4.23 ± 1.52 for the phantom images and 6.09 ± 0.72 for the in vivo data. Conclusion The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest.