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Item type:Publication, Access status: with files , Do we need pre-processing for deep learning based ultrasound shear wave elastography?(De Gruyter, 2026-01-20); ; ; ; Estimating the elasticity of soft tissue can provide useful information for various diagnostic applications. Ultrasound shear wave elastography offers a non-invasive approach. However, its generalizability and standardization across different systems and processing pipelines remain limited. Considering the influence of image processing on ultrasound based diagnostics, recent literature has discussed the impact of different image processing steps on reliable and reproducible elasticity analysis. In this work, we investigate the need of ultrasound pre-processing steps for deep learning-based ultrasound shear wave elastography. We evaluate the performance of a 3D convolutional neural network in predicting shear wave velocities from spatio-temporal ultrasound images, studying different degrees of pre-processing on the input images, ranging from fully beamformed and filtered ultrasound images to raw radiofrequency data. We compare the predictions from our deep learning approach to a conventional timeof- flight method across four gelatin phantoms with different elasticity levels. Our results demonstrate statistically significant differences in the predicted shear wave velocity among all elasticity groups, regardless of the degree of pre-processing. Although pre-processing slightly improves performance metrics, our results show that the deep learning approach can reliably differentiate between elasticity groups using raw, unprocessed radiofrequency data. These results show that deep learning-based approaches could reduce the need and the bias of traditional ultrasound pre-processing steps in ultrasound shear wave elastography, enabling faster and more reliable clinical elasticity assessments.Publicationtype: Journal Article22 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Learning-based target localization in robotic radioguided surgery in noisy environments(De Gruyter, 2025-12-01); ; ; ; Rojas, M.In radioguided surgery, G-probes are used intraoperatively to localize targets marked by radionuclides. However, the interpretation of G-probe measurements is challenging due to background activity from surrounding organs. This work investigated whether deep neural networks can localize radiation sources in such environments and how different background activities impact this task. A physics-guided forward model simulated G-probe measurements for different intra-abdominal distributions, including an anatomically inspired bladder activity. A convolutional neural network was trained on simulated measurements to predict 3D target source positions. Results indicated that prediction accuracy improved with more Gprobe measurements and degraded with increased background activity. In particular, proximity to high-activity regions like the bladder significantly reduced accuracy. This study demonstrates the need to consider background activity distributions for target localization and that a convolutional neural network could solve this task.Publicationtype: Journal Article16 8