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Lewandowski M., Jarosik P., Tasinkevych Y., Walczak M., Efficient GPU implementation of 3D spectral domain synthetic aperture imaging,
IUS 2020, IEEE International Ultrasonics Symposium, 2020-09-07/09-11, Las Vegas (US), DOI: 10.1109/IUS46767.2020.9251552, pp.1-3, 2020 Streszczenie: In this work, we considered the implementation of a 3D volume reconstruction algorithm for single plane-wave ultrasound insonification. We review the theory behind the Hybrid Spectral-Domain Imaging (HSDI) algorithm, provide details of the algorithm implementation for Nvidia CUDA GPU cards, and discuss the performance evaluation results. The average time required to reconstruct a single data volume using our GPU implementation of the HSDI algorithm was 22 ms. We also present an iso-surface extraction result using a marching cubes algorithm. Our work constitutes a preliminary research for further development and implementation of 3D volume reconstruction using GPU implementation of the spectral domain imaging algorithm. Słowa kluczowe: ultrasound imaging, 3D ultrasound, volumetric imaging, gpu Afiliacje autorów:
Lewandowski M. | - | IPPT PAN | Jarosik P. | - | IPPT PAN | Tasinkevych Y. | - | IPPT PAN | Walczak M. | - | IPPT PAN |
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Jarosik P., Lewandowski M., Automatic Ultrasound Guidance Based on Deep Reinforcement Learning,
IUS 2019, IEEE, International Ultrasonics Symposium, 2019-10-06/10-09, Glasgow (GB), DOI: 10.1109/ULTSYM.2019.8926041, pp.475-478, 2019 Streszczenie: Ultrasound is becoming the modality of choice for everyday medical diagnosis, due to its mobility and decreasing price. As the availability of ultrasound diagnostic devices for untrained users grows, appropriate guidance becomes desirable. This kind of support could be provided by a software agent, who easily adapts to new conditions, and whose role is to instruct the user on how to obtain optimal settings of the imaging system during an examination. In this work, we verified the feasibility of implementing and training such an agent for ultrasound, taking the deep reinforcement learning approach. The tasks it was given were to find the optimal position of the transducer’s focal point (FP task) and to find an appropriate scanning plane (PP task). The ultrasound environment consisted of a linear-array transducer acquiring information from a tissue phantom with cysts forming an object-of-interest (OOI). The environment was simulated in the Field-II software. The agent could perform the following actions: move the position of the probe to the left/right, move focal depth upwards/downwards, rotate the probe clockwise/counter-clockwise, or do not move. Additional noise was applied to the current probe setting. The only observations the agent received were B-mode frames. The agent acted according to stochastic policy modeled by a deep convolutional neural network, and was trained using the vanilla policy gradient update algorithm. After the training, the agent’s ability to accurately locate the position of the focal depth and scanning plane improved. Our preliminary results confirmed that deep reinforcement learning can be applied to the ultrasound environment. Słowa kluczowe: ultrasound guidance, reinforcement learning, deep learning Afiliacje autorów:
Jarosik P. | - | IPPT PAN | Lewandowski M. | - | IPPT PAN |
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Jarosik P., Lewandowski M., The feasibility of deep learning algorithms integration on a GPU-based ultrasound research scanner,
IUS 2017, IEEE International Ultrasonics Symposium, 2017-09-06/09-09, Washington (US), DOI: 10.1109/ULTSYM.2017.8091750, pp.1-4, 2017 Streszczenie: Ultrasound medical diagnostics is a real-time modality based on a doctor's interpretation of images. So far, automated Computer-Aided Diagnostic tools were not widely applied to ultrasound imaging. The emerging methods in Artificial Intelligence, namely deep learning, gave rise to new applications in medical imaging modalities. The work's objective was to show the feasibility of implementing deep learning algorithms directly on a research scanner with GPU software beamforming. We have implemented and evaluated two deep neural network architectures as part of the signal processing pipeline on the ultrasound research platform USPlatform (us4us Ltd., Poland). The USPlatform is equipped with a GPU cluster, enabling full software-based channel data processing as well as the integration of open source Deep Learning frameworks. The first neural model (S-4-2) is a classical convolutional network for one-class classification of baby body parts. We propose a simple 6-layer network for this task. The model was trained and evaluated on a dataset consisting of 786 ultrasound images of a fetal training phantom. The second model (Gu-net) is a fully convolutional neural network for brachial plexus localisation. The model uses 'U-net'-like architecture to compute the overall probability of target detection and the probability mask of possible target locations. The model was trained and evaluated on 5640 ultrasound B-mode frames. Both training and inference were performed on a multi-GPU (Nvidia Titan X) cluster integrated with the platform. As performance metrics we used: accuracy as a percentage of correct answers in classification, dice coefficient for object detection, and mean and std. dev. of a model's response time. The 'S-4-2' model achieved 96% classification accuracy and a response time of 3 ms (334 predictions/s). This simple model makes accurate predictions in a short time. The 'Gu-net' model achieved a 0.64 dice coefficient for object detection and a 76% target's presence classification accuracy with a response time of 15 ms (65 predictions/s). The brachial plexus detection task is more challenging and requires more effort to find the right solution. The results show that deep learning methods can be successfully applied to ultrasound image analysis and integrated on a single advanced research platform Słowa kluczowe: Ultrasonic imaging, Neural networks, Convolution, Machine learning, Image segmentation, Kernel Afiliacje autorów:
Jarosik P. | - | IPPT PAN | Lewandowski M. | - | IPPT PAN |
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