Generative adversarial network-based photoacoustic image reconstruction from bandlimited and limited-view data

Kalloor Joseph, Francis and Arora, Aayush and Kancharla, Parimala and Kuniyil Ajith Singh, Mithun and Steenbergen, Wiendelt and Channappayya, Sumohana S. and et al, . (2021) Generative adversarial network-based photoacoustic image reconstruction from bandlimited and limited-view data. In: Photons Plus Ultrasound: Imaging and Sensing 2021, 6 March 2021 through 11 March 2021, Virtual, Online.

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Ultrasound transducers used in photoacoustic imaging are bandlimited and have a limited detection angle, which degrades the reconstructed image quality. One way to address this problem is to have transducers with multiple frequency bands with acquisition around the sample. This approach is expensive and it is not feasible for systems with a handheld probe using a linear transducer array. In this work, we aim to develop a deep learning method for photoacoustic reconstruction from bandlimited and limited-view data. We have developed a Generative Adversarial Networks (GANs)-based framework conditioned with a photoacoustic measurement for image reconstruction. In this way, the transducer used in the measurement can be incorporated and the generator trying to compensate for the limited data problem. We have developed the model for a handheld photoacoustic system using a linear transducer array with 128 elements having a center frequency of 7MHz and -6dB bandwidth from 4-10 MHz. We trained the network using simulated blood vessel images and tested it on in vivo measurements from the human forearm. We have compared the reconstructed images using the proposed method with the time-reversal on simulated data for detection using a bandlimited and directional transducer and compared it using the ground truth. Further, we compare our results to the in vivo images from the system which uses a delay and sum algorithm. The results from both simulations and experiments show that the proposed approach can remove bandlimited and limited view artifacts and can achieve a better image quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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IITH Creators:
IITH CreatorsORCiD
Channappayya, Sumohana S.UNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: artifact removal; convolutional neural networks; deep learning; generative adversarial network; image reconstruction; Photoacoustic imaging
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 24 Aug 2022 11:13
Last Modified: 24 Aug 2022 11:13
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