Ultracompression: Framework For High Density Compression Of Ultrasound Volumes Using Physics Modeling Deep Neural Networks

Abstract

Ultrasound image compression by preserving specklebased key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achieving very high-density compression factors. The compressor employs a tissue segmentation method, transmitting segments along with transducer frequency, number of samples and image size as essential information required for decompression. The decompressor is based on a convolutional network trained to generate patho-realistic ultrasound images which convey essential information pertinent to tissue pathology visible in the images. We demonstrate generalizability of the building blocks using two variants to build the compressor. We have evaluated the quality of decompressed images using distortion losses as well as perception loss and compared it with other off the shelf solutions. The proposed method achieves a compression ratio of 725 : 1 while preserving the statistical distribution of speckles. This enables image segmentation on decompressed images to achieve dice score of 0.89 ± 0.11, which evidently is not so accurately achievable when images are compressed with current standards like JPEG, JPEG 2000, WebP and BPG. We envision this frame work to serve as a roadmap for speckle image compression standards.

Real-time element position tracking of flexible array transducer for ultrasound beamforming

“…Ultrasound (US) imaging in biomedical applications such as image-guided radiotherapy (IGRT) has increased in the last decade because of its non-ionizing nature and real-time imaging capability [1][2][3][4][5][6][7][8][9][10][11][12][13] . The use of other imaging modalities has been more common [14][15][16][17][18][19][20][21][22][23][24] than the use of US-based techniques in clinical applications of IGRT due to the rigid architecture of traditional US probe transducers.…”

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

“…In another study, Perdios et al [26] presented an approach for ultrasound image recovery where compression and decompression of ultrasound signals were achieved using a stacked denoising autoencoder (SDA). Moreover, China et al introduced an ultrasound image compression system that preserves speckle information, with a CNN-based decompressor generating patho-realistic ultrasound images that convey essential information about pathological tissues [27].…”

A Pseudo-Dynamic Delay Calculation Using Optimal Zone Segmentation for Ultra-Compact Ultrasound Imaging Systems

“…Several efficient beamforming and/or compressive sensing algorithms have been proposed to address such demands [5][6][7][8][9][10]. The dynamic receive beamformer is one of the most complex processing components in a US imaging system; our previous research found that it contains 46.5% of the total hardware resources and 25.4% of the total power consumption of a system-on-chip solution for portable US imaging [11].…”