research-article

High-Speed Medical Imaging in 3D Ultrasound Computer Tomography

Authors:

Nicole V. RuiterAuthors Info & Claims

IEEE Transactions on Parallel and Distributed Systems, Volume 27, Issue 2

Pages 455 - 467

https://doi.org/10.1109/TPDS.2015.2405508

Published: 01 February 2016 Publication History

Abstract

A promising candidate for sensitive imaging of breast cancer is 3D Ultrasound Computer Tomography (3D USCT). So far its clinical applicability for diagnosis has been limited by the duration of the demanding image reconstruction. In this paper we investigate how signal processing and image reconstruction can be accelerated for diagnosis by using heterogeneous hardware. Additionally, the time and costs for real-time system for a future diagnosis and therapy device is estimated. Reusing the device's built-in FPGA-based data acquisition system (DAQ) through reconfiguration results in a speed-up by a factor of 7 for signal processing and by a factor of 2 for image reconstruction. Applying cutting-edge single FPGAs and GPUs, speed-ups by a factor of 10 (FPGA) and 6 (GPU) for signal processing and 15 (FPGA) and 37 (GPU) for image reconstruction were achieved compared to a recent quad-core Intel Core-i7 CPU. Using quad-core CPUs and a cluster of eight GPUs allowed us for the first time to calculate volumes in less than 30 min with an overall speed-up by a factor of 47, enabling a first clinical study. Based on these results we extrapolated that real-time reconstruction for a therapeutic 3D USCT will be possible in the year 2020 if the trend in density follows the ITRS roadmap.

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Cited By

Mondal P(2022)FPGA-accelerated adaptive cartesian to polar conversion and efficient MI computation for image registrationJournal of Real-Time Image Processing10.1007/s11554-022-01205-319:3(529-537)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11554-022-01205-3
Mondal PBanerjee S(2021)FPGA-accelerated adaptive projection-based image registrationJournal of Real-Time Image Processing10.1007/s11554-020-00952-518:1(113-125)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11554-020-00952-5

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Published: 01 February 2016

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Cited By

Mondal P(2022)FPGA-accelerated adaptive cartesian to polar conversion and efficient MI computation for image registrationJournal of Real-Time Image Processing10.1007/s11554-022-01205-319:3(529-537)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11554-022-01205-3
Mondal PBanerjee S(2021)FPGA-accelerated adaptive projection-based image registrationJournal of Real-Time Image Processing10.1007/s11554-020-00952-518:1(113-125)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11554-020-00952-5

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