Abstract
Large-scale numerical simulations of high-intensity focused ultrasound (HIFU), important for model-based treatment planning, generate large amounts of data. Typically, it is necessary to save hundreds of gigabytes during simulation. We propose a novel algorithm for time-varying simulation data compression specialised for HIFU. Our approach is particularly focused on on-the-fly parallel data compression during simulations. The algorithm is able to compress 3D pressure time series of linear and non-linear simulations with very acceptable compression ratios and errors (over 80% of the space can be saved with an acceptable error). The proposed compression enables significant reduction of resources, such as storage space, network bandwidth, CPU time, and so forth, enabling better treatment planning using fast volume data visualisations. The paper describes the proposed method, its experimental evaluation, and comparisons to the state of the arts.
Highlights
The application of high-intensity focused ultrasound (HIFU) in human medicine is an emerging non-invasive perspective therapy
We present a novel compression method for time-varying HIFU simulation data
The first set was focused on testing the compression method on 1D signals, and the second set was conducted for large 4D datasets
Summary
The application of high-intensity focused ultrasound (HIFU) in human medicine is an emerging non-invasive perspective therapy. The focused beam of ultrasound, typically generated using a large transducer, is sent into the tissue. The cells of tissue in a localised region are rapidly heated, which causes tissue death while the surrounding tissue is left unharmed. Many HIFU clinical trials for the treatment of tumours in the prostate, kidney, liver, breast, or brain have been performed. The most important issue is the precise placement of the ultrasound focus and dosage assessment [1]. There are some tissue properties that can significantly distort ultrasound distribution, for example, in the skull. This is the main reason that large-scale numerical HIFU simulations are needed
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