The C++ source code provided in this repository was written by me as part of the Ph.D thesis "Statistical Image Processing of Medical Ultrasound Radio Frequency Data ". Goal of my thesis was to leverage the statistical patterns of raw ultrasound data (radio-frequency/RF data) that has not been processed for visual appeal (B-mode). Given the large memory footprint of RF data, the software is highly optimized and features several high performance massively parallelized routines for reconstruction. The software has multiple purposes: First, to record B-mode or RF sequences in combination with 3D tracking data. Second, to reconstruct 3D volumes from raw RF ultrasound data (radio-frequency data) (see RFcomppunding.cpp or conventional B-mode (see IntensityCompounding.cpp. Third, there was some preliminary functionality to reconstruct 3D flow velocity fields from Doppler ultrasound video streams. To acquire the data, the ultrasound transducer was tracked with an optical tracking system. The software was mainly used to conduct a study on using 3D ultrasound for the early diagonis of Parkinson's disease - see paper.
Conventional ultrasound images, commonly referred to as B-Mode, are the result of many processing steps optimizing data for visual assessment by physicians. However, at the core of ultrasound imaging pipeline lies the radio frequency (RF) data. Just lately, RF data has become more readily available to the research community such that its potential has not fully unveiled yet. From a data processing standpoint using RF data over B-Mode suggests many advantages. First of all, it is generally much richer in information due to the comparably higher resolution. Furthermore, it is not affected by non-linear post-processing steps such as log-compression and proprietary filter algorithms that change the noise statistics for reasons of improved visual appeal. In addition, it has nice probabilistic properties facilitating various ways of distributional modeling of ultrasound specific texture patterns, referred to as speckle noise. If you interested in more detail, you might want to have a look at my Ph.D. thesis.
If you use this code or find it somehow useful for your research, I would appreciate citation:
@inproceedings{Klein2012StatisticalIP,
title={Statistical Image Processing of Medical Ultrasound Radio Frequency Data},
author={Tassilo Klein},
year={2012}
}
@InProceedings{10.1007/978-3-642-33415-3_52,
author="Klein, T.
and Hansson, M.
and Navab, Nassir",
editor="Ayache, Nicholas
and Delingette, Herv{\'e}
and Golland, Polina
and Mori, Kensaku",
title="Modeling of Multi-View 3D Freehand Radio Frequency Ultrasound",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2012",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="422--429"
}
@InProceedings{10.1007/978-3-319-24571-3_71,
author="Klein, Tassilo
and Wells, William M.",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro",
title="RF Ultrasound Distribution-Based Confidence Maps",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="595--602"
}
@InProceedings{10.1007/978-3-642-33415-3_52,
author="Klein, T.
and Hansson, M.
and Navab, Nassir",
editor="Ayache, Nicholas
and Delingette, Herv{\'e}
and Golland, Polina
and Mori, Kensaku",
title="Modeling of Multi-View 3D Freehand Radio Frequency Ultrasound",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2012",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="422--429"
}