Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography

BMC Medical Imaging

Tran Duc-Tan¹  Tran Quang Huy²  Ton That Long³  Huynh Huu Tue³ 

[1] Faculty of Electronics & Telecommunications, VNU University of Engineering & Technology;HaNoi Pedagogical University 2;School of Electrical Engineering, VNU International University;

关键词: Mammography;    Ultrasound tomography;    Inverse scattering;    Distorted born iterative method (DBIM);    Deterministic compressive sampling (DCS);   

DOI  :  10.1186/s12880-017-0206-8

来源: DOAJ

【 摘 要 】

Abstract Background A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammography. Based on inverse scattering technique, ultrasound tomography uses some material properties such as sound contrast or attenuation to detect small targets. The Distorted Born Iterative Method (DBIM) based on first-order Born approximation is an efficient diffraction tomography approach. One of the challenges for a high quality reconstruction is to obtain many measurements from the number of transmitters and receivers. Given the fact that biomedical images are often sparse, the compressed sensing (CS) technique could be therefore effectively applied to ultrasound tomography by reducing the number of transmitters and receivers, while maintaining a high quality of image reconstruction. Methods There are currently several work on CS that dispose randomly distributed locations for the measurement system. However, this random configuration is relatively difficult to implement in practice. Instead of it, we should adopt a methodology that helps determine the locations of measurement devices in a deterministic way. For this, we develop the novel DCS-DBIM algorithm that is highly applicable in practice. Inspired of the exploitation of the deterministic compressed sensing technique (DCS) introduced by the authors few years ago with the image reconstruction process implemented using l 1 regularization. Results Simulation results of the proposed approach have demonstrated its high performance, with the normalized error approximately 90% reduced, compared to the conventional approach, this new approach can save half of number of measurements and only uses two iterations. Universal image quality index is also evaluated in order to prove the efficiency of the proposed approach. Conclusions Numerical simulation results indicate that CS and DCS techniques offer equivalent image reconstruction quality with simpler practical implementation. It would be a very promising approach in practical applications of modern biomedical imaging technology.

【 授权许可】

Unknown