Ultrasound computed tomography his paper designs and implements a high throughout, extensible and flexible ultrasound excitation and data acquisition system that transmits sustained high-speed ultrasound data to the server by Ethernet technology. The system is mainly used for the second-generation ultrasound computed tomography system designed in the medical ultrasound lab, but can also be utilized by other types of ultrasound imaging systems. The system consists of one or more ultrasonic excitation and acquisition boards. Each board includes multiplexing switches, pulse generators with T/R switches, analog front ends, analog-to-digital converters, and an FPGA, and can be used to excite a 256-element probe to transmit and receive ultrasound signals. The peak and the average bandwidth of one single board are 44.8Gbps and 4Gbps, respectively. Potential users can combine several excitation and acquisition boards to build high-end ultrasound imaging systems. The system has been applied to upgrade our ultrasound computed tomography system.
Ultrasound computed tomography (USCT) is a 3D imaging tool, especially for breast screening. Sound-speed tomography as one imaging modal of USCT is widely studied by researchers because of its great clinical potential for early breast cancer detection. Sound-speed reconstruction methods include ray-based methods and wave-based methods. In this study, a ray-based method for sound speed reconstruction: Fresnel volume tomography (FVT) is implemented. We use Limitedmemory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization algorithm to solve the large and sparse equation for the inversion step. Considering the great computation burden in the L-BFGS inversion process, two kinds of acceleration schemes: CPU parallel and GPU parallel schemes are used and evaluated by in vitro experiment. The corresponding acceleration ratios are 5.3 and 18.6 for the 512×512 sound speed image reconstruction, compared to CPU serial computation.
An x-ray energy spectrum plays an essential role in computed tomography (CT) imaging and related tasks. Because of the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and usually suffer from various limitations. In this study, we aim to provide a segmentation-free, indirect transmission measurement–based energy spectrum estimation method using dual-energy material decomposition. The general principle of this method is to minimize the quadratic error between the polychromatic forward projection and the raw projection to calibrate a set of unknown weights, which are used to express the unknown spectrum together with a set of model spectra. The polychromatic forward projection is performed using material-specific images, which are obtained using dual-energy material decomposition. The algorithm was evaluated using numerical simulations, experimental phantom data, and realistic patient data. The results show that the estimated spectrum matches the reference spectrum quite well and the method is robust. Extensive studies suggest that the method provides an accurate estimate of the CT spectrum without dedicated physical phantom and prolonged workflow. This paper may be attractive for CT dose calculation, artifacts reduction, polychromatic image reconstruction, and other spectrum-involved CT applications.
X-ray energy spectrum plays an essential role in imaging and related tasks. Due to the high photon flux
of clinical CT scanners, most of the spectrum estimation methods are indirect and are usually suffered from
various limitations. The recently proposed indirect transmission measurement-based method requires at least
the segmentation of one material, which is insufficient for CT images of highly noisy and with artifacts. To
combat for the bottleneck of spectrum estimation using segmented CT images, in this study, we develop a
segmentation-free indirect transmission measurement based energy spectrum estimation method using dual-energy
material decomposition. The general principle of the method is to compare polychromatic forward
projection with raw projection to calibrate a set of unknown weights which are used to express the unknown
spectrum together with a set of model spectra. After applying dual-energy material decomposition using high-and
low-energy raw projection data, polychromatic forward projection is conducted on material-specific images.
The unknown weights are then iteratively updated to minimize the difference between the raw projection and
estimated projection. Both numerical simulations and experimental head phantom are used to evaluate the
proposed method. The results indicate that the method provides accurate estimate of the spectrum and it may
be attractive for dose calculations, artifacts correction and other clinical applications.