Simulated sensor characterization for virtual clinical trials in mammography and digital breast tomosynthesis

Virtual clinical trials in X-ray breast imaging permit to compare different technical solutions and imaging modalities at reduced costs, involved personnel, reduced times and reduced radiation risks to patients. In this context, the detector characteristics (spatial resolution, noise level and efficiency) play a key role for an appropriate generation of simulated images. The project AGATA proposes to compute images as dose deposit maps in a detector layer of defined materials. Simulated images are then post-processed on the basis of suitable comparison between intrinsic characteristics of real and simulated detectors. With this scope, as first step for the post-processing manipulations, we evaluated the presampled modulation transfer function (MTF), the detector-response function and the noise power spectrum (NPS) of the simulated detectors. Two detectors were simulated: (1) 0.20 mm-thick a-Se direct flat panel with 70 µm pixel pitch and (2) CsI(Tl) indirect flat panel with 100 µm pixel pitch and scintillator layer 0.25 mm thick. In addition, the impact of simulating the de-excitation processes (Auger emission and fluorescence) was explored. Simulated detector characteristics were evaluated for W/Rh spectra between 25 kV and 31 kV. The in-silico platform used a Monte Carlo software based on Geant4 toolkit (vers. 6). First, the simulation and tracking of electrons generated from photoelectric or Compton interactions was shown to have neglectable influence on the pixel values for the explored spectra, with the produced electrons presenting short ranges with respect to the pixel dimension. In the case of the CsI detector, which has fluorescence energies higher than those of the simulated X-ray photons, the deexcitation processes have not noticeable influence on the calculated pixel values. On the other hand, the MTF of the a-Se detector resulted slightly lower when the fluorescence is simulated in the detector materials, due to the dose spread derived from the fluorescence photons, which can travel far from the initial ionization interaction. Regarding the a-Se detector, the noise power spectrum resulted lower with simulated deexcitation.

Microphone array speech enhancement based on optimized IMCRA

Microphone array speech enhancement algorithm uses temporal and spatial informa- tion to improve the performance of speech noise reduction significantly. By combining noise estimation algorithm with microphone array speech enhancement, the accuracy of noise estimation is improved, and the computation is reduced. In traditional noise es- timation algorithms, the noise power spectrum is not updated in the presence of speech, which leads to the delay and deviation of noise spectrum estimation. An optimized im- proved minimum controlled recursion average speech enhancement algorithm, based on a microphone matrix is proposed in this paper. It consists of three parts. The first part is the preprocessing, divided into two branches: the upper branch enhances the speech signal, and the lower branch gets the noise. The second part is the optimized improved minimum controlled recursive averaging. The noise power spectrum is updated not only in the non-speech segments but also in the speech segments. Fi- nally, according to the estimated noise power spectrum, the minimum mean-square error log-spectral amplitude algorithm is used to enhance speech. Testing data are from TIMIT and Noisex-92 databases. Short-time objective intelligibility and seg- mental signal-to-noise ratio are chosen as evaluation metrics. Experimental results show that the proposed speech enhancement algorithm can improve the segmental signal-to-noise ratio and short-time objective intelligibility for various noise types at different signal-to-noise ratio levels.

Efektifitas Penggunaan Sistem AEC (Smart mA) untuk Optimisasi Dosis dan Kualitas Citra pada Protokol CT Abdomen

This study aims to analyze Automatic Exposure Control (AEC) with Smart mA software on water phantom image quality and CTDIvol dose. The researched image quality is CT Number and noise. In addition, the CT Number is evaluated for accuracy, uniformity, and noise using the Noise Power Spectrum method. The results of image measurements with and without Smart mA on CT Number accuracy are still in the Standard range of ± 4 CT, the uniformity value of CT Number and noise is also still within the Standard, namely ± 2 CT. The use of Smart mA increases the noise value by 14.29%. The noise value from the noise power spectrum analysis when using Smart mA is higher than without using Smart mA. Meanwhile, the CTDIvol radiation dose from using Smart mA decreases by 52.33%. Image quality using Smart mA has a CT Number value almost the same or uniform with the test object, namely water phantom, so that the use of Smart mA can characterize body tissues well, but the noise value generated is more significant than without using Smart mA. Although the noise value generated by Smart mA is more excellent, visually, the noise value does not disturb the radiologist too much in determining the diagnosis because the image quality is still in good condition so that it can give a dose according to the patient's body thickness according to the ALARA principle. Keywords: CT Number, CTDIvol, AEC, NPS.