Probabilistic expert systems for handling artifacts in complex DNA mixtures

This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example

Identification and separation of DNA mixtures using peak area information (Updated version of Statistical Research Paper No. 25)

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed

Plane wave imaging challenge

The plane wave imaging challenge (PICMUS) has been introduced for the first time to IUS in order to encourage participants to compete and share their knowledge in medical ultrasound plane wave imaging. To participate in this challenge, we have chosen the contrast enhanced delay and sum (CEDAS) post signal processing method. This technique have been used to improve B-mode image contrast to noise ratio (CNR) without effecting the image spatial resolution. With CEDAS the energy of every envelope signal is calculated, mapped, and clustered in order to identify the cyst and clutter location. CEDAS significantly reduces the clutter inside the cyst by attenuating it from envelope signals before the new B-Mode image is formed. This paper describes in more details the techniques and parameters we have been using for the challenge. Results obtained for CEDAS shows that it outperforms conventional DAS by 18.33% in experiment and 79.24% in simulation for CNR

Clutter noise reduction in B-Mode image through mapping and clustering signal energy for better cyst classification

Improving the ultrasound image contrast ratio (CR) and contrast to noise ratio (CNR) has many clinical advantages. Breast cancer detection is one example. Anechoic cysts which fill with clutter noise can be easily misinterpreted and classified as malignant lesions instead of benign. Beamforming techniques contribute to off-axis side lobes and clutter. These two side effects inherent in beamforming are undesirable since they will degrade the image quality by lowering the image CR and CNR. To overcome this issue a new post-processing technique known as contrast enhanced delay and sum (CEDAS) is proposed. Here the energy of every envelope signals are calculated, mapped, and clustered in order to identify the cyst and clutter location. CEDAS reduce clutter inside the cyst by attenuating it from envelope signals before the new B-Mode image is formed. With CEDAS, the image CR and CNR improved by average 12 dB and 1.1 dB respectively for cysts size 2 mm to 6 mm and imaging depth from 40 mm to 80 mm

Contrast-Enhanced Ultrasound Imaging with Chirps: Signal Processing and Pulse Compression

Contrast-enhanced ultrasound imaging creates one of the worst case scenarios for pulse compression due to depth and frequency dependent attenuation, high level of harmonic generation, phase variations due to resonance behavior of microbubbles, and increased broadband noise by microbubble destruction. This study investigates the feasibility of pulse compression with a matched filter in the existence of microbubbles with resonant behavior. Simulations and experimental measurements showed that the scattered pressure from a microbubble population excited by a chirp waveform preserves its chirp rate even for harmonic frequencies. Although, pulse compression by a matched filter was possible due to the conservation of the chirp rate, an increase on sidelobe levels were observed at fundamental and second harmonic frequencies. Therefore, using chirp excitation and a matched filter pair will increase the contrast-to-tissue ratio with a trade-off of decreased image quality

Velocity estimation error reduction in stenosis areas using a correlation correction method

The advent of ultrafast ultrasound imaging proved beneficial for capturing transient flow patterns which was never readily achievable before. Velocity estimation methods based on 2D block-matching outperform Doppler based methods by offering higher frame rate with the cost of increased uncertainty in presence of out-of-plane motion as a result of turbulent flow. Local median filtering can partially address the estimation error reduction in stenosis areas at the risk of higher inaccuracy, since neighboring values may be also outliers. In this study, a correlation correction method is proposed, where the out-of-plane motion is eliminated by means of multiplying correlation maps from a same area but in two adjacent pairs of RF images. Experimental investigations were performed on a wall-less flow phantom, and proposed method achieved an error reduction of 66% in turbulent flow regions

Phonon transport in large scale carbon-based disordered materials: Implementation of an efficient order-N and real-space Kubo methodology

We have developed an efficient order-N real-space Kubo approach for the calculation of the phonon conductivity which outperforms state-of-the-art alternative implementations based on the Green's function formalism. The method treats efficiently the time-dependent propagation of phonon wave packets in real space, and this dynamics is related to the calculation of the thermal conductance. Without loss of generality, we validate the accuracy of the method by comparing the calculated phonon mean free paths in disordered carbon nanotubes (isotope impurities) with other approaches, and further illustrate its upscalability by exploring the thermal conductance features in large width edge-disordered graphene nanoribbons (up to ~20 nm), which is out of the reach of more conventional techniques. We show that edge-disorder is the most important scattering mechanism for phonons in graphene nanoribbons with realistic sizes and thermal conductance can be reduced by a factor of ~10.Comment: Accepted for publication in Physical Review B - Rapid Communication