Determination of contrast-detail curves in mammography image quality assessment by a parametric model observer.

A novel approach is proposed for the determination of contrast-detail curves in mammography image quality assessment. The approach is compared with current practice using virtual mammography. A binary parametric model observer is applied to images of the CDMAM phantom. The observer accounts for the simple disc shaped objects in the phantom and is applied separately to each cell of the phantom. For each of these applications, the area under the ROC curve (AUC) of the model observer is determined. The different AUCs, calculated from different applications of the parametric model observer, are then combined to a single contrast-detail curve quantifying the ability of the observer to detect details in the images. Virtual mammography is developed as a tool to simulate X-ray images of single CDMAM cells and to quantitatively assess the approach in comparison with current practice. It is shown that the proposed approach can lead to similar contrast-detail curves as current practice. The precision of the estimated contrast-detail curves is increased, i.e. using 5 images yields about the same precision for the proposed approach as 16 images when applying current practice. We conclude that contrast-detail curves in mammography image quality assessment can also be determined through the AUC of a binary parametric model observer. Since the proposed approach has higher precision than current practice, it is a promising candidate for contrast-detail analysis in mammography image quality assessment.

A simple parametric model observer for quality assurance in computer tomography.

Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10-15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB® function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer and the nonprewhitening matched filter for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems.

Machine-learning approach for local classification of crystalline structures in multiphase systems.

Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.

Evidence of a field-induced Berezinskii-Kosterlitz-Thouless scenario in a two-dimensional spin-dimer system.

Two-dimensional (2D) systems with continuous symmetry lack conventional long-range order because of thermal fluctuations. Instead, as pointed out by Berezinskii, Kosterlitz and Thouless (BKT), 2D systems may exhibit so-called topological order driven by the binding of vortex-antivortex pairs. Signatures of the BKT mechanism have been observed in thin films, specially designed heterostructures, layered magnets and trapped atomic gases. Here we report on an alternative approach for studying BKT physics by using a chemically constructed multilayer magnet. The novelty of this approach is to use molecular-based pairs of spin S=½ ions, which, by the application of a magnetic field, provide a gas of magnetic excitations. On the basis of measurements of the magnetic susceptibility and specific heat on a so-designed material, combined with density functional theory and quantum Monte Carlo calculations, we conclude that these excitations have a distinct 2D character, consistent with a BKT scenario, implying the emergence of vortices and antivortices.