The regression detectability index RDI for mammography images of breast phantoms with calcification-like objects and anatomical background.

Currently, quality assurance measurements in mammography are performed on unprocessed images. For diagnosis, however, radiologists are provided with processed images. This image processing is optimised for images of human anatomy and therefore does not always perform satisfactorily with technical phantoms. To overcome this problem, it may be possible to use anthropomorphic phantoms reflecting the anatomic structure of the human breast in place of technical phantoms when carrying out task-specific quality assessment using model observers. However, the use of model observers is hampered by the fact that a large number of images needs to be acquired. A recently published novel observer called the regression detectability index (RDI) needs significantly fewer images, but requires the background of the images to be flat. Therefore, to be able to apply the RDI to images of anthropomorphic phantoms, the anatomic background needs to be removed. For this, a procedure in which the anatomical structures are fitted by thin plate spline (TPS) interpolation has been developed. When the object to be detected is small, such as a calcification-like lesion, it is shown that the anatomic background can be removed successfully by subtracting the TPS interpolation, which makes the background-free image accessible to the RDI. We have compared the detectability obtained by the RDI with TPS background subtraction to results of the channelized Hotelling observer (CHO) and human observers. With the RDI, results for the detectabilityd'can be obtained using 75% fewer images compared to the CHO, while the same uncertainty ofd'is achieved. Furthermore, the correlation ofd'(RDI) with the results of human observers is at least as good as that ofd'(CHO) with human observers.

RDI[Formula: see text]a regression detectability index for quality assurance in: x-ray imaging.

Novel iterative image reconstruction methods can help reduce the required radiation dose in x-ray diagnostics such as computed tomography (CT), while maintaining sufficient image quality. Since some of the established image quality measures are not appropriate for reliably judging the quality of images derived by iterative methods, alternative approaches such as task-specific quality assessment would be highly desirable for acceptance or constancy testing. Task-based image quality methods are also closer to tasks performed by the radiologists, such as lesion detection. However, this approach is usually hampered by a huge workload, since hundreds of images are usually required for its application. It is demonstrated that the proposed approach works reliably on the basis of significantly fewer images, and that it correlates well with results obtained from human observers.

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.

Determination of reference values for optical properties of liquid phantoms based on Intralipid and India ink.

A multi-center study has been set up to accurately characterize the optical properties of diffusive liquid phantoms based on Intralipid and India ink at near-infrared (NIR) wavelengths. Nine research laboratories from six countries adopting different measurement techniques, instrumental set-ups, and data analysis methods determined at their best the optical properties and relative uncertainties of diffusive dilutions prepared with common samples of the two compounds. By exploiting a suitable statistical model, comprehensive reference values at three NIR wavelengths for the intrinsic absorption coefficient of India ink and the intrinsic reduced scattering coefficient of Intralipid-20% were determined with an uncertainty of about 2% or better, depending on the wavelength considered, and 1%, respectively. Even if in this study we focused on particular batches of India ink and Intralipid, the reference values determined here represent a solid and useful starting point for preparing diffusive liquid phantoms with accurately defined optical properties. Furthermore, due to the ready availability, low cost, long-term stability and batch-to-batch reproducibility of these compounds, they provide a unique fundamental tool for the calibration and performance assessment of diffuse optical spectroscopy instrumentation intended to be used in laboratory or clinical environment. Finally, the collaborative work presented here demonstrates that the accuracy level attained in this work for optical properties of diffusive phantoms is reliable.

Shifted factor analysis for the separation of evoked dependent MEG signals.

Decomposition of evoked magnetoencephalography (MEG) data into their underlying neuronal signals is an important step in the interpretation of these measurements. Often, independent component analysis (ICA) is employed for this purpose. However, ICA can fail as for evoked MEG data the neuronal signals may not be statistically independent. We therefore consider an alternative approach based on the recently proposed shifted factor analysis model, which does not assume statistical independence of the neuronal signals. We suggest the application of this model in the time domain and present an estimation procedure based on a Taylor series expansion. We show in terms of synthetic evoked MEG data that the proposed procedure can successfully separate evoked dependent neuronal signals while standard ICA fails. Latency estimation of neuronal signals is an inherent part of the proposed procedure and we demonstrate that resulting latency estimates are superior to those obtained by a maximum likelihood method.

Absolute profile measurement of large moderately flat optical surfaces with high dynamic range.

We present a novel procedure for absolute, highly-accurate profile measurement with high dynamic range for large, moderately flat optical surfaces. The profile is reconstructed from many sub-profiles measured by a small interferometer which is scanned along the specimen under test. Additional angular and lateral distance measurements are used to account for the tilt of the interferometer and its precise lateral location during the measurements. Accurate positioning of the interferometer is not required. The algorithm proposed for the analysis of the data allows systematic errors of the interferometer and height offsets of the scanning stage to be eliminated and it does not reduce the resolution. By utilizing a realistic simulation scenario we show that accuracies in the nanometer range can be reached.

Latency analysis of single auditory evoked M100 responses by spatio-temporal filtering.

Appropriate spatial filtering followed by temporal filtering is well suited for the single-trial analysis of multi-channel magnetoencephalogram or electroencephalogram recordings. This is demonstrated by the results of a single-trial latency analysis obtained for auditory evoked M100 responses from nine subjects using two different stimulation frequencies. Spatial filters were derived automatically from the data via noise-adjusted principle component analysis, and single-trial latencies were estimated from the signal phase after complex bandpass filtering. For each of the two stimulation frequencies, estimated single-trial latencies were consistent with results obtained from a standard approach using averaged evoked responses. The quality of the estimated single-trial latencies was additionally assessed by their ability to separate between the two different stimulation frequencies. As a result, more than 80% of the single trials can be classified correctly by their estimated latencies.

A template-free approach for determining the latency of single events of auditory evoked M100.

The phase of the complex output of a narrow band Gaussian filter is taken to define the latency of the auditory evoked response M100 recorded by magnetoencephalography. It is demonstrated that this definition is consistent with the conventional peak latency. Moreover, it provides a tool for reducing the number of averages needed for a reliable estimation of the latency. Single-event latencies obtained by this procedure can be used to improve the signal quality of the conventional average by latency adjusted averaging.

Systematic latency variation of the auditory evoked M100: from average to single-trial data.

Standard analyses of neurophysiologically evoked response data rely on signal averaging across many epochs associated with specific events. The amplitudes and latencies of these averaged events are subsequently interpreted in the context of the given perceptual, motor, or cognitive tasks. Can such critical timing properties of event-related responses be recovered from single-trial data? Here, we make use of the M100 latency paradigm used in previous magnetoencephalography (MEG) research to evaluate a novel single-trial analysis approach. Specifically, the latency of the auditory evoked M100 varies systematically with stimulus frequency over a well-defined time range (lower frequencies, e.g., 125 Hz, yield up to 25 ms longer latencies than higher frequencies, e.g., 1000 Hz). Here, we show that the complex filtering approach to single-trial analysis recovers this key characteristic of the M100 response, as well as some other important response properties relating to lateralization. The results illustrate (i) the utility of the complex filtering method and (ii) the potential of the M100 latency to be used for stimulus encoding, since the relevant variation can be observed in single trials.

MEG-analysis using the Hilbert transform.

Event-related fields (ERFs) measured by magnetoencephalography (MEG) upon visual stimulation are analysed by Hilbert transformation. The Hilbert transform of real-valued measured ERF is an analytic complex signal, represented by phase and amplitude. The temporal behaviour of the derivative of the phase, i.e. the instantaneous frequency, allows to distinguish time intervals containing meaningful signal from noise. On the basis of both phase and amplitude, energies and latencies of single event ERFs can be determined.

Quantitative MRS: comparison of time domain and time domain frequency domain methods using a novel test procedure.

For quantitative analysis of in vivo MR spectra, a state-of-the-art time domain method was compared with a recently reported time domain frequency domain method which uses wavelets for background characterization. The comparison was made on the basis of results for simulated test problems that were constructed by combining measured and simulated MRS data at different signal-to-noise ratios in order to simultaneously reflect real world difficulties, in particular the overlapping background problem, and to allow for quantitative judgment of a method's accuracy. Incorporating prior knowledge was also considered. The results obtained give insight into the accuracy of the methods when applied to measured MRS data. Due to the improved background characterization, the time domain frequency domain method outperformed the time domain method in some of the test cases. Both methods were also applied to serial brain MR spectra of a healthy volunteer on 10 occasions.

Exact two-dimensional wave-front reconstruction from lateral shearing interferograms with large shears.

A method is proposed for exact discrete reconstruction of a two-dimensional wave front from four suitably designed lateral shearing experiments. The method reconstructs any wave front at evaluation points of a circular aperture exactly up to an arbitrary constant for noiseless data, and it shows excellent stability properties in the case of noisy data. Application of large shears is allowed, and high resolution of the reconstructed wave front can be achieved. Results of numerical experiments are presented that demonstrate the capability of the method.

Solution to the shearing problem.

Lateral shearing interferometry is a promising reference-free measurement technique for optical wave-front reconstruction. The wave front under study is coherently superposed by a laterally sheared copy of itself, and from the interferogram difference measurements of the wave front are obtained. From these difference measurements the wave front is then reconstructed. Recently, several new and efficient algorithms for evaluating lateral shearing interferograms have been suggested. So far, however, all evaluation methods are somewhat restricted, e.g., assume a priori knowledge of the wave front under study, or assume small shears, and so on. Here a new, to our knowledge, approach for the evaluation of lateral shearing interferograms is presented, which is based on an extension of the difference measurements. This so-called natural extension allows for reconstruction of that part of the underlying wave front whose information is contained in the given difference measurements. The method is not restricted to small shears and allows for high lateral resolution to be achieved. Since the method uses discrete Fourier analysis, the reconstructions can be efficiently calculated. Furthermore, it is shown that, by application of the method to the analysis of two shearing interferograms with suitably chosen shears, exact reconstruction of the underlying wave front at all evaluation points is obtained up to an arbitrary constant. The influence of noise on the results obtained by this reconstruction procedure is investigated in detail, and its stability is shown. Finally, applications to simulated measurements are presented. The results demonstrate high-quality reconstructions for single shearing interferograms and exact reconstructions for two shearing interferograms.

Influenza virus M1 protein binds to RNA through its nuclear localization signal.

The RNA-binding activity of influenza A virus M1 protein was studied by cross-linking the protein to viral RNA followed by sequence analysis of the oligoribonucleotide bound to the protein as well as sequence analysis of the M1 peptide bound to the RNA. M1 was found to bind to RNA without any RNA sequence specificity, as verified in a series of filter-binding experiments using a large variety of nucleic acids including DNA. The peptide sequence that bound to the RNA was the RKLKR nuclear localization signal of M1. Site-specific mutagenesis of recombinant M1 showed that most of the basic residues in that region had to be mutated in order to inhibit RNA-binding. We also constructed an M1 mutant that no longer bound to RNA but which was still able to inhibit the in vitro transcription activity of isolated viral ribonucleoprotein, albeit to a lower extent. Mutation of the zinc-binding sequence had no effect on RNA-binding or transcription-inhibition activity.

Salmonellal splenic abscess in the antibiotic era: a Latin American perspective.

Ten cases of salmonellal splenic abscesses recently documented in various Latin American countries are discussed. All patients were adults; the mean age was 32.6 years, and there was a predominance of males (seven). Predisposing conditions were identified in four cases. All 10 cases were documented by diagnostic imaging techniques; in one case, exploratory diagnostic laparotomy was also performed. Splenectomy was performed on eight patients, while two other patients responded to long courses of intravenous antimicrobial therapy alone. One patient died as the result of perioperative splenic rupture, and two patients underwent second laparotomies because of left subphrenic abscesses. Except for one human immunodeficiency virus-infected individual, all patients were immunocompetent and had large solitary lesions. Salmonella typhi was the predominant organism isolated and was recovered in six of the 10 cases.

A small percentage of influenza virus M1 protein contains zinc but zinc does not influence in vitro M1-RNA interaction.

A peptide containing the CCHH motif, the putative zinc-binding sequence of influenza virus M1 protein, was found to bind zinc in a one-to-one complex with the characteristics of a typical zinc-binding peptide. Intact influenza virus also contained zinc and we show that this zinc is bound to the M1 protein in the virus. However, only a small proportion of M1 contained zinc: 4% in virus and 6 to 9% in isolated protein. One strain, B/Yamagata/16/88, consistently contained more zinc: 15 to 20% both in virus and in isolated protein. We also determined the RNA binding and transcription inhibition activities of various M1 proteins and found that the zinc content of M1 had no influence on either activity. We suggest that the zinc in M1 has a structural role in the virion other than nucleic acid binding.