The D-bar Method for Diffuse Optical Tomography: a computational study

The D-bar method at negative energy is numerically implemented. Using the method we are able to numerically reconstruct potentials and investigate exceptional points at negative energy. Subsequently, applying the method to Diffusive Optical Tomography, a new way of reconstructing the diffusion coefficient from the associated Complex Geometrics Optics solution is suggested and numerically validated

The D-Bar Method for Diffuse Optical Tomography : A Computational Study

The D-bar method at negative energy is numerically implemented. Using the method, we are able to numerically reconstruct potentials and investigate exceptional points at negative energy. Subsequently, applying the method to diffuse optical tomography, a new way of reconstructing the diffusion coefficient from the associated Complex Geometrics Optics solution is suggested and numerically validated.Peer reviewe

A direct D-bar reconstruction algorithm for recovering a complex conductivity in 2-D

A direct reconstruction algorithm for complex conductivities in $W^{2,\infty}(\Omega)$, where $\Omega$ is a bounded, simply connected Lipschitz domain in $\mathbb{R}^2$, is presented. The framework is based on the uniqueness proof by Francini [Inverse Problems 20 2000], but equations relating the Dirichlet-to-Neumann to the scattering transform and the exponentially growing solutions are not present in that work, and are derived here. The algorithm constitutes the first D-bar method for the reconstruction of conductivities and permittivities in two dimensions. Reconstructions of numerically simulated chest phantoms with discontinuities at the organ boundaries are included.Comment: This is an author-created, un-copyedited version of an article accepted for publication in [insert name of journal]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/0266-5611/28/9/09500

Time-resolved fluoroimmunoassay for bactericidal/permeability-increasing protein

Bactericidal/permeability-increasing protein (BPI) is a cationic antimicrobial protein produced by polymorphonuclear leukocytes, that specifically interacts with and kills Gram-negative bacteria. BPl competes with lipopolysaccharide-binding protein (LBP) secreted by liver cells into blood plasma for binding to lipopolysaccharide (LPS) and thus reduces the proinflammatory effects of LPS. We have developed a time-resolved fluoroimmunoassay for BPI and measured the concentration of BPI in human serum and plasma samples. The assay is based on a rabbit antibody against recombinant BPI. This antibody specifically adheres to polymorphonuclear leukocytes in immunostained human tissues. The difference in the serum concentration of BPI between unselected hospitalized patients with and without an infection was statistically significant. The mean concentration of BPI in serum samples was 28.3 μg/l (range 1.64–132, S.D. 26.8, n = 83). In contrast, there was no difference between the two groups in the BPI levels in plasma samples. For all individuals tested, BPI levels were consistently higher in plasma samples compared to the matched serum samples. The mean concentration of BPI in plasma samples was 52.3 μg/l (range 0.9–403, S.D. 60.6, n = 90). There was a positive correlation between the concentration of BPI and the white blood cell count as well as between the BPI concentration and C-reactive protein (CRP) in serum samples. In conclusion, the present study demonstrates that BPI can be quantified reliably by time-resolved fluoroimmunoassay in human serum samples

Deep Neural Networks for Inverse Problems with Pseudodifferential Operators: An Application to Limited-Angle Tomography

We propose a novel convolutional neural network (CNN), called \Psi DONet, designed for learning pseudodifferential operators (\Psi DOs) in the context of linear inverse problems. Our starting point is the iterative soft thresholding algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow us to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling, and convolution, which characterize our \Psi DONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited-angle Xray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of \Psi DONet on simulated data from limited-angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are \Psi DOs or Fourier integral operators

Psychosocial issues need more attention in COPD self-management education

Objective: To find out how regularly the contents of patient education regarded as essential for COPD patients' self-management are provided by healthcare professionals in specialised healthcare (SHC) and primary healthcare (PHC) in Finland. Design: A cross-sectional study based on an e-questionnaire with 42 items on the content of self-management education of COPD patients. Setting: The study sample included all public SHC units with pulmonary outpatient clinics (n = 29) and nine out of 160 health centres in Finland. Subjects: 83 doctors and 162 nurses. Main outcome measures: The respondents' answers on how regularly they included the contents regarded as essential for COPD patients' self-management in their education of COPD patients. Results: COPD patients were educated regularly on medical issues regarding COPD treatment, such as smoking cessation, exercise and pharmacological treatment. However, issues vital for coping with the disease, such as psychological well-being, stress management or fatigue, were often ignored. Patient education in SHC seemed to be more systematic than education in PHC. The education provided by the asthma/COPD nurses (n = 70) was more systematic than the education provided by the other nurses (n = 84). Conclusion: Healthcare professionals' continuous education should cover not only the medical but also the psychosocial aspects of coping with COPD. The role of doctors and nurses should be considered to ensure that there is no gap in COPD patients' education. Training asthma/COPD nurses and promoting specialised nurse-led asthma/COPD clinics in primary care could be beneficial while improving practices of patient education that enhance patients' ability to cope with the disease

An Automatic Regularization Method : An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data

X-ray tomography is a reliable tool for determining the inner structure of 3-D object with penetrating X-rays. However, traditional reconstruction methods, such as Feldkamp-Davis-Kress (FDK), require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high-resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets. However, computing 3-D reconstruction from such a sparsely sampled data set is difficult because the measurement data are usually contaminated by errors, and linear measurement models do not contain sufficient information to solve the problem in practice. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the 3-D shearlet transform domain. The inputs of the algorithm are the projection data and a priori known expected degree of sparsity, denoted as 0 <C-prPeer reviewe