To describe use of the emergency department (ED) among late preterm versus term infants enrolled in a home visiting program and to determine whether home visiting frequency was associated with ...outcome differences.
Retrospective, cohort study.
Regional home visiting program in southwest Ohio from 2007–2010.
Late preterm and term infants born to mothers enrolled in home visiting. Program eligibility requires ≥ one of four characteristics: unmarried, low income, < 18 years, or suboptimal prenatal care.
Data were derived from vital statistics, hospital discharges, and home visiting records. Negative binomial regression was used to determine association of ED visits in the first year with late preterm birth and home visit frequency, adjusting for maternal and infant characteristics.
Of 1,804 infants, 9.2% were born during the late preterm period. Thirty‐eight percent of all infants had at least one ED visit, 15.6% had three or more. No significant difference was found between the number of ED visits for late preterm and term infants (39.4% vs. 37.8% with at least one ED visit, p = .69). In multivariable analysis, late preterm birth combined with a maternal mental health diagnosis was associated with an ED incident rate ratio (IRR) of 1.26, p = .03; high frequency of home visits was not significant (IRR = .92, p = .42).
Frequency of home visiting service over the first year of life is not significantly associated with reduced ED visits for infants with at‐risk attributes and born during the late preterm period. Research on how home visiting can address ED use, particularly for those with prematurity and maternal mental health conditions, may strengthen program impact and cost benefits.
Purpose: To estimate in-room breathing motion from a limited number of 2D cone-beam (CB) projection images by registering them to a phase of the 4D planning CT. Methods: Breathing motion was modelled ...using a piecewise continuous B-spline representation 1, allowing to preserve the sliding along the thoracic wall while limiting the degrees of freedom. The deformed target 3D image was subsequently used to generate Digitally Reconstructed Radiographs (DRR). The Normalized Correlation Coefficient (NCC) between the measured projection images and the DRR was computed in the 2D projection space. However, the partial derivatives of the NCC relative to the transform parameters were backprojected into the 3D space, avoiding the projection of the transform Jacobian matrix which is computationally intractable 2. Results: The method was quantitatively evaluated on 16 lung cancer patients. 40 CB projection images were simulated using the end-exhale phase of the 4D planning CT and the geometric parameters of a clinical CB protocol. The end-inhale phase was deformed to match these simulated projections. The Target Registration Error (TRE) decreased from 8.8 mm to 2.0 mm while the TRE obtained from the 3D/3D registration of the reconstructed CBCT was significantly worse (2.6 mm), due to view aliasing artefacts. We also provide the motion compensated image reconstructed from a real CB acquisition showing the quality improvement brought by the in-room deformation model compared to the planning motion model. Conclusions: We have developed a 2D/3D deformable registration algorithm that enables in-room breathing motion estimation from cone-beam projection images.
Purpose
Exploiting the x‐ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3‐D description of a patient in a material basis. This ...may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x‐ray projection images, which is a nonlinear ill‐posed problem.
Methods
Our main contribution is to introduce a material‐dependent spatial regularization in the projection domain. The decomposition problem is solved iteratively using a Gauss–Newton algorithm that can benefit from fast linear solvers. A Matlab implementation is available online. The proposed regularized weighted least squares Gauss–Newton algorithm (RWLS‐GN) is validated on numerical simulations of a thorax phantom made of up to five materials (soft tissue, bone, lung, adipose tissue, and gadolinium), which is scanned with a 120 kV source and imaged by a 4‐bin photon counting detector. To evaluate the method performance of our algorithm, different scenarios are created by varying the number of incident photons, the concentration of the marker and the configuration of the phantom. The RWLS‐GN method is compared to the reference maximum likelihood Nelder–Mead algorithm (ML‐NM). The convergence of the proposed method and its dependence on the regularization parameter are also studied.
Results
We show that material decomposition is feasible with the proposed method and that it converges in few iterations. Material decomposition with ML‐NM was very sensitive to noise, leading to decomposed images highly affected by noise, and artifacts even for the best case scenario. The proposed method was less sensitive to noise and improved contrast‐to‐noise ratio of the gadolinium image. Results were superior to those provided by ML‐NM in terms of image quality and decomposition was 70 times faster. For the assessed experiments, material decomposition was possible with the proposed method when the number of incident photons was equal or larger than 105 and when the marker concentration was equal or larger than 0.03 g·cm−3.
Conclusions
The proposed method efficiently solves the nonlinear decomposition problem for spectral CT, which opens up new possibilities such as material‐specific regularization in the projection domain and a parallelization framework, in which projections are solved in parallel.
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