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Importance Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. Objective To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning. Design, Setting, and Participants This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018. Main Outcomes and Measures Performance and generalizability of prognostic models. Results A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD. Conclusions and Relevance Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well.

Differential phase-contrast X-ray imaging using a Talbot-Lau interferometer has recently shown promising results for applications in medical imaging. However, reducing the applied radiation dose remains a major challenge. In this study, we consider the realization of a Talbot-Lau interferometer in a high Talbot order to increase the signal-to-noise ratio for low-dose applications. The quantitative performance of π and π/2 systems at high Talbot orders is analyzed through simulations, and the design energy and X-ray spectrum are optimized for mammography. It is found that operation even at very high Talbot orders is feasible and beneficial for image quality. As long as the X-ray spectrum is matched to the visibility spectrum, the SNR continuously increases with the Talbot order for π-systems. We find that the optimal X-ray spectra and design energies are almost independent of the Talbot order and that the overall imaging performance is robust against small variations in these parameters. Discontinuous spectra, such as that from molybdenum, are less robust because the characteristic lines may coincide with minima in the visibility spectra; however, they may offer slightly better performance. We verify this hypothesis by realizing a prototype system with a mean fringe visibility of above 40% at the seventh Talbot order. With this prototype, a proof-of-principle measurement of a freshly dissected breast at reasonable compression to 4 cm is conducted with a mean glandular dose of only 3 mGy but with a high SNR.

Numerical wave-optical simulations of X-ray differential phase-contrast imaging using grating interferometry require the oversampling of gratings and object structures in the range of few micrometers. Consequently, fields of view of few millimeters already use large amounts of a computer’s main memory to store the propagating wave front, limiting the scope of the investigations to only small-scale problems. In this study, we apply an approximation to the Fresnel-Kirchhoff diffraction theory to overcome these restrictions by dividing the two-dimensional wave front up into 1D lines, which are processed separately. The approach enables simulations with samples of clinically relevant dimensions by significantly reducing the memory footprint and the execution time and, thus, allows the qualitative comparison of different setup configurations. We analyze advantages as well as limitations and present the simulation of a virtual mammography phantom of several centimeters of size.

X-ray phase-contrast and dark-field imaging are two new modalities that have great potential for applications in different fields like medical diagnostics or materials science. The use of grating interferometers allows the detection of both differential phase shift and dark-field signal together with the absorption signal in a single acquisition. We present wave-optical simulations to quantitatively analyze the response of a grating-based X-ray phase-contrast and dark-field imaging setup to variations of the sample relative to the system. Specifically, we investigated changes in the size and the position of the object. Furthermore, we examined the influence of different detector pixel sizes while sample and interferometer remained unchanged. The results of this study contribute to a better understanding of the signal formation and represent a step towards the full characterization of the response of grating interferometry setups to specific sample geometries.

Phase retrieval in differential X-ray phase contrast imaging involves a one dimensional integration step. In the presence of noise, standard integration methods result in image blurring and streak artifacts. This work proposes a regularized integration method which takes the availability of two dimensional data as well as the integration-specific frequency-dependent noise amplification into account. In more detail, a Fourier-domain algorithm is developed comprising a frequency-dependent minimization of the total variation orthogonal to the direction of integration. For both simulated and experimental data, the novel method yielded strong artefact reduction without increased blurring superior to the results obtained by standard integration methods or regularization techniques in the image domain.

An X-ray phase contrast grating interferometer consists of three gratings in the beam line to generate interference patterns and acquire phase shift information if an object is placed into the beam. These gratings have a pitch of a few micrometers and therefore very accurate alignment is needed to acquire phase information reliably. In this work we have investigated different setup parameters and environmental influences on the detected signal quality by Six Sigma methods. We have used this information to optimize our setup and established a semi-automatic alignment procedure to reproduce a stable signal quality.

Energy dispersive X-ray diffraction (EDXRD) is a very effective method for explosive and narcotic threat detection in baggage screening. The XRD profiles arise from the molecular interference when X-rays are coherently scattered by a substance. The accurate identification of the target material depends on the ability to detect and resolve the peaks present in the coherent scatter profiles. A high-energy resolution High Purity Germanium (HPGe) detector is therefore generally used in such type of systems. To evaluate the suitability of cost-effective room-temperature semiconductor detectors for next-generation baggage screening systems, an assessment of the minimal requirements for the system resolution is required. In this study a hybrid Monte Carlo code has been modified to account for the molecular interference function that gives rise to the coherent scatter signature. A model for a realistic response function for Cadmium Zinc Telluride (CZT) detectors is then used to convolve the spectral output. This simulation tool is then used to assess the system design features and their influence on spectral resolution.

High temporal resolution and high spatial resolution are required to image the coronary arteries without motion artifacts. Several approaches have been pursued to achieve better temporal resolution including faster rotational speeds, and dual tube systems. In this paper, we present an alternative approach using motion estimation and compensation. The results demonstrate that the proposed methods can significantly reduce motion artifacts in coronary artery imaging.

X-ray computed tomography is a powerful medical imaging device. It allows high-resolution 3-D visualization of the human body. However, one drawback is the health risk associated with ionizing radiation. Simply downscaling the radiation intensities over the entire scan results in increased quantum noise. This paper proposes the concept of computer-assisted scan protocol and reconstruction. More specifically, we propose a method to compute patient and task-specific intensity profiles that achieve an optimal tradeoff between radiation dose and image quality. Therefore, reasonable image variance and dose metrics are derived. Conventional third-generation systems as well as inverted geometry concepts are considered. Two dose/noise minimization problems are formulated and solved by an efficient algorithm providing optimized milliampere (mA)-profiles. Thorax phantom simulations demonstrate the promising advantage of this technique: in this particular example, the dose is reduced by 53% for third-generation systems and by 86% for an inverted geometry in comparison to a sinusoidal mA-profile at a constant upper noise limit.

We describe an efficient method for computing the system response (sensitivity and point spread function) of a conventional gamma camera with multiple keel-edge pinhole collimators, taking into account geometric acceptance effects at the collimator as well as detector response. An efficient way of storing the resulting system matrix permits us to achieve very fast iterative reconstruction with reasonable memory requirements.

In 3 rd generation CT systems projection data, generated by X-rays emitted from a single source and passing through the imaged object, are acquired by a single detector covering the entire field of view (FOV). Novel CT system architectures employing distributed sources [1,2] could extend the axial coverage, while removing cone-beam artifacts and improving spatial resolution and dose. The sources can be distributed in plane and/or in the longitudinal direction. We investigate statistical iterative reconstruction of multi-axial data, acquired with simulated CT systems with multiple sources distributed along the in-plane and longitudinal directions. The current study explores the feasibility of 3D iterative Full and Half Scan reconstruction methods for CT systems with two different architectures. In the first architecture the sources are distributed in the longitudinal direction, and in the second architecture the sources are distributed both longitudinally and trans-axially. We used Penalized Weighted Least Squares Transmission Reconstruction (PWLSTR) and incorporated a projector-backprojector model matching the simulated architectures. The proposed approaches minimize artifacts related to the proposed geometries. The reconstructed images show that the investigated architectures can achieve good image quality for very large coverage without severe cone-beam artifacts.

Third-generation CT architectures are approaching fundamental limits. Spatial resolution is limited by the focal spot size and the detector cell size. Temporal resolution is limited by mechanical constraints on gantry rotation speed, and alternative geometries such as electron-beam CT and two-tube-two-detector CT come with severe tradeoffs in terms of image quality, dose-efficiency and complexity. Image noise is fundamentally linked to patient dose, and dose-efficiency is limited by finite detector efficiency and by limited spatio-temporal control over the X-ray flux. Finally, volumetric coverage is limited by detector size, scattered radiation, conebeam artifacts, Heel effect, and helical over-scan. We propose a new concept, multi-source inverse geometry CT, which allows CT to break through several of the above limitations. The proposed architecture has several advantages compared to third-generation CT: the detector is small and can have a high detection efficiency, the optical spot size is more consistent throughout the field-of-view, scatter is minimized even when eliminating the anti-scatter grid, the X-ray flux from each source can be modulated independently to achieve an optimal noise-dose tradeoff, and the geometry offers unlimited coverage without cone-beam artifacts. In this work we demonstrate the advantages of multi-source inverse geometry CT using computer simulations.

A universal benchtop CT system has recently been developed and installed at GE Global Research. The system consists of a source, detector and phantom table that are mounted on a set of high accuracy motion stages. These stages permit positioning and orienting the source, detector and phantom with respect to each other. The benchtop system is thereby able to simulate any realistic clinical CT system geometry. This work provides a geometrical model of this benchtop system and illustrates how the stages need to be controlled in order to simulate a particular CT system. This illustrates at the same time the geometrical capabilities of the system. The method and first results of the geometrical calibration of the system are also shown.

Many scientific research areas rely on accurate electron density characterization of various materials. For instance in X-ray optics and radiation therapy, there is a need for a fast and reliable technique to quantitatively characterize samples for electron density. We present how a precise measurement of electron density can be performed using an X-ray phase-contrast grating interferometer in a radiographic mode of a homogenous sample in a controlled geometry. A batch of various plastic materials was characterized quantitatively and compared with calculated results. We found that the measured electron densities closely match theoretical values. The technique yields comparable results between a monochromatic and a polychromatic X-ray source. Measured electron densities can be further used to design dedicated X-ray phase contrast phantoms and the additional information on small angle scattering should be taken into account in order to exclude unsuitable materials.