Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis.

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. ain 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.

Fourier domain image fusion for differential X-ray phase-contrast breast imaging.

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.

Low-dose, phase-contrast mammography with high signal-to-noise ratio.

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.

Fast one-dimensional wave-front propagation for x-ray differential phase-contrast imaging.

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.

A Fourier-domain algorithm for total-variation regularized phase retrieval in differential X-ray phase contrast imaging.

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.

Computer-assisted scan protocol and reconstruction (CASPAR)-reduction of image noise and patient dose.

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.

Morphologic and functional changes in the unilateral 6-hydroxydopamine lesion rat model for Parkinson's disease discerned with microSPECT and quantitative MRI.

OBJECT: In the present study, we aimed to evaluate the impact of neurodegeneration of the nigrostriatal tract in a rodent model of Parkinson's disease on the different MR contrasts (T(2), T(1), CBF and CBV) measured in the striatum. MATERIAL AND METHODS: Animals were injected with 6-hydroxydopamine (6OHDA) in the substantia nigra resulting in massive loss of nigrostriatal neurons and hence dopamine depletion in the ipsilateral striatum. Using 7T MRI imaging, we have quantified T(2), T(1), CBF and CBV in the striata of 6OHDA and control rats. To validate the lesion size, behavioral testing, dopamine transporter muSPECT and tyrosine hydroxylase staining were performed. RESULTS: No significant differences were demonstrated in the absolute MRI values between 6OHDA animals and controls; however, 6OHDA animals showed significant striatal asymmetry for all MRI parameters in contrast to controls. CONCLUSIONS: These PD-related asymmetry ratios might be the result of counteracting changes in both intact and affected striatum and allowed us to diagnose PD lesions. As lateralization is known to occur also in PD patients and might be expected in transgenic PD models as well, we propose that MR-derived asymmetry ratios in the striatum might be a useful tool for in vivo phenotyping of animal models of PD.

Non-invasive imaging of neuropathology in a rat model of alpha-synuclein overexpression.

Parkinson's disease is a neurodegenerative disorder affecting the dopaminergic neurons in the substantia nigra. Aggregation of alpha-synuclein appears to play a central role in the pathogenesis. Novel animal models for neurodegeneration have been generated by lentiviral vector-mediated locoregional overexpression of disease-associated genes in the adult brain. We have used lentiviral vectors to overexpress a clinical mutant of alpha-synuclein, A30P, in the rat substantia nigra. This overexpression induced time-dependent cytoplasmic and neuritic accumulation of alpha-synuclein and neurodegeneration. A subgroup of the rats developed asymmetric rotational behavior after administration of amphetamine. In addition, these animals displayed reduced dopamine transporter binding visualized by 123I-FP-CIT microSPECT imaging. The behavioral and microSPECT data were validated by histological analysis. There was a strong correlation between the reduction of dopaminergic neurons in the substantia nigra and the reduction of dopamine transporter binding in the striatum. MicroSPECT imaging enables non-invasive imaging of the neurodegeneration allowing longitudinal follow-up in this new animal model for Parkinson's disease and the evaluation of neuroprotective drugs.

Comparison between MAP and postprocessed ML for image reconstruction in emission tomography when anatomical knowledge is available.

Previously, the noise characteristics obtained with penalized-likelihood reconstruction [or maximum a posteriori (MAP)] have been compared to those obtained with postsmoothed maximum-likelihood (ML) reconstruction, for emission tomography applications requiring uniform resolution. It was found that penalized-likelihood reconstruction was not superior to postsmoothed ML. In this paper, a similar comparison is made, but now for applications where the noise suppression is tuned with anatomical information. It is assumed that limited but exact anatomical information is available. Two methods were compared. In the first method, the anatomical information is incorporated in the prior of a MAP-algorithm and is, therefore, imposed during MAP-reconstruction. The second method starts from an unconstrained ML-reconstruction, and imposes the anatomical information in a postprocessing step. The theoretical analysis was verified with simulations: small lesions were inserted in two different objects, and noisy PET data were produced and reconstructed with both methods. The resulting images were analyzed with bias-noise curves, and by computing the detection performance of the nonprewhitening observer and a channelized Hotelling observer. Our analysis and simulations indicate that the postprocessing method is inferior, unless the noise correlations between neighboring pixels are taken into account. This can be done by applying a so-called prewhitening filter. However, because the prewhitening filter is shift variant and object dependent, it seems that MAP reconstruction is the more efficient method.

Optimization of geometrical calibration in pinhole SPECT.

Previously, we developed a method to determine the acquisition geometry of a pinhole camera. This information is needed for the correct reconstruction of pinhole single photon emission computed tomography images. The method uses a calibration phantom consisting of three point sources and their positions in the field of view (FOV) influence the accuracy of the geometry estimate. This paper proposes two particular configurations of point sources with specific positions and orientations in the FOV for optimal image reconstruction accuracy. For the proposed calibration setups, inaccuracies of the geometry estimate due to noise in the calibration data, only cause subresolution inaccuracies in reconstructed images. The calibration method also uses a model of the point source configuration, which is only known with limited accuracy. The study demonstrates, however, that, with the proposed calibration setups, the error in reconstructed images is comparable to the error in the phantom model.

Characterization of pinhole SPECT acquisition geometry.

A method is presented to estimate the acquisition geometry of a pinhole single photon emission computed tomography (SPECT) camera with a circular detector orbit. This information is needed for the reconstruction of tomographic images. The calibration uses the point source projection locations of a tomographic acquisition of three point sources located at known distances from each other. It is shown that this simple phantom provides the necessary and sufficient information for the proposed calibration method. The knowledge of two of the distances between the point sources proves to be essential. The geometry is estimated by fitting analytically calculated projections to the measured ones, using a simple least squares Powell algorithm. Some mild a priori knowledge is used to constrain the solutions of the fit. Several of the geometrical parameters are however highly correlated. The effect of these correlations on the reconstructed images is evaluated in simulation studies and related to the estimation accuracy. The highly correlated detector tilt and electrical shift are shown to be the critical parameters for accurate image reconstruction. The performance of the algorithm is finally demonstrated by phantom measurements. The method is based on a single SPECT scan of a simple calibration phantom, executed immediately after the actual SPECT acquisition. The method is also applicable to cone-beam SPECT and X-ray CT.