Smoothly Clipped Absolute Deviation (SCAD) regularization for compressed sensing MRI Using an augmented Lagrangian scheme

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Objective.Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem.Approach.To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well asin vivoexperiments.Main results.The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution.Siginificance.These findings indicate that the SCAD-GML method has the potential to advance the application of FMT inin vivobiological research.

The Role of Social Support and Socioeconomic Factors in the Prediction of Depression among First-Year Undergraduate Students

Aim

This study was devoted to determining the role of social support and socioeconomic factors in predicting students' depression.

Methods

In this cross-sectional study, all first-year undergraduate students in the Shahrekord University of Medical Sciences, Iran, during the 2019-2020 academic year were included via the census method. Data collection tools include a researcher-made checklist about demographic and socioeconomic status, a standard questionnaire of perceived social support, and Beck's depression questionnaire. Smoothly clipped absolute deviation (SCAD) linear regression was used to model the role of social support and socioeconomic factors in predicting depression.

Results

Out of the 220 first-year undergraduate students, 174 (79.1%) were female, and 176 (80.0%) were single. The mean ± SD of depression score among the first-year undergraduate students was 10.56 ± 5.19, and the mean ± SD of social support score was 48.86 ± 5.46. The mean score of depression was significantly higher in female students than in males (11.09 versus 8.59, P = 0.001) but was not statistically significant in different categories of age (P = 0.70), marital status (P = 0.37), ethnicity (P = 0.10), parents' education, and the other demographic variables. Pearson's correlation showed an inverse and significant correlation between depression and social support (R = -0.20, P = 0.003). The mean score of depression was at the highest level for students of public health and environmental health majors and was the lowest for students of laboratory sciences, which was statistically significant (P < 0.001). After adjusting the other variables, SCAD regression showed that social support plays a key role in depression prediction, and increasing social support leads to a decrease in depression score.

Conclusion

Considering the existence of an inverse and significant correlation between depression and social support, any intervention to promote social support for first-year undergraduate students may decrease depression.

Hybrid reconstruction method for multispectral bioluminescence tomography with log-sum regularization

Bioluminescence tomography (BLT) has important applications in the in vivo visualization of a pathological process for preclinical studies. However, the reconstruction of BLT is severely ill-posed. To recover the bioluminescence source stably and efficiently, we use a log-sum regularization term in the objective function and utilize a hybrid optimization algorithm for solving the nonconvex regularized problems (HONOR). The hybrid optimization scheme of HONOR merges second-order information and first-order information to reconstruction by choosing either the quasi-Newton (QN) or gradient descent step at each iteration. The QN step uses the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) to acquire second-order information. Simulations and in vivo experiments based on multispectral measurements demonstrated the remarkable performance of the proposed hybrid method in the sparse reconstruction of BLT.

Prediction of progression in idiopathic pulmonary fibrosis using CT scans at baseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, there are two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans and their follow-up status; and (b) simultaneously selecting important features from high-dimensional space, and optimizing the prediction performance. We resolved the first challenge by implementing a study design and having an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-up visits. For the second challenge, we integrated the feature selection with prediction by developing an algorithm using a wrapper method that combines quantum particle swarm optimization to select a small number of features with random forest to classify early patterns of progression. We applied our proposed algorithm to analyze anonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields a parsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROI level. These results are superior to other popular feature selections and classification methods, in that our method produces higher accuracy in prediction of progression and more balanced sensitivity and specificity with a smaller number of selected features. Our work is the first approach to show that it is possible to use only baseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence.

An autoencoder based formulation for compressed sensing reconstruction

This work proposes a new formulation for image reconstruction based on the autoencoder framework. The work follows the adaptive approach used in prior dictionary and transform learning based reconstruction techniques. Existing autoencoder based reconstructions are non-adaptive; they are trained on a separate training set and applied on another. In this work, the autoencoder is learnt from the patches of the image it is reconstructing. Experimental studies on MRI reconstruction shows that the proposed method outperforms state-of-the-art methods in dictionary learning, transform learning and (non-adaptive) autoencoder based approaches.