Determinants Driving the Student’s Decision Making to Opt Institution for Higher Education in India: An Exploratory Factor Analysis

This study intends to build up a thorough understanding of the determinants that largely influence student’s decision-making to opt institution for higher education empirically from the Indian perspective. It also attempts to develop the rank of importance of the determinants influencing their institution selection decision. Prior research has various constructs that influence the design of the student’s career path, but this is the first time, “uncertainty” a new construct has been introduced which made this study relevant. The factor analysis was conducted to analyze the data of 558 students of Delhi NCR, India, and identified seven determinants namely Holistic institutional environment, Conformity influence, Human intelligence, Institutional prominence, Geographic characteristics, Student residential life, and Financial viability that impact student’s decision-making to opt institution for higher education. The findings of the study can guide educational institutions by empowering them to set objectives to draw the interest of students to pursue higher studies

Simplified Statistical Image Reconstruction Algorithm for Polyenergetic X-ray CT

In X-ray computed tomography (CT), bony structures cause beam-hardening artifacts that appear on the reconstructed image as streaks and shadows. Currently, there are two classes of methods for correcting for bone-related beam hardening. The standard approach used with filtered backprojection (FBP) reconstruction is the Joseph and Spital (JS) method. In the current simulation study (which is inspired by a clinical head scan), the JS method requires a simple table or polynomial model for correcting water-related beam hardening, and two additional tuning parameters to compensate for bone. Like all FBP methods, it is sensitive to data noise. Statistical methods have also been proposed recently for image reconstruction from noisy polyenergetic X-ray data. However, these methods have required more knowledge of the X-ray spectrum than is needed in the JS method, hampering their use in practice. This paper proposes a simplified statistical image reconstruction approach for polyenergetic X-ray CT that uses the same calibration data and tuning parameters used in the JS method, thereby facilitating its practical use. Simulation results indicate that the proposed method provides improved image quality (reduced beam hardening artifacts and noise) compared to the JS method, at the price of increased computation. The results also indicate that the image quality of the proposed method is comparable to a method requiring more beam-hardening information.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85884/1/Fessler213.pd

Penalized Likelihood Transmission Image Reconstruction:Unconstrained Monotonic Algorithms

Statistical reconstruction algorithms in transmission tomography yield improved images relative to the conventional FBP method. The most popular iterative algorithms for this problem are the conjugate gradient (CG) method and ordered subsets (OS) methods. Neither method is ideal. OS methods "converge" quickly, but are suboptimal for problems with factored system matrices. Nonnegativity constraints are not imposed easily by the CG method. To speed convergence, we propose to abandon the nonnegativity constraints (letting the regularization discourage the negative values), and to use quadratic surrogates to choose the step size rather than using an expensive line search. To ensure monotonicity, we develop a modification of the transmission log-likelihood. The resulting algorithm is suitable for large-scale problems with factored system matrices such as X-ray CT image reconstruction with afterglow models. Preliminary results show that the regularization ensures minimal negative values, and that the algorithm is indeed monotone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85849/1/Fessler195.pd

Accelerated Statistical Image Reconstruction Algorithms and Simplified Cost Functions for X-ray Computed Tomography.

Statistical image reconstruction methods are poised to replace traditional methods like filtered back-projection (FBP) in commercial X-ray computed tomography (CT) scanners. Statistical methods offer many advantages over FBP, including incorporating physical effects and physical constraints, modeling of complex imaging geometries, and imaging at lower X-ray doses. But, the use of statistical methods is limited due to many practical problems. This thesis proposes methods to improve four aspects of statistical methods: reconstruction time, beam hardening, non-negativity constraints, and organ motion. To reduce the reconstruction time, several novel iterative algorithms are proposed that are adapted to multi-core computing, including a hybrid ordered subsets (OS) / iterative coordinate descent (ICD) approach. This approach leads to a reduction in reconstruction time, and it also makes the ICD algorithm robust to the initial guess image. Statistical methods have accounted for beam hardening by using more information than needed by traditional FBP-based methods like the Joseph-Spital (JS) method. This thesis proposes a statistical method that uses exactly the same beam hardening information as the JS method while suppressing beam hardening artifacts. Directly imposing the non-negativity constraints can increase the computation time of algorithms such as the preconditioned conjugate gradient (PCG) method. This thesis proposes a modification of the penalized-likelihood cost function for monoenergetic transmission tomography, and a corresponding PCG algorithm, that reduce reconstruction time when enforcing nonnegativity. Organ motion during a scan causes image artifacts, and in some cases these artifacts are more apparent when standard statistical methods are used. A preliminary simulation study of a new approach to remove motion artifacts is presented. The distinguishing feature of this approach is that it does not require any new information from the scanner. The target applications of this research effort are 3-D volume reconstructions for axial cone-beam and helical cone-beam scanning geometries of multislice CT (MSCT) scanners.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60749/1/someshs_1.pd

Effect of interventions in improving awareness, knowledge and practices of ppfp among women and health-care providers in bihar:a pre-and post-intervention study

Background: Post-partum family planning (PPFP) within first 12 months of childbirth is known to improve maternal and neonatal health outcomes. This study evaluates the effect a package of PPFP interventions in improving level of awareness, knowledge and practices of post-partum women. Methods: A before and after intervention cross sectional study was conducted in 18 public health facilities and their catchment areas across 5 districts of Bihar. Participants included randomly selected postpartum women and purposively selected health service providers. A standard questionnaire was used to assess the level of knowledge, awareness and practices related to post-partum family planning before and after the intervention. Results: A total of 972 postpartum women, 27 doctors, 46 nurses, 89 Auxiliary Nurse Midwives (ANM) and 89 Accredited Social Health Activists (ASHA) as well as 981 postpartum women, 18 doctors, 53 nurses, 90 ANMs and 90 ASHAs were interviewed during baseline and end line respectively. This intervention package increased knowledge regarding postpartum return to fertility, modern FP methods and criteria of lactational amenorrhoea method. Also, the proportion of post-partum women who reported receiving FP counselling were increased. Conclusion: The findings of this study demonstrate that effective implementation of a package of PPFP interventions at a scale can lead to improvement in the knowledge and awareness levels of both health workers and post-partum women

2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation

While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown to have better image quality than Filtered Back Projection (FBP), its use has been limited by its high computational cost. More recently, deep convolutional neural networks (CNN) have shown great promise in both denoising and reconstruction applications. In this research, we propose a fast reconstruction algorithm, which we call Deep Learning MBIR (DL-MBIR), for approximating MBIR using a deep residual neural network. The DL-MBIR method is trained to produce reconstructions that approximate true MBIR images using a 16 layer residual convolutional neural network implemented on multiple GPUs using Google Tensorflow. In addition, we propose 2D, 2.5D and 3D variations on the DL-MBIR method and show that the 2.5D method achieves similar quality to the fully 3D method, but with reduced computational cost.Comment: IEEE Asilomar conference on signals systems and computers, 201

Implementation of a large-scale breast cancer early detection program in a resource-constrained setting: real-world experiences from 2 large states in India

Background: The Breast Health Initiative (BHI) was launched to demonstrate a scalable model to improve access to early diagnosis and treatment of breast cancer. Methods: A package of evidence-based interventions was codesigned and implemented with the stakeholders, as part of the national noncommunicable disease program, through the existing primary health care system. Data from the first 18 months of the BHI are presented. Results: A total of 108,112 women received breast health education; 48% visited the health facilities for clinical breast examination (CBE), 3% had a positive CBE result, and 41% were referred to a diagnostic facility. The concordance of CBE findings between health care providers and adherence to follow-up care improved considerably, with more women visiting the diagnostic facilities and completing diagnostic evaluation within 1 month from initial screening, and with only 9% lost to follow-up. The authors observed a clinically meaningful decrease in time to complete diagnostic evaluation with biopsy, from 37 to 9 days. Conclusions: The results demonstrate the feasibility and effectiveness of implementing a large-scale, decentralized breast cancer early detection program delivered through the existing primary health care system in India