This study investigates the application of Cluster Initialized Factor Analysis (CIFA) to enhance the quantitative analysis of dynamic tau brain PET imaging utilizing F-18-Flurotaucipir (FTP). We analyzed PET scans from 18 subjects (9 Pittsburgh Compound-B (PIB)-positive and 9 PIB-negative) to uncover distinct tracer-binding dynamics, which describe the temporal patterns of radiotracer uptake in brain regions. Specific binding refers to the interaction of the radiotracer with its intended target, such as tau proteins in Alzheimer's disease. By examining factor curves resulting from the dynamic image factorization and comparing the corresponding coefficients for specific-binding (C-specific) values with Standardized Uptake Value Ratio (SUVR) values, we identified a significant correlation between the two. SUVR is a commonly used metric in PET imaging that represents the ratio of tracer uptake in a target region to a reference region, normalized over a specific time frame. Unlike SUVR, which requires a reference region, our method does not. This study underscores CIFA's potential in advancing our understanding of tau dynamics, providing a robust and reference-region-independent alternative. Future research will aim to validate these findings with larger datasets and investigate their clinical relevance.

In our time cybersecurity has grown to be a topic of massive proportion at the national and enterprise levels. Our thesis is that the economic perspective and investment decision-making are vital factors in determining the outcome of the struggle. To build our economic framework, we borrow from the pioneering work of Gordon and Loeb in which the Defender optimally trades-off investments for lower likelihood of its system breach. Our two-sided model additionally has an Attacker, assumed to be rational and also guided by economic considerations in its decision-making, to which the Defender responds. Our model is a simplified adaptation of a model proposed during the Cold War for weapons deployment in the US. Our model may also be viewed as a Stackelberg game and, from an analytic perspective, as a Max-Min problem, the analysis of which is known to have to contend with discontinuous behavior. The complexity of our simple model is rooted in its inherent nonlinearity and, more consequentially, non-convexity of the objective function in the optimization. The possibilities of the Attacker's actions add substantially to the risk to the Defender, and the Defender's rational, risk-neutral optimal investments in general substantially exceed the optimal investments predicted by the one-sided Gordon-Loeb model. We obtain a succinct set of three decision types that categorize all of the Defender's optimal investment decisions. Also, the Defender's optimal decisions exhibit discontinuous behavior as the initial vulnerability of its system is varied. The analysis is supplemented by extensive numerical illustrations. The results from our model open several major avenues for future work.

Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Propagation-Reconstruction Network (PRNet) for few-shot classification and localization, respectively, in Single Photon Emission Computed Tomography (SPECT) images. For the proof of concept we used a 2D-sliced image cropped around heart. The Prototypical Network, with a pre-trained ResNet-18 backbone, classified ventricles, myocardium, and liver tissues with 96.67% training and 93.33% validation accuracy. PRNet, adapted for 2D imaging with an encoder-decoder architecture and skip connections, achieved a training loss of 1.395, accurately reconstructing patches and capturing spatial relationships. These results highlight the potential of Prototypical Networks for tissue classification with limited labeled data and PRNet for anatomical landmark localization, paving the way for improved performance in deep learning frameworks.

We present a reconstruction of jet geometry models using numerical methods based on a Markov ChainMonte Carlo (MCMC) and limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimized algorithm. Our aim is to model the three-dimensional geometry of an AGN jet using observations, which are inherently two-dimensional. Many AGN jets display complex hotspots and bends over the kiloparsec scales. The structure of these bends in the jets frame may be quite different than what we see in the sky frame, transformed by our particular viewing geometry. The knowledge of the intrinsic structure will be helpful in understanding the appearance of the magnetic field and hence emission and particle acceleration processes over the length of the jet. We present the method used, as well as a case study based on a region of the M87 jet.

Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem.

We love Life and want to make our Life Cycle hassle free and sustainable. When I am writing the word “we”, it is not only referring human being but also addressing the entire eco system comprising of animals, plants, rivers , forests and everything which we use and surround us. We all live with work on targets and this paper trying to explore organizational problems which demand for customized solution with more focused and specific NDE application. NDE developer community and service provider need to be more aligned with user organization/ industries in order to feel and understand their technical issues, complexities and nightmares and have to streamline NDE focus on these KPA to develop customized solution which can solve similar problems globally. These specific technical solutions not only promote the basic NDE intention of “global safety and welfare” but also will open up avenue for NDE R&D to innovate customized gadgets and solutions. This paper will try to have focused discussion on 19 certain cases and organizational nightmares where readymade solutions are still unavailable or unknown and where ASNT/other NDE community requested to develop customized solutions.

In this proof-of-concept work, we have developed a 3D-CNN architecture that is guided by the tumor mask for classifying several patient-outcomes in breast cancer from the respective 3D dynamic contrast-enhanced MRI (DCE-MRI) images. The tumor masks on DCE-MRI images were generated using pre- and post-contrast images and validated by experienced radiologists. We show that our proposed mask-guided classification has a higher accuracy than that from either the full image without tumor masks (including background) or the masked voxels only. We have used two patient outcomes for this study: (1) recurrence of cancer after 5 years of imaging and (2) HER2 status, for comparing accuracies of different models. By looking at the activation maps, we conclude that an image-based prediction model using 3D-CNN could be improved by even a conservatively generated mask, rather than overly trusting an unguided, blind 3D-CNN. A blind CNN may classify accurately enough, while its attention may really be focused on a remote region within 3D images. On the other hand, only using a conservatively segmented region may not be as good for classification as using full images but forcing the model's attention toward the known regions of interest.

Previously, we have shown that an image location, size, or even constant attenuation factor may be estimated by deep learning from the images Radon transformed representation. In this project, we go a step further to estimate a few other mathematical transformation parameters under Radon transformation. The motivation behind the project is that many medical imaging problems are related to estimating similar invariance parameters. Such estimations are typically performed after image reconstruction from detector images that are in the Radon transformed space. The image reconstruction process introduces additional noise of its own. Deep learning provides a framework for direct estimation of required information from the detector images. A specific case we are interested in is dynamic nuclear imaging, where the quantitative estimations of the target tissues are queried. Motion inherent in biological systems, e.g., in vivo imaging with breathing motion, may be modeled as a transformation in the spatial domain. Motion is particularly prevalent in dynamic imaging, while tracer dynamics in the imaged object are a second source of transformation in the time domain. Our neural network model attempts to discern the two types of transformation (motion and intensity variation dynamics), i.e., tries to learn one type of transformation, ignoring the other.

In this paper we address motion correction in image reconstruction. Patient motion, including breathing, is a persistent problem in medical imaging. Cardiac motion is particularly enigmatic and often ignored, except in high-speed imaging modalities like MRI. Motions may create artifacts to the extent that the image may have to be discarded. Since the beginning of medical imaging, motion correction remained an important subcategory of research. Motion corrections may be applied during or after tomographic image reconstruction. In this work we considered motion as a Gaussian blur at the image level. Discrete radon transform is applied to the blurred images to create corresponding noisy sinograms that mimic real imaging scenario. Our deep learning based tool recovered accurately (1) the blurring functions with an artificial convolutional neural network (CNN) directly from the sinograms, and (2) successfully reconstructed (inverse radon transformed) the noise-free images utilizing an adaptation of the convolutional encoder-decoder network (CED) from the literature. Our work shows that neural networks are not only capable of eliminating systematic noise in reconstruction but can also recover the noise model.

In this work, we present a whole-body image segmentation algorithm that is completely unsupervised. It uses diverse topological features of intensity distributions over different organs to perform segmentations. We compared our results on Computed Tomography (CT) images, with that of traditional graph-cut segmentation algorithm that is semi-manual (for a given organ).

Radiomics is a relatively new field that purports to relate a large number of 3D medical images to phenotypes and specific disease outcomes. The paradigm relies on past similar and successful results in other '-omics' research made possible by the advent of fast computing tools. Radiomics bases itself on recent advances in machine learning. Most existing review articles in the area relate to results of studies of a specific imaging modality or patient outcome. This chapter provides an overview on the available and developing tools underlying radiomics studies. Here we address image features from the point of view of their usefulness in predicting outcomes in radiomics and, furthermore, classify the current literature based on machine learning algorithms used therein.

In this article a few of the qualitative spatio-temporal knowledge representation techniques developed by the constraint reasoning community within artificial intelligence are reviewed. The objective is to provide a broad exposure to any other interested group who may utilize these representations. The author has a particular interest in applying these calculi (in a broad sense) in topological data analysis, as these schemes are highly qualitative in nature.

Iterative reconstruction algorithms often have relatively large computation time affecting their clinical deployment. This is especially true for 4D reconstruction in dynamic imaging (DI). In this work, we have shown how sparse domain approaches and parallelization for static 3D image reconstruction and 4D dynamic image reconstruction (directly from sinogram) in Single Photon Emission Computed Tomography (SPECT), without any intermediate 3D reconstructions, can improve computational efficiency. DI in SPECT is one of the hardest inverse problems in medical image reconstruction area and slow reconstruction is a challenge for this promising protocol. Our work hopefully, paves a new direction toward making DI in SPECT clinically viable. Our 4D reconstruction also is a novel application of non-negative matrix factorization (NNMF) in an inverse problem.

Conventional gamma cameras are heavy and occupy large volume for all required hardware components. Direct conversion solid-state detectors like cadmium zinc telluride (CZT) enable a compact design of gamma cameras. In addition, conventional gamma cameras require different collimators for different gamma-ray photon energies, and the change of collimators between studies sometimes disrupts the workflow. To ease this inconvenience (i.e., bulkiness and collimator change), we have recently developed a compact, CZT-based, energy-independent gamma camera that requires only one collimator to cover a broad range of photon energies. In this paper, we show our design parameters and system specifications as well as simulation studies that support our design principles.

A model of an industrial laboratory is presented and analyzed. It is a highly simplified abstraction, and consists of a network with two stages in series, Research (R) and Development (D). Ideas and prototypes are incubated in the R stage, possibly patented and documented, and brought to a sufficiently stable level for transfer to teams of professional developers in the D stage. Revenue is generated from sale and licensing of patents at both stages, and the sale of products/solutions that are outputs of the D stage. The model is dynamic, evolving with time, which is discrete.

Plant hormones (Indole-3-Acetic Acid) regulate and influence the developmental range of cellular and physiological process of plant health. In the present study, ten PGPA bacteria were isolated from rhizospheric soil of Asparagus racemosus(L). Morphological and biochemical test showed that the isolates named KNAD1, KNAD2, KNAD6, KNAD9, KNAD10 belong to genus Bacillus. These isolates were further screened for PGPA like Phosphate Solubilization, production of ammonia and cell wall degrading enzymes. KNAD2, KNAD4, KNAD5, KNAD7, KNAD9 & KNAD10 showed high potential in Phosphate Solubilization and others PGPA. In this study,indole-3- acetic acid (IAA) production by these isolates were carried out day wises and then results showed that on Day2: KNAD6 (86.72ppm); Day3: KNAD7 (85.63ppm); Day4: KNAD2 (116.83ppm); Day5: KNAD4 (121.54ppm); Day6: KNAD1 (86.17ppm); Day7: KNAD6 (126.60ppm) of IAA (μg ml-1) were produced in the presence of tryptophan (1 mg ml-1) at optimum conditions of 37°C and 0.5% NaCl. Additionally, it was further observed that at 1%, 2%and 7% tryptone concentration in the nutrient broth the bacterial growth increased. These results suggest that production of IAA in presence of tryptone might be promising factor for the implementation in plant growth and development.

In this paper, we propose and validate an algorithm of extracting voxel-by-voxel time activity curves directly from inconsistent projections applied in dynamic cardiac SPECT. The algorithm was derived based on factor analysis of dynamic structures (FADS) approach and imposes prior information by applying several regularization functions with adaptively changing relative weighting. The anatomical information of the imaged subject was used to apply the proposed regularization functions adaptively in the spatial domain. The algorithm performance is validated by reconstructing dynamic datasets simulated using the NCAT phantom with a range of different input tissue time-activity curves. The results are compared to the spline-based and FADS methods. The validated algorithm is then applied to reconstruct pre-clinical cardiac SPECT data from canine and murine subjects. Images, generated from both simulated and experimentally acquired data confirm the ability of the new algorithm to solve the inverse problem of dynamic SPECT with slow gantry rotation.