| 1. |
- Goswami, Mayank, et al.
(author)
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Optimal Spatial Filtering Schemes and Compact Tomography Setups
- 2016
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In: Research in nondestructive evaluation (Print). - : Taylor & Francis. - 0934-9847 .- 1432-2110. ; 27:2, s. 69-85
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Journal article (peer-reviewed)abstract
- Three compact computerized tomography (CT) scanner prototypes are established and tested for acceptable reconstruction results. Performance of conventional iterative reconstruction algorithm is enhanced via incorporating a spatial filtering/masking step. Generally, these masking strategies incorporate an arbitrary (3 3 or 2 2) size of square averaging mask to subdue the ill-posedness. Three different spatial filtering schemes are tested in this work. The objective is to remove any dependency on a user for deciding an appropriate masking parameter. The outcome of the simulation study is successfully verified for three real data situations using three specimens with pre-assigned/known inner profile. Such austere scanning situations arise in real-time environment especially for undetachable/fixed small size objects situated in inaccessible locations. The present study encourages the development of low budget CT setups.
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| 2. |
- Liefke, Jonas, et al.
(author)
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Early-Onset Fetal Growth Restriction Increases Left Ventricular Sphericity in Adolescents Born Very Preterm
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In: Pediatric Cardiology. - 0172-0643.
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Journal article (peer-reviewed)abstract
- Left ventricular shape alterations predict cardiovascular outcomes and have been observed in children born preterm and after fetal growth restriction (FGR). The aim was to investigate whether left ventricular shape is altered in adolescents born very preterm and if FGR has an additive effect. Adolescents born very preterm due to verified early-onset FGR and two control groups with birthweight appropriate for gestational age (AGA), born at similar gestational age and at term, respectively, underwent cardiac MRI. Principal component analysis was applied to find the modes of variation best explaining shape variability for end-diastole, end-systole, and for the combination of both, the latter indicative of function. Seventy adolescents were included (13-16 years; 49% males). Sphericity was increased for preterm FGR versus term AGA for end-diastole (36[0-60] vs - 42[- 82-8]; p = 0.01) and the combined analysis (27[- 23-94] vs - 51[- 119-11]; p = 0.01), as well as for preterm AGA versus term AGA for end-diastole (30[- 56-115] vs - 42[- 82-8]; p = 0.04), for end-systole (57[- 29-89] vs - 30[- 79-34]; p = 0.03), and the combined analysis (44[- 50-145] vs - 51[- 119-11]; p = 0.02). No group differences were observed for left ventricular mass or ejection fraction (all p ≥ 0.33). Sphericity was increased after very preterm birth and exacerbated by early-onset FGR, indicating an additive effect to that of very preterm birth on left ventricular remodeling. Increased sphericity may be a prognostic biomarker of future cardiovascular disease in this cohort that as of yet shows no signs of cardiac dysfunction using standard clinical measurements.
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| 3. |
- Puri, Ashishi, et al.
(author)
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A fractional order-based mixture of central Wishart (FMoCW) model for reconstructing white matter fibers from diffusion MRI
- 2023
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In: Psychiatry Research - Neuroimaging. - 0925-4927. ; 333
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Journal article (peer-reviewed)abstract
- This paper introduces an algorithm for reconstructing the brain's white matter fibers (WMFs). In particular, a fractional order mixture of central Wishart (FMoCW) model is proposed to reconstruct the WMFs from diffusion MRI data. The pseudo super diffusive modality of anomalous diffusion is coupled with the mixture of central Wishart (MoCW) model to derive the proposed model. We have shown results on multiple synthetic simulations, including fibers orientations in 2 and 3 directions per voxel and experiments on real datasets of rat optic chiasm and a healthy human brain. In synthetic simulations, a varying Rician distributed noise levels, σ=0.01−0.09 is also considered. The proposed model can efficiently distinguish multiple fibers even when the angle of separation between fibers is very small. This model outperformed, giving the least angular error when compared to fractional mixture of Gaussian (MoG), MoCW and mixture of non-central Wishart (MoNCW) models.
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| 4. |
- Puri, Ashishi, et al.
(author)
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An enhanced multi-fiber reconstruction technique using adaptive gradient directions coupled with MoNCW model in diffusion MRI
- 2021
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In: Journal of Magnetic Resonance. - : Elsevier BV. - 1090-7807. ; 325
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Journal article (peer-reviewed)abstract
- In this paper, we introduced a novel approach for generating unit gradient vectors named as adaptive gradient directions (AGD) for reconstructing single and decussating (crossing or kissing) white matter fibers in brain. The present study is focusing on reconstruction process of brain's white matter fibers but not dealing with data acquisition where scanning is performed. The gradient vectors used in the state-of-art methodologies for reconstruction are uniformly distributed vectors on a unit sphere but AGD, in contrary, are non-uniformly distributed points on a unit sphere. These points are uniformly distributed in some pattern on the surface of a unit sphere. For reconstruction, we have coupled the proposed AGD approach with mixture of non-central Wishart (MoNCW) model. We uphold the proposed approach with different simulations including synthetic as well as real data experiments. Resistivity to different Rician noise levels (σ=0.02-0.1) is demonstrated in simulated data for single as well as two and three decussating fibers. Our approach of using AGD dissipates the limitations that are encountered by the state-of-art technique of uniformly distributed points over the surface of unit sphere and outperforms showing significant reduction in angular errors.
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| 5. |
- Sadasivam, Rajkumar, et al.
(author)
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Non-invasive multimodal imaging of diabetic retinopathy : A survey on treatment methods and nanotheranostics
- 2021
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In: Nanotheranostics. - : Ivyspring International Publisher. - 2206-7418. ; 5:2, s. 166-181
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Journal article (peer-reviewed)abstract
- Diabetes Retinopathy (DR) is one of the most prominent microvascular complications of diabetes. It is one of the pre-eminent causes for vision impairment followed by blindness among the working-age population worldwide. The de facto cause for DR remains challenging, despite several efforts made to unveil the mechanism underlying the pathology of DR. There is quite less availability of the low cost pre-emptive theranostic imaging tools in terms of in-depth resolution, due to the multiple factors involved in the etiology of DR. This review work comprehensively explores the various reports and research works on all perspectives of diabetic retinopathy (DR), and its mechanism. It also discusses various advanced non-destructive imaging modalities, current, and future treatment approaches. Further, the application of various nanoparticle-based drug delivery strategies used for the treatment of DR are also discussed. In a nutshell, the present review work bolsters the pursuit of the development of an advanced non-invasive optical imaging modal with a nano-theranostic approach for the future diagnosis and treatment of DR and its associated ocular complications.
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| 6. |
- Shakya, Snehlata, et al.
(author)
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Adaptive Discretization for Computerized Tomography
- 2018
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In: Research in nondestructive evaluation (Print). - : TAYLOR & FRANCIS INC. - 0934-9847 .- 1432-2110. ; 29:2, s. 78-94
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Journal article (peer-reviewed)abstract
- Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.
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| 7. |
- Shakya, Snehlata, et al.
(author)
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Deep Learning Algorithm for Satellite Imaging Based Cyclone Detection
- 2020
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In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - 1939-1404. ; 13, s. 827-839
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Journal article (peer-reviewed)abstract
- Satellite images are primary data in weather prediction modeling. Deep learning-based approach, a viable candidate for automatic image processing, requires large sets of annotated data with diverse characteristics for training purposes. Accuracy of weather prediction improves with data having a relatively dense temporal resolution. We have employed interpolation and data augmentation techniques for enhancement of the temporal resolution and diversifications of characters in a given dataset. Algorithm requires classical approaches during preprocessing steps. Three optical flow methods using 14 different constraint optimization techniques and five error estimates are tested here. The artificially enriched data (optimal combination from the previous exercise) are used as a training set for a convolutional neural network to classify images in terms of storm or nonstorm. Several cyclone data (eight cyclone datasets of a different class) were used for training. A deep learning model is trained and tested with artificially densified and classified storm data for cyclone classification and locating the cyclone vortex giving minimum 90% and 84% accuracy, respectively. In the final step, we show that the linear regression method can be used for predicting the path.
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| 8. |
- Shakya, Snehlata, et al.
(author)
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Multi-fiber estimation and tractography for diffusion mri using mixture of non-central wishart distributions
- 2017
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In: 2017 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2017. - : Eurographics Association. - 9783038680369 ; , s. 119-123
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Conference paper (peer-reviewed)abstract
- Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.
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| 9. |
- Shakya, Snehlata, 1985-, et al.
(author)
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Multi-fiber reconstruction using probabilistic mixture models for diffusion MRI examinations of the brain
- 2017
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In: Modeling, Analysis, and Visualization of Anisotropy. - Cham : Springer Berlin/Heidelberg. - 9783319613574 - 9783319613581 ; , s. 283-308
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Book chapter (peer-reviewed)abstract
- In the field of MRI brain image analysis, Diffusion tensor imaging (DTI) provides a description of the diffusion of water through tissue and makes it possible to trace fiber connectivity in the brain, yielding a map of how the brain is wired. DTI employs a second order diffusion tensor model based on the assumption of Gaussian diffusion. The Gaussian assumption, however, limits the use of DTI in solving intra-voxel fiber heterogeneity as the diffusion can be non-Gaussian in several biological tissues including human brain. Several approaches to modeling the non-Gaussian diffusion and intra-voxel fiber heterogeneity reconstruction have been proposed in the last decades. Among such approaches are the multi-compartmental probabilistic mixture models. These models include the discrete or continuous mixtures of probability distributions such as Gaussian, Wishart or von Mises-Fisher distributions. Given the diffusion weighted MRI data, the problem of resolving multiple fibers within a single voxel boils down to estimating the parameters of such models. In this chapter, we focus on such multi-compartmental probabilistic mixture models. First we present a review including mathematical formulations of the most commonly applied mixture models. Then, we present a novel method based on the mixture of non-central Wishart distributions. A mixture model of central Wishart distributions has already been proposed earlier to resolve intra-voxel heterogeneity. However, we show with detailed experiments that our proposed model outperforms the previously proposed probabilistic models specifically for the challenging scenario when the separation angles between crossing fibers (two or three) are small. We compare our results with the recently proposed probabilistic models of mixture of central Wishart distributions and mixture of hyper-spherical von Mises-Fisher distributions. We validate our approach with several simulations including fiber orientations in two and three directions and with real data. Resistivity to noise is also demonstrated by increasing levels of Rician noise in simulated data. The experiments demonstrate the superior performance of our proposed model over the prior probabilistic mixture models.
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