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  1. Article ; Online: DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain.

    Paluru, Naveen / Susan Mathew, Raji / Yalavarthy, Phaneendra K

    NMR in biomedicine

    2024  , Page(s) e5163

    Abstract: Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the ... ...

    Abstract Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the susceptibility distribution from the measured local field obtained from the MR phase. Although existing deep learning based QSM methods can produce high-quality reconstruction, they are highly biased toward training data distribution with less scope for generalizability. This work proposes a hybrid two-step reconstruction approach to improve deep learning based QSM reconstruction. The susceptibility map prediction obtained from the deep learning methods has been refined in the framework developed in this work to ensure consistency with the measured local field. The developed method was validated on existing deep learning and model-based deep learning methods for susceptibility mapping of the brain. The developed method resulted in improved reconstruction for MRI volumes obtained with different acquisition settings, including deep learning models trained on constrained (limited) data settings.
    Language English
    Publishing date 2024-04-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 1000976-0
    ISSN 1099-1492 ; 0952-3480
    ISSN (online) 1099-1492
    ISSN 0952-3480
    DOI 10.1002/nbm.5163
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2869: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article: A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms.

    Gupta, Utkarsh / Paluru, Naveen / Nankani, Deepankar / Kulkarni, Kanchan / Awasthi, Navchetan

    Heliyon

    2024  Volume 10, Issue 5, Page(s) e26787

    Abstract: Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to ... ...

    Abstract Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.
    Language English
    Publishing date 2024-02-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2024.e26787
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Model resolution-based deconvolution for improved quantitative susceptibility mapping.

    Mathew, Raji Susan / Paluru, Naveen / Yalavarthy, Phaneendra K

    NMR in biomedicine

    2023  Volume 37, Issue 2, Page(s) e5055

    Abstract: Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution- ...

    Abstract Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution-based deconvolution for improved estimation of the susceptibility map obtained using the thresholded k-space division (TKD). A two-step approach has been proposed, wherein the first step involves the TKD susceptibility map computation and the second step involves the correction of this susceptibility map using the model-resolution matrix. The TKD-estimated susceptibility map can be expressed as the weighted average of the true susceptibility map, where the weights are determined by the rows of the model-resolution matrix, and hence a deconvolution of the TKD susceptibility map using the model-resolution matrix yields a better approximation to the true susceptibility map. The model resolution-based deconvolution is realized using closed-form, iterative, and sparsity-regularized implementations. The proposed approach was compared with L2 regularization, TKD, rescaled TKD in superfast dipole inversion, the modulated closed-form method, and iterative dipole inversion, as well as sparsity-regularized dipole inversion. It was observed that the proposed approach showed a substantial reduction in the streaking artifacts across 94 test volumes considered in this study. The proposed approach also showed better error reduction and edge preservation compared with other approaches. The proposed model resolution-based deconvolution compensates for the truncation of zero coefficients in the dipole kernel at the magic angle and hence provides a closer approximation to the true susceptibility map compared with other direct methods.
    MeSH term(s) Magnetic Resonance Imaging/methods ; Algorithms ; Brain ; Brain Mapping/methods ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2023-10-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 1000976-0
    ISSN 1099-1492 ; 0952-3480
    ISSN (online) 1099-1492
    ISSN 0952-3480
    DOI 10.1002/nbm.5055
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2869: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: 3D deformation measurement in digital holographic interferometry using a multitask deep learning architecture.

    Vengala, Krishna Sumanth / Paluru, Naveen / Subrahmanyam Gorthi, Rama Krishna Sai

    Journal of the Optical Society of America. A, Optics, image science, and vision

    2022  Volume 39, Issue 1, Page(s) 167–176

    Abstract: The extraction of absolute phase from an interference pattern is a key step for 3D deformation measurement in digital holographic interferometry (DHI) and is an ill-posed problem. Estimating the absolute unwrapped phase becomes even more challenging when ...

    Abstract The extraction of absolute phase from an interference pattern is a key step for 3D deformation measurement in digital holographic interferometry (DHI) and is an ill-posed problem. Estimating the absolute unwrapped phase becomes even more challenging when the obtained wrapped phase from the interference pattern is noisy. In this paper, we propose a novel multitask deep learning approach for phase reconstruction and 3D deformation measurement in DHI, referred to as TriNet, that has the capability to learn and perform two parallel tasks from the input image. The proposed TriNet has a pyramidal encoder-two-decoder framework for multi-scale information fusion. To our knowledge, TriNet is the first multitask approach to accomplish simultaneous denoising and phase unwrapping of the wrapped phase from the interference fringes in a single step for absolute phase reconstruction. The proposed architecture is more elegant than recent multitask learning methods such as Y-Net and state-of-the-art segmentation approaches such as UNet++. Further, performing denoising and phase unwrapping simultaneously enables deformation measurement from the highly noisy wrapped phase of DHI data. The simulations and experimental comparisons demonstrate the efficacy of the proposed approach in absolute phase reconstruction and 3D deformation measurement with respect to the existing conventional methods and state-of-the-art deep learning methods.
    Language English
    Publishing date 2022-02-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 283633-6
    ISSN 1520-8532 ; 1084-7529 ; 0740-3232
    ISSN (online) 1520-8532
    ISSN 1084-7529 ; 0740-3232
    DOI 10.1364/JOSAA.444949
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.B 2689: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images.

    Paluru, Naveen / Dayal, Aveen / Jenssen, Havard Bjorke / Sakinis, Tomas / Cenkeramaddi, Linga Reddy / Prakash, Jaya / Yalavarthy, Phaneendra K

    IEEE transactions on neural networks and learning systems

    2021  Volume 32, Issue 3, Page(s) 932–946

    Abstract: Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The ... ...

    Abstract Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at https://github.com/NaveenPaluru/Segmentation-COVID-19.
    MeSH term(s) COVID-19/diagnostic imaging ; COVID-19/epidemiology ; Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods ; Lung/diagnostic imaging ; Neural Networks, Computer ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2021-03-01
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2021.3054746
    Database MEDical Literature Analysis and Retrieval System OnLINE

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