Zobrazeno 1 - 10
of 7 980
pro vyhledávání: '"P. Nagesh"'
Autor:
Naghdi, Parisa, Bhurwani, Mohammad Mahdi Shiraz, Rahmatpour, Ahmad, Mondal, Parmita, Udin, Michael, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance
Externí odkaz:
http://arxiv.org/abs/2411.14407
Autor:
Mondal, Parmita, Williams, Kyle A, Naghdi, Parisa, Rahmatpour, Ahmad, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evalu
Externí odkaz:
http://arxiv.org/abs/2411.14475
Autor:
Rahmatpour, Ahmad, Shields, Allison, Mondal, Parmita, Naghdi, Parisa, Udin, Michael, Williams, Kyle A, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific I
Externí odkaz:
http://arxiv.org/abs/2411.09632
Autor:
Newlin, Nancy R., Schilling, Kurt, Koudoro, Serge, Chandio, Bramsh Qamar, Kanakaraj, Praitayini, Moyer, Daniel, Kelly, Claire E., Genc, Sila, Chen, Jian, Yang, Joseph Yuan-Mou, Wu, Ye, He, Yifei, Zhang, Jiawei, Zeng, Qingrun, Zhang, Fan, Adluru, Nagesh, Nath, Vishwesh, Pathak, Sudhir, Schneider, Walter, Gade, Anurag, Rathi, Yogesh, Hendriks, Tom, Vilanova, Anna, Chamberland, Maxime, Pieciak, Tomasz, Ciupek, Dominika, Vega, Antonio Tristán, Aja-Fernández, Santiago, Malawski, Maciej, Ouedraogo, Gani, Machnio, Julia, Ewert, Christian, Thompson, Paul M., Jahanshad, Neda, Garyfallidis, Eleftherios, Landman, Bennett A.
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructu
Externí odkaz:
http://arxiv.org/abs/2411.09618
Autor:
Mondal, Parmita, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Siddiqui, Adnan H, Ionita, Ciprian N
This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro met
Externí odkaz:
http://arxiv.org/abs/2411.08185
Autor:
Mondal, Parmita, Nagesh, Swetadri Vasan Setlur, Sommers-Thaler, Sam, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Baig, Ammad, Snyder, Kenneth, Siddiqui, Adnan H, Levy, Elad, Ionita, Ciprian N
Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed to
Externí odkaz:
http://arxiv.org/abs/2411.03655
Autor:
Shang, Liang, Lou, Zhengyang, Alexander, Andrew L., Prabhakaran, Vivek, Sethares, William A., Nair, Veena A., Adluru, Nagesh
Deep neural networks have demonstrated exceptional efficacy in stroke lesion segmentation. However, the delineation of small lesions, critical for stroke diagnosis, remains a challenge. In this study, we propose two straightforward yet powerful appro
Externí odkaz:
http://arxiv.org/abs/2408.02929
Publikováno v:
A&A 690, A119 (2024)
Dynamical friction works very differently for Newtonian gravity with dark matter and in modified Newtonian dynamics (MOND). While the absence of dark matter considerably reduces the friction in major galaxy mergers, analytic calculations indicate the
Externí odkaz:
http://arxiv.org/abs/2407.11139
Autor:
Nagesh, Srikanth T., Freundlich, Jonathan, Famaey, Benoit, Bílek, Michal, Candlish, Graeme, Ibata, Rodrigo, Müller, Oliver
Publikováno v:
A&A 690, A149 (2024)
Ultra-diffuse galaxies (UDGs) in the Coma cluster have velocity dispersion profiles that are in full agreement with the predictions of Modified Newtonian Dynamics (MOND) in isolation. However, the external field effect (EFE) from the cluster seriousl
Externí odkaz:
http://arxiv.org/abs/2407.03413
Autor:
Jia, Jinghan, Komma, Abi, Leffel, Timothy, Peng, Xujun, Nagesh, Ajay, Soliman, Tamer, Galstyan, Aram, Kumar, Anoop
In task-oriented conversational AI evaluation, unsupervised methods poorly correlate with human judgments, and supervised approaches lack generalization. Recent advances in large language models (LLMs) show robust zeroshot and few-shot capabilities a
Externí odkaz:
http://arxiv.org/abs/2406.17304