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pro vyhledávání: '"Pirkl A"'
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions remains challenging, particularl
Externí odkaz:
http://arxiv.org/abs/2410.23318
Artificial Intelligence (AI)-based radio fingerprinting (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model and data orchestration of FP are time-consuming and costly, as it requ
Externí odkaz:
http://arxiv.org/abs/2410.00617
Autor:
Mayo, Perla, Cencini, Matteo, Pirkl, Carolin M., Menzel, Marion I., Tosetti, Michela, Menze, Bjoern H., Golbabaee, Mohammad
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping. The reconstruction of quantitative maps requires tailored algorithms for removing aliasing artefacts from the compressed samp
Externí odkaz:
http://arxiv.org/abs/2408.02367
Autor:
Mayo, Perla, Cencini, Matteo, Fatania, Ketan, Pirkl, Carolin M., Menzel, Marion I., Menze, Bjoern H., Tosetti, Michela, Golbabaee, Mohammad
The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during imag
Externí odkaz:
http://arxiv.org/abs/2407.19866
Autor:
Fuderer, Miha, Wichtmann, Barbara, Crameri, Fabio, deSouza, Nandita M., Baeßler, Bettina, Gulani, Vikas, Wang, Meiyun, Poot, Dirk, de Boer, Ruud, Cashmore, Matt, de Graaf, Wolter, Keenan, Kathryn E., Ma, Dan, Pirkl, Carolin, Sollmann, Nico, Weingärtner, Sebastian, Mandija, Stefano, Golay, Xavier
Purpose: To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing T1 and T2 maps. Methods: Perceptually linearized color-maps were chosen to have similar color settings as those pro
Externí odkaz:
http://arxiv.org/abs/2407.03906
Autor:
Di Teodoro, Giulia, Pirkl, Martin, Incardona, Francesca, Vicenti, Ilaria, Sönnerborg, Anders, Kaiser, Rolf, Palagi, Laura, Zazzi, Maurizio, Lengauer, Thomas
Publikováno v:
Bioinformatics, Volume 40, Issue 6, June 2024, btae327
Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical k
Externí odkaz:
http://arxiv.org/abs/2311.04846
Publikováno v:
Proc of the 14th International Conference on Cloud Computing, GRIDs, and Virtualization (Cloud Computing 2023), Nice, France, June 2023, pp. 21-29, ISSN 2308-4294
Security challenges for Cloud or Fog-based machine learning services pose several concerns. Securing the underlying Cloud or Fog services is essential, as successful attacks against these services, on which machine learning applications rely, can lea
Externí odkaz:
http://arxiv.org/abs/2310.19459
Autor:
Fatania, Ketan, Chau, Kwai Y., Pirkl, Carolin M., Menzel, Marion I., Hall, Peter, Golbabaee, Mohammad
Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawback of requiring high-fidelity ground truth tissue map training data whi
Externí odkaz:
http://arxiv.org/abs/2211.12786
Autor:
Riley Walton Jackson, Ann Cao-Nasalga, Amy Chieng, Amy Pirkl, Annemarie D Jagielo, Cindy Xu, Emilio Goldenhersch, Nicolas Rosencovich, Cristian Waitman, Judith J Prochaska
Publikováno v:
JMIR Formative Research, Vol 8, p e54817 (2024)
BackgroundSmoking contributes to 1 in 3 cancer deaths. At the Stanford Cancer Center, tobacco cessation medication management and counseling are provided as a covered benefit. Patients charted as using tobacco are contacted by a tobacco treatment spe
Externí odkaz:
https://doaj.org/article/cd14c11dd84d4a7aa3dda748896394a7
Autor:
Kaushik, Sandeep, Bylund, Mikael, Cozzini, Cristina, Shanbhag, Dattesh, Petit, Steven F, Wyatt, Jonathan J, Menzel, Marion I, Pirkl, Carolin, Mehta, Bhairav, Chauhan, Vikas, Chandrasekharan, Kesavadas, Jonsson, Joakim, Nyholm, Tufve, Wiesinger, Florian, Menze, Bjoern
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss f
Externí odkaz:
http://arxiv.org/abs/2203.16288