WITHDRAWN: A canonical transform for strengthening the local <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e21" altimg="si1.svg"><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msup></mml:math>-type universal approximation property

Kratsios, Anastasis; Zamanlooy, Behnoosh

doi:10.1016/j.jat.2022.105716

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Deep learning algorithms are essentially complex nonlinear functions which can represent nearly any underlying distribution provided enough variable input is provided 34 35 . This essentially implies that most, if not all, conventional processing steps that are applied to the raw perfusion data can be learned by a neural network.…”

Section: Quantitative Analysismentioning

confidence: 99%

Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning

Rotkopf

Zhang

Tavakoli

et al. 2022

Rofo

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Background Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years. Despite promising results from novel methods, their relatively minimal improvement compared to established methods did not generally warrant significant changes to clinical perfusion processing. Results and Conclusion Machine learning in general and deep learning in particular, which are currently revolutionizing computer-aided diagnosis, may carry the potential to change this situation and truly capture the potential of perfusion imaging. Recent advances in the training of recurrent neural networks make it possible to predict and classify time series data with high accuracy. Combining physics-based tissue models and deep learning, using either physics-informed neural networks or universal differential equations, simplifies the training process and increases the interpretability of the resulting models. Due to their versatility, these methods will potentially be useful in bridging the gap between microvascular architecture and perfusion parameters, akin to MR fingerprinting in structural MR imaging. Still, further research is urgently needed before these methods may be used in clinical practice. Key Points: Citation Format

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Section: Quantitative Analysismentioning

confidence: 99%