Erratum to: Fast quantitative parameter maps without fitting: Integration yields accurate mono‐exponential signal decay rates (Magn Reson Med 2018;79:2978–2985)
Sign Up to like & getrecommendations! Published in 2019 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.27405
Abstract: It should be noted that the integral of Eq. [2] was approximated by the Riemann sum for a simplified presentation of Eqs. [10] ‐ [12]. Other methods, such as the midpoint integral interpolation, approximate the… read more here.
Keywords: maps without; quantitative parameter; erratum fast; without fitting ... See more keywords
Efficient phase‐cycling strategy for high‐resolution 3D gradient‐echo quantitative parameter mapping
Sign Up to like & getrecommendations! Published in 2022 at "NMR in Biomedicine"
DOI: 10.1002/nbm.4700
Abstract: Magnetization‐prepared (MP) gradient‐echo (GRE) sequences suffer from signal contaminations from T1 recovery during the readout train, which can be eliminated by paired RF phase cycling (PC) at the cost of doubling the scan time. The… read more here.
Keywords: quantitative parameter; gradient echo; resolution; strategy ... See more keywords
Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series
Sign Up to like & getrecommendations! Published in 2017 at "Studies in health technology and informatics"
DOI: 10.3233/978-1-61499-808-2-202
Abstract: The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady… read more here.
Keywords: magnetic resonance; quantitative parameter; deep learning; parameter ... See more keywords