Methods of measuring spin‐lattice (T1) relaxation times: An annotated bibliography

Kingsley, Peter B.

doi:10.1002/(sici)1099-0534(1999)11:4<243::aid-cmr5>3.3.co;2-3

Cited by 56 publications

(71 citation statements)

References 18 publications

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“…MFIR is a variation on the standard inversion recovery experiment, in which images are collected at different inversion times (TI), where the repetition time (TR) is fixed at a value that is shorter than required for full recovery of longitudinal magnetization. This method realizes a portion of the time savings of fast inversion recovery (FIR), for which both TI and TR are varied in concert in order to fix the pre-delay, but, unlike FIR, can be modeled even in the presence of imperfect flip angles by the following three parameter equation,

S (TI) = A + B exp (- {normalR}_{1} \cdot TI),

where S(TI) is the signal intensity for inversion time TI and R 1 is the longitudinal relaxation rate constant, the inverse of the time constant, T 1 [7,8]. …”

Section: Methodsmentioning

confidence: 99%

“…To ensure images at each TI had approximately the same SNR, variable numbers of transients (NT) were averaged for each image, corresponding, respectively, to the different TI values: NT = 8, 16, 32, and 8. To assure that no steady-state transverse magnetization was present during the MFIR experiment, the shortest value of Δ = TR−TI was set equal to three times the transverse relaxation time constant (T 2 ) of the bulk (non-flowing) solution: TR = 13.5 s for 0 mM gadodiamide experiments and 0.8 s for 1 mM gadodiamide experiments [8]. (Note: T 2 was measured independently for 0 mM and 1 mM gadodiamide solutions via spectroscopic methods with a standard CPMG sequence.)…”

Section: Methodsmentioning

confidence: 99%

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Diffusion effects on longitudinal relaxation in poorly mixed compartments

Anderson¹,

Ye²,

Ackerman³

et al. 2011

Journal of Magnetic Resonance

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Diffusion of spins between physical or virtual, communicating compartments having different states of longitudinal magnetization leads to diffusion-driven longitudinal relaxation. Herein, in two model systems, the effects of diffusion-driven longitudinal relaxation are explored experimentally and analyzed quantitatively. In the first case, longitudinal relaxation in a single slice of a water phantom is monitored spectroscopically as a function of slice thickness. In the second case, mimicking vascular flow/diffusion effects, longitudinal relaxation is monitored in a two-compartment, semi-permeable fiber phantom. In both cases, apparent longitudinal relaxation, though clearly multi-exponential, is well-modeled as bi-exponential.

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S (TI) = A + B exp (- {normalR}_{1} \cdot TI),

where S(TI) is the signal intensity for inversion time TI and R 1 is the longitudinal relaxation rate constant, the inverse of the time constant, T 1 [7,8]. …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Diffusion effects on longitudinal relaxation in poorly mixed compartments

Anderson¹,

Ye²,

Ackerman³

et al. 2011

Journal of Magnetic Resonance

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“…Furthermore, it is often desirable to compare T 1 measurements across subjects and across scanners. While there are many techniques for T 1 mapping (1), there is also a wide range of reported T 1 values in tissue (2), an inconsistency that raises the issue of reproducibility and standardization.…”

Section: Introductionmentioning

confidence: 99%

“…The method is known as inversion recovery T 1 mapping (IR), and it consists of inverting the longitudinal magnetization M z and sampling the MR signal as it recovers with an exponential recovery time T 1 . Different models have been used for T 1 mapping (1). With all models, the fit is traditionally performed using a Levenberg-Marquardt (LM) algorithm (5).…”

Section: Introductionmentioning

confidence: 99%

A robust methodology for in vivo T₁ mapping

Barral¹,

Gudmundson²,

Stikov³

et al. 2010

Magnetic Resonance in Med

199

191

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In this article, a robust methodology for in vivo T 1 mapping is presented. The approach combines a gold standard scanning procedure with a novel fitting procedure. Fitting complex data to a five-parameter model ensures accuracy and precision of the T 1 estimation. A reduced-dimension nonlinear least squares method is proposed. This method turns the complicated multiparameter minimization into a straightforward one-dimensional search. As the range of possible T 1 values is known, a global grid search can be used, ensuring that a global optimal solution is found. When only magnitude data are available, the algorithm is adapted to concurrently restore polarity. The performance of the new algorithm is demonstrated in simulations and phantom experiments. The new algorithm is as accurate and precise as the conventionally used Levenberg-Marquardt algorithm but much faster. This gain in speed makes the use of the fiveparameter model viable. In addition, the new algorithm does not require initialization of the search parameters. Finally, the methodology is applied in vivo to conventional brain imaging and to skin imaging. INTRODUCTIONThe T 1 parameter is an intrinsic MR property of tissue, and mapping T 1 in vivo is useful in several ways. First, knowledge of T 1 helps in optimizing the MR protocol, e.g., by setting the Ernst angle appropriately. In addition, it provides a tool to evaluate contrast uptake, blood perfusion and volume, as well as disease progression during a longitudinal study. Furthermore, it is often desirable to compare T 1 measurements across subjects and across scanners. Although there are many techniques for T 1 mapping (1), there is also a wide range of reported T 1 values in tissue (2), an inconsistency that raises the issue of reproducibility and standardization. The gold standard for T 1 mapping was developed from NMR experiments performed in the late 1940s (3,4). The method is known as inversion recovery T 1 mapping (IR), and it consists of inverting the longitudinal magnetization M z and sampling the MR signal as it recovers with an exponential recovery time T 1 . Different models have been used for T 1 mapping (1). With all models, the fit is traditionally performed using a Levenberg-Marquardt (LM) algorithm (5). Many methods have been proposed to speed up the scanning and fitting procedures, at the expense of accuracy and precision.In this article, we first justify the need for a fourparameter model when accurate T 1 mapping is desired. We show that this model is equivalent in terms of accuracy and precision of the T 1 estimation to a more general five-parameter model. We propose to solve a nonlinear least squares (NLS) problem to fit complex data to the five-parameter model. The problem is reduced to a search over one dimension, which substantially decreases the computational complexity. When only magnitude data are available, the algorithm is adapted to concurrently restore polarity. We perform Monte-Carlo simulations to compare the proposed algorithms to the conventional LM algorithm and...

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“…These issues motivate efforts to generate pre-contrast T 1 maps for DCE analyses. T 1 maps can be produced by a variety of methods (15). Many of the classical methods, such as multiple inversion or multiple repetition time acquisitions, typically require lengthy acquisition times that pose a problem in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses

Fennessy

Fedorov

Gupta

et al. 2012

Magnetic Resonance Imaging

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There are many challenges in developing robust imaging biomarkers that can be reliably applied in a clinical trial setting. In the case of Dynamic Contrast Enhanced (DCE) MRI, one such challenge is to obtain accurate pre-contrast T1 maps for subsequent use in two-compartment pharmacokinetic models commonly used to fit the MR enhancement time courses. In the prostate, a convenient and common approach for this task has been to use the same 3D SPGR sequence used to collect the DCE data, but with variable flip angles (VFA’s) to collect data suitable for T1 mapping prior to contrast injection. However, inhomogeneous radiofrequency conditions within the prostate have been found to adversely affect the accuracy of this technique. Herein we demonstrate the sensitivity of DCE pharmacokinetic parameters to pre-contrast T1 values and examine methods to improve the accuracy of T1 mapping with flip angle corrected VFA SPGR methods, comparing T1 maps from such methods with reference T1 maps generated with saturation recovery experiments performed with fast spin echo (FSE) sequences.

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Methods of measuring spin‐lattice (T1) relaxation times: An annotated bibliography

Cited by 56 publications

References 18 publications

Diffusion effects on longitudinal relaxation in poorly mixed compartments

Diffusion effects on longitudinal relaxation in poorly mixed compartments

A robust methodology for in vivo T₁ mapping

Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses

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Methods of measuring spin‐lattice (T1) relaxation times: An annotated bibliography

Cited by 56 publications

References 18 publications

Diffusion effects on longitudinal relaxation in poorly mixed compartments

Diffusion effects on longitudinal relaxation in poorly mixed compartments

A robust methodology for in vivo T1 mapping

Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses

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A robust methodology for in vivo T₁ mapping