Improved estimation of <scp>MR</scp> relaxation parameters using complex‐valued data

Rudrapatna, Umesh; Bakker, Chris J.G.; Viergever, Max A.; Toorn, Annette van der; Dijkhuizen, Rick M.

doi:10.1002/mrm.26088

Cited by 10 publications

(11 citation statements)

References 37 publications

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“…Given multiple TSL acquisitions, the following monoexponential signal relaxation model using complex‐valued data was applied for curve fitting 40 :

S ((), italicTSL goodbreak\pm) goodbreak= goodbreak\pm S_{A} e^{- \frac{TSL}{T_{1 ρ}}} goodbreak+ S_{B},

where S ( TSL + ) and S ( TSL –) are the signal from the TSL acquisitions with positive and negative PC, respectively, and S B is the contaminating term originated from T 1 recovery. For each voxel, S , S A , and S B are complex numbers.…”

Section: Methodsmentioning

confidence: 99%

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Efficient phase‐cycling strategy for high‐resolution 3D gradient‐echo quantitative parameter mapping

Peng

Kim

et al. 2022

NMR in Biomedicine

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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 objective of this study was to develop and validate a novel unpaired PC strategy to eliminate the time penalty for high‐resolution quantitative parameter mapping in 3D MP‐GRE sequences. Based on the observation that the contaminating T1 recovery signal along the GRE readout train is independent of magnetization preparation, its impact can be eliminated using a novel curve‐fitting approach with complex‐valued data without needing paired PC acquisitions. Four new unpaired PC schemes were compared with two traditional paired PC schemes in both phantom and in vivo human knee studies at 3 T using a MP angle‐modulated partitioned k‐space spoiled gradient‐echo snapshots (MAPSS) T1ρ mapping sequence. In the phantom study, all methods resulted in consistent T1ρ measurements (∆T1ρ < 0.5%) at the center slice when B0/B1 values were uniform. Results were not consistent when off‐center slices with nonideal B0/B1 were included. Two unpaired PC schemes had comparable or significantly improved quantitative accuracy and scan‐rescan reproducibility compared with the paired PC schemes. There was no significant T1ρ quantitative variability increase or spatial fidelity loss using the new unpaired PC schemes. Unpaired PC schemes also had different T1ρ spectral responses at different B0 frequency offsets, which can potentially be exploited to reduce sensitivity to B0 field inhomogeneities. The human knee study results were consistent with the phantom study findings. In conclusion, an unpaired PC strategy potentially allows more accurate quantitative parameter mapping with halved scan time compared with the paired PC approach to eliminate signal contaminations from T1 recovery. It therefore offers additional flexibility in SNR optimization, spatial resolution improvement, and choice of imaging sampling points to obtain more accurate quantitative parameter mapping.

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“…Given multiple TSL acquisitions, the following monoexponential signal relaxation model using complex‐valued data was applied for curve fitting 40 :

S ((), italicTSL goodbreak\pm) goodbreak= goodbreak\pm S_{A} e^{- \frac{TSL}{T_{1 ρ}}} goodbreak+ S_{B},

Section: Methodsmentioning

confidence: 99%

“…Given multiple TSL acquisitions, the following monoexponential signal relaxation model using complex-valued data was applied for curve fitting 40 :…”

Section: Quantitative Map Generationmentioning

confidence: 99%

Efficient phase‐cycling strategy for high‐resolution 3D gradient‐echo quantitative parameter mapping

Peng

Kim

et al. 2022

NMR in Biomedicine

View full text Add to dashboard Cite

show abstract

“…Usually, the curve fitting is done with magnitude-only data, here we used the complex-valued data. Magnitude-only fitting must account for Rician distributed noise and also rely on data weighting and noise thresholding to improve performance 31 . Complex-valued estimators are statistically efficient 31 and do not require these additional steps.…”

Section: Methodsmentioning

confidence: 99%

“…Magnitude-only fitting must account for Rician distributed noise and also rely on data weighting and noise thresholding to improve performance 31 . Complex-valued estimators are statistically efficient 31 and do not require these additional steps. Supporting information Fig.…”

Section: Methodsmentioning

confidence: 99%

Optimization of spin-lock times for T1ρ mapping of human knee cartilage with bi- and stretched-exponential models

Moura

Menon

Zibetti

et al. 2022

Sci Rep

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Two optimization criteria based on Cramér-Rao Bounds are compared between each other and with non-optimized schedules for T1ρ mapping using synthetic data, model phantoms, and in-vivo knee cartilage. The curve fitting is done on complex-valued data using an iterative Nonlinear Least Squares (NLS) approach. The optimization criteria are compared based on the Mean Normalized Absolute Error (MNAE) and variance of the estimated parameters. The optimized spin-lock time (TSL) schedules provided improved results over the non-optimized schedules for all cases that were tested. The simulations showed that optimized schedules can reach the same precision and reduce acquisition times by 16.5 min (42%) for the bi-exponential model, and 6.6 min (22%) for the stretched-exponential model. In the model phantoms experiments, the bi-exponential MNAE was reduced from 0.47 to 0.36, while stretched-exponential from 0.28 to 0.20 with a Modified Cramér-Rao Lower Bound (MCRLB). In-vivo knee cartilage experiments show a reduction in bi-exponential MNAE from 0.47 to 0.31, and stretched-exponential from 0.047 to 0.039. The optimized spin-lock times criteria reduced the error in all cases, being more significant in the synthetic data and model phantoms. The optimized TSL schedules can be either used to improve the quality of parameter maps or reduce scan time.

show abstract

“…Usually, the curve fitting is done with magnitude-only data, here we used the complex-valued data. Magnitude-only fitting must account for Rician distributed noise and also rely on data weighting and noise thresholding to improve performance 28 . Complex-valued estimators are statistically efficient 28 and do not require these additional steps.…”

Section: Curve Fitting Algorithmmentioning

confidence: 99%

Optimization of Spin-Lock Times for T1ρ Mapping of Human Knee Cartilage with Bi- and Stretched-exponential Models

Moura

Menon

Zibetti

et al. 2022

Preprint

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Two optimization criteria based on Cramér-Rao Bounds are compared between each other and with non-optimized schedules for T1ρ mapping using synthetic data, model phantoms, and in-vivo knee cartilage. The curve fitting is done on complex-valued data using an iterative Nonlinear Least Squares (NLS) approach. The optimization criteria are compared based on the Mean Normalized Absolute Error (MNAE) and variance of the estimated parameters. The optimized spin-lock time (TSL) schedules provided improved results over the non-optimized schedules for all cases that were tested. The simulations showed that optimized schedules can reach the same precision and reduce acquisition times by 16.5 minutes (42%) for the bi-exponential model, and 6.6 minutes (22%) for the stretched-exponential model. In the model phantoms experiments, the bi-exponential MNAE was reduced from 0.47 to 0.36, while stretched-exponential from 0.28 to 0.20 with MCRLB. In-vivo knee cartilage experiments show a reduction in bi-exponential MNAE from 0.47 to 0.31, and stretched-exponential from 0.047 to 0.039. The optimized spin-lock times criteria reduced the error in all cases, being more significant in the synthetic data and model phantoms. The optimized TSL schedules can be either used to improve the quality of parameter maps or reduce scan time.

show abstract

Improved estimation of MR relaxation parameters using complex‐valued data

Cited by 10 publications

References 37 publications

Efficient phase‐cycling strategy for high‐resolution 3D gradient‐echo quantitative parameter mapping

Efficient phase‐cycling strategy for high‐resolution 3D gradient‐echo quantitative parameter mapping

Optimization of spin-lock times for T1ρ mapping of human knee cartilage with bi- and stretched-exponential models

Optimization of Spin-Lock Times for T1ρ Mapping of Human Knee Cartilage with Bi- and Stretched-exponential Models

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