Intravascular signal suppression and microvascular signal mapping using delays alternating with nutation for tailored excitation (DANTE) pulse for arterial spin labeling perfusion imaging

Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks

PURPOSE

Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNNSup), physics-informed unsupervised DNN (DNNUns), and the conventional lookup table method (LUT) using simulation and in vivo data.

METHODS

MP-ASL was performed twice during resting state and once during the breath-holding task. First, the accuracy and noise immunity were evaluated in the first resting state. Second, CBF and CBVa values were statistically compared between the first resting state and the breath-holding task using the Wilcoxon signed-rank test and Cliff's delta. Finally, reproducibility of the two resting states was assessed.

RESULTS

Simulation and first resting-state analyses demonstrated that DNNSup had higher accuracy, noise immunity, and a six-fold faster computation time than LUT. Furthermore, all methods detected task-induced CBF and CBVa elevations, with the effect size being larger with the DNNSup (CBF, p = 0.055, Δ = 0.286; CBVa, p = 0.008, Δ = 0.964) and DNNUns (CBF, p = 0.039, Δ = 0.286; CBVa, p = 0.008, Δ = 1.000) than that with LUT (CBF, p = 0.109, Δ = 0.214; CBVa, p = 0.008, Δ = 0.929). Moreover, all the methods exhibited comparable and satisfactory reproducibility.

CONCLUSION

DNNSup outperforms DNNUns and LUT with respect to estimation performance and computation time.

Direct angiographic comparison of different velocity-selective saturation, inversion, and DANTE labeling modules on cerebral arteries

PURPOSE

This study evaluated the velocity-selective (VS) MRA with different VS labeling modules, including double refocused hyperbolic tangent, eight-segment B1-insensitive rotation, delay alternating with nutation for tailored excitation, Fourier transform-based VS saturation, and Fourier transform-based inversion.

METHODS

These five VS labeling modules were evaluated first through Bloch simulations, and then using VSMRA directly on various cerebral arteries of healthy subjects. The relative signal ratios from arterial ROIs and surrounding tissues as well as relative arteria-tissue contrast ratios of different methods were compared.

RESULTS

Double refocused hyperbolic tangent and eight-segment B1-insensitive rotation showed very similar labeling effects. Delay alternating with nutation for tailored excitation yielded high arterial signal but with residual tissue signal due to the spatial banding effect. Fourier transform-based VS saturation with half the time of other techniques serves as an efficient nonsubtractive VSMRA method, but the remaining tissue signal still obscured some small distal arteries that were delineated by other subtraction-based VSMRA, allowing more complete cancelation of static tissue. Fourier transform-based inversion produced the highest arterial signal in VSMRA with minimal tissue background.

CONCLUSION

This is the first study that angiographically compared five different VS labeling modules. Their labeling characteristics on arteries and tissue and implications for VSMRA and VS arterial spin labeling are discussed.

Estimation of Cerebral Blood Flow and Arterial Transit Time From Multi-Delay Arterial Spin Labeling MRI Using a Simulation-Based Supervised Deep Neural Network

BACKGROUND

An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time (ATT) when using arterial spin labeling (ASL). Deep neural network (DNN)-based parameter estimation can solve these problems.

PURPOSE

To reduce the effects of Rician noise on ASL parameter estimation and compute unbiased CBF and ATT using simulation-based supervised DNNs.

STUDY TYPE

Retrospective.

POPULATION

One million simulation test data points, 17 healthy volunteers (five women and 12 men, 33.2 ± 14.6 years of age), and one patient with moyamoya disease.

FIELD STRENGTH/SEQUENCE

3.0 T/Hadamard-encoded pseudo-continuous ASL with a three-dimensional fast spin-echo stack of spirals.

ASSESSMENT

Performances of DNN and conventional methods were compared. For test data, the normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) between the ground truth and predicted values were evaluated. For in vivo data, baseline CBF and ATT and their relative changes with respect to SNR using artificial noise-added images were assessed.

STATISTICAL TESTS

One-way analysis of variance with post-hoc Tukey's multiple comparison test, paired t-test, and the Bland-Altman graphical analysis. Statistical significance was defined as P < 0.05.

RESULTS

For both CBF and ATT, NMAE and NRMSE were lower with DNN than with the conventional method. The baseline values were significantly smaller with DNN than with the conventional method (CBF in gray matter, 66 ± 10 vs. 71 ± 12 mL/100 g/min; white matter, 45 ± 6 vs. 46 ± 7 mL/100 g/min; ATT in gray matter, 1424 ± 201 vs. 1471 ± 154 msec). CBF and ATT increased with decreasing SNR; however, their change rates were smaller with DNN than were those with the conventional method. Higher CBF in the prolonged ATT region and clearer contrast in ATT were identified by DNN in a clinical case.

DATA CONCLUSION

DNN outperformed the conventional method in terms of accuracy, precision, and noise immunity.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 1.

Separating spin compartments in arterial spin labeling using delays alternating with nutation for tailored excitation (DANTE) pulse: A validation study using T2 -relaxometry and application to arterial cerebral blood volume imaging

PURPOSE

To clarify the type of spin compartment in arterial spin labeling (ASL) that is eliminated by delays alternating with nutation for tailored excitation (DANTE) pulse using T₂ -relaxometry, and to demonstrate the feasibility of arterial cerebral blood volume (CBV_a ) imaging using DANTE-ASL in combination with a simplified two-compartment model.

METHOD

The DANTE and T₂ -preparation modules were combined into a single ASL sequence. T₂ values under the application of DANTE were determined to evaluate changes in T₂ , along with the post-labeling delay (PLD) and the relationship between transit time without DANTE (TT_noVS ) and T₂ . The reference tissue T₂ (T_{2_ref} ) was also obtained. Subsequently, the DANTE module was embedded into the Hadamard-encoded ASL. Cerebral blood flow (CBF) and CBV_a were computed using two Hadamard-encoding datasets (with and without DANTE) in a rest and breath-holding (BH) task.

RESULTS

While T₂ without DANTE (T_{2_noVS} ) decreased as the PLD increased, T₂ with DANTE (T_{2_DANTE} ) was equivalent to T_{2_ref} and did not change with the PLD. Although there was a significant positive correlation between TT_noVS and T_{2_noVS} with short PLD, T_{2_DANTE} was not correlated with TT_noVS nor PLD. Baseline CBV_a values obtained at rest were 0.64 ± 0.12, 0.64 ± 0.11, and 0.58 ± 0.15 mL/100 g for anterior, middle, and posterior cerebral arteries, respectively. Significant CBF and CBV_a elevations were observed in the BH task.

CONCLUSION

Microvascular compartment signals were eliminated from the total ASL signals by DANTE. CBV_a can be measured using Hadamard-encoded DANTE-ASL in combination with a simplified two-compartment model.