Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling

Wright, Katherine Landau; Jiang, Yun; Ma, Dan; Noll, Douglas C.; Griswold, Mark A.; Gulani, Vikas; Hernandez‐García, Luis

doi:10.1016/j.mri.2018.03.011

Cited by 41 publications

(64 citation statements)

References 40 publications

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“…A previous report has also demonstrated the use of MRF principles in ASL . The method proposed in the present study is different from that previous report in the following areas.…”

Section: Discussioncontrasting

confidence: 79%

“…Thus, by comparing the experimentally measured time course with the database, one can find the best match, through which multiple MR properties can be obtained in one step. Arterial spin labeling presents an ideal application for the MRF principle for two reasons : (i) ASL signal is inherently affected by multiple parameters. As a matter of fact, ASL researchers are often frustrated by the fact that in the kinetic perfusion model there are too many unknown parameters (eg, CBF, bolus arrival time, tissue T 1 property).…”

Section: Introductionmentioning

confidence: 99%

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Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL

Pan

Mao

Liu

et al. 2016

Magnetic Resonance in Med

154

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Purpose Assessment of brain hemodynamics without exogenous contrast agents is of increasing importance in clinical applications. This study aims to develop an MR perfusion technique that can provide non-contrast and multi-parametric estimation of hemodynamic markers. Methods We devised an Arterial-Spin-Labeling (ASL) method based on the principle of MR Fingerprinting (MRF), referred to as MRF-ASL. By taking advantage of the rich information contained in MRF sequence, up to seven hemodynamic parameters can be estimated concomitantly. Feasibility demonstration, flip angle optimization, comparison with Look-Locker ASL, reproducibility test, sensitivity to hypercapnia challenge, and initial clinical application in an intracranial steno-occlusive process, Moyamoya disease, were performed to evaluate this technique. Results MRF-ASL provided estimation of up to seven parameters, including B1+, tissue T1, cerebral blood flow (CBF), tissue bolus-arrival-time (BAT), pass-through arterial BAT, pass-through blood volume, and pass-through blood travel time. Coefficients-of-variation (CoV) of the estimated parameters ranged from 0.2% to 9.6%. Hypercapnia resulted in an increase in CBF by 57.7%, and a decrease in BAT by 13.7% and 24.8% in tissue and vessels, respectively. Patients with Moyamoya disease showed diminished CBF and lengthened BAT that could not be detected with regular ASL. Conclusion MRF-ASL is a promising technique for non-contrast, multi-parametric perfusion assessment.

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“…A previous report has also demonstrated the use of MRF principles in ASL . The method proposed in the present study is different from that previous report in the following areas.…”

Section: Discussioncontrasting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL

Pan

Mao

Liu

et al. 2016

Magnetic Resonance in Med

154

View full text Add to dashboard Cite

show abstract

“…We validated our methods in silico using a simulated anthropomorphic phantom, and in vivo against a multi‐PLD method as well as two reference ASL‐MRF scans. For easy comparison with “state‐of‐the‐art” MRF ASL, “decreasing affine scan” was very similar to the Perlin schedule used in, but comparatively yielded lower predictions of normalized standard deviation, while “random scan” was similar to the one used in . In most cases, the CRLB predictions were reflected in the performance of various methods in a relative sense.…”

Section: Discussionmentioning

confidence: 99%

“…The imaging parameters were: a single slice placed above the ventricles, single shot spiral readout, nominal We tested our methods on data acquired from six human subjects. For four of these subjects, we also acquired data with two other MRF-ASL scan designs (similar to the ones used in 22,23 and described in more detail later). We compared our optimized scan design to them using similar estimation techniques (same neural network architecture and training target distribution in Table 1).…”

Section: In Vivo Experimentsmentioning

confidence: 99%

Optimizing MRF‐ASL scan design for precise quantification of brain hemodynamics using neural network regression

Lahiri

Fessler

Hernandez‐García

2019

Magnetic Resonance in Med

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Purpose Arterial Spin Labeling (ASL) is a quantitative, non‐invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological parameters simultaneously. In this work, we introduce an optimization scheme to increase the sensitivity of the ASL fingerprint. We also propose a regression based estimation framework for MRF‐ASL. Methods To improve the sensitivity of MRF‐ASL signals to underlying parameters, we optimized ASL labeling durations using the Cramer‐Rao Lower Bound (CRLB). This paper also proposes a neural network regression based estimation framework trained using noisy synthetic signals generated from our ASL signal model. We tested our methods in silico and in vivo, and compared with multiple post labeling delay (multi‐PLD) ASL and unoptimized MRF‐ASL. We present comparisons of estimated maps for the six parameters of our signal model. Results The scan design process facilitated precise estimates of multiple hemodynamic parameters and tissue properties from a single scan, in regions of normal gray and white matter, as well as regions with anomalous perfusion activity in the brain. In particular, there was a 86.7% correlation of perfusion estimates with the ground truth in silico, using our proposed techniques. In vivo, there was roughly a 7 fold improvement in the Coefficient of Variation (CoV) for white matter perfusion, and 2 fold improvement in gray matter perfusion CoV in comparison to a reference Multi PLD method. The regression based estimation approach provided perfusion estimates rapidly, with estimation times of around 1s per map. Conclusions Scan design optimization, coupled with regression‐based estimation is a powerful tool for improving precision in MRF‐ASL

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“…A third potential limitation of ASL‐MRICloud is that only the most common forms of ASL schemes were currently implemented. Future work will consider the inclusion of other promising ASL sequences such as velocity selective ASL, Hadamard time‐encoded ASL, and MR fingerprinting ASL …”

Section: Discussionmentioning

confidence: 99%

ASL‐MRICloud: An online tool for the processing of ASL MRI data

Liu

et al. 2018

NMR in Biomedicine

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Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud‐based tool for ASL data processing, referred to as ASL‐MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL‐MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL‐MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo‐continuous ASL and pulsed ASL, and can process single‐delay and multi‐delay ASL data. The outputs of ASL‐MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel‐wise masks indicating regions with potential hyper‐perfusion and hypo‐perfusion, and an image quality index. The ASL tool is also integrated with a T1‐based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region‐of‐interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

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Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling

Cited by 41 publications

References 40 publications

Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL

Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL

Optimizing MRF‐ASL scan design for precise quantification of brain hemodynamics using neural network regression

ASL‐MRICloud: An online tool for the processing of ASL MRI data

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