Multi-band MR fingerprinting (MRF) ASL imaging using artificial-neural-network trained with high-fidelity experimental data

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Effects of the Training Data Condition on Arterial Spin Labeling Parameter Estimation Using a Simulation-Based Supervised Deep Neural Network

OBJECTIVE

A simulation-based supervised deep neural network (DNN) can accurately estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multidelay arterial spin labeling signals. However, the performance of deep learning depends on the characteristics of the training data set. We aimed to investigate the effects of the ground truth (GT) ranges of CBF and ATT on the performance of the DNN when training data were prepared using arterial spin labeling signal simulation.

METHODS

Deep neural networks were individually trained using 36 patterns of the training data sets. Simulation test data (1,000,000 points), 17 healthy volunteers, and 1 patient with moyamoya disease were included. The simulation test data were used to evaluate accuracy, precision, and noise immunity of the DNN. The best-performing DNN was determined by the normalized mean absolute error (NMAE), normalized root mean squared error (NRMSE), and normalized coefficient of variation over repeated training (CV Net ). Cerebral blood flow and ATT values and their histograms were compared between the GT and predicted values. For the in vivo data, the dependency of the predicted values on the GT ranges was visually evaluated by comparing CBF and ATT maps between the best-performing DNN and the other DNNs. Moreover, using the synthesized noisy images, noise immunity was compared between the best-performing DNN based on the simulation study and a conventional method.

RESULTS

The simulation study showed that a network trained by the GT of CBF and ATT in the ranges of 0 to 120 mL/100 g/min and 0 to 4500 milliseconds, respectively, had the highest performance (NMAE CBF , 0.150; NRMSE CBF , 0.231; CV NET CBF , 0.028; NMAE ATT , 0.158; NRMSE ATT , 0.257; and CV NET ATT , 0.028). Although the predicted CBF and ATT varied with the GT range of the training data sets, the appropriate settings preserved the accuracy, precision, and noise immunity of the DNN. In addition, the same results were observed in in vivo studies.

CONCLUSIONS

The GT ranges to prepare the training data affected the performance of the simulation-based supervised DNNs. The predicted CBF and ATT values depended on the GT range; inappropriate settings degraded the accuracy, whereas appropriate settings of the GT range provided accurate and precise estimates.

Deeply Accelerated Arterial Spin Labeling Perfusion MRI for Measuring Cerebral Blood Flow and Arterial Transit Time

Cerebral blood flow (CBF) indicates both vascular integrity and brain function. Regional CBF can be non-invasively measured with arterial spin labeling (ASL) perfusion MRI. By repeating the same ASL MRI sequence several times, each with a different post-labeling delay (PLD), another important neurovascular index, the arterial transit time (ATT) can be estimated by fitting the acquired ASL signal to a kinetic model. This process however faces two challenges: one is the multiplicatively prolonged scan time, making it impractically for clinical use due to the escalated risk of motions; the other is the reduced signal-to-noise-ratio (SNR) in the long PLD scans due to the T1 decay of the labeled spins. Increasing SNR needs more repetitions which will further increase the total scan time. Currently, there lacks a way to accurately estimate ATT from a parsimonious number of PLDs. In this paper, we proposed a deep learning-based algorithm to reduce the number of PLDs and to accurately estimate ATT and CBF. Two separate deep networks were trained: one is designed to estimate CBF and ATT from ASL data with a single PLD; the other is to estimate CBF and ATT from ASL data with two PLDs. The models were trained and tested using the large Human Connectome Project multiple-PLD ASL MRI. Performance of the DL-based approach was compared to the traditional full dataset-based data fitting approach. Our results showed that ATT and CBF can be reliably estimated using deep networks even with one PLD.

High-Resolution 3D Magnetic Resonance Fingerprinting With a Graph Convolutional Network

Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging framework for rapid and simultaneous quantification of multiple tissue properties. 3D MRF allows higher through-plane resolution, but the acquisition process is slow when whole-brain coverage is needed. Existing methods for acceleration mainly rely on GRAPPA for k-space interpolation in the partition-encoding direction, limiting the acceleration factor to 2 or 3. In this work, we replace GRAPPA with a deep learning approach for accurate tissue quantification with greater acceleration. Specifically, a graph convolution network (GCN) is developed to cater to the non-Cartesian spiral sampling trajectories typical in MRF acquisition. The GCN maintains high quantification accuracy with up to 6-fold acceleration and allows 1mm isotropic resolution whole-brain 3D MRF data to be acquired in 3min and submillimeter 3D MRF (0.8mm) in 5min, greatly improving the feasibility of MRF in clinical settings.

Recent Technical Developments in ASL: A Review of the State of the Art

This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of nonperfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultrahigh field ASL. We aim to provide a high level of understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics outside the scope of this article that are reviewed at length in separate articles include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations.

Timing of Revascularization in Ischemic Moyamoya Disease: Association of Early Versus Delayed Surgery with Perioperative and Long-Term Outcomes

OBJECTIVE

Patients with nmoyamoya disease (MMD) who present primarily with ischemic stroke are known to have greater rates of perioperative strokes as compared with those who present with nonstroke symptoms. The optimal timing for revascularization for these patients remains unclear.

METHODS

From 1994 to 2015, 91 patients with MMD presented with signs and symptoms of an acute ischemic stroke with diffusion restriction correlate on magnetic resonance imaging, and these patients were subdivided into those who underwent early revascularization (<90 days from last stroke), versus those who underwent delayed revascularization (≥90 days after last stroke), based on evidence that most neurological recovery after stroke occurs during the first three months. Perioperative and long-term outcomes were compared between the 2 surgical cohorts.

RESULTS

In total, 27 patients underwent early revascularization, and 64 patients underwent delayed revascularization. Patients who underwent early revascularization had a statistically greater rate of perioperative stroke (P = 0.04) and perioperative mortality (P = 0.03), and overall complication rate (P = 0.049). At last follow-up of 5.2 ± 4.3 years, patients who underwent delayed revascularization had a lower mortality rate (P = 0.01) and a lower overall postoperative stroke incidence (P = 0.002). As a function of time, patients with MMD undergoing delayed revascularization had a statistically higher length of stroke-free survival (P = 0.005).

CONCLUSIONS

Patients with MMD who present with ischemic stroke are more likely to have perioperative strokes, overall perioperative complications, worse long-term mortality rates, and lower rates of stroke-free survival if revascularization surgery occurred within 90 days of last stroke.

Contrast-agent-free state-of-the-art MRI on cerebral small vessel disease-part 1. ASL, IVIM, and CVR

Cerebral small vessel disease (cSVD), a common cause of stroke and dementia, is traditionally considered the small vessel equivalent of large artery occlusion or rupture that leads to cortical and subcortical brain damage. Microvessel endothelial dysfunction can also contribute to it. Brain imaging, including MRI, is useful to show the presence of lesions of several types, although the association between conventional MRI measures and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast-agent-free, state-of-the-art MRI techniques such as arterial spin labeling (ASL), diffusion tensor imaging, functional MRI, and intravoxel incoherent motion (IVIM) applied to cSVD in the existing literature. We performed a review following the PICO Worksheet and Search Strategy, including original papers in English, published between 2000 and 2022. For each MRI method, we extracted information about their contributions, in addition to those established with traditional MRI methods and related information about the origins, pathology, markers, and clinical outcomes in cSVD. This paper presents the first part of the review, which includes 37 studies focusing on ASL, IVIM, and cerebrovascular reactivity (CVR) measures. In general, they have shown that, in addition to white matter hyperintensities, alterations in other neuroimaging parameters such as blood flow and CVR also indicate the presence of cSVD. Such quantitative parameters were also related to cSVD risk factors. Therefore, they are promising, noninvasive tools to explore questions that have not yet been clarified about this clinical condition. However, protocol standardization is essential to increase their clinical use.

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated hemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. For 20-min signals, the 10-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-min signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. The method proposed in this article has potential for detecting and screening moyamoya with high proficiency. Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases.

Simultaneous Hemodynamic and Structural Imaging of Ischemic Stroke With Magnetic Resonance Fingerprinting Arterial Spin Labeling

BACKGROUND

Perfusion and structural imaging play an important role in ischemic stroke. Magnetic resonance fingerprinting (MRF) arterial spin labeling (ASL) is a novel noninvasive method of ASL perfusion that allows simultaneous estimation of cerebral blood flow (CBF), bolus arrival time (BAT), and tissue T₁ map in a single scan of <4 minutes. Here, we evaluated the utility of MRF-ASL in patients with ischemic stroke in terms of detecting hemodynamic and structural damage and predicting neurological deficits and disability.

METHODS

A total of 34 patients were scanned on 3T magnetic resonance imaging. MRF-ASL, standard single-delay pseudo-continuous ASL, T₂-weighted, and diffusion magnetic resonance imaging were performed. Regions of interest of lesion and contralateral normal tissues were manually delineated. CBF (with 2 different compartmental models), BAT, and tissue T₁ parameters were quantified. Cross-sectional linear regression analyses were performed to examine the relationship between MRF-ASL parameters and National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale. Receiver operating characteristic analyses were performed to determine the utility of MRF-ASL in the classification of stroke lesion voxels.

RESULTS

MRF-ASL derived parameters revealed a significant difference between stroke lesion and contralateral normal regions of interest, in that lesion regions manifested a lower CBF_{1-compartment} (P<0.001), lower CBF_{2-compartment} (P<0.001), longer BAT (P=0.002), and longer T₁ (P<0.001) compared with normal regions of interest. NIHSS scores at acute stage revealed a strong association with lesion-normal differences in CBF_{1-compartment,diff} (β=-0.11, P=0.008), CBF_{2-compartment,diff} (β=-0.16, P=0.003), and T_1,diff (β=0.008, P=0.001). MRF-ASL parameters were also predictive of NIHSS score and modified Rankin Scale scale measured at a later stage, although the degree of the associations was weaker. These associations tended to be even stronger when the MRF-ASL data were acquired at the acute/subacute stage. Compared with standard pseudo-continuous ASL, the multiparametric capability of MRF-ASL yielded higher area under curve values in the receiver operating characteristic analyses of stroke voxel classifications.

CONCLUSIONS

MRF-ASL may provide a new approach for quantitative hemodynamic and structural imaging in ischemic stroke.

Automated generation of cerebral blood flow and arterial transit time maps from multiple delay arterial spin-labeled MRI

PURPOSE

Develop and evaluate a deep learning approach to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-labeling delay (PLD) ASL MRI.

METHODS

ASL MRI were acquired with 6 PLDs on a 1.5T or 3T GE system in adults with and without cognitive impairment (N = 99). Voxel-level CBF and ATT maps were quantified by training models with distinct convolutional neural network architectures: (1) convolutional neural network (CNN) and (2) U-Net. Models were trained and compared via 5-fold cross validation. Performance was evaluated using mean absolute error (MAE). Model outputs were trained on and compared against a reference ASL model fitting after data cleaning. Minimally processed ASL data served as another benchmark. Model output uncertainty was estimated using Monte Carlo dropout. The better-performing neural network was subsequently re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules.

RESULTS

Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4 ± 1.4 mL/100 g/min for CBF and 0.22 ± 0.09 s for ATT. A significant association was observed between MAE and Monte Carlo dropout-based uncertainty estimates. Neural network performance remained stable despite systematically reducing the number of input images (i.e., up to 3 missing PLD images). Mean processing time was 10.77 s for the U-Net neural network compared to 10 min 41 s for the reference pipeline.

CONCLUSION

It is feasible to generate CBF and ATT maps from 1.5T and 3T multi-PLD ASL MRI with a fast deep learning image-generation approach.

Macrohistory of Moyamoya Disease Analyzed Using Artificial Intelligence

INTRODUCTION

Moyamoya disease is characterized by progressive stenotic changes in the terminal segment of the internal carotid artery and the development of abnormal vascular networks called moyamoya vessels. The objective of this review was to provide a holistic view of the epidemiology, etiology, clinical findings, treatment, and pathogenesis of moyamoya disease. A literature search was performed in PubMed using the term "moyamoya disease," for articles published until 2021.

RESULTS

Artificial intelligence (AI) clustering was used to classify the articles into 5 clusters: (1) pathophysiology (23.5%); (2) clinical background (37.3%); (3) imaging (13.2%); (4) treatment (17.3%); and (5) genetics (8.7%). Many articles in the "clinical background" cluster were published from the 1970s. However, in the "treatment" and "genetics" clusters, the articles were published from the 2010s through 2021. In 2011, it was confirmed that a gene called Ringin protein 213 (RNF213) is a susceptibility gene for moyamoya disease. Since then, tremendous progress in genomic, transcriptomic, and epigenetic profiling (e.g., methylation profiling) has resulted in new concepts for classifying moyamoya disease. Our literature survey revealed that the pathogenesis involves aberrations of multiple signaling pathways through genetic mutations and altered gene expression.

CONCLUSION

We analyzed the content vectors in abstracts using AI, and reviewed the pathophysiology, clinical background, radiological features, treatments, and genetic peculiarity of moyamoya disease.

Non-contrast hemodynamic imaging of Moyamoya disease with MR fingerprinting ASL: A feasibility study

PURPOSE

MR Fingerprinting (MRF) Arterial Spin Labeling (ASL) is a non-contrast technique to estimate multiple brain hemodynamic and structural parameters in a single scan. The purpose of this study is to examine the feasibility and initial utility of MRF-ASL in Moyamoya disease.

METHODS

MRF-ASL, conventional single-delay ASL, Time-of-flight (TOF) MR angiography, and contrast-based dynamic-susceptibility-contrast (DSC) MRI were prospectively collected from a group of Moyamoya patients in North America (N = 21, 4 men and 17 women). Sixteen healthy subjects (7 men and 9 women) also underwent an MRF-ASL scan. Cerebral blood flow (CBF), bolus arrival time (BAT), and tissue T1 were compared between Moyamoya patients and healthy controls. Perfusion parameters from MRF-ASL were compared to those from other MRI sequences. Multi-linear regression was used for comparisons of parameter values between Moyamoya and control groups. Linear mixed-effects models was used when comparing MRF-ASL to PCASL and DSC parameters. Spearman's Rank Correlation Coefficient was calculated when comparing MRF-ASL to and MRA grades. A P value of 0.05 or less was considered significant.

RESULTS

BAT in stenotic internal carotid artery (ICA) territories was prolonged (P < 0.001) in Moyamoya patients, when compared with healthy controls. CBF in stenotic ICA territories of Moyamoya patients was not different from CBF in healthy controls; but in the PCA territories, CBF in Moyamoya patients was higher (P < 0.01) than controls. Quantitative T1 values in the stenotic ICA territories was longer (P < 0.05) than that in controls. Hemodynamic parameters estimated from MRF-ASL were significantly correlated with single-delay ASL and DSC. Longer BAT was associated with more severe intracranial artery stenosis in ICA.

CONCLUSIONS

MRF-ASL is a promising technique to assess perfusion and structural abnormalities in Moyamoya patients.

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

PURPOSE

Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting.

METHODS

We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data.

RESULTS

When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations.

CONCLUSION

This work highlights that estimation of model parameters using supervised ML depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.

Progression in Moyamoya Disease: Clinical Feature, Neuroimaging Evaluation and Treatment

Moyamoya disease (MMD) is a chronic cerebrovascular disease characterized by progressive stenosis of the arteries of the circle of Willis, with the formation of collateral vascular network at the base of the brain. Its clinical manifestations are complicated. Numerous studies have attempted to clarify the clinical features of MMD, including its epidemiology, genetic characteristics, and pathophysiology. With the development of neuroimaging techniques, various neuroimaging modalities with different advantages have deepened the understanding of MMD in terms of structural, functional, spatial, and temporal dimensions. At present, the main treatment for MMD focuses on neurological protection, cerebral blood flow reconstruction, and neurological rehabilitation, such as pharmacological treatment, surgical revascularization, and cognitive rehabilitation. In this review, we discuss recent progress in understanding the clinical features, in the neuroimaging evaluation and treatment of MMD.