Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI

Neurovascular coupling dysfunction associated with cognitive impairment in presbycusis

The neuropathological mechanism underlying presbycusis remains unclear. This study aimed to illustrate the mechanism of neurovascular coupling associated with cognitive impairment in patients with presbycusis. We assessed the coupling of cerebral blood perfusion with spontaneous neuronal activity by calculating the correlation coefficients between cerebral blood flow and blood oxygen level-dependent-derived quantitative maps (amplitude of low-frequency fluctuation, fractional amplitude of low-frequency fluctuation, regional homogeneity, degree centrality). Four neurovascular coupling metrics (cerebral blood flow-amplitude of low-frequency fluctuation, cerebral blood flow-fractional amplitude of low-frequency fluctuation, cerebral blood flow-regional homogeneity and cerebral blood flow-degree centrality) were compared at the global and regional levels between the presbycusis group and the healthy control group, and the intrinsic association between the altered neurovascular coupling metrics and the neuropsychological scale was further analysed in the presbycusis group. At the global level, neurovascular coupling was significantly lower in the presbycusis group than in the control group and partially related to cognitive level. At the regional level, neurovascular biomarkers were significantly elevated in three brain regions and significantly decreased in one brain region, all of which involved the Papez circuit. Regional neurovascular coupling provides more information than global neurovascular coupling, and neurovascular coupling dysfunction within the Papez circuit has been shown to reveal the causes of poor cognitive and emotional responses in age-related hearing loss patients.

Simultaneous arterial spin labeling functional MRI and fluorodeoxyglucose PET in mild chronic traumatic brain injury

BACKGROUND AND PURPOSE

To determine the effect of mild chronic traumatic brain injury (cTBI) on cerebral blood flow and metabolism.

METHODS

62 cTBI and 40 healthy controls (HCs) with no prior history of cTBI underwent both pulsed arterial spin labeling functional magnetic resonance imaging (PASL-fMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) scanning via a Siemens mMR (simultaneous PET/MRI) scanner. 30 participants also took part in a series of neuropsychological clinical measures (NCMs). Images were processed using statistical parametric mapping software relevant to each modality to generate relative cerebral blood flow (rCBF) and glucose metabolic standardized uptake value ratio (gSUVR) grey matter maps. A voxel-wise two-sample T-test and two-tailed gaussian random field correction for multiple comparisons was performed.

RESULTS

cTBI patients showed a significant increase in rCBF and gSUVR in the right thalamus as well as a decrease in bilateral occipital lobes and calcarine sulci. An inverse relationship between rCBF and gSUVR was found in the left frontal lobe, the left precuneus and regions in the right temporal lobe. Within those regions rCBF values correlated with 9 distinct NCMs and gSUVR with 3.

CONCLUSION

Simultaneous PASL-fMRI and FDG-PET can identify functional changes in a mild cTBI population. Within this population FDG-PET identified more regions of functional disturbance than ASL fMRI and NCMs are shown to correlate with rCBF and glucose metabolism (gSUVR) in various brain regions. As a result, both imaging modalities contribute to understanding the underlying pathophysiology and clinical course of mild chronic traumatic brain injury.

Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps

BACKGROUND

Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality.

PURPOSE

To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps.

STUDY TYPE

Retrospective.

POPULATION

Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury.

FIELD STRENGTH/SEQUENCE

Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts.

ASSESSMENT

The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric.

STATISTICAL TESTS

Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant.

RESULTS

The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72).

DATA CONCLUSION

Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

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.

Hypo- and hyper-perfusion in MCI and AD identified by different ASL MRI sequences

Arterial spin labeling (ASL) perfusion MRI has been increasingly used in Alzheimer's Disease (AD) research. However, ASL MRI sequences differ greatly in terms of arterial blood signal preparations and data acquisition strategies, both leading to a large difference of signal-to-noise ratio (SNR). It is of great translational importance to compare the several widely used ASL MRI sequences regarding sensitivity of ASL measured cerebral blood flow (CBF) for detecting the between-group difference across the AD continuum. To this end, this study compared three ASL MRI sequences in AD research, including the 2D Pulsed ASL (PASL), 3D Background Suppressed (BS) PASL, and 3D BS Pseudo-Continuous ASL (PCASL). We used data from 100 healthy and cognitively normal elderly control (NC) subjects, 75 patients with mild cognitive impairment (MCI), and 57 Alzheimer's disease (AD) subjects from the AD neuroimaging initiative (ADNI). Both cross-sectional perfusion difference and perfusion versus clinical assessment correlations were examined. The major findings included: 3D PCASL sequence identified stronger patient versus control CBF/rCBF differences than 2D PASL and 3D PASL; MCI showed reduced CBF and CBF redistribution; CBF in orbito-frontal cortex presents a new U-shape change pattern from normal aging to MCI and to AD; 3D PCASL identified a negative rCBF to memory correlation while 2D PASL showed a positive correlation.

Arterial Spin Labeling Perfusion MRI Signal Processing Through Traditional Methods and Machine Learning

Arterial spin labeling (ASL) perfusion MRI is a non-invasive technique for quantifying and mapping cerebral blood flow (CBF). Depending on the tissue signal change after magnetically labeled arterial blood enters the brain tissue, ASL MRI signal can be affected by several factors, including the volume of arrived arterial blood, signal decay of labeled blood, physiological fluctuations of the brain and CBF, and head motion, etc. Some of them can be controlled using sophisticated state-of-art ASL MRI sequences, but the others can only be resolved with post-processing strategies. Over the decades, various post-processing methods have been proposed in the literature, and many post processing software packages have been released. This self-contained review provides a brief introduction to ASL MRI, recommendations for typical ASL MRI data acquisition protocols, an overview of the ASL data processing pipeline, and an introduction to typical methods used at each step in the pipeline. Although the main focus is on traditional heuristic model-based methods, a brief introduction to recent machine learning-based approaches is provided too.

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.

Characterizing the Dose Response of Hyperoxia with Brain Perfusion

BACKGROUND: Tactical aviators require administration of enhanced inspired oxygen concentrations (hyperoxia) to reduce risk of hypobaric hypoxia and decompression injuries. Hyperoxia is not without consequence; it reduces cerebral perfusion (CBF). Characterizing the relationship between FIO2 and CBF is necessary to establish FIO2 levels that do not reduce CBF yet are sufficient to mitigate risk of in-flight physiological stressors. To achieve that goal, this study’s objective was to determine whether a dose-response relationship exists between FIO2 and CBF and, if so, the FIO2 at which CBF significantly declines.METHODS: Healthy male and female subjects (N = 26) were randomized to receive either low dose FIO2 of 30%, 40%, 50%, and 100% (Arm 1) or high dose FIO2 of 60%, 70%, 80%, and 100% (Arm 2), followed by a return to 21% for both groups. Subjects were placed within a 3-Tesla MRI scanner equipped with pseudocontinuous arterial spin labeling software (pCASL) to measure CBF. Baseline CBF measurements were obtained during exposure to 21% FIO2, with subsequent CBF measurements obtained at each predetermined FIO2 level.RESULTS: Baseline CBF did not differ between subjects in Arm 1 and Arm 2. Low dose FIO2 ≤ 50% did not affect CBF. In contrast, high dose FIO2 ≥ 60% significantly reduced CBF. Exposure to 100% FIO2 led to similar reductions of CBF for subjects in both Arm 1 and Arm 2.DISCUSSION: The neurovascular system appears to respond to increasing FIO2 levels in a dose dependent manner, with significant reductions in CBF with FIO2 exposures ≥ 60%.Damato EG, Fillioe SJ, Vannix IS, Norton LK, Margevicius SP, Beebe JL, Decker MJ. Characterizing the dose response of hyperoxia with brain perfusion. Aerosp Med Hum Perform. 2022; 93(6):493–498.

Cerebral Blood Flow and Brain Functional Connectivity Changes in Older Adults Participating in a Mindfulness-Based Stress Reduction Program

There is a growing interest in the potential beneficial effects of mindfulness meditation training in protecting against age-related physical, emotional, and cognitive decline. The current prospective, single-center, single-arm study investigated if functional magnetic resonance imaging-based changes in cerebral blood flow and brain functional connectivity could be observed in 11 elderly adults (mean age 79) after participation in a Mindfulness-Based Stress Reduction (MBSR) program. The results showed significantly (p < 0.05) altered cerebral blood flow and functional connectivity in the cingulate gyrus, limbic structures, and subregions of the temporal and frontal lobes, similar to findings of other meditation-related studies in younger populations. Furthermore, these changes were also associated with significant improvements in depression symptoms. This study suggests that the MBSR program can potentially modify cerebral blood flow and connectivity in this population.

Individual Differences in Cerebral Perfusion as a Function of Age and Loneliness

Loneliness is defined as the subjective feeling that one's social needs are not satisfied by both quantity and quality of one's social relationships. Loneliness has been linked to a broad range of adverse physical and mental health consequences. There is an interest in identifying the neural and molecular processes by which loneliness adversely affects health. Prior imaging studies reported divergent networks involved in cognitive, emotional, and social processes associated with loneliness. Although loneliness is common among both younger and older adults, it is experienced differently across the lifespan and has different antecedents and consequences. The current study measured regional cerebral blood flow (CBF) using pulsed arterial spin labeling imaging. Forty-five older (M_age = 63.4) and forty-four younger adults (M_age = 20.9) with comparable degrees of loneliness were included. Whole-brain voxel-wise analysis revealed a main effect of age (in superior temporal and supramarginal gyri), but no main effect of loneliness. Furthermore, the age effect was only observed among people who reported higher level of loneliness. These regions have previously been implicated in social- and attention-related functions. The moderation of loneliness on age and regional CBF suggests that younger and older individuals present differential neural manifestations in response to loneliness, even with comparable levels of loneliness.

Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease Using Transfer Learning of Deep Learning-Based ASL Denoising

BACKGROUND

Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) denoising through deep learning (DL) often faces insufficient training data from patients. One solution is to train DL models using healthy subjects' data which are more widely available and transfer them to patients' data.

PURPOSE

To evaluate the transferability of a DL-based ASL MRI denoising method (DLASL).

STUDY TYPE

Retrospective.

SUBJECTS

Four hundred and twenty-eight subjects (189 females) from three cohorts.

FIELD STRENGTH/SEQUENCE

3 T two-dimensional (2D) echo-planar imaging (EPI)-based pseudo-continuous ASL (PCASL) and 2D EPI-based pulsed ASL (PASL) sequences.

ASSESSMENT

DLASL was trained using young healthy adults' PCASL data (Dataset 1: 250/30 subjects as training/validation set) and was directly transferred (DTF) to PCASL data from Dataset 2 (45 subjects test set) of normal controls (NC) and Alzheimer's disease (AD) groups. DLASL was fine-tuned (DLASLFT) and tested on PASL data from Dataset 3 (103 subjects test set) of NC and AD. An existing non-DL method (NonDL) was used for comparison. Cerebral blood flow (CBF) images from ASL MRI were compared between NC and AD to assess characteristic hypoperfusion (lower CBF) patterns in AD. CBF image quality and CBF map sensitivity for detecting hypoperfusion using peak t-value and suprathreshold cluster size are outcome measures.

STATISTICAL TESTS

Paired t-test, two-sample t-test, one-way analysis of variance, and Tukey honestly significant difference, and linear mixed-effects models were used. P < 0.05 was considered statistically significant.

RESULTS

Mean contrast-to-noise ratio (CNR) of Dataset 2 showed that DTF outperformed NonDL (AD: 3.38 vs. 2.64, NC: 3.80 vs. 3.36). On Dataset 3, DLASLFT outperformed NonDL measured by mean CNR (AD: 2.45 vs. 1.87, NC: 2.54 vs. 2.17) and mean radiologic score (2.86 vs. 2.44). Image quality improvement was significant on both test sets. DTF and DLASLFT improved sensitivity for detecting AD-related hypoperfusion patterns compared with NonDL.

DATA CONCLUSION

We demonstrated the DLASL's transferability across different ASL sequences and different populations.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

N-acetyl cysteine administration affects cerebral blood flow as measured by arterial spin labeling MRI in patients with multiple sclerosis

BACKGROUND

The purpose of this study was to explore if administration of N-acetyl-cysteine (NAC) in patients with multiple sclerosis (MS) resulted in altered cerebral blood flow (CBF) based on Arterial Spin Labeling (ASL) magnetic resonance imaging (MRI).

METHODS

Twenty-three patients with mild to moderate MS, (17 relapsing remitting and 6 primary progressive) were randomized to either NAC plus standard of care (N = 11), or standard of care only (N = 12). The experimental group received NAC intravenously (50 mg/kg) once per week and orally (500mg 2x/day) the other six days. Patients in both groups were evaluated initially and after 2 months (of receiving the NAC or waitlist control) with ASL MRI to measure CBF. Clinical symptom questionnaires were also completed at both time points.

RESULTS

The CBF data showed significant differences in several brain regions including the pons, midbrain, left temporal and frontal lobe, left thalamus, right middle frontal lobe and right temporal/hippocampus (p < 0.001) in the MS group after treatment with NAC, when compared to the control group. Self-reported scores related to cognition and attention were also significantly improved in the NAC group as compared to the control group.

CONCLUSIONS

The results of this study suggest that NAC administration alters resting CBF in MS patients, and this is associated with qualitative improvements in cognition and attention. Given these findings, large scale efficacy studies will be of value to determine the potential clinical impact of NAC over the course of illness in patients with MS, as well as the most effective dosages and differential effects across subpopulations.

Longitudinal Cerebral Blood Flow Changes in Normal Aging and the Alzheimer's Disease Continuum Identified by Arterial Spin Labeling MRI

BACKGROUND

Cross-sectional studies have shown lower cerebral blood flow (CBF) in Alzheimer's disease (AD), but longitudinal CBF changes in AD are still unknown.

OBJECTIVE

To reveal the longitudinal CBF changes in normal control (NC) and the AD continuum using arterial spin labeling perfusion magnetic resonance imaging (ASL MRI).

METHODS

CBF was calculated from two longitudinal ASL scans acquired 2.22±1.43 years apart from 140 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At the baseline scan, the cohort contained 41 NC, 74 mild cognitive impairment patients (MCI), and 25 AD patients. 21 NC converted into MCI and 17 MCI converted into AD at the follow-up. Longitudinal CBF changes were assessed using paired-t test for non-converters and converters separately at each voxel and in the meta-ROI. Age and sex were used as covariates.

RESULTS

CBF reductions were observed in all subjects. Stable NC (n = 20) showed CBF reduction in the hippocampus and precuneus. Stable MCI patients (n = 57) showed spatially more extended CBF reduction patterns in hippocampus, middle temporal lobe, ventral striatum, prefrontal cortex, and cerebellum. NC-MCI converters showed CBF reduction in hippocampus and cerebellum and CBF increase in caudate. MCI-AD converters showed CBF reduction in hippocampus and prefrontal cortex. CBF changes were not related with longitudinal neurocognitive changes.

CONCLUSION

Normal aging and AD continuum showed common longitudinal CBF reductions in hippocampus independent of disease and its conversion. Disease conversion independent longitudinal CBF reductions escalated in MCI subjects.

Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning

BACKGROUND

Arterial spin labeling (ASL) is a useful tool for measuring cerebral blood flow (CBF). However, due to the low signal-to-noise ratio (SNR) of the technique, multiple repetitions are required, which results in prolonged scan times and increased susceptibility to artifacts.

PURPOSE

To develop a deep-learning-based algorithm for simultaneous denoising and suppression of transient artifacts in ASL images.

STUDY TYPE

Retrospective.

SUBJECTS

131 pediatric neuro-oncology patients for model training and 11 healthy adult subjects for model evaluation.

FIELD STRENGTH/SEQUENCE

3T / pseudo-continuous and pulsed ASL with 3D gradient-and-spin-echo readout.

ASSESSMENT

A denoising autoencoder (DAE) model was designed with stacked encoding/decoding convolutional layers. Reference standard images were generated by averaging 10 pairwise ASL subtraction images. The model was trained to produce perfusion images of a similar quality using a single subtraction image. Performance was compared against Gaussian and non-local means (NLM) filters. Evaluation metrics included SNR, peak SNR (PSNR), and structural similarity index (SSIM) of the CBF images, compared to the reference standard.

STATISTICAL TESTS

One-way analysis of variance (ANOVA) tests for group comparisons.

RESULTS

The DAE model was the only model to produce a significant increase in SNR compared to the raw images (P < 0.05), providing an average SNR gain of 62%. The DAE model was also effective at suppressing transient artifacts, and was the only model to show a significant improvement in accuracy in the generated CBF images, as assessed using PSNR values (P < 0.05). In addition, using data from multiple inflow time acquisitions, the DAE images produced the best fit to the Buxton kinetic model, offering a 75% reduction in the fitting error compared to the raw images.

DATA CONCLUSION

Deep-learning-based algorithms provide superior accuracy when denoising ASL images, due to their ability to simultaneously increase SNR and suppress artifactual signals in raw ASL images.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 1.

Denoising arterial spin labeling perfusion MRI with deep machine learning

PURPOSE

Arterial spin labeling (ASL) perfusion MRI is a noninvasive technique for measuring cerebral blood flow (CBF) in a quantitative manner. A technical challenge in ASL MRI is data processing because of the inherently low signal-to-noise-ratio (SNR). Deep learning (DL) is an emerging machine learning technique that can learn a nonlinear transform from acquired data without using any explicit hypothesis. Such a high flexibility may be particularly beneficial for ASL denoising. In this paper, we proposed and validated a DL-based ASL MRI denoising algorithm (DL-ASL).

METHODS

The DL-ASL network was constructed using convolutional neural networks (CNNs) with dilated convolution and wide activation residual blocks to explicitly take the inter-voxel correlations into account, and preserve spatial resolution of input image during model learning.

RESULTS

DL-ASL substantially improved the quality of ASL CBF in terms of SNR. Based on retrospective analyses, DL-ASL showed a high potential of reducing 75% of the original acquisition time without sacrificing CBF measurement quality.

CONCLUSION

DL-ASL achieved improved denoising performance for ASL MRI as compared with current routine methods in terms of higher PSNR, SSIM and Radiologic scores. With the help of DL-ASL, much fewer repetitions may be prescribed in ASL MRI, resulting in a great reduction of the total acquisition time.