Automatic MRI-based rotator cuff muscle segmentation using U-Nets.

BACKGROUND: The rotator cuff (RC) is a crucial anatomical element within the shoulder joint, facilitating an extensive array of motions while maintaining joint stability. Comprised of the subscapularis, infraspinatus, supraspinatus, and teres minor muscles, the RC plays an integral role in shoulder functionality. RC injuries represent prevalent, incapacitating conditions that impose a substantial impact on approximately 8% of the adult population in the USA. Segmentation of these muscles provides valuable anatomical information for evaluating muscle quality and allows for better treatment planning. MATERIALS AND METHODS: We developed a model based on residual deep convolutional encoder-decoder U-net to segment RC muscles on oblique sagittal T1-weighted images MRI. Our data consisted of shoulder MRIs from a cohort of 157 individuals, consisting of individuals without RC tendon tear (N=79) and patients with partial RC tendon tear (N=78). We evaluated different modeling approaches. The performance of the models was evaluated by calculating the Dice coefficient on the hold out test set. RESULTS: The best-performing model's median Dice coefficient was measured to be 89% (Q1:85%, Q3:96%) for the supraspinatus, 86% (Q1:82%, Q3:88%) for the subscapularis, 86% (Q1:82%, Q3:90%) for the infraspinatus, and 78% (Q1:70%, Q3:81%) for the teres minor muscle, indicating a satisfactory level of accuracy in the model's predictions. CONCLUSION: Our computational models demonstrated the capability to delineate RC muscles with a level of precision akin to that of experienced radiologists. As hypothesized, the proposed algorithm exhibited superior performance when segmenting muscles with well-defined boundaries, including the supraspinatus, subscapularis, and infraspinatus muscles.

Arterial cerebral blood volume-weighted functional MRI using pseudocontinuous arterial spin tagging (AVAST).

PURPOSE: Neurovascular regulation, including responses to neural activation that give rise to the blood oxygenation level-dependent (BOLD) effect, occurs mainly at the arterial and arteriolar level. The purpose of this study is to develop a framework for fast imaging of arterial cerebral blood volume (aCBV) signal suitable for functional imaging studies. METHODS: A variant of the pseudocontinuous arterial spin tagging technique was developed in order to achieve a contrast that depends on aCBV with little contamination from perfusion signal by taking advantage of the kinetics of the tag through the vasculature. This technique tailors the tagging duration and repetition time for each subject. The proposed technique, called AVAST, is compared empirically with BOLD imaging and standard (perfusion-weighted) arterial spin labeling (ASL) technique, in a motor-visual activation paradigm. RESULTS: The average Z-scores in the activated area obtained over all the subjects were 4.25, 5.52, and 7.87 for standard ASL, AVAST, and BOLD techniques, respectively. The aCBV contrast obtained from AVAST provided 80% higher average signal-to-noise ratio and 95% higher average contrast-to-noise ratio compared with that of the standard ASL measurements. CONCLUSION: AVAST exhibits improved activation detection sensitivity and temporal resolution over the standard ASL technique, in functional MRI experiments, while preserving its quantitative nature and statistical advantages. AVAST particularly could be useful in clinical studies of pathological conditions, longitudinal studies of cognitive function, and studies requiring sustained periods of the condition.

Noncontrast mapping of arterial delay and functional connectivity using resting-state functional MRI: a study in Moyamoya patients.

PURPOSE: To investigate if delays in resting-state spontaneous fluctuations of the BOLD (sfBOLD) signal can be used to create maps similar to time-to-maximum of the residue function (Tmax) in Moyamoya patients and to determine whether sfBOLD delays affect the results of brain connectivity mapping. MATERIALS AND METHODS: Ten patients were scanned at 3 Tesla using a gradient-echo echo planar imaging sequence for sfBOLD imaging. Cross correlation analysis was performed between each brain voxel signal and a reference signal comprised of either the superior sagittal sinus (SSS) or whole brain (WB) average time course. sfBOLD delay maps were created based on the time shift necessary to maximize the correlation coefficient, and compared with dynamic susceptibility contrast Tmax maps. Standard and time-shifted resting-state BOLD connectivity analyses of the default mode network were compared. RESULTS: Good linear correlations were found between sfBOLD delays and Tmax using the SSS as reference (r(2) = 0.8, slope = 1.4, intercept = -4.6) or WB (r(2) = 0.7, slope = 0.8, intercept = -3.2). New nodes of connectivity were found in delayed regions when accounting for delays in the analysis. CONCLUSION: Resting-state sfBOLD imaging can create delay maps similar to Tmax maps without the use of contrast agents in Moyamoya patients. Accounting for these delays may affect the results of functional connectivity maps.

Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling.

Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( A ß ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A ß biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A ß detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A ß status, and offer key insights into dynamic functional connectivity analysis in AD.

Real-time functional MRI using pseudo-continuous arterial spin labeling.

The first implementation of real-time acquisition and analysis of arterial spin labeling-based functional magnetic resonance imaging time series is presented in this article. The implementation uses a pseudo-continuous labeling scheme followed by a spiral k-space acquisition trajectory. Real-time reconstruction of the images, preprocessing, and regression analysis of the functional magnetic resonance imaging data were implemented on a laptop computer interfaced with the MRI scanner. The method allows the user to track the current raw data, subtraction images, and the cumulative t-statistic map overlaid on a cumulative subtraction image. The user is also able to track the time course of individual time courses and interactively selects a region of interest as a nuisance covariate. The pulse sequence allows the user to adjust acquisition and labeling parameters while observing their effect on the image within two successive pulse repetition times. This method is demonstrated by two functional imaging experiments: a simultaneous finger-tapping and visual stimulation paradigm, and a bimanual finger-tapping task.

B0 field inhomogeneity considerations in pseudo-continuous arterial spin labeling (pCASL): effects on tagging efficiency and correction strategy.

Pseudo-continuous arterial spin labeling (pCASL) is a very powerful technique to measure cerebral perfusion, which circumvents the problems affecting other continuous arterial spin labeling schemes, such as magnetization transfer and duty cycle. However, some variability in the tagging efficiency of the pCASL technique has been reported. This article investigates the effect of B(0) field inhomogeneity on the tagging efficiency of the pCASL pulse sequence as a possible cause of this variability. Both theory and simulated data predict that the efficiency of pseudo-continuous labeling pulses can be degraded in the presence of off-resonance effects. These findings are corroborated by human in vivo measurements of tagging efficiency. On the basis of this theoretical framework, a method utilizing B(0) field map information is proposed to correct for the possible loss in tagging efficiency of the pCASL pulse sequence. The efficiency of the proposed correction method is evaluated using numerical simulations and in vivo implementation. The data show that the proposed method can effectively recover the lost tagging efficiency and signal-to-noise ratio of pCASL caused by off-resonance effects.

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates.

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using data extracted from magnetic resonance imaging (MRI). This protocol allows for the generation of 3D printed anatomically accurate physical models of the brain and skull, as well as an agarose gel model of the brain modeling some of the mechanical properties of the brain. These models can be extracted from MRI using brain extraction software for the model of the brain, and custom code for the model of the skull. The preparation protocol takes advantage of state-of-the-art 3D printing technology to make interfacing brains, skulls, and molds for gel brain models. The skull and brain models can be used to visualize brain tissue inside the skull with the addition of a craniotomy in the custom code, allowing for better preparation for surgeries directly involving the brain. The applications of these methods are designed for surgeries involved in neurological stimulation and recording as well as injection, but the versatility of the system allows for future expansion of the protocol, extraction techniques, and models to a wider scope of surgeries.

Advantages of short repetition time resting-state functional MRI enabled by simultaneous multi-slice imaging.

BACKGROUND: Recent advancements in simultaneous multi-slice (SMS) imaging techniques have enabled whole-brain resting-state fMRI (rs-fMRI) scanning at sub-second temporal resolution, providing spectral ranges much wider than the typically used range of 0.01-0.1 Hz. However, the advantages of this accelerated acquisition for rs-fMRI have not been evaluated. NEW METHOD: In this study, we used SMS Echo Planar Imaging (EPI) to probe whole-brain functional connectivity with a short repetition time (TR = 350 ms) and compared it with standard EPI with a longer TR of 2000 ms. We determined the effect of scan length and investigated the temporal filtration strategies that optimize results based on metrics of signal-noise separation and test-retest reliability using both seed-based and independent component analysis (ICA). RESULTS: We found that use of either the entire frequency range of 0.01-1.4 Hz or the entire frequency range with the exclusion of typical cardiac and respiratory frequency values tended to provide the best functional connectivity maps. COMPARISON WITH EXISTING METHODS: We found that the SMS-acquired rs-fMRI scans had improved the signal-noise separation, while preserving the same level of test-retest reliability compared to conventional EPI, and enabled the detection of reliable functional connectivity networks with scan times as short as 3 min. CONCLUSIONS: Our findings suggest that whole-brain rs-fMRI studies may benefit from the increased temporal resolution enabled by the SMS-EPI acquisition, leading to drastic scan time reductions, which in turn should enable the more widespread use of rs-fMRI in clinical research protocols.

Erroneous Resting-State fMRI Connectivity Maps Due to Prolonged Arterial Arrival Time and How to Fix Them.

In resting-state functional MRI (rs-fMRI), functional networks are assessed utilizing the temporal correlation between spontaneous blood oxygen level-dependent signal fluctuations of spatially remote brain regions. Recently, several groups have shown that temporal shifts are present in rs-fMRI maps in patients with cerebrovascular disease due to spatial differences in arterial arrival times, and that this can be exploited to map arrival times in the brain. This suggests that rs-fMRI connectivity mapping may be similarly sensitive to such temporal shifts, and that standard rs-fMRI analysis methods may fail to identify functional connectivity networks. To investigate this, we studied the default mode network (DMN) in Moyamoya disease patients and compared it with normal healthy volunteers. Our results show that using standard independent component analysis (ICA) and seed-based approaches, arterial arrival delays lead to inaccurate incomplete characterization of functional connectivity within the DMN in Moyamoya disease patients. Furthermore, we propose two techniques to correct these errors, for seed-based and ICA methods, respectively. Using these methods, we demonstrate that it is possible to mitigate the deleterious effects of arterial arrival time on the assessment of functional connectivity of the DMN. As these corrections have not been applied to the vast majority of >200 prior rs-fMRI studies in patients with cerebrovascular disease, we suggest that they be interpreted with great caution. Correction methods should be applied in any rs-fMRI connectivity study of subjects expected to have abnormally delayed arterial arrival times.

Nuisance Regression of High-Frequency Functional Magnetic Resonance Imaging Data: Denoising Can Be Noisy.

Recently, emerging studies have demonstrated the existence of brain resting-state spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood oxygen level-dependent contrasts (the upper limit of the canonical model is ∼0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue) or rather is artificially introduced by improper preprocessing operations. In this study, we examined the influence of a common preprocessing step-whole-band linear nuisance regression (WB-LNR)-on resting-state functional connectivity (RSFC) and demonstrated through both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of functional magnetic resonance imaging (fMRI) signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.

Measuring vascular reactivity with resting-state blood oxygenation level-dependent (BOLD) signal fluctuations: A potential alternative to the breath-holding challenge?

Measurement of the ability of blood vessels to dilate and constrict, known as vascular reactivity, is often performed with breath-holding tasks that transiently raise arterial blood carbon dioxide (PaCO2) levels. However, following the proper commands for a breath-holding experiment may be difficult or impossible for many patients. In this study, we evaluated two approaches for obtaining vascular reactivity information using blood oxygenation level-dependent signal fluctuations obtained from resting-state functional magnetic resonance imaging data: physiological fluctuation regression and coefficient of variation of the resting-state functional magnetic resonance imaging signal. We studied a cohort of 28 older adults (69 ± 7 years) and found that six of them (21%) could not perform the breath-holding protocol, based on an objective comparison with an idealized respiratory waveform. In the subjects that could comply, we found a strong linear correlation between data extracted from spontaneous resting-state functional magnetic resonance imaging signal fluctuations and the blood oxygenation level-dependent percentage signal change during breath-holding challenge ( R2 = 0.57 and 0.61 for resting-state physiological fluctuation regression and resting-state coefficient of variation methods, respectively). This technique may eliminate the need for subject cooperation, thus allowing the evaluation of vascular reactivity in a wider range of clinical and research conditions in which it may otherwise be impractical.

Support vector machine classification of arterial volume-weighted arterial spin tagging images.

INTRODUCTION: In recent years, machine-learning techniques have gained growing popularity in medical image analysis. Temporal brain-state classification is one of the major applications of machine-learning techniques in functional magnetic resonance imaging (fMRI) brain data. This article explores the use of support vector machine (SVM) classification technique with motor-visual activation paradigm to perform brain-state classification into activation and rest with an emphasis on different acquisition techniques. METHODS: Images were acquired using a recently developed variant of traditional pseudocontinuous arterial spin labeling technique called arterial volume-weighted arterial spin tagging (AVAST). The classification scheme is also performed on images acquired using blood oxygenation-level dependent (BOLD) and traditional perfusion-weighted arterial spin labeling (ASL) techniques for comparison. RESULTS: The AVAST technique outperforms traditional pseudocontinuous ASL, achieving classification accuracy comparable to that of BOLD contrast images. CONCLUSION: This study demonstrates that AVAST has superior signal-to-noise ratio and improved temporal resolution as compared with traditional perfusion-weighted ASL and reduced sensitivity to scanner drift as compared with BOLD. Owing to these characteristics, AVAST lends itself as an ideal choice for dynamic fMRI and real-time neurofeedback experiments with sustained activation periods.

Comparison of cerebral blood flow measurement with [15O]-water positron emission tomography and arterial spin labeling magnetic resonance imaging: A systematic review.

Noninvasive imaging of cerebral blood flow provides critical information to understand normal brain physiology as well as to identify and manage patients with neurological disorders. To date, the reference standard for cerebral blood flow measurements is considered to be positron emission tomography using injection of the [(15)O]-water radiotracer. Although [(15)O]-water has been used to study brain perfusion under normal and pathological conditions, it is not widely used in clinical settings due to the need for an on-site cyclotron, the invasive nature of arterial blood sampling, and experimental complexity. As an alternative, arterial spin labeling is a promising magnetic resonance imaging technique that magnetically labels arterial blood as it flows into the brain to map cerebral blood flow. As arterial spin labeling becomes more widely adopted in research and clinical settings, efforts have sought to standardize the method and validate its cerebral blood flow values against positron emission tomography-based cerebral blood flow measurements. The purpose of this work is to critically review studies that performed both [(15)O]-water positron emission tomography and arterial spin labeling to measure brain perfusion, with the aim of better understanding the accuracy and reproducibility of arterial spin labeling relative to the positron emission tomography reference standard.

Chronic kidney disease, cerebral blood flow, and white matter volume in hypertensive adults.

OBJECTIVE: To determine the relation between markers of kidney disease-estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR)-with cerebral blood flow (CBF) and white matter volume (WMV) in hypertensive adults. METHODS: We used baseline data collected from 665 nondiabetic hypertensive adults aged ≥50 years participating in the Systolic Blood Pressure Intervention Trial (SPRINT). We used arterial spin labeling to measure CBF and structural 3T images to segment tissue into normal and abnormal WMV. We used quantile regression to estimate the association between eGFR and UACR with CBF and abnormal WMV, adjusting for sociodemographic and clinical characteristics. RESULTS: There were 218 participants (33%) with eGFR <60 mL/min/1.73 m(2) and 146 participants (22%) with UACR ≥30 mg/g. Reduced eGFR was independently associated with higher adjusted median CBF, but not with abnormal WMV. Conversely, in adjusted analyses, there was a linear independent association between UACR and larger abnormal WMV, but not with CBF. Compared to participants with neither marker of CKD (eGFR ≥60 mL/min/1.73 m(2) and UACR <30 mg/g), median CBF was 5.03 mL/100 g/min higher (95% confidence interval [CI] 0.78, 9.29) and abnormal WMV was 0.63 cm(3) larger (95% CI 0.08, 1.17) among participants with both markers of CKD (eGFR <60 mL/min/1.73 m(2) and UACR ≥30 mg/g). CONCLUSIONS: Among nondiabetic hypertensive adults, reduced eGFR was associated with higher CBF and higher UACR was associated with larger abnormal WMV.

Controlling the false positive rate in fuzzy clustering using randomization: application to fMRI activation detection.

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.

Spontaneous BOLD signal fluctuations in young healthy subjects and elderly patients with chronic kidney disease.

Spontaneous fluctuations in blood oxygenation level-dependent (BOLD) images are the basis of resting-state fMRI and frequently used for functional connectivity studies. However, there may be intrinsic information in the amplitudes of these fluctuations. We investigated the possibility of using the amplitude of spontaneous BOLD signal fluctuations as a biomarker for cerebral vasomotor reactivity. We compared the coefficient of variation (CV) of the time series (defined as the temporal standard deviation of the time series divided by the mean signal intensity) in two populations: 1) Ten young healthy adults and 2) Ten hypertensive elderly subjects with chronic kidney disease (CKD). We found a statistically significant increase (P<0.01) in the CV values for the CKD patients compared with the young healthy adults in both gray matter (GM) and white matter (WM). The difference was independent of the exact segmentation method, became more significant after correcting for physiological signals using RETROICOR, and mainly arose from very low frequency components of the BOLD signal fluctuation (f<0.025 Hz). Furthermore, there was a strong relationship between WM and GM signal fluctuation CV's (R(2)= 0.87) in individuals, with a ratio of about 1:3. These results suggest that amplitude of the spontaneous BOLD signal fluctuations may be used to assess the cerebrovascular reactivity mechanisms and provide valuable information about variations with age and different disease states.

Quantitative analysis of arterial spin labeling FMRI data using a general linear model.

Arterial spin labeling techniques can yield quantitative measures of perfusion by fitting a kinetic model to difference images (tagged-control). Because of the noisy nature of the difference images investigators typically average a large number of tagged versus control difference measurements over long periods of time. This averaging requires that the perfusion signal be at a steady state and not at the transitions between active and baseline states in order to quantitatively estimate activation induced perfusion. This can be an impediment for functional magnetic resonance imaging task experiments. In this work, we introduce a general linear model (GLM) that specifies Blood Oxygenation Level Dependent (BOLD) effects and arterial spin labeling modulation effects and translate them into meaningful, quantitative measures of perfusion by using standard tracer kinetic models. We show that there is a strong association between the perfusion values using our GLM method and the traditional subtraction method, but that our GLM method is more robust to noise.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PURPOSE: To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. MATERIALS AND METHODS: Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. RESULTS: The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. CONCLUSION: More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.