Anomalous Change Point Detection Using Probabilistic Predictive Coding

Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods are often constrained to univariate data, face scalability challenges with large datasets due to computational demands, and experience reduced performance with high-dimensional or intricate data, as well as hidden anomalies. Furthermore, they often lack interpretability and adaptability to domain-specific knowledge, which limits their versatility across different fields. In this work, we propose a deep learning-based CPD/AD method called Probabilistic Predictive Coding (PPC) that jointly learns to encode sequential data to low dimensional latent space representations and to predict the subsequent data representations as well as the corresponding prediction uncertainties. The model parameters are optimized with maximum likelihood estimation by comparing these predictions with the true encodings. At the time of application, the true and predicted encodings are used to determine the probability of conformity, an interpretable and meaningful anomaly score. Furthermore, our approach has linear time complexity, scalability issues are prevented, and the method can easily be adjusted to a wide range of data types and intricate applications. We demonstrate the effectiveness and adaptability of our proposed method across synthetic time series experiments, image data, and real-world magnetic resonance spectroscopic imaging data

Changes in volumetric and metabolic parameters relate to differences in exposure to sub-concussive head impacts

Structural and calibrated magnetic resonance imaging data were acquired on 44 collegiate football players prior to the season (PRE), following the first four weeks in-season (PTC) and one month after the last game (POST). Exposure data collected from g-Force accelerometers mounted to the helmet of each player were used to split participants into HIGH (N = 22) and LOW (N = 22) exposure groups, based on the frequency of impacts sustained by each athlete. Significant decreases in grey-matter volume specific to the HIGH group were documented at POST (P = 0.009), compared to baseline. Changes in resting cerebral blood flow (CBF0), corrected for partial volume effects, were observed within the HIGH group, throughout the season (P < 0.0001), suggesting that alterations in perfusion may follow exposure to sub-concussive collisions. Co-localized significant increases in cerebral metabolic rate of oxygen consumption (CMRO2|0) mid-season were also documented in the HIGH group, with respect to both PRE- and POST values. No physiological changes were observed in the LOW group. Therefore, cerebral metabolic demand may be elevated in players with greater exposure to head impacts. These results provide novel insight into the effects of sub-concussive collisions on brain structure and cerebrovascular physiology and emphasize the importance of multi-modal imaging for a complete characterization of cerebral health

Hemodynamic imaging parameters in brain metastases patients - Agreement between multi-delay ASL and hypercapnic BOLD

Arterial spin labeling (ASL) MRI is a routine clinical imaging technique that provides quantitative cerebral blood flow (CBF) information. A related technique is blood oxygenation level-dependent (BOLD) MRI during hypercapnia, which can assess cerebrovascular reactivity (CVR). ASL is weighted towards arteries, whereas BOLD is weighted towards veins. Their associated parameters in heterogeneous tissue types or under different hemodynamic conditions remains unclear. Baseline multi-delay ASL MRI and BOLD MRI during hypercapnia were performed in fourteen patients with brain metastases. In the ROI analysis, the CBF and CVR values were positively correlated in regions showing sufficient reserve capacity (i.e. non-steal regions, r rm  = 0.792). Additionally, longer hemodynamic lag times were related to lower baseline CBF ( r rm  = -0.822) and longer arterial arrival time (AAT; r rm  = 0.712). In contrast, in regions exhibiting vascular steal an inverse relationship was found with higher baseline CBF related to more negative CVR ( r rm  = -0.273). These associations were confirmed in voxelwise analyses. The relationship between CBF, AAT and CVR measures seems to be dependent on the vascular status of the underlying tissue. Healthy tissue relationships do not hold in tissues experiencing impaired or exhausted autoregulation. CVR metrics can possibly identify at-risk areas before perfusion deficiencies become visible on ASL MRI, specifically within vascular steal regions

Quantifying cerebral blood arrival times using hypoxia-mediated arterial BOLD contrast

Cerebral blood arrival and tissue transit times are sensitive measures of the efficiency of tissue perfusion and can provide clinically meaningful information on collateral blood flow status. We exploit the arterial blood oxygen level dependent (BOLD) signal contrast established by precisely decreasing, and then increasing, arterial hemoglobin saturation using respiratory re-oxygenation challenges to quantify arterial blood arrival times throughout the brain. We term this approach the Step Hemoglobin re-Oxygenation Contrast Stimulus (SHOCS). Carpet plot analysis yielded measures of signal onset (blood arrival), global transit time (gTT) and calculations of relative total blood volume. Onset times averaged across 12 healthy subjects were 1.1 ± 0.4 and 1.9 ± 0.6 for cortical gray and deep white matter, respectively. The average whole brain gTT was 4.5 ± 0.9 s. The SHOCS response was 1.7 fold higher in grey versus white matter; in line with known differences in tissue-specific blood volume fraction. SHOCS was also applied in a patient with unilateral carotid artery occlusion revealing ipsilateral prolonged signal onset with normal perfusion in the unaffected hemisphere. We anticipate that SHOCS will further inform on the extent of collateral blood flow in patients with upstream steno-occlusive vascular disease, including those already known to manifest reductions in vasodilatory reserve capacity or vascular steal

A silent echo-planar spectroscopic imaging readout with high spectral bandwidth MRSI using an ultrasonic gradient axis

Purpose: We present a novel silent echo-planar spectroscopic imaging (EPSI) readout, which uses an ultrasonic gradient insert to accelerate MRSI while producing a high spectral bandwidth (20 kHz) and a low sound level. Methods: The ultrasonic gradient insert consisted of a single-axis (z-direction) plug-and-play gradient coil, powered by an audio amplifier, and produced 40 mT/m at 20 kHz. The silent EPSI readout was implemented in a phase-encoded MRSI acquisition. Here, the additional spatial encoding provided by this silent EPSI readout was used to reduce the number of phase-encoding steps. Spectroscopic acquisitions using phase-encoded MRSI, a conventional EPSI-readout, and the silent EPSI readout were performed on a phantom containing metabolites with resonance frequencies in the ppm range of brain metabolites (0–4 ppm). These acquisitions were used to determine sound levels, showcase the high spectral bandwidth of the silent EPSI readout, and determine the SNR efficiency and the scan efficiency. Results: The silent EPSI readout featured a 19-dB lower sound level than a conventional EPSI readout while featuring a high spectral bandwidth of 20 kHz without spectral ghosting artifacts. Compared with phase-encoded MRSI, the silent EPSI readout provided a 4.5-fold reduction in scan time. In addition, the scan efficiency of the silent EPSI readout was higher (82.5% vs. 51.5%) than the conventional EPSI readout. Conclusions: We have for the first time demonstrated a silent spectroscopic imaging readout with a high spectral bandwidth and low sound level. This sound reduction provided by the silent readout is expected to have applications in sound-sensitive patient groups, whereas the high spectral bandwidth could benefit ultrahigh-field MR systems

Proton metabolic mapping of the brain at 7 T using a two-dimensional free induction decay-echo-planar spectroscopic imaging readout with lipid suppression

The increased signal-to-noise ratio (SNR) and chemical shift dispersion at high magnetic fields (≥7 T) have enabled neuro-metabolic imaging at high spatial resolutions. To avoid very long acquisition times with conventional magnetic resonance spectroscopic imaging (MRSI) phase-encoding schemes, solutions such as pulse-acquire or free induction decay (FID) sequences with short repetition time and inner volume selection methods with acceleration (echo-planar spectroscopic imaging [EPSI]), have been proposed. With the inner volume selection methods, limited spatial coverage of the brain and long echo times may still impede clinical implementation. FID-MRSI sequences benefit from a short echo time and have a high SNR per time unit; however, contamination from strong extra-cranial lipid signals remains a problem that can hinder correct metabolite quantification. L2-regularization can be applied to remove lipid signals in cases with high spatial resolution and accurate prior knowledge. In this work, we developed an accelerated two-dimensional (2D) FID-MRSI sequence using an echo-planar readout and investigated the performance of lipid suppression by L2-regularization, an external crusher coil, and the combination of these two methods to compare the resulting spectral quality in three subjects. The reduction factor of lipid suppression using the crusher coil alone varies from 2 to 7 in the lipid region of the brain boundary. For the combination of the two methods, the average lipid area inside the brain was reduced by 2% to 38% compared with that of unsuppressed lipids, depending on the subject's region of interest. 2D FID-EPSI with external lipid crushing and L2-regularization provides high in-plane coverage and is suitable for investigating brain metabolite distributions at high fields

A fully synthetic three-dimensional human cerebrovascular model based on histological characteristics to investigate the hemodynamic fingerprint of the layer BOLD fMRI signal formation

Recent advances in functional magnetic resonance imaging (fMRI) at ultra-high field (≥7 tesla), novel hardware, and data analysis methods have enabled detailed research on neurovascular function, such as cortical layer-specific activity, in both human and nonhuman species. A widely used fMRI technique relies on the blood oxygen level-dependent (BOLD) signal. BOLD fMRI offers insights into brain function by measuring local changes in cerebral blood volume, cerebral blood flow, and oxygen metabolism induced by increased neuronal activity. Despite its potential, interpreting BOLD fMRI data is challenging as it is only an indirect measurement of neuronal activity. Computational modeling can help interpret BOLD data by simulating the BOLD signal formation. Current developments have focused on realistic 3D vascular models based on rodent data to understand the spatial and temporal BOLD characteristics. While such rodent-based vascular models highlight the impact of the angioarchitecture on the BOLD signal amplitude, anatomical differences between the rodent and human vasculature necessitate the development of human-specific models. Therefore, a computational framework integrating human cortical vasculature, hemodynamic changes, and biophysical properties is essential. Here, we present a novel computational approach: a three-dimensional VAscular MOdel based on Statistics (3D VAMOS), enabling the investigation of the hemodynamic fingerprint of the BOLD signal within a model encompassing a fully synthetic human 3D cortical vasculature and hemodynamics. Our algorithm generates microvascular and macrovascular architectures based on morphological and topological features from the literature on human cortical vasculature. By simulating specific oxygen saturation states and biophysical interactions, our framework characterizes the intravascular and extravascular signal contributions across cortical depth and voxel-wise levels for gradient-echo and spin-echo readouts. Thereby, the 3D VAMOS computational framework demonstrates that using human characteristics significantly affects the BOLD fingerprint, making it an essential step in understanding the fundamental underpinnings of layer-specific fMRI experiments

Deuterium echo-planar spectroscopic imaging (EPSI) in the human liver in vivo at 7 T

PURPOSE: To demonstrate the feasibility of deuterium echo-planar spectroscopic imaging (EPSI) to accelerate 3D deuterium metabolic imaging in the human liver at 7 T. METHODS: A deuterium EPSI sequence, featuring a Hamming-weighted k-space acquisition pattern for the phase-encoding directions, was implemented. Three-dimensional deuterium EPSI and conventional MRSI were performed on a water/acetone phantom and in vivo in the human liver at natural abundance. Moreover, in vivo deuterium EPSI measurements were acquired after oral administration of deuterated glucose. The effect of acquisition time on SNR was evaluated by retrospectively reducing the number of averages. RESULTS: The SNR of natural abundance deuterated water signal in deuterium EPSI was 6.5% and 5.9% lower than that of MRSI in the phantom and in vivo experiments, respectively. In return, the acquisition time of in vivo EPSI data could be reduced retrospectively to 2 min, beyond the minimal acquisition time of conventional MRSI (of 20 min in this case), while still leaving sufficient SNR. Three-dimensional deuterium EPSI, after administration of deuterated glucose, enabled monitoring of hepatic glucose dynamics with full liver coverage, a spatial resolution of 20 mm isotropic, and a temporal resolution of 9 min 50 s, which could retrospectively be shortened to 2 min. CONCLUSION: In this work, we demonstrate the feasibility of accelerated 3D deuterium metabolic imaging of the human liver using deuterium EPSI. The acceleration obtained with EPSI can be used to increase temporal and/or spatial resolution, which will be valuable to study tissue metabolism of deuterated compounds over time

Anesthesia Depresses Cerebrovascular Reactivity to Acetazolamide in Pediatric Moyamoya Vasculopathy

Measurements of cerebrovascular reactivity (CVR) are essential for treatment decisions in moyamoya vasculopathy (MMV). Since MMV patients are often young or cognitively impaired, anesthesia is commonly used to limit motion artifacts. Our aim was to investigate the effect of anesthesia on the CVR in pediatric MMV. We compared the CVR with multidelay-ASL and BOLD MRI, using acetazolamide as a vascular stimulus, in all awake and anesthesia pediatric MMV scans at our institution. Since a heterogeneity in disease and treatment influences the CVR, we focused on the (unaffected) cerebellum. Ten awake and nine anesthetized patients were included. The post-acetazolamide CBF and ASL-CVR were significantly lower in anesthesia patients (47.1 ± 15.4 vs. 61.4 ± 12.1, p = 0.04; 12.3 ± 8.4 vs. 23.7 ± 12.2 mL/100 g/min, p = 0.03, respectively). The final BOLD-CVR increase (0.39 ± 0.58 vs. 3.6 ± 1.2% BOLD-change (mean/SD), p < 0.0001), maximum slope of increase (0.0050 ± 0.0040%/s vs. 0.017 ± 0.0059%, p < 0.0001), and time to maximum BOLD-increase (~463 ± 136 and ~697 ± 144 s, p = 0.0028) were all significantly lower in the anesthesia group. We conclude that the response to acetazolamide is distinctively different between awake and anesthetized MMV patients, and we hypothesize that these findings can also apply to other diseases and methods of measuring CVR under anesthesia. Considering that treatment decisions heavily depend on CVR status, caution is warranted when assessing CVR under anesthesia