Ideational Slippage in Middle-Aged and Older Adults: A Preliminary Study.

Ideational slippage-characterized by incorrect word usage and strained logic during dialogue-is common in aging and, at greater frequency, is an indicator of pre-clinical cognitive decline. Performance-based assessment of ideational slippage may be useful in the study of cognitive aging and Alzheimer's-disease-related pathology. In this preliminary study, we examine the association between corpus callosum volume and a performance-based assessment of ideational slippage in middle-aged and older adults (age 61-79 years). Ideational slippage was indexed from cognitive special scores using the Rorschach Inkblot Method (RIM), which are validated indices of deviant verbalization and logical inaccuracy (Sum6, WSum6). Among middle-aged and older adults, smaller splenium volume was associated with greater ideational slippage (ηp2 = 0.48), independent of processing speed and fluid intelligence. The observed negative associations are consistent with visuospatial perception and cognitive functions of the splenium. The effect was strongest with the splenium, and volumes of the genu and total white matter had small effects that were not statistically significant. Conclusions: Results are discussed with future application of RIM special scores for the assessment of pre-clinical cognitive decline and, based on observed effect sizes, power analyses are reported to inform future study planning.

Automating aortic cross-sectional measurement of 3D aorta models.

Purpose: Aortic dissection carries a mortality as high as 50%, but surgical palliation is also fraught with morbidity risks of stroke or paralysis. As such, a significant focus of medical decision making is on longitudinal aortic diameters. We hypothesize that three-dimensional (3D) modeling affords a more efficient methodology toward automated longitudinal aortic measurement. The first step is to automate the measurement of manually segmented 3D models of the aorta. We developed and validated an algorithm to analyze a 3D segmented aorta and output the maximum dimension of minimum cross-sectional areas in a stepwise progression from the diaphragm to the aortic root. Accordingly, the goal is to assess the diagnostic validity of the 3D modeling measurement as a substitute for existing 2D measurements. Approach: From January 2021 to June 2022, 66 3D non-contrast steady-state free precession magnetic resonance images of aortic pathology with clinical aortic measurements were identified; 3D aorta models were manually segmented. A novel mathematical algorithm was applied to each model to generate maximal aortic diameters from the diaphragm to the root, which were then correlated to clinical measurements. Results: With a 76% success rate, we analyzed the resulting 50 3D aortic models utilizing the automated measurement tool. There was an excellent correlation between the automated measurement and the clinical measurement. The intra-class correlation coefficient and p-value for each of the nine measured locations of the aorta were as follows: sinus of valsalva, 0.99, <0.001; sino-tubular junction, 0.89, <0.001; ascending aorta, 0.97, <0.001; brachiocephalic artery, 0.96, <0.001; transverse segment 1, 0.89, <0.001; transverse segment 2, 0.93, <0.001; isthmus region, 0.92, <0.001; descending aorta, 0.96, <0.001; and aorta at diaphragm, 0.3, <0.001. Conclusions: Automating diagnostic measurements that appease clinical confidence is a critical first step in a fully automated process. This tool demonstrates excellent correlation between measurements derived from manually segmented 3D models and the clinical measurements, laying the foundation for transitioning analytic methodologies from 2D to 3D.

Neurovascular Mechanisms of Cognitive Aging: Sex-Related Differences in the Average Progression of Arteriosclerosis, White Matter Atrophy, and Cognitive Decline.

Arterial stiffness (arteriosclerosis) has been linked to heightened risks for cognitive decline, and ultimately for Alzheimer's disease and other forms of dementia. Importantly, neurovascular outcomes generally vary according to one's biological sex. Here, capitalizing on a large sample of participants with neuroimaging and behavioral data ( N = 203, age range = 18-87 years), we aimed to provide support for a hierarchical model of neurocognitive aging, which links age-related declines in cerebrovascular health to the rate of cognitive decline via a series of intervening variables, such as white matter integrity. By applying a novel piecewise regression approach to our cross-sectional sample to support Granger-like causality inferences, we show that, on average, a precipitous decline in cerebral arterial elasticity (measured with diffuse optical imaging of the cerebral arterial pulse; pulse-DOT) temporally precedes an acceleration in the development of white matter lesions by nearly a decade, with women protected from these deleterious effects until approximately age 50, the average onset of menopause. By employing multiple-mediator path analyses while controlling for sex, we show that age may impair cognition via the sequential indirect effects of arteriosclerosis and white matter atrophy on fluid, but not crystallized, abilities. Importantly, we replicate these results using pulse pressure, an independent index of arterial health, thereby providing converging evidence for the central role of arteriosclerosis as an accelerating factor in normal and pathological aging and identifying robust sex-related differences in the progression of cerebral arteriosclerosis and white matter degradation.

Sex differences in interacting genetic and functional connectivity biomarkers in Alzheimer's disease.

As of 2023, it is estimated that 6.7 million individuals in the United States live with Alzheimer's disease (AD). Prior research indicates that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research shows that hippocampal functional connectivity differs by sex and may be related to the observed sex differences in AD, and apolipoprotein E (ApoE) ε4 carriers have reduced hippocampal functional connectivity. The purpose of this study was to determine if the ApoE genotype plays a role in the observed sex differences in hippocampal functional connectivity in Alzheimer's disease. The resting state fMRI and T2 MRI of individuals with AD (n = 30, female = 15) and cognitively normal individuals (n = 30, female = 15) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the functional connectivity toolbox (CONN). Our results demonstrated intrahippocampal functional connectivity differed between those without an ε4 allele and those with at least one ε4 allele in each group. Additionally, intrahippocampal functional connectivity differed only by sex when Alzheimer's participants had at least one ε4 allele. These results improve our current understanding of the role of the interacting relationship between sex, ApoE genotype, and hippocampal function in AD. Understanding these biomarkers may aid in the development of sex-specific interventions for improved AD treatment.

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

Measuring CSF shunt flow with MRI using flow enhancement of signal intensity (FENSI).

PURPOSE: To develop and validate a noninvasive imaging technique for accurately assessing very slow CSF flow within shunt tubes in pediatric patients with hydrocephalus, aiming to identify obstructions that might impede CSF drainage. THEORY AND METHODS: A simulation of shunt flow enhancement of signal intensity (shunt-FENSI) signal is used to establish the relationship between signal change and flow rate. The quantification of flow enhancement of signal intensity data involves normalization, curve fitting, and calibration to match simulated data. Additionally, a phase sweep method is introduced to accommodate the impact of magnetic field inhomogeneity on the flow measurement. The method is tested in flow phantoms, healthy adults, intensive care unit patients with external ventricular drains (EVD), and shunt patients. EVDs enable shunt-flow measurements to be acquired with a ground truth measure of CSF drainage. RESULTS: The flow-rate-to-signal simulation establishes signal-flow relationships and takes into account the T1 of draining fluid. The phase sweep method accurately accounts for phase accumulation due to frequency offsets at the shunt. Results in phantom and healthy human participants reveal reliable quantification of flow rates using controlled flows and agreement with the flow simulation. EVD patients display reliable measures of flow rates. Shunt patient results demonstrate feasibility of the method and consistent flow rates for functional shunts. CONCLUSION: The results demonstrate the technique's applicability, accuracy, and potential for diagnosing and noninvasively monitoring hydrocephalus. Limitations of the current approach include a high sensitivity to motion and strict requirement of imaging slice prescription.

Optimization of 3D dynamic speech MRI: Poisson-disc undersampling and locally higher-rank reconstruction through partial separability model with regional optimized temporal basis.

PURPOSE: To improve the spatiotemporal qualities of images and dynamics of speech MRI through an improved data sampling and image reconstruction approach. METHODS: For data acquisition, we used a Poisson-disc random under sampling scheme that reduced the undersampling coherence. For image reconstruction, we proposed a novel locally higher-rank partial separability model. This reconstruction model represented the oral and static regions using separate low-rank subspaces, therefore, preserving their distinct temporal signal characteristics. Regional optimized temporal basis was determined from the regional-optimized virtual coil approach. Overall, we achieved a better spatiotemporal image reconstruction quality with the potential of reducing total acquisition time by 50%. RESULTS: The proposed method was demonstrated through several 2-mm isotropic, 64 mm total thickness, dynamic acquisitions with 40 frames per second and compared to the previous approach using a global subspace model along with other k-space sampling patterns. Individual timeframe images and temporal profiles of speech samples were shown to illustrate the ability of the Poisson-disc under sampling pattern in reducing total acquisition time. Temporal information of sagittal and coronal directions was also shown to illustrate the effectiveness of the locally higher-rank operator and regional optimized temporal basis. To compare the reconstruction qualities of different regions, voxel-wise temporal SNR analysis were performed. CONCLUSION: Poisson-disc sampling combined with a locally higher-rank model and a regional-optimized temporal basis can drastically improve the spatiotemporal image quality and provide a 50% reduction in overall acquisition time.

Associations between infant amygdala functional connectivity and social engagement following a stressor: A preliminary investigation.

Functional architecture of the infant brain, especially functional connectivity (FC) within the amygdala network and between the amygdala and other networks (i.e., default-mode [DMN] and salience [SAL] networks), provides a neural basis for infant socioemotional functioning. Yet, little is known about the extent to which early within- and between-network amygdala FC are related to infant stress recovery across the first year of life. In this study, we examined associations between amygdala FC (i.e., within-network amygdala connectivity, and between-network amygdala connectivity with the DMN and SAL) at 3 months and infant recovery from a mild social stressor at 3, 6 and 9 months. At 3 months, thirty-five infants (13 girls) underwent resting-state functional magnetic resonance imaging during natural sleep. Infants and their mothers completed the still-face paradigm at 3, 6, and 9 months, and infant stress recovery was assessed at each time point as the proportion of infant social engagement during the reunion episode. Bivariate correlations indicated that greater positive within-network amygdala FC and greater positive amygdala-SAL FC, but not amygdala-DMN FC, at 3 months predicted lower levels of stress recovery at 3 and 6 months, but were nonsignificant at 9 months. These findings provide preliminary evidence that early functional synchronization within the amygdala network, as well as segregation between the amygdala and the SAL, may contribute to infant stress recovery in the context of infant-mother interaction.

Quantifying Velopharyngeal Motion Variation in Speech Sound Production Using an Audio-Informed Dynamic MRI Atlas.

New developments in dynamic magnetic resonance imaging (MRI) facilitate high-quality data acquisition of human velopharyngeal deformations in real-time speech. With recently established speech motion atlases, group analysis is made possible via spatially and temporally aligned datasets in the atlas space from a desired population of interest. In practice, when analyzing motion characteristics from various subjects performing a designated speech task, it is observed that different subjects' velopharyngeal deformation patterns could vary during the pronunciation of the same utterance, regardless of the spatial and temporal alignment of their MRI. Since such variation can be subtle, identification and extraction of unique patterns out of these high-dimensional datasets is a challenging task. In this work, we present a method that computes and visualizes subtle deformation variation patterns as principal components of a subject group's dynamic motion fields in the atlas space. Coupled with the real-time speech audio recordings during image acquisition, the key time frames that contain maximum speech variations are identified by the principal components of temporally aligned audio waveforms, which in turn inform the temporal location of the maximum spatial deformation variation. Henceforth, the motion fields between the key frames and the reference frame for each subject are computed and warped into the common atlas space, enabling a direct extraction of motion variation patterns via quantitative analysis. The method was evaluated on a dataset of twelve healthy subjects. Subtle velopharyngeal motion differences were visualized quantitatively to reveal pronunciation-specific patterns among different subjects.

Cardiothoracic and Vascular Surgery Implant Compatibility With Ultrahigh Field Magnetic Resonance Imaging (4.7 Tesla and 7 Tesla).

The use of 7 Tesla (T) magnetic resonance imaging (MRI) is expanding across medical specialties, particularly, clinical neurosciences and orthopedics. Investigational 7 T MRI has also been performed in cardiology. A limiting factor for expansion of the role of 7 T, irrespective of the body part being imaged, is the sparse testing of biomedical implant compatibility at field strengths >3 T. Implant compatibility can be tested following the American Society for Testing and Materials International guidelines. To assess the current state of cardiovascular implant safety at field strengths >3 T, a systematic search was performed using PubMed, Web of Science, and citation matching. Studies written in English that included at least 1 cardiovascular-related implant and at least 1 safety outcome (deflection angle, torque, or temperature change) were included. Data were extracted for the implant studied, implant composition, deflection angle, torque, and temperature change, and the American Society for Testing and Materials International standards were followed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines for scoping reviews were followed. A total of 9 studies were included. A total of 34 cardiovascular-related implants tested ex vivo at 7 T and 91 implants tested ex vivo at 4.7 T were included. The implants included vascular grafts and conduits, vascular access ports, peripheral and coronary stents, caval filters, and artificial valves. A total of 2 grafts, 1 vascular access port, 2 vena cava filters, and 5 stents were identified as incompatible with the 7 T MRI. All incompatible stents were 40 mm in length. Based on the safety outcomes reported, we identify several implants that may be compatible with >3 T MRI. This scoping review seeks to concisely summarize all the cardiovascular-related implants tested for ultrahigh field MRI compatibility to date.

Preliminary Development of an MRI Atlas for Application to Cleft Care: Findings and Future Recommendations.

OBJECTIVE: To introduce a highly innovative imaging method to study the complex velopharyngeal (VP) system and introduce the potential future clinical applications of a VP atlas in cleft care. DESIGN: Four healthy adults participated in a 20-min dynamic magnetic resonance imaging scan that included a high-resolution T2-weighted turbo-spin-echo 3D structural scan and five custom dynamic speech imaging scans. Subjects repeated a variety of phrases when in the scanner as real-time audio was captured. SETTING: Multisite institution and clinical setting. PARTICIPANTS: Four adult subjects with normal anatomy were recruited for this study. MAIN OUTCOME: Establishment of 4-D atlas constructed from dynamic VP MRI data. RESULTS: Three-dimensional dynamic magnetic resonance imaging was successfully used to obtain high quality dynamic speech scans in an adult population. Scans were able to be re-sliced in various imaging planes. Subject-specific MR data were then reconstructed and time-aligned to create a velopharyngeal atlas representing the averaged physiological movements across the four subjects. CONCLUSIONS: The current preliminary study examined the feasibility of developing a VP atlas for potential clinical applications in cleft care. Our results indicate excellent potential for the development and use of a VP atlas for assessing VP physiology during speech.

Development and validation of a quality control procedure for automatic segmentation of hippocampal subfields.

Automatic segmentation methods for in vivo magnetic resonance imaging are increasing in popularity because of their high efficiency and reproducibility. However, automatic methods can be perfectly reliable and consistently wrong, and the validity of automatic segmentation methods cannot be taken for granted. Quality control (QC) by trained and reliable human raters is necessary to ensure the validity of automatic measurements. Yet QC practices for applied neuroimaging research are underdeveloped. We report a detailed QC and correction procedure to accompany our validated atlas for hippocampal subfield segmentation. We document a two-step QC procedure for identifying segmentation errors, along with a taxonomy of errors and an error severity rating scale. This detailed procedure has high between-rater reliability for error identification and manual correction. The latter introduces at maximum 3% error variance in volume measurement. All procedures were cross-validated on an independent sample collected at a second site with different imaging parameters. The analysis of error frequency revealed no evidence of bias. An independent rater with a third sample replicated procedures with high within-rater reliability for error identification and correction. We provide recommendations for implementing the described method along with hypothesis testing strategies. In sum, we present a detailed QC procedure that is optimized for efficiency while prioritizing measurement validity and suits any automatic atlas.

Yoga, aerobic and stretching exercise effects on neurocognition: Randomized controlled trial protocol.

As the global population ages, the prevalence of cognitive decline and dementia is expected to rise, creating a significant health and economic burden. The purpose of this trial is to rigorously test, for the first time, the efficacy of yoga training as a physical activity intervention to mitigate age-related cognitive decline and impairment. We are conducting a 6-month randomized controlled trial (RCT) of exercise among 168 middle aged and older adults to compare the efficacy of yoga vs. aerobic exercise on cognitive function, brain structure and function, cardiorespiratory fitness, and circulating inflammatory and molecular markers. Using a single-blind, three arm RCT, 168 older adults ages 55-79 will be assigned to either: a Hatha yoga group, an aerobic exercise group or a stretching-toning active control group. Participants will engage in hour long group exercise 3x/week for 6-months. A comprehensive neurocognitive test battery, brain imaging, cardiovascular fitness test, and a blood draw will take place at baseline; end of the 6-month intervention, and at 12-month follow-up. Our primary outcomes of interest are brain regions, such as hippocampal volume and prefrontal cortex, and cognitive functions, such as episodic memory, working memory and executive functions, that are typically affected by aging and Alzheimer's disease. Not only will this RCT test whether yoga is a means to mitigate age-related cognitive decline, but it may also offer an alternative to aerobic exercise, which could be particularly appealing to older adults with compromised physical functioning. ClinicalTrials.gov Identifier: NCT04323163.

Functional neural network connectivity at 3 months predicts infant-mother dyadic flexibility during play at 6 months.

Early functioning of neural networks likely underlies the flexible switching between internal and external orientation and may be key to the infant's ability to effectively engage in social interactions. To test this hypothesis, we examined the association between infants' neural networks at 3 months and infant-mother dyadic flexibility (denoting the structural variability of their interaction dynamics) at 3, 6, and 9 months. Participants included thirty-five infants (37% girls) and their mothers (87% White). At 3 months, infants participated in a resting-state functional magnetic resonance imaging session, and functional connectivity (FC) within the default mode (DMN) and salience (SN) networks, as well as DMN-SN internetwork FC, were derived using a seed-based approach. When infants were 3, 6, and 9 months, infant-mother dyads completed the Still-Face Paradigm where their individual engagement behaviors were observed and used to quantify dyadic flexibility using state space analysis. Results revealed that greater within-DMN FC, within-SN FC, and DMN-SN anticorrelation at 3 months predicted greater dyadic flexibility at 6 months, but not at 3 and 9 months. Findings suggest that early synchronization and interaction between neural networks underlying introspection and salience detection may support infants' flexible social interactions as they become increasingly active and engaged social partners.

Myeloid sarcoma of the thoracic spine: A case report.

Background: Myeloid sarcoma is an uncommon malignant neoplasm that typically arises at extramedullary sites and is associated with a diagnosis of acute myeloid leukemia. While myeloid sarcoma can involve any organ, central nervous system involvement is rare, particularly in the adult population. Case Description: An 87-year-old female presented with progressive paraparesis of 5 days' duration. The magnetic resonance imaging (MRI) revealed an epidural tumor from T4 to T7 with cord compression. When she underwent a laminectomy for tumor resection, the pathology revealed a myeloid sarcoma with monocytic differentiation. Although she improved postoperatively, she elected to pursue hospice care and expired 4 months later. Conclusion: Myeloid sarcoma is an uncommon malignant spinal neoplasm rarely seen in adults. For this 87-year-old female, MRI-documented cord compression warranted decompressive surgery. Although this patient did not opt for adjuvant therapy, other patients with such lesions may undergo additional chemotherapy or radiation therapy. Nevertheless, optimal management for such malignant tumor is still undefined.

Enhancing linguistic research through 2-mm isotropic 3D dynamic speech MRI optimized by sparse temporal sampling and low-rank reconstruction.

PURPOSE: To enable a more comprehensive view of articulations during speech through near-isotropic 3D dynamic MRI with high spatiotemporal resolution and large vocal-tract coverage. METHODS: Using partial separability model-based low-rank reconstruction coupled with a sparse acquisition of both spatial and temporal models, we are able to achieve near-isotropic resolution 3D imaging with a high frame rate. The total acquisition time of the speech acquisition is shortened by introducing a sparse temporal sampling that interleaves one temporal navigator with four randomized phase and slice-encoded imaging samples. Memory and computation time are improved through compressing coils based on the region of interest for low-rank constrained reconstruction with an edge-preserving spatial penalty. RESULTS: The proposed method has been evaluated through experiments on several speech samples, including a standard reading passage. A near-isotropic 1.875 × 1.875 × 2 mm3 spatial resolution, 64-mm through-plane coverage, and a 35.6-fps temporal resolution are achieved. Investigations and analysis on specific speech samples support novel insights into nonsymmetric tongue movement, velum raising, and coarticulation events with adequate visualization of rapid articulatory movements. CONCLUSION: Three-dimensional dynamic images of the vocal tract structures during speech with high spatiotemporal resolution and axial coverage is capable of enhancing linguistic research, enabling visualization of soft tissue motions that are not possible with other modalities.

Establishing a Clinical Protocol for Velopharyngeal MRI and Interpreting Imaging Findings.

Traditional imaging modalities used to assess velopharyngeal insufficiency (VPI) do not allow for direct visualization of underlying velopharyngeal (VP) structures and musculature which could impact surgical planning. This limitation can be overcome via structural magnetic resonance imaging (MRI), the only current imaging tool that provides direct visualization of salient VP structures. MRI has been used extensively in research; however, it has had limited clinical use. Factors that restrict clinical use of VP MRI include limited access to optimized VP MRI protocols and uncertainty regarding how to interpret VP MRI findings. The purpose of this paper is to outline a framework for establishing a novel VP MRI scan protocol and to detail the process of interpreting scans of the velopharynx at rest and during speech tasks. Additionally, this paper includes common scan parameters needed to allow for visualization of velopharynx and techniques for the elicitation of speech during scans.

Establishing an MRI-Based Protocol and Atlas of the Bearded Dragon (Pogona vitticeps) Brain.

The bearded dragon (Pogona vitticeps) has become a popular companion lizard, and as such, clients have increasingly come to expect the application of advanced diagnostic and therapeutic options in their care. The purpose of this study was to establish an MRI-based protocol and brain atlas to improve diagnostic capabilities in bearded dragons presenting with neurologic dysfunction. Using a high-field 3T magnet, in vivo MRI of the brain was successfully performed in seven healthy bearded dragons utilizing an injectable anesthetic protocol utilizing intravenous alfaxalone. From this, we created an atlas of the brain in three planes, identifying nine regions of interest. A total scan time of 35 min allowed for the collection of a quality diagnostic scan and all lizards recovered without complication. This study provides practitioners a neuroanatomic reference when performing brain MRI on the bearded dragon along with a concise and rapid MRI protocol.

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging. METHODS: We propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end-to-end mapping from the initial sensitivity to the high-resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Cross-validation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods. RESULTS: The proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross-validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin-echo and MPRAGE datasets. CONCLUSION: A deep learning-based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE-based reconstructions especially when the ACS data are limited.

4D magnetic resonance imaging atlas construction using temporally aligned audio waveforms in speech.

Magnetic resonance (MR) imaging is becoming an established tool in capturing articulatory and physiological motion of the structures and muscles throughout the vocal tract and enabling visual and quantitative assessment of real-time speech activities. Although motion capture speed has been regularly improved by the continual developments in high-speed MR technology, quantitative analysis of multi-subject group data remains challenging due to variations in speaking rate and imaging time among different subjects. In this paper, a workflow of post-processing methods that matches different MR image datasets within a study group is proposed. Each subject's recorded audio waveform during speech is used to extract temporal domain information and generate temporal alignment mappings from their matching pattern. The corresponding image data are resampled by deformable registration and interpolation of the deformation fields, achieving inter-subject temporal alignment between image sequences. A four-dimensional dynamic MR speech atlas is constructed using aligned volumes from four human subjects. Similarity tests between subject and target domains using the squared error, cross correlation, and mutual information measures all show an overall score increase after spatiotemporal alignment. The amount of image variability in atlas construction is reduced, indicating a quality increase in the multi-subject data for groupwise quantitative analysis.