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Background and Purpose Previous studies have assessed the relationship between cerebral vessel tortuosity and intracranial aneurysm (IA) based on two-dimensional brain image analysis. We evaluated the relationship between cerebral vessel tortuosity and IA according to the hemodynamic location using three-dimensional (3D) analysis and studied the effect of tortuosity on the recurrence of treated IA.Methods We collected clinical and imaging data from patients with IA and disease-free controls. IAs were categorized into outer curvature and bifurcation types. Computerized analysis of the images provided information on the length of the arterial segment and tortuosity of the cerebral arteries in 3D space.Results Data from 95 patients with IA and 95 controls were analyzed. Regarding parent vessel tortuosity index (TI; P

Background The purpose of this study was to develop a software tool and evaluate different T1 map calculation methods in terms of computation time in cardiac magnetic resonance imaging. Methods The modified Look-Locker inversion recovery (MOLLI) sequence was used to acquire multiple inversion time (TI) images for pre- and post-contrast T1 mapping. The T1 map calculation involved pixel-wise curve fitting based on the T1 relaxation model. A variety of methods were evaluated using data from 30 subjects for computational efficiency: MRmap, python Levenberg–Marquardt (LM), python reduced-dimension (RD) non-linear least square, C++ single- and multi-core LM, and C++ single- and multi-core RD. Results Median (interquartile range) computation time was 126 s (98–141) for the publicly available software MRmap, 261 s (249–282) for python LM, 77 s (74–80) for python RD, 3.4 s (3.1–3.6) for C++ multi-core LM, and 1.9 s (1.9–2.0) for C++ multi-core RD. The fastest C++ multi-core RD and the publicly available MRmap showed good agreement of myocardial T1 values, resulting in 95% Bland–Altman limits of agreement of (− 0.83 to 0.58 ms) and (− 6.57 to 7.36 ms) with mean differences of − 0.13 ms and 0.39 ms, for the pre- and post-contrast, respectively. Conclusion The C++ multi-core RD was the fastest method on a regular eight-core personal computer for pre- or post-contrast T1 map calculation. The presented software tool (fT1fit) facilitated rapid T1 map and extracellular volume fraction map calculations.

In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where automatic classification of the slice range of interest is preferable. Standard cardiac image post-processing guidelines indicate the importance of the correct identification of a short axis slice range for accurate quantification. We investigated the feasibility of applying transfer learning of deep convolutional neural networks (CNNs) as a means to automatically classify the short axis slice range, as transfer learning is well suited to medical image data where labeled data is scarce and expensive to obtain. The short axis slice images were classified into out-of-apical, apical-to-basal, and out-of-basal, on the basis of short axis slice location in the LV. We developed a custom user interface to conveniently label image slices into one of the three categories for the generation of training data and evaluated the performance of transfer learning in nine popular deep CNNs. Evaluation with unseen test data indicated that among the CNNs the fine-tuned VGG16 produced the highest values in all evaluation categories considered and appeared to be the most appropriate choice for the cardiac slice range classification.

Background and Purpose— We hypothesized that the pial collateral status at the time of presentation could predict the infarct size on magnetic resonance imaging in patients with similar degrees of early ischemic changes on computed tomography. We tested the association between serial changes in collateral status and infarct volume defined on diffusion-weighted imaging (DWI) in patients with large vessel occlusion and small core. Methods— Consecutive patients who were candidates for endovascular treatment (Alberta Stroke Program Early CT Score [ASPECTS] of ≥6 points) and who underwent both pretreatment multiphasic computed tomography angiography (mCTA) and multimodal magnetic resonance imaging were enrolled. The baseline early ischemic changes and collateral status were determined using both mCTA and magnetic resonance imaging–based collateral maps. Multivariable linear regression was used to evaluate adjusted estimates of the effect of collateral status on predicting MR DWI lesion volume before endovascular treatment. Results— Of 65 patients (39 men; median age, 76 years; median ASPECTS, 8 points [range, 6–10]), 10 (15.4%), 8 (12.3%), and 47 (72.3%) presented poor, intermediate, and good collaterals on mCTA, respectively. After adjusting for the initial stroke severity, ASPECTS, time to DWI, and mismatch volume, the mCTA collateral grade was the only factor independently associated with the DWI lesion volume (β=−35.657, SE mean=3.539; P Conclusions— The mCTA assessed collateral adequacy is the sole predictor of eventual DWI lesion volume before endovascular treatment. The added value of collateral assessment in early ischemic changes and large vessel occlusion for decision-making regarding more aggressive revascularizations requires further evaluation. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT03234634 and NCT02668627.

Background and Purpose— Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ADC values vary over time after stroke onset. Deep learning has the potential to improve the accuracy, provided that a large set of correctly annotated lesion data is used for training. The purpose of this study was to evaluate deep learning–based methods and compare them with commercial software in terms of lesion volume measurements. Methods— U-net, an encoder-decoder convolutional neural network, was adopted to train segmentation models. Two U-net models were developed: a U-net (DWI+ADC) model, trained on DWI and ADC data, and a U-net (DWI) model, trained on DWI data only. A total of 296 subjects were used for training and 134 for external validation. An expert neurologist manually delineated the stroke lesions on DWI images, which were used as the ground-truth reference. Lesion volume measurements from the U-net methods were compared against the expert’s manual segmentation and Rapid Processing of Perfusion and Diffusion (RAPID; iSchemaView Inc) analysis. Results— In external validation, U-net (DWI+ADC) showed the highest intraclass correlation coefficient with manual segmentation (intraclass correlation coefficient, 1.0; 95% CI, 0.99–1.00) and sufficiently high correlation with the RAPID results (intraclass correlation coefficient, 0.99; 95% CI, 0.98–0.99). U-net (DWI+ADC) and manual segmentation resulted in the smallest 95% Bland-Altman limits of agreement (−5.31 to 4.93 mL) with a mean difference of −0.19 mL. Conclusions— The presented deep learning–based method is fully automatic and shows a high correlation of diffusion lesion volume measurements with manual segmentation and commercial software. The method has the potential to be used in patient selection for endovascular reperfusion therapy in the late time window of acute stroke.

Objective Clot characteristics can provide information on the cause of cerebral artery occlusion and may guide acute revascularization and secondary prevention strategies. We developed a rapid automated clot analysis system using machine learning (ML) and validated its accuracy in patients undergoing endovascular treatment. Methods Pre‐endovascular treatment gradient echo (GRE) images from consecutive patients with middle cerebral artery occlusion were utilized to develop and validate an ML system to predict whether atrial fibrillation (AF) was the underlying cause of ischemic stroke. The accuracy of the ML algorithm was compared with that of visual inspection by neuroimaging specialists for the presence of blooming artifact. Endovascular procedures and outcomes were compared in patients with and without AF. Results Of 67 patients, 29 (43.3%) had AF. Of these, 13 had known AF and 16 were newly diagnosed with cardiac monitoring. By visual inspection, interrater correlation for blooming artifact was 0.73 and sensitivity and specificity for AF were 0.79 and 0.63, respectively. For AF classification, the ML algorithms yielded an average accuracy of > 75.4% in fivefold cross‐validation with clot signal profiles obtained from 52 patients and an area under the curve >0.87 for the average AF probability from five signal profiles in external validation (n = 15). Analysis with an in‐house interface took approximately 3 min per patient. Absence of AF was associated with increased number of passes by stentriever, high reocclusion frequency, and additional use of rescue stenting and/or glycogen IIb/IIIa blocker for recanalization. Interpretation ML‐based rapid clot analysis is feasible and can identify AF with high accuracy, enabling selection of endovascular treatment strategy.

While the penumbra zone is traditionally assessed based on perfusion&ndash, diffusion mismatch, it can be assessed based on machine learning (ML) prediction of infarct growth. The purpose of this work was to develop and validate an ML method for the prediction of infarct growth distribution and volume, in cases of successful (SR) and unsuccessful recanalization (UR). Pre-treatment perfusion-weighted, diffusion-weighted imaging (DWI) data, and final infarct lesions annotated from day-7 DWI from patients with middle cerebral artery occlusion were utilized to develop and validate two ML models for prediction of tissue fate. SR and UR models were developed from data in patients with modified treatment in cerebral infarction (mTICI) scores of 2b&ndash, 3 and 0&ndash, 2a, respectively. When compared to manual infarct annotation, ML-based infarct volume predictions resulted in an intraclass correlation coefficient (ICC) of 0.73 (95% CI = 0.31&ndash, 0.91, p &lt, 0.01) for UR, and an ICC of 0.87 (95% CI = 0.73&ndash, 0.94, p &lt, 0.001) for SR. Favorable outcomes for mismatch presence and absence in SR were 50% and 36%, respectively, while they were 61%, 56%, and 25%, respectively, for the low, intermediate, and high infarct growth groups. The presented method can offer novel and alternative insights into selecting patients for recanalization therapy and predicting functional outcome.

The human upper airway is involved in various functions, including speech, swallowing, and respiration. Magnetic resonance imaging (MRI) can visualize the motion of the upper airway and has been used in scientific studies to understand the dynamics of vocal tract shaping during speech and for assessment of upper airway abnormalities related to obstructive sleep apnea and swallowing disorders. Acceleration technologies in MRI are crucial in improving spatiotemporal resolution or spatial coverage. Recent trends in technical aspects of upper airway MRI are to develop state-of-the-art image acquisition methods for improved dynamic imaging of the upper airway and develop automatic image analysis methods for efficient and accurate quantification of upper airway parameters of interest. This review covers the fast upper airway magnetic resonance (MR) acquisition and reconstruction, MR experimental issues, image analysis techniques, and applications, mainly with respect to studies of speech production and sleep apnea.

Automatic localization of anatomical landmarks in myocardial perfusion magnetic resonance (MR) data is considered to be a preliminary step toward fully automatic quantification of regional upslope or blood flow in the myocardium. The goal of this work is to develop an automatic method based on supervised learning and detect anatomical landmarks with high accuracy from myocardial perfusion MR data. Dynamic contrast enhanced myocardial MR perfusion data were acquired using a standard perfusion sequence. To effectively extract characteristic features for left ventricle (LV) center point and anterior right ventricle (RV) insertion point, we performed feature extraction of the two landmarks independently from an LV enhancement frame and an RV enhancement frame. Feature extraction of pixel intensity, Sobel gradient, and Haar-like features was performed. Cross-validation from training data was used to build a random forests classifier. We used 38 subjects’ data as training datasets and 21 subjects’ data as test datasets. The proposed method provided high accuracy in localization of the LV center point and anterior RV insertion point. The mean (±SD) localization errors of the proposed method were 3.38 (±2.36) mm for the LV center point and 4.23 (±1.97) mm for the anterior RV insertion point. The proposed method shows the potential to automatically localize anatomical landmarks for the segmental analysis of myocardial perfusion in MRI.

Background Intracranial atherosclerotic stroke is prevalent in Asians. We hypothesized that patients with the ring finger protein 213 ( RNF213 ) variant, a susceptibility locus for moyamoya disease in Asians, have different neuroimaging characteristics in terms of the vessel wall and hemodynamics. Methods and Results We analyzed consecutive patients with ischemic events in middle cerebral artery distribution and relevant plaques of the distal internal carotid artery or proximal middle cerebral artery on high‐resolution magnetic resonance imaging. Patients with carotid/cardiac sources of embolism or moyamoya disease were excluded. High‐resolution magnetic resonance imaging features (eg, outer vessel diameters and plaque characteristics) and fractional flow (as measured by adjusted signal intensity ratio on time‐of‐flight magnetic resonance angiography) were compared between RNF213 p.Arg4810Lys variant carriers and noncarriers. Among 144 patients included, 44 (29.9%) had the RNF213 variant. Clinical characteristics, including age, sex, body mass index, and vascular risk factors, were not significantly different between RNF213 variant carriers and noncarriers. However, the outer vessel diameter was smaller in RNF213 variant carriers than in noncarriers ( P P P Conclusions The RNF213 variant may be associated with vasculogenesis, but not with atherogenesis. Patients with this variant had small intracranial arteries predisposing hemodynamic compromise in the presence of intracranial atherosclerosis. In addition to antiatherosclerotic strategies, further studies are warranted to develop novel therapeutic strategies against RNF213 vasculopathy in Asians.

Background: Intracranial atherosclerotic stroke is prevalent in Asians. We hypothesized patients with the Ring Finger protein 213 ( RNF213 ) variant, a susceptibility locus for moyamoya disease in East Asians, have different neuroimaging characteristics in terms of vessel wall and hemodynamics. Methods: We analyzed consecutive patients with ischemic events in the middle cerebral artery (MCA) distribution and relevant plaque on the distal internal carotid artery or proximal MCA on high-resolution MRI (HR-MRI). Patients with carotid/cardiac sources of embolism or angiographic features of moyamoya disease were excluded. HR-MRI features (e.g., outer vessel diameters and plaque characteristics) and fractional flow (as measured by adjusted signal intensity ratio on time-of-flight MRA) were compared between RNF213 p.Arg4810Lys variant carriers and non-carriers. Results: Of 144 patients included, 44 (29.9%) patients had the RNF213 variant. Clinical characteristics including age, gender, and vascular risk factors were not different between RNF213 variant carriers and non-carriers. However, outer vessel diameter was smaller in RNF213 variant carriers than in non-carriers (p Conclusions: The RNF213 variant may be associated with vasculogenesis but not with atherogenesis. Patients with this variant had small intracranial arteries predisposing hemodynamic compromise in the presence of intracranial atherosclerosis. Beside anti-atherosclerotic strategies, further studies are warranted to develop a novel therapeutic strategies against RNF213 vasculopathy in Asians.

BackgroundRecently there have been several clinical MR perfusion studies in patients with hypertrophic cardiomyopathy (HCM) who may suffer from myocardial ischemia due to coronary microvascular dysfunction. In these studies, data analysis relied on a manual procedure of tracing epicardial and endocardial borders. The goal of this work is to develop and validate a robust semi-automated analysis method for myocardial perfusion quantification in clinical HCM data. MethodDynamic multi-slice stress perfusion MRI data were acquired from 18 HCM patients. The proposed semi-automated method required user input of two landmark selections: LV center point and RV insertion point. Automated segmentations of the endocardial and epicardial borders were performed in three short-axis slices using distance regularized level set evolution on RV, LV, and myocardial enhancement frames. ResultsThe proposed automated epicardial border detection method resulted in average radial distance errors of 7.5%, 9.5%, and 11.6% in basal, mid, and apical slices, respectively, when compared to manual tracing of the borders as a reference. In linear regression analysis, the highest correlation of myocardial upslope measurements was observed between the manual method and the proposed method in the anterolateral section (r=0.964), and the lowest correlation was observed in the inferoseptal section (r=0.866). ConclusionThe proposed semi-automated method for myocardial MR perfusion quantification is feasible in HCM patients who typically show (1) irregular myocardial shape and (2) low image contrast between the myocardium and its surrounding regions due to coronary microvascular disease. Semi-automated quantification of multi-slice cardiac MR perfusion is proposed.LV center and RV insertion points are manually chosen, and remaining steps are automated.Myocardium is automatically segmented using distance regularized level set evolution.Accuracy of myocardial upslope quantification is evaluated on clinical HCM data.

Purpose The aim of this work was to develop and evaluate an MRI-based system for study of dynamic vocal tract shaping during speech production, which provides high spatial and temporal resolution. Methods The proposed system utilizes (a) custom eight-channel upper airway coils that have high sensitivity to upper airway regions of interest, (b) two-dimensional golden angle spiral gradient echo acquisition, (c) on-the-fly view-sharing reconstruction, and (d) off-line temporal finite difference constrained reconstruction. The system also provides simultaneous noise-cancelled and temporally aligned audio. The system is evaluated in 3 healthy volunteers, and 1 tongue cancer patient, with a broad range of speech tasks. Results We report spatiotemporal resolutions of 2.4 × 2.4 mm2 every 12 ms for single-slice imaging, and 2.4 × 2.4 mm2 every 36 ms for three-slice imaging, which reflects roughly 7-fold acceleration over Nyquist sampling. This system demonstrates improved temporal fidelity in capturing rapid vocal tract shaping for tasks, such as producing consonant clusters in speech, and beat-boxing sounds. Novel acoustic-articulatory analysis was also demonstrated. Conclusion A synergistic combination of custom coils, spiral acquisitions, and constrained reconstruction enables visualization of rapid speech with high spatiotemporal resolution in multiple planes. Magn Reson Med 77:112–125, 2017. © 2016 Wiley Periodicals, Inc.

Background and Objective Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation. Methods A U-Net segmentation model was trained on the cardiac cine data. Monte Carlo dropout sampling of the U-Net model was performed on the dynamic perfusion datasets frame-by-frame to estimate the standard deviation (SD) maps. The uncertainty estimate based on the sum of the SD values was used to select the optimal frames for endocardial and epicardial segmentations. DCE perfusion data from 35 subjects (14 subjects with coronary artery disease, 8 subjects with hypertrophic cardiomyopathy, and 13 healthy volunteers) were evaluated. The Dice similarity scores of the proposed method were compared with those of a semi-automatic U-Net segmentation method, which involved user selection of an image frame for segmentation in the cardiac perfusion dataset. Results The proposed method was fully automatic and did not require manual labeling of the cardiac perfusion image data for model development. The mean Dice similarity score of the proposed automatic method was 0.806 (±0.096), which was comparable to the 0.808 (±0.084) Dice score of the semi-automatic U-Net segmentation method (intraclass correlation coefficient = 0.61, P Conclusions Our study demonstrated the feasibility of applying an existing model trained on cardiac cine data to dynamic cardiac perfusion data to achieve robust and automatic segmentation of the myocardium. The uncertainty estimates can be used for screening purposes, which would facilitate the cases with high endocardial and epicardial uncertainty estimates to be sent for further evaluation and correction by human experts.

Rationale: The use of real-time magnetic resonance imaging (MRI) for the evaluation during sleep-related respiratory events can lead to better understanding of airway dynamics. Objectives: To investigate the dynamic anatomy of the upper airway during central apnea. Methods: The study included obese adolescents who snore and were otherwise healthy. Subjects underwent an overnight baseline polysomnogram. Subjects slept during a 24-minute real-time upper airway MRI scan wearing a full face mask attached to a pneumotach. Sleep versus wakefulness during the MRI was inferred from the heart rate and respiratory patterns. Central apneas were scored using tracings of facemask airflow and abdominal bellows. The cross-sectional area of the upper airway before, during, and after each central apnea event was recorded. Results: Eight subjects were studied and 57 central apnea events were observed during real-time MRI scanning during natural sleep. The median age of subjects was 14.0 years (interquartile range [IQR], 13.5 to 15.5). The median average reduction in cross-sectional area during central apnea events was −38% (IQR, −27 to −51) for primary snorers and −45% (IQR, −40 to −54) for subjects with obstructive sleep apnea. The percentage decrease in cross-sectional area of upper airway during a central apnea event was positively correlated to the length of the central apnea (ρ = 0.389; r(2) = 0.152; P = 0.003). Conclusions: We observed that there is upper airway narrowing during central apneas during natural sleep in obese adolescent subjects, using real-time MRI.

To determine the usefulness of extracellular contrast agent (ECA)-enhanced multiphasic liver magnetic resonance imaging (MRI) using a pseudo-golden-angle radial acquisition scheme by intra-individual comparison with gadoxetic acid-MRI (EOB-MRI) with regard to image quality and the diagnosis of hepatocellular carcinoma (HCC). This prospective study enrolled 15 patients with 18 HCCs who underwent EOB-MRI using a Cartesian approach and ECA-MRI using the pseudo-golden-angle radial acquisition scheme (free-breathing continuous data acquisition for 64 s following ECA injection, generating six images). Two reviewers evaluated the arterial and portal phases of each MRI for artifacts, organ sharpness, and conspicuity of intrahepatic vessels and the hepatic tumors. A Liver Imaging Reporting and Data System category was also assigned to each lesion. There were no differences in the subjective image quality analysis between the arterial phases of two MRIs (p > 0.05). However, ghosting artifact was seen only in EOB-MRI (N = 3). Six HCCs showed different signal intensities in the arterial phase or portal phase between the two MRIs; five HCCs showed arterial hyperenhancement on ECA-MRI, but not on EOB-MRI. The capsule was observed in 15 HCCs on ECA-MRI and 6 HCCs on EOB-MRI. Five and one HCC were assigned as LR-5 and LR-4 with ECA-MRI and LR-4 and LR-3 with EOB-MRI, respectively. Free-breathing ECA-enhanced multiphasic liver MRI using a pseudo-golden-angle radial acquisition was more sensitive in detecting arterial hyperenhancement of HCC than conventional EOB-MRI, and the image quality was acceptable. • The pseudo-golden-angle radial acquisition scheme can be applied to perform free-breathing multiphasic dynamic liver MRI. • Adopting the pseudo-golden-angle radial acquisition scheme can improve the detection of arterial enhancement of HCC. • The pseudo-golden-angle radial acquisition scheme enables motion-free liver MRI.

Revascularization therapies have been established as the treatment mainstay for acute ischemic stroke. However, a substantial number of patients are either ineligible for revascularization therapy, or the treatment fails or is futile. At present, non-contrast computed tomography is the first-line neuroimaging modality for patients with acute stroke. The use of magnetic resonance imaging (MRI) to predict the response to early revascularization therapy and to identify patients for delayed treatment is desirable. MRI could provide information on stroke pathophysiologies, including the ischemic core, perfusion, collaterals, clot, and blood-brain barrier status. During the past 20 years, there have been significant advances in neuroimaging as well as in revascularization strategies for treating patients with acute ischemic stroke. In this review, we discuss the role of MRI and post-processing, including machine-learning techniques, and recent advances in MRI-based triage for revascularization therapies in acute ischemic stroke.

Obstructive sleep apnea (OSA) is the most common type of sleep apnea and is characterized by repetitive episodes of airflow cessation during sleep as a result of the collapse of the pharyngeal airway (1). The prevalence of OSA syndrome has been estimated at 2% of the general population of children (2), 13–66% of obese children (3), and 4–9% of adults (2). Airway collapse during sleep is caused by a combination of anatomical and functional factors. Anatomical factors include inherently narrow upper airway resulting from 1) enlarged adenoids or tonsils, 2) a large soft palate, and 3) a large tongue. Functional factors include decrease in the activation of pharyngeal dilator muscles with sleep onset. Obesity contributes to a narrow airway and airway collapsibility (4), and the prevalence of obesity is on the rise. Despite breathing efforts, airway obstruction results in no gas exchange leading to oxygen desaturation and frequent arousals during sleep. Pathologic central sleep apnea (CSA) is relatively rare when compared with OSA and is characterized by absent respiratory effort as well as airflow cessation during sleep, accompanied by arousal and/or hypoxemia. Airway narrowing and/or closure occur during CSA (5). CSAs have been reported in obese children and adolescents with sleep-disordered breathing (3). Complex sleep apnea is a combination of OSA and CSA, and patients with complex sleep apnea exhibit CSAs even during a continuous positive airway pressure treatment of airway obstruction (6). Knowledge of obstruction sites with an imaging tool has been of great interest to researchers because direct inspection of the airway may facilitate treatment planning and may have the potential to improve treatment outcome (7–9). Imaging techniques including endoscopy, fluoroscopy, computed tomography, and magnetic resonance imaging (MRI) have been applied to subjects with OSA, and each approach has its own unique limitations (10). MRI is noninvasive, involves no ionizing radiation, and has provided valuable insights into understanding upper airway anatomy and function in patients with OSA syndrome. In prior studies, static 3D MRI revealed narrowed airway volume in patients with OSA when compared with age-matched controls without OSA (11). Respiratory-gated dynamic 2D (12) and 3D (13,14) MRI have been proposed to measure the changes in the upper airway caliber in relation to the respiratory cycle during tidal breathing and compare them between subjects with and without OSA syndrome. Real-time MRI (i.e., MRI using an acquisition time per frame that is short relative to the dynamic process of interest) has been used to demonstrate airway collapse in OSA patients during sleep with (15,16) and without sedation (17–19), but has been limited to a 2D slice of axial or mid-sagittal orientation. Recently, Shin et al. (18,19) demonstrated visualization of airway collapse in adult patients with OSA using real-time interactive multislice MRI, in which slice prescriptions were made in axial, mid-sagittal, and coronal slices. The most common approach is 2D mid-sagittal MRI, which provides only partial coverage of a narrowed upper airway and is very sensitive to imperfect localization and to patient head motion. Similarly, 2D axial MRI can reveal temporal changes in airway cross-sectional area, but it is easy to miss the actual site of collapse. Real-time 3D MRI would be desirable, because it provides complete coverage of the airway and does not require prior knowledge of the location of collapse. In this work, we propose a real-time 3D MRI acquisition using a novel temporal view order and a reconstruction technique that provides high spatial and temporal resolution with the combined use of parallel imaging and constrained reconstruction. We demonstrate its effectiveness in visualizing marked reduction of the pharyngeal airway during inspiratory loading in volunteers and identifying locations of airway narrowing and/or collapse in pediatric subjects with sleep-disordered breathing during natural sleep.

Antimony (Sb) doping of SnO 2 nanowires (NWs) was investigated for its optical and electrical effects. The low-temperature photoluminescence spectra of SnO 2 NWs varied significantly with increasing Sb content, where the temperature-dependence of the visible emission at ca. 400 nm was distinctive with Sb-doping, indicating different defect states, such as neutral and positively charged oxygen vacancies. Field effect transistors (FETs) with low-level Sb-doped SnO 2 NW channels exhibited higher mobility, charge concentration, and faster response and recovery to UV light than intrinsic SnO 2 NW FETs.

We report on the fabrication and electronic properties of single-walled carbon nanotube (SWCNT)/tin oxide nanowire (SnO 2 NW) heterojunction device arrays on fl exible polyimide (PI) substrates. Hetero-NW junctions consisting of crossed SnO 2 NWs and SWCNTs were fabricated by sliding transfer of SnO 2 NWs onto the SWCNT channels on PI substrate. Individual SWCNTs and SnO 2 NWs fi eld effect transistors showed pand n-type transfer properties with current on/off ratios of 7.0 10 5 and 2.7 10 6 , respectively. The heterojunction diode showed a rectifying behavior with a rectifi cation ratio of higher than 10 3 at ± 1 V and the analysis with an equivalent circuit model of serially connected diode and resistor estimated an ideality factor of 1.5 and the resistance of 20 M Ω . The rectifi cation of AC input signal was clearly demonstrated by fabricating a full-wave bridge circuit of heterojunctions. In addition, the heterojunctions showed a high UV photosensitivity of ∼ 10 4 under reverse bias, suggesting their implicit applications in UV sensors.

In speech production research using real-time magnetic resonance imaging (MRI), the analysis of articulatory dynamics is performed retrospectively. A flexible selection of temporal resolution is highly desirable because of natural variations in speech rate and variations in the speed of different articulators. The purpose of the study is to demonstrate a first application of golden-ratio spiral temporal view order to real-time speech MRI and investigate its performance by comparison with conventional bit-reversed temporal view order. Golden-ratio view order proved to be more effective at capturing the dynamics of rapid tongue tip motion. A method for automated blockwise selection of temporal resolution is presented that enables the synthesis of a single video from multiple temporal resolution videos and potentially facilitates subsequent vocal tract shape analysis.

In speech-production research, three-dimensional (3D) MRI of the upper airway has provided insights into vocal tract shaping and data for its modeling. Small movements of articulators can lead to large changes in the produced sound, therefore improving the resolution of these data sets, within the constraints of a sustained speech sound (6 –12 s), is an important area for investigation. The purpose of the study is to provide a first application of compressed sensing (CS) to high-resolution 3D upper airway MRI using spatial finite difference as the sparsifying transform, and to experimentally determine the benefit of applying constraints on image phase. Estimates of image phase are incorporated into the CS reconstruction to improve the sparsity of the finite difference of the solution. In a retrospective subsampling experiment with no sound production, 5 and 4 were the highest acceleration factors that produced acceptable image quality when using a phase constraint and when not using a phase constraint, respectively. The prospective use of a 5 undersampled acquisition and phaseconstrained CS reconstruction enabled 3D vocal tract MRI during sustained sound production of English consonants /s/, //, /l/, and /r/ with 1.5 1.5 2.0 mm 3 spatial resolution an d7so fscan

When transmission images are obtained using conventional reconstruction methods in stand alone PET scanners, such as standard clinical PET, microPET, and dedicated brain scanners, the results may be noisy and/or inaccurate. For example, the popular penalized maximum-likelihood method effectively reduces noise, but it does not address the bias problem that results from the incorporation of a penalty function and contamination from emission data due to patient activity. In this paper, we present an algorithm that simultaneously reconstructs transmission images and performs a ldquosoftrdquo segmentation of voxels into the classes: air, patient bed, lung, soft-tissue, and bone. It is through the segmentation step that the algorithm, which we refer to as the concurrent segmentation and estimation (CSE) algorithm, provides a means for incorporating accurate attenuation coefficients. The CSE algorithm is obtained by applying an expectation-maximization like formulation to a certain maximum a posterior objective function. This formulation enables us to show that the CSE algorithm monotonically increases the objective function. In experiments using real phantom and synthetic data, the CSE images produced attenuation correction factors and emission images that were more accurate than those obtained using a popular segmentation based attenuation correction method, and the penalized maximum likelihood and filtered backprojection methods.

In this paper real-time (RT) magnetic resonance imaging (MRI) is used to study speech production especially capturing vocal tract shaping.

Goal : We demonstrate a novel and robust approach for visualization of upper airway dynamics and detection of obstructive events from dynamic 3-D magnetic resonance imaging (MRI) scans of the pharyngeal airway. Methods: This approach uses 3-D region growing, where the operator selects a region of interest that includes the pharyngeal airway, places two seeds in the patent airway, and determines a threshold for the first frame. Results: This approach required 5 s/frame of CPU time compared to 10 min/frame of operator time for manual segmentation. It compared well with manual segmentation, resulting in Dice Coefficients of 0.84 to 0.94, whereas the Dice Coefficients for two manual segmentations by the same observer were 0.89 to 0.97. It was also able to automatically detect 83% of collapse events. Conclusion: Use of this simple semiautomated segmentation approach improves the workflow of novel dynamic MRI studies of the pharyngeal airway and enables visualization and detection of obstructive events. Significance: Obstructive sleep apnea (OSA) is a significant public health issue affecting 4–9% of adults and 2% of children. Recently, 3-D dynamic MRI of the upper airway has been demonstrated during natural sleep, with sufficient spatiotemporal resolution to noninvasively study patterns of airway obstruction in young adults with OSA. This study makes it practical to analyze these long scans and visualize important factors in an MRI sleep study, such as the time, site, and extent of airway collapse.

Purpose: To develop a method that automatically corrects ghosting artifacts due to echo-misalignment in interleaved gradient-echo echo-planar imaging (EPI) in arbitrary oblique or double-oblique scan planes. Materials and Methods: An automatic ghosting correction technique was developed based on an alternating EPI acquisition and the phased-array ghost elimination (PAGE) reconstruction method. The direction of k-space traversal is alternated at every temporal frame, enabling lower temporal-resolution ghost-free coil sensitivity maps to be dynamically estimated. The proposed method was compared with conventional one-dimensional (1D) phase correction in axial, oblique, and double-oblique scan planes in phantom and cardiac in vivo studies. The proposed method was also used in conjunction with two-fold acceleration. Results: The proposed method with nonaccelerated acquisition provided excellent suppression of ghosting artifacts in all scan planes, and was substantially more effective than conventional 1D phase correction in oblique and double-oblique scan planes. The feasibility of real-time reconstruction using the proposed technique was demonstrated in a scan protocol with 3.1-mm spatial and 60-msec temporal resolution. Conclusion: The proposed technique with nonaccelerated acquisition provides excellent ghost suppression in arbitrary scan orientations without a calibration scan, and can be useful for real-time interactive imaging, in which scan planes are frequently changed with arbitrary oblique orientations.

This study was carried out to investigate the protective effects of the hot-water extract from Artemisia iwayomogi (Compositae) on carbon tetrachloride-induced liver fibrosis in rats. Liver injury was induced by oral administration of carbon tetrachloride (1 mL kg−1) twice a week during 4 weeks of A. iwayomogi treatment. Extracts from A. iwayomogi were prepared and administered to rats orally (2 g kg−1 as A. iwayomogi for 4 weeks) as follows: group 1, hot-water extract; group 2, ethanol-soluble part of hot-water extract; group 3, ethanol-insoluble part of hot-water extract; and group 4, methanol extract. In rats treated with the ethanol-soluble part of the hot-water extract, liver hydroxyproline content was reduced to 74% that of carbon tetrachloride control rats (P < 0.05). Protein expression of alpha smooth muscle cell like actin was also decreased in rats treated with the ethanol-soluble part of the hot-water extract, which indicates inhibition of hepatic stellate cell activation. Liver malondialdehyde levels were significantly lowered in rats treated with the ethanol-soluble part of hot-water extract (P < 0.05). Serum cholesterol levels in rats treated with hot-water extract, ethanol-soluble or -insoluble parts of hot-water extract or methanol extract were significantly reduced when compared with those of carbon tetrachloride control rats (P < 0.05). The ethanol-soluble part of the hot-water extract from A. iwayomogi inhibited fibrosis and lipid peroxidation in rats with liver fibrosis induced by carbon tetrachloride. Both hot-water extract (either ethanol-soluble or -insoluble) and methanol extract of A. iwayomogi also lowered serum cholesterol levels in fibrotic rats.

Importance Magnetic resonance imaging (MRI) has the advantage of imaging swallow function at any anatomical level without changing the position of patient, which can provide detailed information than modified barium swallow, by far the gold standard of swallow evaluation. Objective To investigate the use of real-time MRI in the evaluation of swallow function of patients with tongue cancer. Design, Setting, and Participants Real-time MRI experiments were performed on a Signa Excite HD 1.5-T scanner (GE Healthcare), with gradients capable of 40-mT/m (milli-Tesla per meter) amplitudes and 150-mT/m/ms (mT/m per millisecond) slew rates. The sequence used was spiral fast gradient echo sequence. Four men with base of tongue or oral tongue squamous cell carcinoma and 3 age-matched healthy men with normal swallowing participated in the experiment. Interventions Real-time MRI of the midsagittal plane was collected during swallowing. Coronal planes between the oral tongue and base of tongue and through the middle of the larynx were collected from 1 of the patients. Main Outcomes and Measures Oral transit time, pharyngeal transit time, submental muscle length change, and the distance change between the hyoid bone and anterior boundary of the thyroid cartilage were measured frame by frame during swallowing. Results All the measurable oral transit and pharyngeal transit times of the patients with cancer were significantly longer than the ones of the healthy participants. The changes in submental muscle length and the distance between the hyoid bone and thyroid cartilage happened in concert for all 60 normal swallows; however, the pattern differed for each patient with cancer. To our knowledge, the coronal view of the tongue and larynx revealed information that has not been previously reported. Conclusions and Relevance This study has demonstrated the potential of real-time MRI to reveal critical information beyond the capacity of traditional videofluoroscopy. Further investigation is needed to fully consider the technique, procedure, and standard scope of applying MRI to evaluate swallow function of patients with cancer in research and clinic practice.

Conventional real-time magnetic resonance imaging (RTMRI) of the upper airway typically acquires information about the vocal tract from a single midsagittal scan plane. This provides insights into the dynamics of all articulators, but does not allow for visualization of several important features in vocal tract shaping, such as grooving/doming of the tongue, asymmetries in tongue shape, and lateral shaping of the pharyngeal airway. In this paper, we present an approach to RT-MRI of multiple scan planes of interest using timeinterleaved acquisition, in which temporal resolution is compromised for greater spatial coverage. We demonstrate simultaneous visualization of vocal tract dynamics from midsagittal, coronal, and axial scan planes in the articulation of English fricatives.