Purpose:To develop an isotropic high-resolution stack-of-spirals UTE sequence for pulmonary imaging at 0.55 Tesla by leveraging a combination of robust respiratory-binning, trajectory correction, and concomitant-field corrections. Methods:A stack-of-spirals golden-angle UTE sequence was used to continuously acquire data for 15.5 minutes. The data was binned to a stable respiratory phase based on superoinferior readout self-navigator signals. Corrections for trajectory errors and concomitant field artifacts, along with image reconstruction with conjugate gradient SENSE, were performed inline within the Gadgetron framework. Finally, data were retrospectively reconstructed to simulate scan times of 5, 8.5, and 12 minutes. Image quality was assessed using signal-to-noise, image sharpness, and qualitative reader scores. The technique was evaluated in healthy volunteers, patients with coronavirus disease 2019 infection, and patients with lung nodules. Results:The technique provided diagnostic quality images with parenchymal lung SNR of 3.18 ± 0.0.60, 4.57 ± 0.87, 5.45 ± 1.02, and 5.89 ± 1.28 for scan times of 5, 8.5, 12, and 15.5 minutes, respectively. The respiratory binning technique resulted in significantly sharper images (p < 0.001) as measured with relative maximum derivative at the diaphragm. Concomitant field corrections visibly improved sharpness of anatomical structures away from iso-center. The image quality was maintained with a slight loss in SNR for simulated scan times down to 8.5 minutes. Inline image reconstruction and artifact correction were achieved in <5 minutes.

The purpose of this study was to evaluate oxygen‐enhanced pulmonary imaging at 0.55 T with 3D stack‐of‐spirals ultrashort‐TE (UTE) acquisition. Oxygen‐enhanced pulmonary MRI offers the measurement of regional lung ventilation and perfusion using inhaled oxygen as a contrast agent. Low‐field MRI systems equipped with contemporary hardware can provide high‐quality structural lung imaging by virtue of the prolonged T2*. Fortuitously, the T1 relaxivity of oxygen increases at lower field strengths, which is expected to improve the sensitivity of oxygen‐enhanced lung MRI. We implemented a breath‐held T1‐weighted 3D stack‐of‐spirals UTE acquisition with a 7 ms spiral‐out readout. Measurement repeatability was assessed using five repetitions of oxygen‐enhanced lung imaging in healthy volunteers (n = 7). The signal intensity at both normoxia and hyperoxia was strongly dependent on lung tissue density modulated by breath‐hold volume during the five repetitions. A voxel‐wise correction for lung tissue density improved the repeatability of percent signal enhancement maps (coefficient of variation = 34 ± 16%). Percent signal enhancement maps were compared in 15 healthy volunteers and 10 patients with lymphangioleiomyomatosis (LAM), a rare cystic disease known to reduce pulmonary function. We measured a mean percent signal enhancement of 9.0 ± 3.5% at 0.55 T in healthy volunteers, and reduced signal enhancement in patients with LAM (5.4 ± 4.8%, p = 0.02). The heterogeneity, estimated by the percent of lung volume exhibiting low enhancement, was significantly increased in patients with LAM compared with healthy volunteers (11.1 ± 6.0% versus 30.5 ± 13.1%, p = 0.01), illustrating the capability to measure regional functional deficits.

Purpose To develop and evaluate an improved velocity‐selective (VS) labeling pulse for myocardial arterial spin labeling (ASL) perfusion imaging that addresses two limitations of current pulses: (1) spurious labeling of moving myocardium and (2) low labeling efficiency. Methods The proposed myocardial VSASL labeling pulse is designed using a Fourier Transform based Velocity‐Selective labeling pulse train. The pulse utilizes bipolar velocity‐encoding gradients, a 9‐tap velocity‐encoding envelope, and double‐refocusing pulses with Malcolm Levitt phase cycling. Amplitudes of the velocity‐encoding envelope were optimized to minimize the labeling of myocardial velocities during stable diastole (±2‐3 cm/s) and maximize the labeling of coronary velocities (10‐130 cm/s during rest/stress or 10‐70 cm/s during rest). Myocardial ASL experiments were performed in seven healthy subjects using the previously developed VS‐ASL protocol by Jao et al with the two proposed VS pulses and original VS pulse. Myocardial ASL experiments were also performed using FAIR ASL. Myocardial perfusion and physiological noise (PN) were evaluated and compared. Results Bloch simulations of the first and second proposed pulses show <2% labeling over ±3 cm/s and ±2 cm/s, respectively. Bloch simulations also show the mean labeling efficiency of arterial blood is 1.23 over the relevant coronary arterial ranges. In‐vivo VSASL experiments show the proposed pulses provided comparable measurements to FAIR ASL and reduced TSNR in 5 of 7 subjects compared to the original VS pulse. Conclusion We demonstrate an improved VS labeling pulse specifically for myocardial ASL perfusion imaging to reduce spurious labeling of moving myocardium and PN.

Background Quantitative assessment of dynamic lung water accumulation is of interest to unmask latent heart failure. We develop and validate a free-breathing 3D ultrashort echo time (UTE) sequence with automated inline image processing to image changes in lung water density (LWD) using high-performance 0.55 T cardiovascular magnetic resonance (CMR). Methods Quantitative lung water CMR was performed on 15 healthy subjects using free-breathing 3D stack-of-spirals proton density weighted UTE at 0.55 T. Inline image reconstruction and automated image processing was performed using the Gadgetron framework. A gravity-induced redistribution of LWD was provoked by sequentially acquiring images in the supine, prone, and again supine position. Quantitative validation was performed in a phantom array of vials containing mixtures of water and deuterium oxide. Results The phantom experiment validated the capability of the sequence in quantifying water density (bias ± SD 4.3 ± 4.8%, intraclass correlation coefficient, ICC = 0.97). The average global LWD was comparable between imaging positions (supine 24.7 ± 3.4%, prone 22.7 ± 3.1%, second supine 25.3 ± 3.6%), with small differences between imaging phases (first supine vs prone 2.0%, p < 0.001; first supine vs second supine − 0.6%, p = 0.001; prone vs second supine − 2.7%, p < 0.001). In vivo test–retest repeatability in LWD was excellent (− 0.17 ± 0.91%, ICC = 0.97). A regional LWD redistribution was observed in all subjects when repositioning, with a predominant posterior LWD accumulation when supine, and anterior accumulation when prone (difference in anterior–posterior LWD: supine − 11.6 ± 2.7%, prone 5.5 ± 2.7%, second supine − 11.4 ± 2.9%). Global LWD maps were calculated inline within 23.2 ± 0.3 s following the image reconstruction using the automated pipeline. Conclusions Redistribution of LWD due to gravitational forces can be depicted and quantified using a validated free-breathing 3D proton density weighted UTE sequence and inline automated image processing pipeline on a high-performance 0.55 T CMR system.

A number of B1 mapping methods have been introduced. A model to facilitate choice among these methods is valuable, as the performance of each technique is affected by a variety of factors, including acquisition SNR. The Bloch-Siegert Shift B1 mapping method has recently garnered significant interest. In this manuscript, we present a statistical model suitable for analysis of the Bloch-Siegert Shift method. Unlike previously presented models, the analysis is valid in both low SNR and high SNR regimes. We present a detailed analysis of the performance of the Bloch-Siegert Shift B1 mapping method across a broad range of acquisition scenarios, and compare it to two other B1 mapping techniques (the Dual Angle method and the Phase Sensitive Method). Further validation of the model is presented through both Monte Carlo simulations and experimental results. The simulations and experimental results match the model well, lending confidence to its accuracy. Each technique is found to perform well with high acquisition SNR. However, our results suggest that the Dual Angle method is not reliable in low SNR environments. Furthermore, the Phase Sensitive method appears to outperform the Bloch-Siegert Shift method in these low-SNR cases, although variations of the Bloch-Siegert method may be possible that improve its performance at low SNR.

Goal We demonstrate a novel and robust approach for visualization of upper airway dynamics and detection of obstructive events from dynamic 3D magnetic resonance imaging (MRI) scans of the pharyngeal airway. Methods This approach uses 3D 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 sec/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 semi-automated 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 is a significant public health issue affecting 4-9% of adults and 2% of children. Recently, 3D dynamic MRI of the upper airway has been demonstrated during natural sleep, with sufficient spatio-temporal resolution to non-invasively study patterns of airway obstruction in young adults with OSA. This work 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.

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Purpose: We propose and evaluate a new structured low-rank method for echoplanar imaging (EPI) ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data. Methods: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable highquality ghost correction. This method works well when the autocalibration data are pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. Second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, including comparisons to state-of-the-art methods. Results: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated EPI acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging). Conclusions: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.