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This work develops and evaluates a self‐navigated variable density spiral (VDS)‐based manifold regularization scheme to prospectively improve dynamic speech magnetic resonance imaging (MRI) at 3 T. Short readout duration spirals (1.3‐ms long) were used to minimize sensitivity to off‐resonance. A custom 16‐channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self‐navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a sensitivity‐encoding–based nonlocal soft‐weighted temporal regularization scheme. Our approach was compared with view‐sharing, low‐rank, temporal finite difference, extra dimension‐based sparsity reconstruction constraints. Undersampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges was performed in a retrospective undersampling experiment on one volunteer. For prospective undersampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring was performed by three experts in voice research. Region of interest analysis at articulator boundaries was performed in both experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective undersampling, a spatial resolution of 2.4 × 2.4 mm2/pixel and a temporal resolution of 17.4 ms/frame for single‐slice imaging, and 52.2 ms/frame for concurrent three‐slice imaging, were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared with all other reconstruction constraints. While it exhibited faint (nonsignificant) alias artifacts that were similar to temporal finite difference, it provided statistically significant improvements compared with the other constraints. In conclusion, the self‐navigated manifold regularized scheme enabled robust high spatiotemporal resolution dynamic speech MRI at 3 T. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a new approach called tailored variable flip‐angle (VFA) scheduling for SNR‐efficient 3D T1ρ mapping of the brain using a magnetization‐prepared gradient‐echo sequence. Methods: Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T1ρ mapping of brain tissue compared with magnetization‐prepared angle‐modulated partitioned k‐space spoiled gradient‐echo snapshots (MAPSS), a state‐of‐the‐art technique for accurate 3D gradient‐echo T1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach. Results: Tailored VFA scheduling can provide a 2‐fold to 4‐fold gain in the SNR of the resulting T1ρ map as compared with MAPSS when using identical sequence parameters while limiting T1ρ quantification errors to 2% or less. In vivo whole‐brain 3D T1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment. Conclusions: Tailored VFA scheduling is an SNR‐efficient GRE technique for 3D T1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications. [ABSTRACT FROM AUTHOR]

Background: A previous study demonstrated the feasibility of using 3D radial ultrashort echo time (UTE) oxygen-enhanced MRI (UTE OE-MRI) for functional imaging of healthy human lungs. The repeatability of quantitative measures from UTE OE-MRI needs to be established prior to its application in clinical research.Purpose: To evaluate repeatability of obstructive patterns in asthma and cystic fibrosis (CF) with UTE OE-MRI with isotropic spatial resolution and full chest coverage.Study Type: Volunteer and patient repeatability.Population: Eighteen human subjects (five asthma, six CF, and seven normal subjects).Field Strength/sequence: Respiratory-gated free-breathing 3D radial UTE (80 μs) sequence at 1.5T.Assessment: Two 3D radial UTE volumes were acquired sequentially under normoxic and hyperoxic conditions. A subset of subjects underwent repeat acquisitions on either the same day or ≤15 days apart. Asthma and CF subjects also underwent spirometry. A workflow including deformable registration and retrospective lung density correction was used to compute 3D isotropic percent signal enhancement (PSE) maps. Median PSE (MPSE) and ventilation defect percent (VDP) of the lung were measured from the PSE map.Statistical Tests: The relations between MPSE, VDP, and spirometric measures were assessed using Spearman correlations. The test-retest repeatability was evaluated using Bland-Altman analysis and intraclass correlation coefficients (ICC).Results: Ventilation measures in normal subjects (MPSE = 8.0%, VDP = 3.3%) were significantly different from those in asthma (MPSE = 6.0%, P = 0.042; VDP = 21.7%, P = 0.018) and CF group (MPSE = 4.5%, P = 0.0006; VDP = 27.2%, P = 0.002). MPSE correlated significantly with forced expiratory lung volume in 1 second percent predicted (ρ = 0.72, P = 0.017). The ICC of the test-retest VDP and MPSE were both ≥0.90. In all subject groups, an anterior/posterior gradient was observed with higher MPSE and lower VDP in the posterior compared to anterior regions (P ≤ 0.0021 for all comparisons).Data Conclusion: 3D radial UTE OE-MRI supports quantitative differentiation of diseased vs. healthy lungs using either whole lung VDP or MPSE with excellent test-retest repeatability.Level Of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1287-1297. [ABSTRACT FROM AUTHOR]

Bilinear models such as low-rank and compressed sensing, which decompose the dynamic data to spatial and temporal factors, are powerful and memory efficient tools for the recovery of dynamic MRI data. These methods rely on sparsity and energy compaction priors on the factors to regularize the recovery. Motivated by deep image prior, we introduce a novel bilinear model, whose factors are regularized using convolutional neural networks. To reduce the run time, we initialize the CNN parameters by pre-training them on pre-acquired data with longer acquistion time. Since fully sampled data is not available, pretraining is performed on undersampled data in an unsupervised fashion. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on on free-breathing and ungated cardiac CINE data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to low-rank and SToRM reconstructions.

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.

Echo-planar imaging (EPI), which is the main workhorse of functional MRI, suffers from field inhomogeneity-induced geometric distortions. The amount of distortion is proportional to the readout duration, which restricts the maximum achievable spatial resolution. The spatially varying nature of the T 2 * decay makes it challenging for EPI schemes with a single echo time to obtain good sensitivity to functional activations in different brain regions. Despite the use of parallel MRI and multislice acceleration, the number of different echo times that can be acquired in a reasonable TR is limited. The main focus of this work is to introduce a rosette-based acquisition scheme and a structured low-rank reconstruction algorithm to overcome the above challenges. The proposed scheme exploits the exponential structure of the time series to recover distortion-free images from several echoes simultaneously.

Purpose: To quantitatively evaluate interday, interreader, and intersite agreement of readers of hyperpolarized helium 3 (HPHe) MR images in patients with exercise-induced bronchoconstriction., Materials and Methods: This HIPAA-compliant, institutional review board approved study included 13 patients with exercise-induced bronchoconstriction. On two separate days, HPHe MR imaging of the lungs was performed at baseline, immediately after a 10-minute exercise challenge (postchallenge), and 45 minutes after exercise (recovery). Patients were imaged at two sites, six at site A and seven at site B. Images were analyzed independently by multiple readers at each site. Lung volume, ventilation defect volume, ventilated volume, and the number of defects were measured quantitatively, and the location of defects was evaluated qualitatively at site A. Interday and interreader agreement were evaluated by using the intraclass correlation coefficient (ICC), and intersite agreement was evaluated by using a modified Bland-Altman analysis., Results: The ICC between days for ventilation defect volume, ventilated volume, and number of defects was at least 0.74 at both sites. The ICC for lung volume was greater at site B (0.83-0.86) than at site A (0.60-0.65). Defects seen in the same location in the lung on both days included 19.7% of those seen on baseline images and 29.2% and 18.6% of defects on postchallenge and recovery images, respectively. Interreader ICC for each measurement was at least 0.82 for each site. Analysis of intersite agreement showed biases of 612 mL for lung volume, -60.7 mL for ventilation defect volume, 2.91% for ventilated volume, and -6.56 for number of defects., Conclusion: The reported measures of reproducibility of HPHe MR imaging may help in the design and interpretation of single- and multicenter studies of patients with exercise-induced bronchoconstriction.