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Purpose: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model for non-rigid motion correction in coronary magnetic resonance angiography (CMRA). Methods: An unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired as part of a CMRA sequence. The unrolled model incorporates a non-uniform FFT operator to perform the data consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with $\textit{l}_{1}$-ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the $\textit{l}_{1}$-ESPIRiT and DL model-based 3D iNAVs are assessed for differences. Results: 3D iNAVs reconstructed using the DL model-based approach and conventional $\textit{l}_{1}$-ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of $\textit{l}_{1}$-ESPIRiT (20x and 3x speed increases, respectively). Conclusion: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of non-rigid motion information offered by them for correction., Comment: 34 pages, 5 figures, 1 table, 6 supporting figures, 1 supporting table

Purpose: Off-resonance artifact correction by deep-learning, to facilitate rapid pediatric body imaging with a scan time efficient 3D cones trajectory. Methods: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of 30 pediatric magnetic resonance angiography exams. Each exam acquired a short-readout scan (1.18 ms +- 0.38) and a long-readout scan (3.35 ms +- 0.74) at 3T. Short-readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long-readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared to short-readout scans by normalized root-mean-square error (NRMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scans were also compared by scoring on eight anatomical features by two radiologists, using analysis of variance with post-hoc Tukey's test. Reader agreement was determined with intraclass correlation. Results: Long-readout scans were on average 59.3% shorter than short-readout scans. Images from Off-ResNet had superior NRMSE, SSIM, and PSNR compared to uncorrected images across +-1kHz off-resonance (P<0.01). The proposed method had superior NRMSE over -677Hz to +1kHz and superior SSIM and PSNR over +-1kHz compared to autofocus (P<0.01). Radiologic scoring demonstrated that long-readout scans corrected with Off-ResNet were non-inferior to short-readout scans (P<0.01). Conclusion: The proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a non-inferior image quality compared to diagnostically standard short-readout scans.

PurposeTo develop a new sequence for non-contrast-enhanced peripheral angiography using a sliding interleaved cylinder (SLINCYL) acquisition.MethodsA venous saturation pulse was incorporated into a three-dimensional magnetization-prepared balanced steady-state free precession sequence for non-contrast-enhanced peripheral angiography to improve artery-vein contrast. The SLINCYL acquisition, which consists of a series of overlapped thin slabs for volumetric coverage similar to the original sliding interleaved ky (SLINKY) acquisition, was used to evenly distribute the venous-suppression effects over the field of view. In addition, the thin-slab-scan nature of SLINCYL and the centric-ordered sampling geometry of its readout trajectory were exploited to implement efficient fluid-suppression and parallel imaging schemes. The sequence was tested in healthy subjects and a patient.ResultsCompared to a multiple overlapped thin slab acquisition, both SLINKY and SLINCYL suppressed the venetian blind artifacts and provided similar artery-vein contrast. However, SLINCYL achieved this with shorter scan times and less noticeable artifacts from k-space amplitude modulation than SLINKY. The fluid-suppression and parallel imaging schemes were also validated. A patient study using the SLINCYL-based sequence well identified stenoses at the superficial femoral arteries, which were also confirmed with digital subtraction angiography.ConclusionNon-contrast-enhanced angiography using SLINCYL can provide angiograms with improved artery-vein contrast in the lower extremities.

PurposeTo improve the spatial/temporal resolution of whole-heart coronary MR angiography by developing a variable-density (VD) 3D cones acquisition suitable for image reconstruction with parallel imaging and compressed sensing techniques.MethodsA VD 3D cones trajectory design incorporates both radial and spiral trajectory undersampling techniques to achieve higher resolution. This design is used to generate a VD 3D cones trajectory with 0.8 mm/66 ms isotropic spatial/temporal resolution, using a similar number of readouts as our previous fully sampled cones trajectory (1.2 mm/100 ms). Scans of volunteers and patients are performed to evaluate the performance of the VD trajectory, using non-Cartesian L1 -ESPIRiT for high-resolution image reconstruction.ResultsWith gridding reconstruction, the high-resolution scans experience an expected drop in signal-to-noise and contrast-to-noise ratios, but with L1 -ESPIRiT, the apparent noise is substantially reduced. Compared with 1.2 mm images, in each volunteer, the L1 -ESPIRiT 0.8 mm images exhibit higher vessel sharpness values in the right and left anterior descending arteries.ConclusionCoronary MR angiography with isotropic submillimeter spatial resolution and high temporal resolution can be performed with VD 3D cones to improve the depiction of coronary arteries.

Purpose: The balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest due to its high signal-to-noise ratio efficiency. However, bSSFP images often suffer from banding artifacts due to off-resonance effects, which we aim to minimize in this article. Methods: We present a general and fast two-step algorithm for 1) estimating the unknowns in the bSSFP signal model from multiple phase-cycled acquisitions, and 2) reconstructing band-free images. The first step, linearization for off-resonance estimation (LORE), solves the nonlinear problem approximately by a robust linear approach. The second step applies a Gauss-Newton algorithm, initialized by LORE, to minimize the nonlinear least squares criterion. We name the full algorithm LORE-GN. Results: We derive the Cramer-Rao bound, a theoretical lower bound of the variance for any unbiased estimator, and show that LORE-GN is statistically efficient. Furthermore, we show that simultaneous estimation of T-1 and T-2 from phase-cycled bSSFP is difficult, since the Cramer-Rao bound is high at common signal-to-noise ratio. Using simulated, phantom, and in vivo data, we illustrate the band-reduction capabilities of LORE-GN compared to other techniques, such as sum-of-squares. Conclusion: Using LORE-GN we can successfully minimize banding artifacts in bSSFP.

PurposeTo implement a nonrigid autofocus motion correction technique to improve respiratory motion correction of free-breathing whole-heart coronary magnetic resonance angiography acquisitions using an image-navigated 3D cones sequence.Methods2D image navigators acquired every heartbeat are used to measure superior-inferior, anterior-posterior, and right-left translation of the heart during a free-breathing coronary magnetic resonance angiography scan using a 3D cones readout trajectory. Various tidal respiratory motion patterns are modeled by independently scaling the three measured displacement trajectories. These scaled motion trajectories are used for 3D translational compensation of the acquired data, and a bank of motion-compensated images is reconstructed. From this bank, a gradient entropy focusing metric is used to generate a nonrigid motion-corrected image on a pixel-by-pixel basis. The performance of the autofocus motion correction technique is compared with rigid-body translational correction and no correction in phantom, volunteer, and patient studies.ResultsNonrigid autofocus motion correction yields improved image quality compared to rigid-body-corrected images and uncorrected images. Quantitative vessel sharpness measurements indicate superiority of the proposed technique in 14 out of 15 coronary segments from three patient and two volunteer studies.ConclusionThe proposed technique corrects nonrigid motion artifacts in free-breathing 3D cones acquisitions, improving image quality compared to rigid-body motion correction.

QC 20130109

QC 20130524

In this article, a robust methodology for in vivo T(1) mapping is presented. The approach combines a gold standard scanning procedure with a novel fitting procedure. Fitting complex data to a five-parameter model ensures accuracy and precision of the T(1) estimation. A reduced-dimension nonlinear least squares method is proposed. This method turns the complicated multi-parameter minimization into a straightforward one-dimensional search. As the range of possible T(1) values is known, a global grid search can be used, ensuring that a global optimal solution is found. When only magnitude data are available, the algorithm is adapted to concurrently restore polarity. The performance of the new algorithm is demonstrated in simulations and phantom experiments. The new algorithm is as accurate and precise as the conventionally used Levenberg-Marquardt algorithm but much faster. This gain in speed makes the use of the five-parameter model viable. In addition, the new algorithm does not require initialization of the search parameters. Finally, the methodology is applied in vivo to conventional brain imaging and to skin imaging. T(1) values are estimated for white matter and gray matter at 1.5T and for dermis, hypodermis, and muscle at 1.5T, 3T, and 7T. Magn Reson Med 64:1057-1067, 2010., QC 20111111

Increasing the field strength seems promising for high-resolution skin imaging but a legitimate concern is the increase in the spin-lattice relaxation parameter T1. Skin T1 maps at 1.5T, 3T, and 7T are compared and a novel non-linear least-square approach for fast and accurate T1-estimation is proposed., QC 20111114