High spatial resolution whole-neck MR angiography using thin-slab stack-of-stars quiescent interval slice-selective acquisition.

PURPOSE: To report a 3D multi-echo thin-slab stack-of-stars (tsSOS) quiescent-interval slice-selective (QISS) strategy for high-resolution magnetic resonance angiography (MRA) of the entire neck in under seven minutes. METHODS: The neck arteries of eight subjects were imaged at 3 Tesla. Multi-echo 3D tsSOS QISS using a FLASH readout was compared with 3D tsSOS FLASH, 2D QISS, 2D TOF, and 3D TOF. A root-mean-square (RMS) combination of echo time images was tested. Evaluation metrics included arterial signal-to-noise ratio (SNR), arterial-to-muscle contrast-to-noise ratio (CNR), and image quality. RESULTS: 3D multi-echo tsSOS QISS using a RMS combination of echo time images increased SNR and CNR by 60% and 63% with respect to the reconstruction obtained with the shortest echo time. 3D tsSOS QISS showed superior CNR with respect to 3D tsSOS FLASH imaging, and more than 3-fold higher SNR and CNR with respect to 2D radial QISS when normalized for voxel size. 3D tsSOS QISS provided good to excellent image quality that exceeded the image quality of 2D QISS, 2D TOF, and 3D TOF (P < .05). CONCLUSION: Whole-neck high-resolution nonenhanced MRA is feasible using 3D tsSOS QISS, and produced image quality that exceeded those of competing nonenhanced MRA protocols at 3 Tesla.

Feasibility of a sub-3-minute imaging strategy for ungated quiescent interval slice-selective MRA of the extracranial carotid arteries using radial k-space sampling and deep learning-based image processing.

PURPOSE: To develop and test the feasibility of a sub-3-minute imaging strategy for non-contrast evaluation of the extracranial carotid arteries using ungated quiescent interval slice-selective (QISS) MRA, combining single-shot radial sampling with deep neural network-based image processing to optimize image quality. METHODS: The extracranial carotid arteries of 12 human subjects were imaged at 3 T using ungated QISS MRA. In 7 healthy volunteers, the effects of radial and Cartesian k-space sampling, single-shot and multishot image acquisition (1.1-3.3 seconds/slice, 141-423 seconds/volume), and deep learning-based image processing were evaluated using segmental image quality scoring, arterial temporal SNR, arterial-to-background contrast and apparent contrast-to-noise ratio, and structural similarity index. Comparison of deep learning-based image processing was made with block matching and 3D filtering denoising. RESULTS: Compared with Cartesian sampling, radial k-space sampling increased arterial temporal SNR 107% (P < .001) and improved image quality during 1-shot imaging (P < .05). The carotid arteries were depicted with similar image quality on the rapid 1-shot and much lengthier 3-shot radial QISS protocols (P = not significant), which was corroborated in patient studies. Deep learning-based image processing outperformed block matching and 3D filtering denoising in terms of structural similarity index (P < .001). Compared with original QISS source images, deep learning image processing provided 24% and 195% increases in arterial-to-background contrast (P < .001) and apparent contrast-to-noise ratio (P < .001), and provided source images that were preferred by radiologists (P < .001). CONCLUSION: Rapid, sub-3-minute evaluation of the extracranial carotid arteries is feasible with ungated single-shot radial QISS, and benefits from the use of deep learning-based image processing to enhance source image quality.

Accelerated, free-breathing, noncontrast, electrocardiograph-triggered, thoracic MR angiography with stack-of-stars k-space sampling and GRASP reconstruction.

PURPOSE: To develop an accelerated, free-breathing, noncontrast, electrocardiograph-triggered, thoracic MR angiography (NC-MRA) pulse sequence capable of achieving high spatial resolution at clinically acceptable scan time and test whether it produces clinically acceptable image quality in patients with suspected aortic disease. METHODS: We modified a "coronary" MRA pulse sequence to use a stack-of-stars k-space sampling pattern and combined it with golden-angle radial sparse parallel (GRASP reconstruction to enable self-navigation of respiratory motion and high data acceleration. The performance of the proposed NC-MRA was evaluated in 13 patients, where clinical standard contrast-enhanced MRA (CE-MRA) was used as control. For visual analysis, two readers graded the conspicuity of vessel lumen, artifacts, and noise level on a 5-point scale (overall score index = sum of three scores). The aortic diameters were measured at seven standardized locations. The mean visual scores, inter-observer variability, and vessel diameters were compared using appropriate statistical tests. RESULTS: The overall mean visual score index (12.1 ± 1.7 for CE-MRA versus 12.1 ± 1.0 for NC-MRA) scores were not significantly different (P > 0.16). The two readers' scores were significantly different for CE-MRA (P = 0.01) but not for NC-MRA (P = 0.21). The mean vessel diameters were not significantly different, except at the proximal aortic arch (P < 0.03). The mean diameters were strongly correlated (R2 ≥ 0.96) and in good agreement (absolute mean difference ≤ 0.01 cm and 95% confidence interval ≤ 0.62 cm). CONCLUSION: This study shows that the proposed NC-MRA produces clinically acceptable image quality in patients at high spatial resolution (1.5 mm × 1.5 mm × 1.5 mm) and clinically acceptable scan time (~6 min).

Accelerated, first-pass cardiac perfusion pulse sequence with radial k-space sampling, compressed sensing, and k-space weighted image contrast reconstruction tailored for visual analysis and quantification of myocardial blood flow.

PURPOSE: To develop an accelerated cardiac perfusion pulse sequence and test whether it is capable of increasing spatial coverage, generating high-quality images, and enabling quantification of myocardial blood flow (MBF). METHODS: We implemented an accelerated first-pass cardiac perfusion pulse sequence by combining radial k-space sampling, compressed sensing (CS), and k-space weighted image contrast (KWIC) filtering. The proposed and clinical standard pulse sequences were evaluated in a randomized order in 13 patients at rest. For visual analysis, 3 readers graded the conspicuity of wall enhancement, artifact, and noise level on a 5-point Likert scale (overall score index = sum of 3 individual scores). Resting MBF was calculated using a Fermi function model with and without KWIC filtering. Mean visual scores and MBF values were compared between sequences using appropriate statistical tests. RESULTS: The proposed pulse sequence produced greater spatial coverage (6-8 slices) with higher spatial resolution (1.6 × 1.6 × 8 mm3 ) and shorter readout duration (78 ms) compared to clinical standard (3-4 slices, 3 × 3 × 8 mm3 , 128 ms, respectively). The overall image score index between accelerated (11.1 ± 1.3) and clinical standard (11.2 ± 1.3) was not significantly different (P = 0.64). Mean resting MBF values with KWIC filtering (0.9-1.2 mL/g/min across different slices) were significantly lower (P < 0.0001) than those without KWIC filtering (3.1-4.3 mL/g/min) and agreed better with values reported in literature. CONCLUSION: An accelerated, first-pass cardiac perfusion pulse sequence with radial k-space sampling, CS, and KWIC filtering is capable of increasing spatial coverage, generating high-quality images, and enabling quantification of MBF.