Purpose: To assess the performance of a three-dimensional (3D) non-contrast respiratory-triggered steady state free precession (SSFP) pulse sequence for detection of renal artery stenosis. Materials and Methods:A total of 64 patients who had non-contrast MR angiography (NC MRA) and 3D contrastenhanced MRA (CE MRA) performed during the same exam and three patients who had NC MRA followed by conventional catheter angiography within one month of the MRI exam were included in this retrospective study. Two blinded readers evaluated NC MRA images for the presence of significant renal artery stenosis and also rated their diagnostic confidence and evaluated the images for artifact. A similar analysis was performed for CE MRA images by two additional blinded readers, and discrepancies were resolved by consensus reading. Results:The 67 patients had 168 main and accessory renal arteries, with significant (>50%) stenosis in 34 arteries on CE MRA or conventional angiography. The two NC MRA readers had sensitivity and specificity for detection of significant stenosis of 94%/82% and 82%/87% respectively on a per renal artery basis. Conclusion:There was good agreement between CE MRA and NC MRA for detection of significant renal artery stenosis. This technique should prove useful in evaluating patients with suspected renovascular hypertension who are unable to undergo CE MRA. THREE-DIMENSIONAL gadolinium contrast-enhanced MR angiography (CE MRA) is a sensitive and accurate technique for detection of renal artery stenosis, and is useful as a primary or confirmatory non-invasive test in patients with suspected renal artery stenosis (1-5). Recent reports linking the use of gadolinium-based MR contrast agents with nephrogenic systemic fibrosis (NSF) (6-9), however, have led to a dramatic reduction in the number of renal MRA exams ordered and performed, since many patients with refractory hypertension and suspected renal artery stenosis also have renal insufficiency.While duplex sonography is effective in many patients, complete visualization of the renal arteries can be limited. Computed tomography (CT) angiography involves the use of iodinated contrast, also problematic in patients with renal insufficiency, and requires ionizing radiation. This state of affairs has stimulated many attempts to reinvestigate non-contrast MRA (NC MRA) methods. While phase contrast and time-of-flight MRA pulse sequences have proven useful in assessment of the renal arteries in previous investigations, they have limitations as a primary technique for non-contrast renal MRA (10-13); more recent investigations have focused predominantly on modified steady state free precession (SSFP) pulse sequences. SSFP sequences are attractive for their high vascular signal to noise ratio, fairly rapid acquisition times, and inherent flow compensation (14-26). Modifications of the basic 3D SSFP pulse sequence for improved visualization of the renal arteries have included arterial spin labeling (14,15), navigator or respiratory gating (14,15,17,18,(20)(21)(22)(23)(24), ...
Arterial transit time (ATT) prolongation causes an error of cerebral blood flow (CBF) measurement during arterial spin labeling (ASL). To improve the accuracy of ATT and CBF in patients with prolonged ATT, we propose a robust ATT and CBF estimation method for clinical practice. The proposed method consists of a three‐delay Hadamard‐encoded pseudo‐continuous ASL (H‐pCASL) with an additional‐encoding and single‐delay with long‐labeled long‐delay (1dLLLD) acquisition. The additional‐encoding allows for the reconstruction of a single‐delay image with long‐labeled short‐delay (1dLLSD) in addition to the normal Hadamard sub‐bolus images. Five different images (normal Hadamard 3 delay, 1dLLSD, 1dLLLD) were reconstructed to calculate ATT and CBF. A Monte Carlo simulation and an in vivo study were performed to access the accuracy of the proposed method in comparison to normal 7‐delay (7d) H‐pCASL with equally divided sub‐bolus labeling duration (LD). The simulation showed that the accuracy of CBF is strongly affected by ATT. It was also demonstrated that underestimation of ATT and CBF by 7d H‐pCASL was higher with longer ATT than with the proposed method. Consistent with the simulation, the 7d H‐pCASL significantly underestimated the ATT compared to that of the proposed method. This underestimation was evident in the distal anterior cerebral artery (ACA; P = 0.0394) and the distal posterior cerebral artery (PCA; 2 P = 0.0255). Similar to the ATT, the CBF was underestimated with 7d H‐pCASL in the distal ACA (P = 0.0099), distal middle cerebral artery (P = 0.0109), and distal PCA (P = 0.0319) compared to the proposed method. Improving the SNR of each delay image (even though the number of delays is small) is crucial for ATT estimation. This is opposed to acquiring many delays with short LD. The proposed method confers accurate ATT and CBF estimation within a practical acquisition time in a clinical setting.
Objectives Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of various contrast-weighted images as well as simultaneous quantification of T1 and T2 relaxation times and proton density. However, to date, it has been challenging to generate magnetic resonance angiography (MRA) images with synthetic MRI. The purpose of this study was to develop a deep learning algorithm to generate MRA images based on 3D synthetic MRI raw data. Materials and Methods Eleven healthy volunteers and 4 patients with intracranial aneurysms were included in this study. All participants underwent a time-of-flight (TOF) MRA sequence and a 3D-QALAS synthetic MRI sequence. The 3D-QALAS sequence acquires 5 raw images, which were used as the input for a deep learning network. The input was converted to its corresponding MRA images by a combination of a single-convolution and a U-net model with a 5-fold cross-validation, which were then compared with a simple linear combination model. Image quality was evaluated by calculating the peak signal-to-noise ratio (PSNR), structural similarity index measurements (SSIMs), and high frequency error norm (HFEN). These calculations were performed for deep learning MRA (DL-MRA) and linear combination MRA (linear-MR), relative to TOF-MRA, and compared with each other using a nonparametric Wilcoxon signed-rank test. Overall image quality and branch visualization, each scored on a 5-point Likert scale, were blindly and independently rated by 2 board-certified radiologists. Results Deep learning MRA was successfully obtained in all subjects. The mean PSNR, SSIM, and HFEN of the DL-MRA were significantly higher, higher, and lower, respectively, than those of the linear-MRA (PSNR, 35.3 ± 0.5 vs 34.0 ± 0.5, P < 0.001; SSIM, 0.93 ± 0.02 vs 0.82 ± 0.02, P < 0.001; HFEN, 0.61 ± 0.08 vs 0.86 ± 0.05, P < 0.001). The overall image quality of the DL-MRA was comparable to that of TOF-MRA (4.2 ± 0.7 vs 4.4 ± 0.7, P = 0.99), and both types of images were superior to that of linear-MRA (1.5 ± 0.6, for both P < 0.001). No significant differences were identified between DL-MRA and TOF-MRA in the branch visibility of intracranial arteries, except for ophthalmic artery (1.2 ± 0.5 vs 2.3 ± 1.2, P < 0.001). Conclusions Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time.
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The feasibility of using a metabolite signal as an internal reference for self-referenced temperature distribution measurement was examined. Line scan echo-planar spectroscopic imaging (LSEPSI) was applied to obtain quick multi-voxel spectroscopic measurements and to avoid possible spectral degradation from motion. Temperature distribution in a rabbit brain in vivo was successfully visualized by means of the chemical shift of water, which was measured by using naturally abundant (up to 10 mM) N-acetyl-aspartate (NAA) as the reference signal. Unlike the phase-mapping approach, this technique does not require a pixel-by-pixel subtraction. Therefore, in theory, it is more resistant to inter-scan motion or changes in susceptibility. The spatial and temporal resolutions of this technique are 1.5 cm3 and 4.5 min. A higher signal-to-noise ratio and optimization of the water and outer-volume suppression capabilities will be required to further enhance the temperature-mapping capabilities.
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