The aim of this study was to evaluate the mechanical triggers that may cause plaque rupture. Wall shear stress (WSS) and pressure gradient are the direct mechanical forces acting on the plaque in a stenotic artery. Their influence on plaque stability is thought to be controversial. This study used a physiologically realistic, pulsatile flow, two-dimensional, cine phase-contrast MRI sequence in a patient with a 70% carotid stenosis. Instead of considering the full patient-specific carotid bifurcation derived from MRI, only the plaque region has been modelled by means of the idealised flow model. WSS reached a local maximum just distal to the stenosis followed by a negative local minimum. A pressure drop across the stenosis was found which varied significantly during systole and diastole. The ratio of the relative importance of WSS and pressure was assessed and was found to be less than 0.07% for all time phases, even at the throat of the stenosis. In conclusion, although the local high WSS at the stenosis may damage the endothelium and fissure plaque, the magnitude of WSS is small compared with the overall loading on plaque. Therefore, pressure may be the main mechanical trigger for plaque rupture and risk stratification using stress analysis of plaque stability may only need to consider the pressure effect.
Background Current self‐calibration and reconstruction methods for wave‐encoded single‐shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed. Purpose To develop and investigate the clinical feasibility of data‐driven self‐calibration and reconstruction of wave‐encoded SSFSE imaging for computation time reduction and quality improvement. Study Type Prospective controlled clinical trial. Subjects With Institutional Review Board approval, the proposed method was assessed on 29 consecutive adult patients (18 males, 11 females, range, 24–77 years). Field Strength/Sequence A wave‐encoded variable‐density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable 3.5× acceleration with full‐Fourier acquisitions. Data‐driven calibration of wave‐encoding point‐spread function (PSF) was developed using a trained deep neural network. Data‐driven reconstruction was developed with another set of neural networks based on the calibrated wave‐encoding PSF. Training of the calibration and reconstruction networks was performed on 15,783 2D wave‐encoded SSFSE abdominal images. Assessment Image quality of the proposed data‐driven approach was compared independently and blindly with a conventional approach using iterative self‐calibration and reconstruction with parallel imaging and compressed sensing by three radiologists on a scale from –2 to 2 for noise, contrast, sharpness, artifacts, and confidence. Computation time of these two approaches was also compared. Statistical Tests Wilcoxon signed‐rank tests were used to compare image quality and two‐tailed t ‐tests were used to compare computation time with P values of under 0.05 considered statistically significant. Results An average 2.1‐fold speedup in computation was achieved using the proposed method. The proposed data‐driven self‐calibration and reconstruction approach significantly reduced the perceived noise level (mean scores 0.82, P < 0.0001). Data Conclusion The proposed data‐driven calibration and reconstruction achieved twice faster computation with reduced perceived noise, providing a fast and robust self‐calibration and reconstruction for clinical abdominal SSFSE imaging. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:841–853.
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing (AGB) technique that automates the process of combining the adversarial and pixel-wise terms and streamlines hyperparameter tuning. In addition, we introduce a Densely Connected Iterative Network, which is an undersampled MRI reconstruction network that utilizes dense connections. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques. To demonstrate the general nature of our method, it is further evaluated on a battery of image-to-image translation experiments, demonstrating an ability to recover from sub-optimal weighting in multi-term adversarial training.
Purpose The goal of this work is to propose a motion robust reconstruction method for diffusion‐weighted MRI that resolves shot‐to‐shot phase mismatches without using phase estimation. Methods Assuming that shot‐to‐shot phase variations are slowly varying, spatial‐shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low‐rank constraint on the spatial‐shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. Results The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase‐estimation‐based methods to achieve even higher image quality. Conclusion We introduced the shot–locally low‐rank method, a reconstruction technique for multishot diffusion‐weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
Purpose To compare the diagnostic value of conventional, bilateral diffusion‐weighted imaging (DWI) and high‐resolution targeted DWI of known breast lesions. Materials and Methods Twenty‐one consecutive patients with known breast cancer or suspicious breast lesions were scanned with the conventional bilateral DWI technique, a high‐resolution, reduced field of view (rFOV) DWI technique, and dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) (3.0 T). We compared bilateral DWI and rFOV DWI quantitatively by measuring the lesions' apparent diffusion coefficient (ADC) values. For qualitative comparison, three dedicated breast radiologists scored image quality and performed lesion interpretation. Results In a phantom, ADC values were in good agreement with the reference values. Twenty‐one patients (30 lesions: 14 invasive carcinomas, 10 benign lesions [of which 5 cysts], 3 high‐risk, and 3 in situ carcinomas) were included. Cysts and high‐risk lesions were excluded from the quantitative analysis. Quantitatively, both bilateral and rFOV DWI measured lower ADC values in invasive tumors than other lesions. In vivo, rFOV DWI gave lower ADC values than bilateral DWI (1.11 × 10 ‐3 mm 2 /s vs. 1.24 × 10 ‐3 mm 2 /s, P = 0.002). Regions of interest (ROIs) were comparable in size between the two techniques (2.90 vs. 2.13 cm 2 , P = 0.721). Qualitatively, all three radiologists scored sharpness of rFOV DWI images as significantly higher than bilateral DWI ( P ≤ 0.002). Receiver operating characteristic (ROC) curve analysis showed a higher area under the curve (AUC) in BI‐RADS classification for rFOV DWI compared to bilateral DWI (0.71 to 0.93 vs. 0.61 to 0.76, respectively). Conclusion Tumor morphology can be assessed in more detail with high‐resolution DWI (rFOV) than with standard bilateral DWI by providing significantly sharper images. J. MAGN. RESON. IMAGING 2015. J. MAGN. RESON. IMAGING 2015;42:1656–1665.
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