Comparison of respiratory-triggered 3D MR cholangiopancreatography and compressed-sensing 3D MR cholangiopancreatography at 1.5T and 3T and impact of individual factors on image quality

2021 ◽  
pp. 109873
Author(s):  
Hélène Blaise ◽  
Thomas Remen ◽  
Khalid Ambarki ◽  
Elisabeth Weiland ◽  
Bernd Kuehn ◽  
...  
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Individual Factors ◽  
Mr Cholangiopancreatography

scholarly journals Comparison of Compressed Sensing and Gradient and Spin-Echo in Breath-Hold 3D MR Cholangiopancreatography: Qualitative and Quantitative Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 634
Author(s):  
Weon Jang ◽  
Ji Soo Song ◽  
Sang Heon Kim ◽  
Jae Do Yang
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Spin Echo ◽  
Contrast Ratio ◽  
Rank Test ◽  
Breath Holding ◽  
Breath Hold ◽  
Background Suppression ◽  
Time Saving ◽  
Noise Ratio

While magnetic resonance cholangiopancreatography (MRCP) is routinely used, compressed sensing MRCP (CS-MRCP) and gradient and spin-echo MRCP (GRASE-MRCP) with breath-holding (BH) may allow sufficient image quality with shorter acquisition times. This study qualitatively and quantitatively compared BH-CS-MRCP and BH-GRASE-MRCP and evaluated their clinical effectiveness. Data from 59 consecutive patients who underwent both BH-CS-MRCP and BH-GRASE-MRCP were qualitatively analyzed using a five-point Likert-type scale. The signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast-to-noise ratio (CNR) of the CBD and liver, and contrast ratio between periductal tissue and the CBD were measured. Paired t-test, Wilcoxon signed-rank test, and McNemar’s test were used for statistical analysis. No significant differences were found in overall image quality or duct visualization of the CBD, right and left 1st level intrahepatic duct (IHD), cystic duct, and proximal pancreatic duct (PD). BH-CS-MRCP demonstrated higher background suppression and better visualization of right (p = 0.004) and left 2nd level IHD (p < 0.001), mid PD (p = 0.003), and distal PD (p = 0.041). Image quality degradation was less with BH-GRASE-MRCP than BH-CS-MRCP (p = 0.025). Of 24 patients with communication between a cyst and the PD, 21 (87.5%) and 15 patients (62.5%) demonstrated such communication on BH-CS-MRCP and BH-GRASE-MRCP, respectively. SNR, contrast ratio, and CNR of BH-CS-MRCP were higher than BH-GRASE-MRCP (p < 0.001). Both BH-CS-MRCP and BH-GRASE-MRCP are useful imaging methods with sufficient image quality. Each method has advantages, such as better visualization of small ducts with BH-CS-MRCP and greater time saving with BH-GRASE-MRCP. These differences allow diverse choices for visualization of the pancreaticobiliary tree in clinical practice.

scholarly journals Accelerated 3D T2-Weighted SPACE Using Compressed Sensing for Paediatric Brain Imaging

2021 ◽  
Author(s):  
Hyun Gi Kim ◽  
Se Won Oh ◽  
Dongyeob Han ◽  
Jee Young Kim ◽  
Gye Yeon Lim
Keyword(s):  
White Matter ◽  
Image Quality ◽  
Compressed Sensing ◽  
Brain Imaging ◽  
Quality Analysis ◽  
Acquisition Time ◽  
Image Quality Analysis ◽  
Space Group ◽  
Quantitative Image ◽  
Noise Ratio

Abstract The purpose of this study was to compare the image quality of the single-slab, 3D T2-weighted turbo-spin-eco sequence with high sampling efficiency (SPACE) with accelerated SPACE using compressed sensing (CS-SPACE) in paediatric brain imaging. A total of 116 brain MRI (53 in SPACE group and 63 in CS-SPACE group) were obtained from children aged 16 years old or younger. Quantitative image quality was evaluated using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The sequences were qualitatively evaluated for overall image quality, SNR, general artifact, cerebrospinal fluid (CSF)-related artifact and grey-white matter differentiation. The two sequences were compared for the total and for two age groups (< 24 months vs. ≥ 24 months). CS application in 3D T2-weighted imaging resulted in 8.5% reduction in scanning time. Quantitative image quality analysis showed higher SNR (Median [Interquartile range]; 29 [25] vs. 23 [14], P = .005) and CNR (0.231 [0.121] vs. 0.165 [0.120], P = .027) with CS-SPACE compared to SPACE. Qualitative image quality analysis showed better image quality with CS-SPACE for general artifact (P = .024) and CSF-related artifact (P < .001). CSF-related artifacts reduction was more prominent in the older age group (≥ 24 months). Overall image quality (P = .162), SNR (P = .726), and grey-white matter differentiation (P = .397) were comparable between SPACE and CS-SPACE. In conclusion, compressed sensing applied 3D T2-weighted images showed comparable or superior image quality compared to conventional images with reduced acquisition time for paediatric brain.

Magnetic resonance cholangiopancreatography at 3 Tesla: Image quality comparison between 3D compressed sensing and 2D single-shot acquisitions

2019 ◽  
Vol 115 ◽  
pp. 53-58 ◽  
Author(s):  
Fabian K. Lohöfer ◽  
Georgios A. Kaissis ◽  
Michael Rasper ◽  
Christoph Katemann ◽  
Andreas Hock ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Compressed Sensing ◽  
Magnetic Resonance Cholangiopancreatography ◽  
3 Tesla ◽  
Single Shot

scholarly journals 3D Dixon water-fat LGE imaging with image navigator and compressed sensing in cardiac MRI

2020 ◽  
Author(s):  
Martin Georg Zeilinger ◽  
Marco Wiesmüller ◽  
Christoph Forman ◽  
Michaela Schmidt ◽  
Camila Munoz ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Quality ◽  
Compressed Sensing ◽  
Cardiac Mri ◽  
Three Dimensional ◽  
Fat Suppression ◽  
Diagnostic Confidence ◽  
Pericardial Disease ◽  
Scan Time ◽  
Wilcoxon Rank Sum Test

Abstract Objectives To evaluate an image-navigated isotropic high-resolution 3D late gadolinium enhancement (LGE) prototype sequence with compressed sensing and Dixon water-fat separation in a clinical routine setting. Material and methods Forty consecutive patients scheduled for cardiac MRI were enrolled prospectively and examined with 1.5 T MRI. Overall subjective image quality, LGE pattern and extent, diagnostic confidence for detection of LGE, and scan time were evaluated and compared to standard 2D LGE imaging. Robustness of Dixon fat suppression was evaluated for 3D Dixon LGE imaging. For statistical analysis, the non-parametric Wilcoxon rank sum test was performed. Results LGE was rated as ischemic in 9 patients and non-ischemic in 11 patients while it was absent in 20 patients. Image quality and diagnostic confidence were comparable between both techniques (p = 0.67 and p = 0.66, respectively). LGE extent with respect to segmental or transmural myocardial enhancement was identical between 2D and 3D (water-only and in-phase). LGE size was comparable (3D 8.4 ± 7.2 g, 2D 8.7 ± 7.3 g, p = 0.19). Good or excellent fat suppression was achieved in 93% of the 3D LGE datasets. In 6 patients with pericarditis, the 3D sequence with Dixon fat suppression allowed for a better detection of pericardial LGE. Scan duration was significantly longer for 3D imaging (2D median 9:32 min vs. 3D median 10:46 min, p = 0.001). Conclusion The 3D LGE sequence provides comparable LGE detection compared to 2D imaging and seems to be superior in evaluating the extent of pericardial involvement in patients suspected with pericarditis due to the robust Dixon fat suppression. Key Points • Three-dimensional LGE imaging provides high-resolution detection of myocardial scarring. • Robust Dixon water-fat separation aids in the assessment of pericardial disease. • The 2D image navigator technique enables 100% respiratory scan efficacy and permits predictable scan times.

An Adaptive Regularized Fast Iterative Shrinkage-Thresholding Algorithm for Image Reconstruction in Compressed Sensing

2013 ◽  
Vol 710 ◽  
pp. 593-597
Author(s):  
Xin Meng ◽  
Shi Fang Duan ◽  
She Xiang Ma
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Objective Function ◽  
Time Complexity ◽  
Regularization Parameter ◽  
Iteration Process ◽  
Reconstructed Image ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

Aiming at the problems of worse reconstructed image quality and larger time complexity of the fast iterative shrinkage-thresholding algorithm in compressed sensing, this paper presents adaptive regularized fast iterative shrinkage-thresholding algorithm. This algorithm brings in the idea of adaptively selecting regularization parameter on the basis of using gradient method and threshold shrinkage to minimize the objective function. During the iteration process regularization parameter is adaptively selected from the whole value in order to adjust the proportion of the former part and the latter part of the objective function value. Simulation results show that the proposed algorithm, compared with the traditional algorithms, obtains the better reconstructed image quality and lower time complexity.

scholarly journals Compressed Sensing Reconstruction based on Adaptive Scale Parameter using Texture Feature

2019 ◽  
Vol 8 (12) ◽  
pp. 5455-5461
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Scale Parameter ◽  
Sparse Matrix ◽  
Texture Feature ◽  
Projection Matrix ◽  
Original Image ◽  
Scaling Factors ◽  
Mri Scan ◽  
The Mean

While taking an MRI scan, the patients cannot static for a long time during the motions; the image formation process can create artifacts that may reduce the image quality. The Compressed Sensing (CS) mechanism is employed to reconstruct the original image from the limited data given as the sparse matrix. Hence, CS can be utilized to reduce the acceleration time for an MRI scan considering the patient's health. So the sensing method is implemented by a suitable projection matrix for reconstructing the sparse signals from a few numbers of measurements using Compressed Sensing. The CS guarantees the recovery of the original image with high probability based on random Gaussian projection matrices. However, sparse ternarius projections are more apt for the implementation of hardware. In this article, the proposed deep learning method is employed to obtain a very sparse ternary projection in Compressed Sensing. Compressed Sensing Reconstruction using an adaptive scale parameter based on the texture feature is used to improve the image quality. The two scaling factors αx and αy are assigned to specify the fixed scale for changing the improvement of the image quality. In the parameter using texture feature, the αx and αy are assigned to α as an adaptive scale based on texture feature. In the TACS-SDANN architecture, there are two layers namely the sensing layer which trains the projection matrix and a reconstruction layer which trains for non-linear sparse matrix continuously using Auto-encoder. Experimentally, the scaling factors are calculated on the training data to get the mean PeakSignal-to-Noise Ratio (PSNR) for improving the image quality. Hence a new deep network layer is employed to improve the image quality in this proposed method. Hence the consequence of the proposed method is compared with the SDANN method based on the mean Peak-Signal-to-Noise Ratio (PSNR) to check the image quality. From that comparisons, the TACS-SDANN architecture is proposed to yield a better performance.

scholarly journals Radial Undersampling-Based Interpolation Scheme for Multislice CSMRI Reconstruction Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Maria Murad ◽  
Abdul Jalil ◽  
Muhammad Bilal ◽  
Shahid Ikram ◽  
Ahmad Ali ◽  
...  
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Imaging Modality ◽  
Similarity Index ◽  
Structural Similarity ◽  
Maximum Information ◽  
Central Target ◽  
Radial Sampling ◽  
Scan Time ◽  
Index Measurement

Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.

scholarly journals Compressed Sensing Inspired Image Reconstruction from Overlapped Projections

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Lin Yang ◽  
Yang Lu ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Compressed Sensing ◽  
Total Variation ◽  
Linear Form ◽  
Imaging System ◽  
Initial Guess ◽  
Filtered Backprojection ◽  
Numerical Tests ◽  
Data Acquisition Process

The key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction approach (e.g., filtered backprojection (FBP) algorithms) cannot be directly used because of two problems. First, overlapped projections represent an imaging system in terms of summed exponentials, which cannot be transformed into a linear form. Second, the overlapped measurement carries less information than the traditional line integrals. To meet these challenges, we propose a compressive sensing-(CS-) based iterative algorithm for reconstruction from overlapped data. This algorithm starts with a good initial guess, relies on adaptive linearization, and minimizes the total variation (TV). Then, we demonstrated the feasibility of this algorithm in numerical tests.

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