Time-of-Flight Magnetic Resonance Angiography With Sparse Undersampling and Iterative Reconstruction

Yamamoto, Takayuki; Fujimoto, Koji; Okada, Tomohisa; Fushimi, Yasutaka; Stalder, Aurélien F.; Natsuaki, Yutaka; Schmidt, Michaela; Togashi, Kaori

doi:10.1097/rli.0000000000000221

Cited by 29 publications

(50 citation statements)

References 41 publications

(35 reference statements)

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“…From our results, CS techniques also can reduce the SENSE ghost artifacts, highlight the blood signal in the blood vessel, and suppress the background signal. Similar findings have been found in previous studies that the image quality of CS‐MRA is higher than MRA which only using a parallel imaging technique, with more than an acceleration factor of 3 . CS‐MRA scan was successfully performed in all enrolled patients without any technical problems in our study, which demonstrated its good feasibility and stability, consistent with a previous study …”

Section: Discussionsupporting

confidence: 92%

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Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing

Lin

Zhang

Guo

et al. 2019

Magnetic Resonance Imaging

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Background Compressed sensing (CS) has been widely used to improve the speed of MRI, but the feasibility of application in 3D intracranial MR angiography (MRA) needs to be evaluated in clinical practice. Purpose To evaluate the clinical feasibility of CS‐MRA in comparison with conventional 3D‐MRA (Con‐MRA). Study Type Retrospective. Subjects Forty‐nine consecutive patients with suspected intracranial arterial disease. Field Strength/Sequence 3T MRI. 3D time‐of‐flight (TOF) MRA using a CS algorithm and conventional 3D TOF MRA scan. Assessment Three radiologists (4, 11, and 12 years of experience in neuroradiology) independently assessed the image quality, vascular lesions, and variations of intracranial arteries of both CS‐MRA and Con‐MRA, respectively. Statistical Tests The Kendall W test was performed to assess the interobserver agreement of image quality and intracranial arterial stenosis. A nonparametric test (Wilcoxon test) was used for comparison of the image quality and definition of the external carotid artery (ECA). Weighted kappa analysis was performed for the interstudy agreement of intracranial arterial stenosis. The aneurysm, decreased branches, congenital hypoplasia, absence, and variant branching of intracranial arteries were observed and evaluated for interobserver agreement and interstudy agreement by kappa analysis. Paired‐t‐tests for signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were conducted. Results Image quality is better for CS‐MRA compared with Con‐MRA with significance (Z = –3.710 to –2.673, with P < 0.01). The interstudy agreement of lesion and variation of intracranial arteries assessment for each observer was excellent. The SNR and CNR were significantly higher in CS‐MRA compared with Con‐MRA (P < 0.001). The definition of ECA of CS‐MRA was significantly better (Z = –4.9, P < 0.001). Data Conclusion CS‐MRA showed significantly higher image quality with less blur, comparable image diagnostic performance of intracranial arteries, and better display of ECA than Con‐MRA. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1843–1851.

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Section: Discussionsupporting

confidence: 92%

“…Our study had a fair amount of samples enrolled, and showed that the image quality of CS‐MRA was preferable than those of Con‐MRA. The image quality of CS‐MRA is better due to the sparse undersampling and inherently denoising procedure of CS reconstruction, which enhances the contrast of vessels by counteracting background noise …”

Section: Discussionmentioning

confidence: 99%

Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing

Lin

Zhang

Guo

et al. 2019

Magnetic Resonance Imaging

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“…Sparse MRI approaches work very well for CE‐MRA because bright signals are sparsified within a low background signal with high contrast‐to‐noise ratio, which matches the basic assumptions of CS . It also works well for non‐contrast time‐of‐flight (TOF) MRA due to sparsity in k ‐space …”

Section: Introductionmentioning

confidence: 82%

“…CS application for non‐contrast TOF‐MRA has been done in previous papers. A retrospective approach using 100% sampled raw data of TOF‐MRA was feasible to assess the reconstructed images with various undersampling rates and various CS reconstruction parameters for the NESTA algorithm In another previous study of the non‐contrast TOF‐MRA adopted CS algorithm mFISTA with prospective undersampling, various undersampling rates were compared in healthy volunteers . Sparse‐TOF can achieve better image quality relative to PI TOF at higher acceleration factors …”

Section: Discussionmentioning

confidence: 99%

Clinical evaluation of time‐of‐flight MR angiography with sparse undersampling and iterative reconstruction for cerebral aneurysms

et al. 2017

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Compressed sensing (CS) MRI has just been introduced to research areas as an innovative approach to accelerate MRI. CS is expected to achieve higher k-space undersampling by exploiting the underlying sparsity in an appropriate transform domain. MR angiography (MRA) provides high spatial resolution information on arteries; however, a relatively long acquisition time is necessary to cover a wide volume. Reduction of acquisition time by CS for time-of-flight (TOF) MR angiography (Sparse-TOF) is beneficial in clinical examinations; therefore, the clinical validity of Sparse-TOF needs to be investigated. The aim of this study was to compare the diagnostic capability of TOF MRA between parallel imaging (PI)-TOF with an acceleration factor of 3 (annotated as 3×) and Sparse-TOF (3× and 5×) in patients with cerebral aneurysms. PI-TOF (3×) and Sparse-TOF (3× and 5×) imaging were performed in 20 patients using a 3 T MRI system. Aneurysms in PI-TOF (3×) and Sparse-TOF (3× and 5×) were blindly rated as visible or scarcely visible by neuroradiologists. The neck, height and width of aneurysms were also measured. Twenty-six aneurysms were visualized and rated as visible in PI-TOF (3×) and Sparse-TOF (3× and 5×), with excellent agreement between two raters. No significant differences were found in measured neck, height or width of aneurysms among them. Sparse-TOF (3× and 5×) were acquired and reconstructed within 6 min, and cerebral aneurysms were visible in both of them with equivalent quality to PI-TOF (3×). Sparse-TOF (5×) is a good alternative to PI-TOF (3×) to visualize cerebral aneurysms.

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“…This algorithm reconstructs undersampled MR images through

l_{1}

minimization using the property of sparsity when transforming an MR image into a specific domain (such as a wavelet or a discrete cosine transform). Recent studies have demonstrated the clinical feasibility of fast TOF MRA using a compressed sensing‐based iterative method …”

Section: Introductionmentioning

confidence: 99%

Parallel imaging in time‐of‐flight magnetic resonance angiography using deep multistream convolutional neural networks

Jun

Shin

et al. 2019

Magnetic Resonance in Med

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Purpose To develop and evaluate a method of parallel imaging time‐of‐flight (TOF) MRA using deep multistream convolutional neural networks (CNNs). Methods A deep parallel imaging network (“DPI‐net”) was developed to reconstruct 3D multichannel MRA from undersampled data. It comprises 2 deep‐learning networks: a network of multistream CNNs for extracting feature maps of multichannel images and a network of reconstruction CNNs for reconstructing images from the multistream network output feature maps. The images were evaluated using normalized root mean square error (NRMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity (SSIM) values, and the visibility of blood vessels was assessed by measuring the vessel sharpness of middle and posterior cerebral arteries on axial maximum intensity projection (MIP) images. Vessel sharpness was compared using paired t tests, between DPI‐net, 2 conventional parallel imaging methods (SAKE and ESPIRiT), and a deep‐learning method (U‐net). Results DPI‐net showed superior performance in reconstructing vessel signals in both axial slices and MIP images for all reduction factors. This was supported by the quantitative metrics, with DPI‐net showing the lowest NRMSE, the highest PSNR and SSIM (except R = 3.8 on sagittal MIP images, and R = 5.7 on axial slices and sagittal MIP images), and significantly higher vessel sharpness values than the other methods. Conclusion DPI‐net was effective in reconstructing 3D TOF MRA from highly undersampled multichannel MR data, achieving superior performance, both quantitatively and qualitatively, over conventional parallel imaging and other deep‐learning methods.

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Time-of-Flight Magnetic Resonance Angiography With Sparse Undersampling and Iterative Reconstruction

Abstract: Sparse TOF can achieve better image quality relative to PI TOF at higher acceleration factors. The diagnostic quality of distal branches (A2/3, M4, P4) was maintained with Sp 6×, which achieved a shorter acquisition time less than half of PI 2×.

Cited by 29 publications

References 41 publications

Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing

Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing

Clinical evaluation of time‐of‐flight MR angiography with sparse undersampling and iterative reconstruction for cerebral aneurysms

Parallel imaging in time‐of‐flight magnetic resonance angiography using deep multistream convolutional neural networks

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