Multiple congenital pericardial cysts mimicking pleural effusion on diagnostic imaging

Sun, Ting; Huang, Jie-Zhou; Xie, Shaobo

doi:10.21037/qims-20-717

Cited by 1 publication

(4 citation statements)

References 3 publications

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“…denotes the number of similar patches, the size of the resultant tensor Γ p is 6×6×6× p patch N . The number of rows for the unfolding matrix of Γ p along the mode-4 (denoted by T (4) ) is much larger than that of other 3 unfolding matrices of Γ p along the mode-1, 2, 3 [denoted by T (1), T (2), T (3) ]. We used a value of 0.004 to provide more weight for the low rankness of T (4) .…”

Section: Methodsmentioning

confidence: 99%

“…Eq. [3] can be converted into the following equivalent constrained optimization problem through variable splitting:…”

Section: Ex Y Xmentioning

confidence: 99%

“…Diseases of the thoracic aorta are some of the most common causes of cardiovascular morbidity and mortality and can result in potentially catastrophic consequences (1)(2)(3). Thoracic aortic imaging can be used to diagnose aortic diseases and may help predict cardiovascular risk (4).…”

Section: Introductionmentioning

confidence: 99%

“…T (2) : reshapes Γ into an N 2 ×( N 1 ×N 3 ×N 4 ) complex matrix. T (3) : reshapes Γ into an N 3 ×( N 1 ×N 2 ×N 4 ) complex matrix. T (4) : reshapes Γ into an N 4 ×( N 1 ×N 2 ×N 3 ) complex matrix.…”

mentioning

confidence: 99%

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High-efficiency 3D black-blood thoracic aorta imaging with patch-based low-rank tensor reconstruction

Shi¹,

Liu²,

Cheng³

et al. 2023

Quant Imaging Med Surg

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Background: Three-dimensional (3D) black-blood (BB) vessel wall imaging is a promising noninvasive imaging technique for assessing thoracic aortic diseases. We aimed to develop and evaluate a fast thoracic aorta vessel wall imaging method with patch-based low-rank tensor (Pt-LRT) reconstruction using the 3D-modulated variable flip angle fast-spin echo (vFA-FSE) sequence. Methods:The Pt-LRT technique adopts a low-rank tensor image model with regularization to explore the local low-rankness and nonlocal redundancies of the images to assess the thoracic aorta vessel wall. It uses high-order tensors to capture correlations between data in multiple dimensions and reconstructs images from highly undersampled data. For this study, 12 healthy participants and 2 patients with thoracic aortic diseases were evaluated at 3T magnetic resonance (MR). The reconstruction results were compared to the traditional generalized autocalibrating partially parallel acquisitions (GRAPPA) and ℓ 1 -SPIRiT reconstruction to assess the feasibility of the proposed framework. Quantitative analyses of the vessel wall thickness (VWT), internal diameter (ID), lumen area (LA), and contrast-to-noise ratio (CNR) between the lumen and vessel wall were performed on all healthy participants.Results: Results demonstrated no significant differences between the GRAPPA and the proposed Pt-LRT in VWT, ID, or LA of the aorta (P<0.05). A higher mean CNR was attained with 3D patch-based low-rank tensor reconstruction than with ℓ 1 -SPIRiT reconstruction (49.4±10.8 vs. 38.9±8.2). Conclusions:The proposed 3D BB thoracic aorta vessel wall imaging method can reduce the scan time and produce an image quality that is in good agreement with the conventional GRAPPA acquisition, which takes approximately more than 8 min. This study also shows that the proposed Pt-LRT method substantially improves the visualization and sharpness of the vessel wall and the definition of the tissue boundary compared to the imaging obtained with ℓ 1 -SPIRiT.

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Section: Methodsmentioning

confidence: 99%

“…Eq. [3] can be converted into the following equivalent constrained optimization problem through variable splitting:…”

Section: Ex Y Xmentioning

confidence: 99%