Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts

Ortuño, Juan E.; Vegas-Sánchez-Ferrero, Gonzalo; Gómez-Valverde, Juan J.; Chen, Marcus Y; Santos, A.; McVeigh, Elliot R.; Ledesma-Carbayo, María J.

doi:10.1016/j.media.2020.101748

Cited by 4 publications

(3 citation statements)

References 57 publications

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“…In cardiovascular CT, 4-dimensional CTA can depict cardiovascular anatomy and dynamics, and track cardiac chambers and valves. 96 Photon-counting CT has emerged as a promising new technique, which will allow for reduced radiation exposure, increased spatial resolution, correction of beam-hardening artifacts, and alternative contrast agent protocols while creating opportunities for quantitative imaging, ready to dramatically change CT. 97 In cardiovascular MRI, 4-dimensional flow MRI offers the opportunity to derive advanced hemodynamic measures, such as vorticity and helicity, wall shear stress, pressure gradients, viscous energy loss, turbulent kinetic energy, and pulse wave velocity, initially focused on the heart and large vessels, now it can be applied to other vascular territory throughout the human circulatory system. 98 Dark-blood LGE method is a promising new tool for noninvasive assessment of myocardial fibrosis.…”

Section: Cardiovascular Imaging: Tomorrow Technical Advances In Cardi...mentioning

confidence: 99%

“…In the future, some promising technical advances in both cardiovascular CT and MRI might have potential for the development of cardiovascular imaging that deserves more attention. In cardiovascular CT, 4-dimensional CTA can depict cardiovascular anatomy and dynamics, and track cardiac chambers and valves 96. Photon-counting CT has emerged as a promising new technique, which will allow for reduced radiation exposure, increased spatial resolution, correction of beam-hardening artifacts, and alternative contrast agent protocols while creating opportunities for quantitative imaging, ready to dramatically change CT 97.…”

Section: Cardiovascular Imaging: Tomorrowmentioning

confidence: 99%

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Cardiovascular Imaging in China

Tang¹,

Zhang²,

Zhang³

et al. 2022

Journal of Thoracic Imaging

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The high prevalence and mortality of cardiovascular diseases in China's large population has increased the use of cardiovascular imaging for the assessment of conditions in recent years. In this study, we review the past 20 years of cardiovascular imaging in China, the increasingly important role played by cardiovascular computed tomography in coronary artery disease and pulmonary embolism assessment, magnetic resonance imaging's use for cardiomyopathy assessment, the development and application of artificial intelligence in cardiovascular imaging, and the future of Chinese cardiovascular imaging.

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Section: Cardiovascular Imaging: Tomorrow Technical Advances In Cardi...mentioning

confidence: 99%

Section: Cardiovascular Imaging: Tomorrowmentioning

confidence: 99%

Cardiovascular Imaging in China

Tang¹,

Zhang²,

Zhang³

et al. 2022

Journal of Thoracic Imaging

View full text Add to dashboard Cite

show abstract

“…In [12], Fu et al classified image segmentation techniques, such as characteristic feature thresholding [13][14][15] or clustering [16,17], edge detection [18,19], and region extraction [20,21]. Other approaches include graph cut methods [22,23] and deep neural network-based methods [24]. Among the existing segmentation methods, thresholding is considered to be an efficient and popular techniques because of its simplicity and high efficiency [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation

Feng

Liu

Zhang

et al. 2021

Entropy

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In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

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