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HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment

Autor: Sultan, K M Arefeen, Hisham, Md Hasibul Husain, Orkild, Benjamin, Morris, Alan, Kholmovski, Eugene, Bieging, Erik, Kwan, Eugene, Ranjan, Ravi, DiBella, Ed, Elhabian, Shireen

The accurate evaluation of left atrial fibrosis via high-quality 3D Late Gadolinium Enhancement (LGE) MRI is crucial for atrial fibrillation management but is hindered by factors like patient movement and imaging variability. The pursuit of automated

Externí odkaz: http://arxiv.org/abs/2407.07254

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Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images

Autor: Sultan, K M Arefeen, Orkild, Benjamin, Morris, Alan, Kholmovski, Eugene, Bieging, Erik, Kwan, Eugene, Ranjan, Ravi, DiBella, Ed, Elhabian, Shireen

Accurate assessment of left atrial fibrosis in patients with atrial fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI images. However, obtaining such images is challenging due to patient motion, changing breathing patterns,

Externí odkaz: http://arxiv.org/abs/2310.08805

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Akademický článek

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network

Autor: Qi Huang, Johnathan Le, Sarang Joshi, Jason Mendes, Ganesh Adluru, Edward DiBella

Publikováno v: Tomography, Vol 10, Iss 5, Pp 660-673 (2024)

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time–concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Pu

Externí odkaz: https://doaj.org/article/429b85042e6b4a4f8259226aeede253f