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Deep Neural Networks for Cardiovascular Magnetic Resonance Imaging.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Neural Networks for Cardiovascular Magnetic Resonance Imaging./
作者:	Ghodrati Kouzehkonan, Vahid.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	257 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Medical imaging. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29060923
ISBN:	9798834011613

Deep Neural Networks for Cardiovascular Magnetic Resonance Imaging.
Ghodrati Kouzehkonan, Vahid.

Deep Neural Networks for Cardiovascular Magnetic Resonance Imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 257 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2022.

This item must not be sold to any third party vendors.

Magnetic Resonance Imaging (MRI) is a powerful diagnostic imaging modalities known to provide high soft-tissue contrast and spatial resolution. Much of the versatility of MRI stems from the fact that the signal from different tissue types can be weighted differently through manipulation of the sequence in which radiofrequency (RF) and gradient events are played out during the data acquisition phase. However, data acquisition for most MRI measurements is sequential, limiting its speed and increasing its susceptibility to motion artifacts. This is particularly the case for cardiovascular applications, where cardiac and respiratory motion complicate all aspects of the data acquisition and signal processing pathways. Moreover, following data acquisition and image reconstruction, clinically relevant post-processing may require substantial time and effort, increasing the burden on clinical centers and medical staff. Thus, general algorithms should be customized to accelerate image acquisition, image reconstruction and image post-processing with the goal of expanding the speed, scope and reliability of cardiovascular MRI applications. This dissertation describes several deep learning-based methods applying tailored image reconstruction, respiratory motion correction, blood vessel segmentation, and instance T1 mapping calculation. The first application is the acceleration of dynamic cardiac MRI. Modern approaches to speeding MR image acquisition involve the use of significantly under-sampled k-space data (with a proportional reduction in acquisition time), such that the Nyquist limit of traditional signal sampling is violated and the missing k-space data are estimated by other means. The missing data are typically recovered either through incorporating independently acquired surface coil spatial sensitivity maps (parallel acquisition) or through iterative reconstruction via optimized approximations that enforce both sparsity in the sampled domain and consistency with the explicitly acquired data (compressed sensing). Although both parallel imaging and compressed sensing (CS) have proved powerful, they manifest hard limits as the degree of undersampling is increased. Moreover, even with fast modern processors and dedicated reconstruction hardware, image reconstruction times can become prohibitive. Deep learning methods have the potential to address several of the limitations noted for current parallel imaging and CS techniques and to expand the scope of clinical applications.

ISBN: 9798834011613Subjects--Topical Terms:

3172799
Medical imaging.
Subjects--Index Terms:

Cardiac MRI

Deep Neural Networks for Cardiovascular Magnetic Resonance Imaging.
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