Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Akcakaya, Mehmet"'
Autor:
Alçalar, Yaşar Utku, Akçakaya, Mehmet
Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, th
Externí odkaz:
http://arxiv.org/abs/2407.11288
Autor:
Gu, Hongyi, Zhang, Chi, Yu, Zidan, Rettenmeier, Christoph, Stenger, V. Andrew, Akçakaya, Mehmet
Functional MRI (fMRI) is an important tool for non-invasive studies of brain function. Over the past decade, multi-echo fMRI methods that sample multiple echo times has become popular with potential to improve quantification. While these acquisitions
Externí odkaz:
http://arxiv.org/abs/2312.05707
Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities of DL-based MRI reconstruction models. However, the approaches of rob
Externí odkaz:
http://arxiv.org/abs/2211.04930
Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications. Unlike CS that is typically implemented with pre-determined linear
Externí odkaz:
http://arxiv.org/abs/2204.07923
Autor:
Hammernik, Kerstin, Küstner, Thomas, Yaman, Burhaneddin, Huang, Zhengnan, Rueckert, Daniel, Knoll, Florian, Akçakaya, Mehmet
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorp
Externí odkaz:
http://arxiv.org/abs/2203.12215
Autor:
Kırış, Talat, Akçakaya, Mehmet Osman
Publikováno v:
In Clinical Neurology and Neurosurgery September 2024 244
Publikováno v:
IEEE Signal Processing Magazine, 2022
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the difficulty of ob
Externí odkaz:
http://arxiv.org/abs/2105.08040
Autor:
Demirel, Omer Burak, Yaman, Burhaneddin, Dowdle, Logan, Moeller, Steen, Vizioli, Luca, Yacoub, Essa, Strupp, John, Olman, Cheryl A., Uğurbil, Kâmil, Akçakaya, Mehmet
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slic
Externí odkaz:
http://arxiv.org/abs/2105.05827
Autor:
Demirel, Omer Burak, Yaman, Burhaneddin, Dowdle, Logan, Moeller, Steen, Vizioli, Luca, Yacoub, Essa, Strupp, John, Olman, Cheryl A., Uğurbil, Kâmil, Akçakaya, Mehmet
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions. Among these, simultaneous multi-slice (SMS) imaging has emerged as a
Externí odkaz:
http://arxiv.org/abs/2105.04532