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Subject: search.filters.subject.Generative adversarial network

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    Virtual contrast enhancement for CT scans of abdomen and pelvis
    (2022) Liu, Jingya; Tian, Yingli; Duzgol, Cihan; Akin, Oguz; Agildere, A. Muhtesem; Haberal, K. Murat; Coskun, Mehmet; 0000-0002-8211-4065; 35914340; R-9398-2019
    Contrast agents are commonly used to highlight blood vessels, organs, and other structures in magnetic resonance imaging (MRI) and computed tomography (CT) scans. However, these agents may cause allergic reactions or nephrotoxicity, limiting their use in patients with kidney dysfunctions. In this paper, we propose a generative adversarial network (GAN) based framework to automatically synthesize contrast-enhanced CTs directly from the non-contrast CTs in the abdomen and pelvis region. The respiratory and peristaltic motion can affect the pixel-level mapping of contrast-enhanced learning, which makes this task more challenging than other body parts. A perceptual loss is introduced to compare high-level semantic differences of the enhancement areas between the virtual contrast-enhanced and actual contrast-enhanced CT images. Furthermore, to accurately synthesize the intensity details as well as remain texture structures of CT images, a dual-path training schema is proposed to learn the texture and structure features simultaneously. Experiment results on three contrast phases (i.e. arterial, portal, and delayed phase) show the potential to synthesize virtual contrast-enhanced CTs directly from non-contrast CTs of the abdomen and pelvis for clinical evaluation.
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    An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
    (2021) Guney, Gokhan; Yigin, Busra Ozgode; Guven, Necdet; Colak, Burcin; Alici, Yasemin Hosgoren; Erzin, Gamze; Saygili, Gorkem; 0000-0003-3384-8131; 33888650
    Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.