Virtual contrast enhancement for CT scans of abdomen and pelvis Journal Article
| Authors: | Liu, J.; Tian, Y.; Duzgol, C.; Akin, O.; Ağıldere, A. M.; Haberal, K. M.; Coşkun, M. |
| Article Title: | Virtual contrast enhancement for CT scans of abdomen and pelvis |
| Abstract: | 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. © 2022 Elsevier Ltd |
| Keywords: | controlled study; magnetic resonance imaging; pelvis; computer assisted tomography; abdomen; image enhancement; computerized tomography; computed tomography images; contrast enhancement; clinical evaluation; motion; contrast agent; blood vessels; learning; contrast-enhanced ct; semantics; textures; contrast-enhanced; body regions; human; article; computed tomography scan; allergic reactions; contrast enhanced computed tomography; x-ray computed tomography; deep learning; generative adversarial network; e-learning; generative adversarial networks; contrast enhanced ct; image synthesize |
| Journal Title: | Computerized Medical Imaging and Graphics |
| Volume: | 100 |
| ISSN: | 0895-6111 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2022-09-01 |
| Start Page: | 102094 |
| Language: | English |
| DOI: | 10.1016/j.compmedimag.2022.102094 |
| PROVIDER: | scopus |
| PUBMED: | 35914340 |
| PMCID: | PMC10227907 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 September 2022 -- Source: Scopus |
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