New imaging techniques and trends in radiology
Özet
Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization. The resolution and diagnostic quality of images obtained through advancements in each modality are steadily improving. Additionally, technological progress has significantly shortened acquisition times for CT and MRI. The use of artificial intelligence (AI), which is becoming increasingly widespread worldwide, has also been incorporated into radiography. This technology can produce more accurate and reproducible results in US examinations. Machine learning offers great potential for improving image quality, creating more distinct and useful imag-es, and even developing new US imaging modalities. Furthermore, AI technologies are increasingly prevalent in CT and MRI for image evaluation, image generation, and enhanced image quality. © 2025, Galenos Publishing House. All rights reserved.
Anahtar Kelimeler
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Kantarcı, Mecit | |
| dc.contributor.author | Aydın, Sonay | |
| dc.contributor.author | Oğul, Hayri | |
| dc.contributor.author | Kızılgöz, Volkan | |
| dc.date.accessioned | 2026-10-18T20:25:54.321511400Z | |
| dc.date.available | 2026-10-18T20:25:54.321511400Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization. The resolution and diagnostic quality of images obtained through advancements in each modality are steadily improving. Additionally, technological progress has significantly shortened acquisition times for CT and MRI. The use of artificial intelligence (AI), which is becoming increasingly widespread worldwide, has also been incorporated into radiography. This technology can produce more accurate and reproducible results in US examinations. Machine learning offers great potential for improving image quality, creating more distinct and useful imag-es, and even developing new US imaging modalities. Furthermore, AI technologies are increasingly prevalent in CT and MRI for image evaluation, image generation, and enhanced image quality. © 2025, Galenos Publishing House. All rights reserved. | |
| dc.identifier.doi | 10.4274/dir.2024.242926 | |
| dc.identifier.eissn | 1305-3612 | |
| dc.identifier.endpage | 517 | |
| dc.identifier.issn | 1305-3825 | |
| dc.identifier.issue | 5 | |
| dc.identifier.pmcid | PMC12417918 | |
| dc.identifier.pmid | 39817634 | |
| dc.identifier.scopus | 2-s2.0-105015623026 | |
| dc.identifier.startpage | 505 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/ebyu/10759 | |
| dc.identifier.volume | 31 | |
| dc.identifier.wos | WOS:001567594100001 | |
| dc.language.iso | en | |
| dc.publisher | Galenos Publishing House | |
| dc.relation.ispartof | Diagnostic and Interventional Radiology | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Arthrographic applications | |
| dc.subject | cerebrospinal fluid flowmetry | |
| dc.subject | imaging techniques | |
| dc.subject | magnetic resonance imaging techniques | |
| dc.subject | magnetic resonance spectroscopy | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Diagnostic Imaging | |
| dc.subject | Humans | |
| dc.subject | Machine Learning | |
| dc.subject | Magnetic Resonance Imaging | |
| dc.subject | Radiology | |
| dc.subject | Tomography, X-Ray Computed | |
| dc.subject | Ultrasonography | |
| dc.subject | adult | |
| dc.subject | cerebrospinal fluid | |
| dc.subject | computer assisted tomography | |
| dc.subject | diagnosis | |
| dc.subject | flow measurement | |
| dc.subject | human | |
| dc.subject | image quality | |
| dc.subject | MRI scanner | |
| dc.subject | nuclear magnetic resonance imaging | |
| dc.subject | nuclear magnetic resonance spectroscopy | |
| dc.subject | radiation dose | |
| dc.subject | review | |
| dc.subject | echography | |
| dc.subject | procedures | |
| dc.subject | x-ray computed tomography | |
| dc.title | New imaging techniques and trends in radiology | |
| dc.type | Review Article | |
| dspace.entity.type | Publication | |
| rdl.citation.scopus | 31 | |
| rdl.citation.trdizin | 4 | |
| rdl.citation.wos | 27 | |
| rdl.department | ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ | |
| rdl.department | ATATÜRK ÜNİVERSİTESİ | |
| rdl.department | ANKARA MEDİPOL ÜNİVERSİTESİ | |
| rdl.oa.status | gold | |
| rdl.source.index | SCOPUS | |
| rdl.source.index | WOS | |
| rdl.source.index | TRDIZIN | |
| rdl.source.index | PUBMED | |
| rdl.wos.index | Science Citation Index Expanded (SCI-EXPANDED) |
