علوم رایانشی

علوم رایانشی

مروری بر روش‌های مبتنی بر هوش مصنوعی جهت کاهش نوفه[1] در تصاویر توموگرافی انتشار با دوز پایین 1. Noise

نوع مقاله : مروری

نویسندگان
1 دانشجوی کارشناسی ارشد علوم کامپیوتر، گرایش محاسبات نرم و هوش مصنوعی، دانشگاه شاهد، ایران
2 دانشیار گروه علوم کامپیوتر، دانشگاه شاهد، ایران
3 استادیار گروه علوم کامپیوتر، دانشگاه شاهد، ایران
4 استادیار گروه فیزیک پزشکی، دانشگاه علوم پزشکی ایران
10.22034/csj.2025.233061
چکیده
توموگرافی انتشار (ET) شامل PET و SPECT، ابزاری کلیدی در تصویربرداری پزشکی است که اطلاعات عملکردی دقیقی از فرآیندهای زیستی بدن را فراهم می‌کند. کاهش دوز تابش برای حفظ ایمنی بیمار، به افزایش نوفه و افت کیفیت تصویر منجر می‌شود که تفسیر بالینی را دشوار و دقت تشخیص را کاهش می‌دهد. مسئله اصلی این مقاله، افت کیفیت تصاویر در شرایط دوز پایین به‌دلیل نوفه ناشی از فرآیند شمارش فوتون‌ها و محدودیت تابش است. هدف، بررسی روش‌های مبتنی بر هوش مصنوعی برای کاهش نوفه و بهبود کیفیت تصاویر در این شرایط است. در این راستا، سه رویکرد نظارت‌شده، بدون‌ نظارت و چندمدالیته بررسی شده‌اند. روش‌های نظارت‌شده مانند U-Net و GAN نتایج قابل‌توجهی ارائه کرده‌اند، و رویکردهای بدون‌نظارت و چندمدالیته نیز به‌ویژه در مواجهه با کمبود داده، عملکرد مؤثری داشته‌اند. این بررسی نشان می‌دهد که الگوریتم‌های هوش مصنوعی، با وجود چالش‌هایی نظیر نوفه بالا و نبود داده‌های مرجع، قابلیت بالایی در بهبود کیفیت تصاویرپزشکی با دوز پایین دارند.
کلیدواژه‌ها
موضوعات

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