VNC-Net architecture.

<p>The model takes contrast enhanced dual-energy CT (CE DECT) images as input and generates virtual non-contrast (VNC) images as output. The numbers denoted below the encoder/decoder blocks represent the number of channels. The bottom of the figure depicts the structure of the down-sampling bl...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Jungye Kim (20485427) (author)
مؤلفون آخرون:	Jimin Lee (2552782) (author), Bitbyeol Kim (17020584) (author), Sangwook Kim (2235727) (author), Hyeongmin Jin (20485430) (author), Seongmoon Jung (9310778) (author)
منشور في:	2024
الموضوعات:	Neuroscience Physiology Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified unlike previous studies showing close correspondence demonstrating high scores deep learning models generating virtual non deep learning model improve imaging protocols maintaining ct numbers using virtual non radiotherapy </ p pseudo dect images model &# 8217 contrast planning ct overall good performance enhanced planning ct contrast ct images radiotherapy planning &# 8217 planning ct ct numbers contrast ct scans using energy ct true non model training images validated contrast agent vnc ct xlink "> ten left study highlights quantitative performance quantitative comparison paper presents outputs instead limitations remained future research dose calculations deterministic method clinical relevance 45 patients
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VNC-Net architecture.

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