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European Radiology: Evaluation of liver metastases by deep learning-based dual-energy CT image reconstruction

 Last Update: 2022-10-10
 Source: Internet
 Author: User

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It is well known that liver metastases are one of the most common complications of abdominal CT in tumor patients.

The contrast of iodine can be increased in VMI at low single energy level (ie, 40–50 KeV) by exploiting the apparent increase in attenuation of iodine due to the increased photoelectric effect of iodine above the K-edge of 33 KeV

Recent studies have shown that compared to traditional iterative reconstruction algorithms, deep learning model (DLM) reconstruction algorithms can improve image quality by suppressing image noise and preserving structural details

A study published in the journal European Radiology evaluated the image quality of 40-keV VMI reconstructed by a provider's diagnostic DLM in liver metastases compared to standard linear mixed images (simulated 100-KVp) and raw 40-keV VMI.

This retrospective study included 131 patients undergoing contrast-enhanced DECT (80-kvp and 150-kvp with tin filter) during the portal venous phase to monitor for liver metastases

DLM 40-keV VMI compared with 40-keV VMI and 100-kvp showed higher CNR of lesions than liver parenchyma (8.

This study demonstrates that DLM-based reconstruction of 40keV VMIs provides better image quality and comparable diagnostic performance in detecting liver metastases compared to standard linear mixed images from ADMIRE

Original source:

Taehee Lee, Jeong Min Lee, Jeong Hee Yoon, et al.

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