Related Experiment Video For Dual-energy CT
Updated: May 1, 2026
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Spectral parametric segmentation of contrast-enhanced dual-energy CT to detect bone metastasis: feasibility
Young Han Lee1, Sungjun Kim1, Daekeon Lim1
1Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Background:
Dual-energy computed tomography (DECT) images may be underutilized for the evaluation of skeletal metastasis. Spectral parametric segmentation of DECT can produce bone-iodine separated images, which have the potential to detect bone metastases.
Purpose:
To evaluate the potential of bone-iodine separation in the detection of bone metastasis with spectral parametric segmentation of DECT images which are acquired at clinical follow-up for patients with prior malignancy.
Material And Methods:
The institutional review board approved the protocol of this retrospective review. Chest DECT scans using fast kV-switching between 80 and 140 kVp were included in this study. Bone-iodine separated reformatted images were produced by spectral parametric segmentation of synthesized monochromatic images. All chest CT images of 702 metastatic lesions from 54 patients were retrospectively evaluated in terms of visualization of metastatic lesions compared with (99m)Tc-MDP (methylene diphosphonate) whole-body bone scintigraphy (WBBS) as reference standard of diagnosis.
Results:
Spectral parametric segmentation images of DECT visualized metastatic lesions in 92.3% (n = 648/702). Osteoblastic metastases were delineated as subtle enhancing lesions on DECT in comparison to WBBS.
Conclusion:
Spectral parametric segmentation of iodine from cortical and medullary bone allowed visualization of bone metastasis. DECT might be utilized for the screening or detection of bone metastases.
