CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility
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| Format: | Artículo Open Access |
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Wiley
2026
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| author | Shaojie Chang Joseph R. Swicklik Zhongxing Zhou Shravani Kharat Jarod Wellinghoff Hao Gong Eric E. Williamson Thomas A. Foley Cynthia H. McCollough Shuai Leng |
| author_facet | Shaojie Chang Joseph R. Swicklik Zhongxing Zhou Shravani Kharat Jarod Wellinghoff Hao Gong Eric E. Williamson Thomas A. Foley Cynthia H. McCollough Shuai Leng Shaojie Chang Joseph R. Swicklik Zhongxing Zhou Shravani Kharat Jarod Wellinghoff Hao Gong Eric E. Williamson Thomas A. Foley Cynthia H. McCollough Shuai Leng |
| collection | Wiley Open Access |
| contents | CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility Shaojie Chang Joseph R. Swicklik Zhongxing Zhou Shravani Kharat Jarod Wellinghoff Hao Gong Eric E. Williamson Thomas A. Foley Cynthia H. McCollough Shuai Leng Medical Physics Abstract Background Radiomics extracts quantitative imaging features from computed tomography (CT) data for clinical decision‐making. However, variations in acquisition parameters—particularly x‐ray tube voltage (kV)—introduce non‐biological variability in attenuation values, limiting the reproducibility of radiomic features across scanners, protocols, and institutions. Purpose To develop and evaluate a CT dAta harmoNiZAtion framework based on deep learNed vIrTual monoEnergetic imaging (TANZANITE), which leverages the keV flexibility of virtual monoenergetic images (VMIs) to enable cross‐kV scan translation and radiomics harmonization. Methods TANZANITE is a model hub consisting of multiple pre‐trained convolutional neural networks (CNNs), each designed to translate VMIs from 1 keV level to another. Phantom‐based calibration was first used to determine energy‐equivalent (Eff_E) keV levels corresponding to each tube potential (e.g., Eff_E(A) keV for source kV and Eff_E(B) keV for target kV). A CNN trained using 69 120 patches from seven patient cases to map VMIs from Eff_E(A) to Eff_E(B) was selected from the TANZANITE hub and applied directly to clinical CT images acquired at the source kV. This harmonized the images to match the attenuation characteristics of the target kV setting. Evaluation was conducted on independent dual‐energy CT datasets acquired at 100/Sn150 kV. Regions of interest (ROIs) were placed in the kidney, liver, and spine to assess CT number consistency and radiomic feature reproducibility. The concordance correlation coefficient (CCC) was calculated across 93 non‐shape radiomic features. Results After TANZANITE processing with 100 kV images, CT numbers in evaluated organs closely matched the Sn150 kV reference values in four testing patient cases. For example, mean kidney CT numbers changed from 320 HU (100 kV) to 156 HU (TANZANITE), approximating the Sn150 kV value of 160 HU. Similar changes were observed in the liver (157–105 HU vs. 104 HU reference) and spine (45–22 HU vs. 19 HU reference). Radiomic reproducibility improved substantially across organs: mean CCC increased from 0.590 to 0.995 in the liver, 0.300 to 0.970 in the kidney, and 0.630 to 0.968 in the spine. Post‐TANZANITE, over 98% of features exceeded the stability threshold (CCC ≥ 0.900) in all three representative organs. Conclusion TANZANITE provides a flexible, image‐domain harmonization framework by learning the keV‐to‐keV translation in the VMI domain and applying pre‐trained CNNs to clinical kV images. It improves CT number consistency and organ‐specific radiomic reproducibility without requiring raw projection data or scanner‐specific training. This approach supports consistent quantitative imaging across multiple‐kV acquisition protocols, enhancing radiomics reliability in clinical settings. 10.1002/mp.70403 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/mp.70403 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_mp_70403 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility Shaojie Chang Joseph R. Swicklik Zhongxing Zhou Shravani Kharat Jarod Wellinghoff Hao Gong Eric E. Williamson Thomas A. Foley Cynthia H. McCollough Shuai Leng Medical Physics CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility Shaojie Chang Joseph R. Swicklik Zhongxing Zhou Shravani Kharat Jarod Wellinghoff Hao Gong Eric E. Williamson Thomas A. Foley Cynthia H. McCollough Shuai Leng Medical Physics Abstract Background Radiomics extracts quantitative imaging features from computed tomography (CT) data for clinical decision‐making. However, variations in acquisition parameters—particularly x‐ray tube voltage (kV)—introduce non‐biological variability in attenuation values, limiting the reproducibility of radiomic features across scanners, protocols, and institutions. Purpose To develop and evaluate a CT dAta harmoNiZAtion framework based on deep learNed vIrTual monoEnergetic imaging (TANZANITE), which leverages the keV flexibility of virtual monoenergetic images (VMIs) to enable cross‐kV scan translation and radiomics harmonization. Methods TANZANITE is a model hub consisting of multiple pre‐trained convolutional neural networks (CNNs), each designed to translate VMIs from 1 keV level to another. Phantom‐based calibration was first used to determine energy‐equivalent (Eff_E) keV levels corresponding to each tube potential (e.g., Eff_E(A) keV for source kV and Eff_E(B) keV for target kV). A CNN trained using 69 120 patches from seven patient cases to map VMIs from Eff_E(A) to Eff_E(B) was selected from the TANZANITE hub and applied directly to clinical CT images acquired at the source kV. This harmonized the images to match the attenuation characteristics of the target kV setting. Evaluation was conducted on independent dual‐energy CT datasets acquired at 100/Sn150 kV. Regions of interest (ROIs) were placed in the kidney, liver, and spine to assess CT number consistency and radiomic feature reproducibility. The concordance correlation coefficient (CCC) was calculated across 93 non‐shape radiomic features. Results After TANZANITE processing with 100 kV images, CT numbers in evaluated organs closely matched the Sn150 kV reference values in four testing patient cases. For example, mean kidney CT numbers changed from 320 HU (100 kV) to 156 HU (TANZANITE), approximating the Sn150 kV value of 160 HU. Similar changes were observed in the liver (157–105 HU vs. 104 HU reference) and spine (45–22 HU vs. 19 HU reference). Radiomic reproducibility improved substantially across organs: mean CCC increased from 0.590 to 0.995 in the liver, 0.300 to 0.970 in the kidney, and 0.630 to 0.968 in the spine. Post‐TANZANITE, over 98% of features exceeded the stability threshold (CCC ≥ 0.900) in all three representative organs. Conclusion TANZANITE provides a flexible, image‐domain harmonization framework by learning the keV‐to‐keV translation in the VMI domain and applying pre‐trained CNNs to clinical kV images. It improves CT number consistency and organ‐specific radiomic reproducibility without requiring raw projection data or scanner‐specific training. This approach supports consistent quantitative imaging across multiple‐kV acquisition protocols, enhancing radiomics reliability in clinical settings. 10.1002/mp.70403 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility |
| topic | Medical Physics |
| url | https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70403 |