Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study
<b>Objectives</b>: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. <b>Methods</b>: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was sca...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Tomography |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2379-139X/11/5/51 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126430361354240 |
|---|---|
| author | Haoyan Li Zhenpeng Chen Shuaiyi Gao Jiaqi Hu Zhihao Yang Yun Peng Jihang Sun |
| author_facet | Haoyan Li Zhenpeng Chen Shuaiyi Gao Jiaqi Hu Zhihao Yang Yun Peng Jihang Sun |
| author_sort | Haoyan Li |
| collection | DOAJ |
| description | <b>Objectives</b>: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. <b>Methods</b>: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. <b>Results</b>: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. <b>Conclusions</b>: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity. |
| format | Article |
| id | doaj-art-e5615024fd9e4f508247d876753c8f60 |
| institution | OA Journals |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj-art-e5615024fd9e4f508247d876753c8f602025-08-20T02:33:55ZengMDPI AGTomography2379-13812379-139X2025-04-011155110.3390/tomography11050051Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom StudyHaoyan Li0Zhenpeng Chen1Shuaiyi Gao2Jiaqi Hu3Zhihao Yang4Yun Peng5Jihang Sun6Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, ChinaQingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, ChinaDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, ChinaDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, ChinaDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, ChinaDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, ChinaDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China<b>Objectives</b>: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. <b>Methods</b>: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. <b>Results</b>: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. <b>Conclusions</b>: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity.https://www.mdpi.com/2379-139X/11/5/51multi-slice CTdual-energy CTimage enhancementU-Net modeldeep learningimage reconstruction |
| spellingShingle | Haoyan Li Zhenpeng Chen Shuaiyi Gao Jiaqi Hu Zhihao Yang Yun Peng Jihang Sun Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study Tomography multi-slice CT dual-energy CT image enhancement U-Net model deep learning image reconstruction |
| title | Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study |
| title_full | Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study |
| title_fullStr | Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study |
| title_full_unstemmed | Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study |
| title_short | Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study |
| title_sort | performance evaluation of image segmentation using dual energy spectral ct images with deep learning image reconstruction a phantom study |
| topic | multi-slice CT dual-energy CT image enhancement U-Net model deep learning image reconstruction |
| url | https://www.mdpi.com/2379-139X/11/5/51 |
| work_keys_str_mv | AT haoyanli performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy AT zhenpengchen performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy AT shuaiyigao performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy AT jiaqihu performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy AT zhihaoyang performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy AT yunpeng performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy AT jihangsun performanceevaluationofimagesegmentationusingdualenergyspectralctimageswithdeeplearningimagereconstructionaphantomstudy |