Lung volume assessment for mean dark-field coefficient calculation using different determination methods
Abstract Background Accurate lung volume determination is crucial for reliable dark-field imaging. We compared different approaches for the determination of lung volume in mean dark-field coefficient calculation. Methods In this retrospective analysis of data prospectively acquired between October 2...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
|
| Series: | European Radiology Experimental |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41747-025-00593-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849326022817742848 |
|---|---|
| author | Florian T. Gassert Jule Heuchert Rafael Schick Henriette Bast Theresa Urban Tina Dorosti Gregor S. Zimmermann Sebastian Ziegelmayer Alexander W. Marka Markus Graf Marcus R. Makowski Daniela Pfeiffer Franz Pfeiffer |
| author_facet | Florian T. Gassert Jule Heuchert Rafael Schick Henriette Bast Theresa Urban Tina Dorosti Gregor S. Zimmermann Sebastian Ziegelmayer Alexander W. Marka Markus Graf Marcus R. Makowski Daniela Pfeiffer Franz Pfeiffer |
| author_sort | Florian T. Gassert |
| collection | DOAJ |
| description | Abstract Background Accurate lung volume determination is crucial for reliable dark-field imaging. We compared different approaches for the determination of lung volume in mean dark-field coefficient calculation. Methods In this retrospective analysis of data prospectively acquired between October 2018 and October 2020, patients at least 18 years of age who underwent chest computed tomography (CT) were screened for study participation. Inclusion criteria were the ability to consent and to stand upright without help. Exclusion criteria were pregnancy, lung cancer, pleural effusion, atelectasis, air space disease, ground-glass opacities, and pneumothorax. Lung volume was calculated using four methods: conventional radiography (CR) using shape information; a convolutional neural network (CNN) trained for CR; CT-based volume estimation; and results from pulmonary function testing (PFT). Results were compared using a Student t-test and Spearman ρ correlation statistics. Results We studied 81 participants (51 men, 30 women), aged 64 ± 12 years (mean ± standard deviation). All lung volumes derived from the various methods were different from each other: CR, 7.27 ± 1.64 L; CNN, 4.91 ± 1.05 L; CT, 5.25 ± 1.36 L; PFT, 6.54 L ± 1.52 L; p < 0.001 for all comparisons. A high positive correlation was found for all combinations (p < 0.001 for all), the highest one being between CT and CR (ρ = 0.88) and the lowest one between PFT and CNN (ρ = 0.78). Conclusion Lung volume and therefore mean dark-field coefficient calculation is highly dependent on the method used, taking into consideration different positioning and inhalation depths. Relevance statement This study underscores the impact of the method used for lung volume determination. In the context of mean dark-field coefficient calculation, CR-based methods are more desirable because both dark-field images and conventional images are acquired at the same breathing state, and therefore, biases due to differences in inhalation depth are eliminated. Key Points Lung volume measurements vary significantly between different determination methods. Mean dark-field coefficient calculations require the same method to ensure comparability. Radiography-based methods simplify workflows and minimize biases, making them most suitable. Graphical Abstract |
| format | Article |
| id | doaj-art-0dd9b8f33d124a17b7e9337fe36ff3e0 |
| institution | Kabale University |
| issn | 2509-9280 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj-art-0dd9b8f33d124a17b7e9337fe36ff3e02025-08-20T03:48:15ZengSpringerOpenEuropean Radiology Experimental2509-92802025-05-01911910.1186/s41747-025-00593-yLung volume assessment for mean dark-field coefficient calculation using different determination methodsFlorian T. Gassert0Jule Heuchert1Rafael Schick2Henriette Bast3Theresa Urban4Tina Dorosti5Gregor S. Zimmermann6Sebastian Ziegelmayer7Alexander W. Marka8Markus Graf9Marcus R. Makowski10Daniela Pfeiffer11Franz Pfeiffer12Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich (TUM)Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Division of Respiratory Medicine, Department of Internal Medicine I, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)Abstract Background Accurate lung volume determination is crucial for reliable dark-field imaging. We compared different approaches for the determination of lung volume in mean dark-field coefficient calculation. Methods In this retrospective analysis of data prospectively acquired between October 2018 and October 2020, patients at least 18 years of age who underwent chest computed tomography (CT) were screened for study participation. Inclusion criteria were the ability to consent and to stand upright without help. Exclusion criteria were pregnancy, lung cancer, pleural effusion, atelectasis, air space disease, ground-glass opacities, and pneumothorax. Lung volume was calculated using four methods: conventional radiography (CR) using shape information; a convolutional neural network (CNN) trained for CR; CT-based volume estimation; and results from pulmonary function testing (PFT). Results were compared using a Student t-test and Spearman ρ correlation statistics. Results We studied 81 participants (51 men, 30 women), aged 64 ± 12 years (mean ± standard deviation). All lung volumes derived from the various methods were different from each other: CR, 7.27 ± 1.64 L; CNN, 4.91 ± 1.05 L; CT, 5.25 ± 1.36 L; PFT, 6.54 L ± 1.52 L; p < 0.001 for all comparisons. A high positive correlation was found for all combinations (p < 0.001 for all), the highest one being between CT and CR (ρ = 0.88) and the lowest one between PFT and CNN (ρ = 0.78). Conclusion Lung volume and therefore mean dark-field coefficient calculation is highly dependent on the method used, taking into consideration different positioning and inhalation depths. Relevance statement This study underscores the impact of the method used for lung volume determination. In the context of mean dark-field coefficient calculation, CR-based methods are more desirable because both dark-field images and conventional images are acquired at the same breathing state, and therefore, biases due to differences in inhalation depth are eliminated. Key Points Lung volume measurements vary significantly between different determination methods. Mean dark-field coefficient calculations require the same method to ensure comparability. Radiography-based methods simplify workflows and minimize biases, making them most suitable. Graphical Abstracthttps://doi.org/10.1186/s41747-025-00593-yLung volume measurementRadiography (thoracic)Respiratory function testsThoraxTomography (x-ray computed) |
| spellingShingle | Florian T. Gassert Jule Heuchert Rafael Schick Henriette Bast Theresa Urban Tina Dorosti Gregor S. Zimmermann Sebastian Ziegelmayer Alexander W. Marka Markus Graf Marcus R. Makowski Daniela Pfeiffer Franz Pfeiffer Lung volume assessment for mean dark-field coefficient calculation using different determination methods European Radiology Experimental Lung volume measurement Radiography (thoracic) Respiratory function tests Thorax Tomography (x-ray computed) |
| title | Lung volume assessment for mean dark-field coefficient calculation using different determination methods |
| title_full | Lung volume assessment for mean dark-field coefficient calculation using different determination methods |
| title_fullStr | Lung volume assessment for mean dark-field coefficient calculation using different determination methods |
| title_full_unstemmed | Lung volume assessment for mean dark-field coefficient calculation using different determination methods |
| title_short | Lung volume assessment for mean dark-field coefficient calculation using different determination methods |
| title_sort | lung volume assessment for mean dark field coefficient calculation using different determination methods |
| topic | Lung volume measurement Radiography (thoracic) Respiratory function tests Thorax Tomography (x-ray computed) |
| url | https://doi.org/10.1186/s41747-025-00593-y |
| work_keys_str_mv | AT floriantgassert lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT juleheuchert lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT rafaelschick lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT henriettebast lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT theresaurban lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT tinadorosti lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT gregorszimmermann lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT sebastianziegelmayer lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT alexanderwmarka lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT markusgraf lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT marcusrmakowski lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT danielapfeiffer lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods AT franzpfeiffer lungvolumeassessmentformeandarkfieldcoefficientcalculationusingdifferentdeterminationmethods |