Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study
Abstract Purpose While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F. Method A movi...
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| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EJNMMI Physics |
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| Online Access: | https://doi.org/10.1186/s40658-025-00784-x |
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| author | Marloes A. de Winter Robin Gevers Jules Lavalaye Jan B. A. Habraken Matteo Maspero |
| author_facet | Marloes A. de Winter Robin Gevers Jules Lavalaye Jan B. A. Habraken Matteo Maspero |
| author_sort | Marloes A. de Winter |
| collection | DOAJ |
| description | Abstract Purpose While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F. Method A moving platform simulated respiratory motion in a NEMA-IEC body phantom with varying amplitudes (0, 7, 10, 20, 30 mm) and target-to-background ratios (2, 5, 10.5). Scans were reconstructed with and without DDMC, and the spherical targets’ maximal and mean recovery coefficient (RC) and contrast-to-noise ratio (CNR) were measured. Results DDMC results in higher RC values in the target spheres. CNR values increase for small, high-motion affected targets but decrease for larger spheres with smaller amplitudes. A sub-analysis shows that DDMC increases the contrast of the sphere along with a 36% increase in background noise. Conclusion While DDMC significantly enhances contrast (RC), its impact on detectability (CNR) is less profound due to increased background noise. CNR improves for small targets with high motion amplitude, potentially enhancing the detectability of low-uptake lesions. Given that the increased background noise may reduce detectability for targets unaffected by motion, we suggest that DDMC reconstructions are used best in addition to non-DDMC reconstructions. |
| format | Article |
| id | doaj-art-50c24f9c6045488a82090f8ca0e2e7d7 |
| institution | Kabale University |
| issn | 2197-7364 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Physics |
| spelling | doaj-art-50c24f9c6045488a82090f8ca0e2e7d72025-08-20T03:46:12ZengSpringerOpenEJNMMI Physics2197-73642025-07-0112111110.1186/s40658-025-00784-xEffect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom studyMarloes A. de Winter0Robin Gevers1Jules Lavalaye2Jan B. A. Habraken3Matteo Maspero4Department of Medical Physics, St Antonius HospitalDepartment of Medical Physics, St Antonius HospitalDepartment of Nuclear Medicines, St Antonius HospitalDepartment of Medical Physics, St Antonius HospitalDepartment of Radiotherapy, University Medical Center UtrechtAbstract Purpose While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F. Method A moving platform simulated respiratory motion in a NEMA-IEC body phantom with varying amplitudes (0, 7, 10, 20, 30 mm) and target-to-background ratios (2, 5, 10.5). Scans were reconstructed with and without DDMC, and the spherical targets’ maximal and mean recovery coefficient (RC) and contrast-to-noise ratio (CNR) were measured. Results DDMC results in higher RC values in the target spheres. CNR values increase for small, high-motion affected targets but decrease for larger spheres with smaller amplitudes. A sub-analysis shows that DDMC increases the contrast of the sphere along with a 36% increase in background noise. Conclusion While DDMC significantly enhances contrast (RC), its impact on detectability (CNR) is less profound due to increased background noise. CNR improves for small targets with high motion amplitude, potentially enhancing the detectability of low-uptake lesions. Given that the increased background noise may reduce detectability for targets unaffected by motion, we suggest that DDMC reconstructions are used best in addition to non-DDMC reconstructions.https://doi.org/10.1186/s40658-025-00784-xPositron emission tomographyComputed tomographyMotion correctionArtificial intelligenceRespiratory motion correction |
| spellingShingle | Marloes A. de Winter Robin Gevers Jules Lavalaye Jan B. A. Habraken Matteo Maspero Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study EJNMMI Physics Positron emission tomography Computed tomography Motion correction Artificial intelligence Respiratory motion correction |
| title | Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study |
| title_full | Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study |
| title_fullStr | Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study |
| title_full_unstemmed | Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study |
| title_short | Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study |
| title_sort | effect of data driven motion correction for respiratory movement on lesion detectability in pet ct a phantom study |
| topic | Positron emission tomography Computed tomography Motion correction Artificial intelligence Respiratory motion correction |
| url | https://doi.org/10.1186/s40658-025-00784-x |
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