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CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Kyounghyoun | - |
dc.contributor.author | Hwang, Donghwi | - |
dc.contributor.author | Oh, Dongkyu | - |
dc.contributor.author | Kim, Ji Hye | - |
dc.contributor.author | Yoo, Jihyung | - |
dc.contributor.author | Lee, Jae Sung | - |
dc.contributor.author | Lee, Won Woo | - |
dc.date.accessioned | 2023-05-12T04:16:41Z | - |
dc.date.available | 2023-05-12T13:17:14Z | - |
dc.date.issued | 2023-03-22 | - |
dc.identifier.citation | EJNMMI Physics, 10(1):20 | ko_KR |
dc.identifier.issn | 2197-7364 | - |
dc.identifier.uri | https://hdl.handle.net/10371/192392 | - |
dc.description.abstract | Purpose
Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid. Methods Quantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification. Results The synthetic μ-map demonstrated a strong correlation (R2 = 0.972) and minimum error (mean square error = 0.936 × 10−4, %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of − 0.72mL, and a short 95% Hausdorff distance of 9.416mm (n = 36). Additionally, %thyroid uptake by synthetic μ-map and automatic thyroid segmentation (CT-free SPECT) was similar to that by the original μ-map and manual thyroid segmentation (SPECT/CT) (3.772 ± 5.735% vs. 3.682 ± 5.516%, p = 0.1090) (n = 36). Furthermore, the synthetic μ-map generation and automatic thyroid segmentation were successfully performed in the salivary SPECT/CT using the deep-learning algorithms trained by thyroid SPECT/CT (n = 29). Conclusion CT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning. | ko_KR |
dc.description.abstract | Key points
Question 1: Can CT-free attenuation correction be realized for SPECT? Pertinent findings: The first deep-learning algorithm produced μ-map similar to CT-derived μ-map. Implications for patient care: Quantitative SPECT can be performed without CT. Therefore, patients can be protected from redundant radiation exposure of CT. Question 2: Can the thyroid be segmented without high-resolution images like CT? Pertinent findings: The second deep-learning algorithm successfully generated the thyroid segmentation map using low-resolution images such as the generated μ-map and SPECT. Implications for patient care: The thyroid segmentation process was dramatically reduced from 40–60min to < 1min, facilitating rapid patient care. Question 3: Can quantitative SPECT/CT be possible without CT? Pertinent findings: The two deep-learning algorithms deprived the quantitative thyroid SPECT/CT of CT. Implications for patient care: Repetitive CT acquisitions may be excluded in multiple SPECT/CT-based nuclear imaging studies, such as dosimetry. | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | Springer | ko_KR |
dc.subject | Quantification | - |
dc.subject | Single-photon emission computed tomography | - |
dc.subject | Deep-learning | - |
dc.subject | Attenuation correction | - |
dc.subject | Segmentation | - |
dc.title | CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning | ko_KR |
dc.type | Article | ko_KR |
dc.identifier.doi | 10.1186/s40658-023-00536-9 | ko_KR |
dc.citation.journaltitle | EJNMMI Physics | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2023-03-30T10:12:34Z | - |
dc.citation.number | 20 | ko_KR |
dc.citation.volume | 10 | ko_KR |
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