CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning

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 − 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (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.

Boa

“…This holds for all but the glands label, which contains parotid, thyroid, and submandibular glands, and resulted in an average Sørensen-Dice score of 0.745, which may be due to the fact that the glands regions are comparably smaller than the other classes and thus are less important in the optimization of the model 40 . However, the best score that was achieved in the literature for the thyroid glands alone was 0.767, 41 which is of similar magnitude. Future work should investigate the impact of separating these 3 classes.…”

Tc-99m DMSA SPECT for Follow-Up of Non-Operative Treatments in Renal Injuries: A Prospective Single-Center Study

“…Absolute quantification of renal uptake was based on delineating renal contours with a relative threshold [ 13 23 ]. A more individualized definition of threshold [ 24 ] or advanced segmentation technique [ 25 ] may contribute to a more precise evaluation of renal cortical integrity with DMSA SPECT.…”

Gamma camera-specific reference standards for radioactive iodine uptake measurements

“…For further standardization, optimal methods for ROI delineation and thyroid volumetry need to be defined, also with regard to the improved contrast and spatial resolution of new generation gamma cameras [ 53 ]. Lastly, the role of quantitative SPECT [ 54 ] and deep-learning technology [ 55 ] for RAIU measurement and their potential for reduction of measurement variability need to be further evaluated in large patient cohorts.…”