Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT

Du, Yu; Shang, Jingjie; Sun, Jingzhang; Wang, Lu; Liu, Yi-Hwa; Xu, Hao; Mok, Greta S. P.

doi:10.1007/s12350-022-03092-4

Cited by 16 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 2 shows the average voxel-based analysis results of S-NAC and S-DLAC For all quantitative measurements in S-PET studies, the S-DLAC method significantly improves PET image quality as compared to S-NAC. These results are consistent with existing literature [37,38] S-PET. This work further investigates the effectiveness of DL-based AC for D-PET.…”

Section: Static [ 13 N]ammonia Pet Acsupporting

confidence: 94%

“…Our work provides a feasible CT-less AC solution to this problem for S-PET and D-PET. On the other hand, DL-based estimation of attenuation map for AC has been proven to be superior to direct generation of AC SPECT for cardiac SPECT [37,42]. Thus, estimation on DL-based AC maps instead of direct AC could potentially further enhance our work.…”

Section: Static [ 13 N]ammonia Pet Acmentioning

confidence: 93%

See 1 more Smart Citation

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network

Sun

Wang

Yang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Purpose Current attenuation correction (AC) of myocardial perfusion (MP) positron emission tomography (PET) remains challenging in routine clinical practice due to the propagation of CT-based artifacts and potential mismatch between PET and CT. The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET (AC PET) images from non-attenuation-corrected PET (NAC PET) images in the reconstruction domain for [13N]ammonia MP PET based on a generative adversarial network (GAN). Methods We recruited 60 patients who underwent rest [13N]ammonia cardiac PET/CT examinations. One static frame and twenty-one dynamic frames were acquired for each patient with both NAC PET and CT-based AC (CTAC) PET images. Paired 3D static or dynamic NAC and CTAC PET images were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) MP PET, respectively. In addition, the pre-trained S-DLAC network was fine-tuned by 3D paired dynamic NAC and CTAC PET frames for then AC in the dynamic PET images (D-DLAC-FT). Qualitative and quantitative assessments were implemented using CTAC PET as reference. Results The proposed S-DLAC, D-DLAC and D-DLAC-FT methods were qualitatively and quantitatively consistent with clinical CTAC. The S-DLAC showed a higher correlation with the reference static CTAC (S-CTAC) as compared to static NAC. The estimated kinetic parameters and blood volume fraction images from D-DLAC and D-DLAC-FT methods showed comparable performances with the reference dynamic CTAC (D-CTAC). D-DLAC-FT was slightly better than D-DLAC in terms of various physical and clinical indices. Conclusion The proposed S-DLAC, D-DLAC and D-DLAC-FT methods reduced attenuation artifacts significantly and achieved comparable performance with clinical CTAC for static and dynamic cardiac PET. The use of transfer learning is effective for the dynamic MP PET AC purpose.

show abstract

Section: Static [ 13 N]ammonia Pet Acsupporting

confidence: 94%

Section: Static [ 13 N]ammonia Pet Acmentioning

confidence: 93%

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network

Sun

Wang

Yang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, the time-consuming manual thyroid segmentation on CT canvas is challenging. In the literature, there are deep-learning-based CT-free AC studies for myocardial perfusion SPECT [ 2 , 5 ], brain perfusion SPECT [ 6 , 7 , 32 ] and dopamine-transporter brain SPECT [ 3 ]. Undoubtedly, AC using CT is essential for quantitative thyroid SPECT/CT, but thyroid-dedicated deep-learning study has not been investigated.…”

Section: Discussionmentioning

confidence: 99%

“…However, application of CT-based AC (CTAC) is yet to be a clinical routine in SPECT because of lack of proper clinical indication, concern about extra-radiation exposure, and necessity for hybrid SPECT/CT scanner [ 1 ]. Recent development of deep-learning may change the concept of CTAC because CT acquisition may be omitted through either μ-map generation from SPECT (indirect approach) [ 2 – 5 ] or creation of attenuation-corrected SPECT (direct approach) [ 6 , 7 ]. Deep-learning was also useful in organ segmentation [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

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

Kwon

Hwang

et al. 2023

EJNMMI Phys

View full text Add to dashboard Cite

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.

show abstract

“…Yang et al ( 15 ) and Chen et al ( 16 ) estimated AC MP SPECT images directly from NAC MP SPECT images (direct deep learning-based attenuation correction [DL-AC]) using different deep convolutional neural networks. Chen et al ( 17 ) and Du et al ( 18 ) compared the AC performance of DL-AC and DL-AC μ and demonstrated that indirect estimation of μ-maps is superior to direct estimation of AC SPECT on MP SPECT. Chen et al ( 19 ) further investigated the feasibility of transfer learning-based AC for MP SPECT images from different scanners, tracers, and acquisition protocols.…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

Du,

Jiang,

Lin

et al. 2023

Front. Med.

Self Cite

View full text Add to dashboard Cite

BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners.MethodsTwo hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACμ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACμ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACμ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-ACμ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods.ResultsThe NMSE for Chang's method, DL-ACμ, DL-AC, cDL-ACμ, cDL-AC, eDL-ACμ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACμ are closer to CT-AC, Followed by DL-AC, eDL-ACμ, cDL-ACμ, cDL-AC, eDL-AC, Chang's method, and NAC.ConclusionAll DL-based AC methods are superior to Chang-AC. DL-ACμ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT.

show abstract

Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT

Cited by 16 publications

References 39 publications

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network

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

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

Contact Info

Product

Resources

About