Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT

Weyts, Kathleen; Lasnon, Charline; Ciappuccini, Renaud; Lequesne, Justine; Corroyer‐Dulmont, Aurélien; Quak, Elske; Clarisse, Bénédicte; Roussel, L.; Bardet, Stéphane; Jaudet, Cyril

doi:10.1007/s00259-022-05800-1

Cited by 24 publications

(31 citation statements)

References 36 publications

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“…Previous work from our group on AI-based PET denoising in a large series of FDG PET scans showed the reassuringly high concordance rate in lesion detection between conventional and AI-processed PET images in the same patient (11). Therefore, the primary aim of PET imaging, which is lesion detection with high sensitivity, does not seem to be jeopardized by AI.…”

Section: Discussionmentioning

confidence: 81%

“…A post-reconstruction PET denoising software (SubtlePET TM , Subtle Medical©, Stanford, USA provided by Incepto©, France) that was recently developed by using a deep convolutional neural network on a library of millions of paired images (native and low-dose images) to learn and tune the optimal parameters to compute an estimate of the native image. Currently, only a few clinical publications have evaluated its use in oncology, all of them dealing with 18 F-FDG PET images (8)(9)(10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime

Quak¹,

Weyts²,

Jaudet³

et al. 2023

Front. Med.

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IntroductionThe yield per elution of a 68Ge/68Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise. We aimed to investigate whether AI-based PET denoising can offset this decrease in image quality parameters.MethodsAll patients addressed to our PET unit for a 68Ga-DOTATOC PET/CT from April 2020 to February 2021 were enrolled. Forty-four patients underwent their PET scans according to Protocol_FixedDose (150 MBq) and 32 according to Protocol_WeightDose (1.5 MBq/kg). Protocol_WeightDose examinations were processed using the Subtle PET software (Protocol_WeightDoseAI). Liver and vascular SUV mean were recorded as well as SUVmax, SUVmean and metabolic tumour volume (MTV) of the most intense tumoural lesion and its background SUVmean. Liver and vascular coefficients of variation (CV), tumour-to-background and tumour-to-liver ratios were calculated.ResultsThe mean injected dose of 2.1 (0.4) MBq/kg per patient was significantly higher in the Protocol_FixedDose group as compared to 1.5 (0.1) MBq/kg for the Protocol_WeightDose group. Protocol_WeightDose led to noisier images than Protocol_FixedDose with higher CVs for liver (15.57% ± 4.32 vs. 13.04% ± 3.51, p = 0.018) and blood-pool (28.67% ± 8.65 vs. 22.25% ± 10.37, p = 0.0003). Protocol_WeightDoseAI led to less noisy images than Protocol_WeightDose with lower liver CVs (11.42% ± 3.05 vs. 15.57% ± 4.32, p < 0.0001) and vascular CVs (16.62% ± 6.40 vs. 28.67% ± 8.65, p < 0.0001). Tumour-to-background and tumour-to-liver ratios were lower for protocol_WeightDoseAI: 6.78 ± 3.49 vs. 7.57 ± 4.73 (p = 0.01) and 5.96 ± 5.43 vs. 6.77 ± 6.19 (p < 0.0001), respectively. MTVs were higher after denoising whereas tumour SUVmax were lower: the mean% differences in MTV and SUVmax were + 11.14% (95% CI = 4.84–17.43) and −3.92% (95% CI = −6.25 to −1.59).ConclusionThe degradation of PET image quality due to a reduction in injected dose at the end of the 68Ge/68Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising.

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Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime

Quak¹,

Weyts²,

Jaudet³

et al. 2023

Front. Med.

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“…In recent years, large datasets have been made available, optimization algorithms have advanced, and efficient network structures have emerged, which have been widely used to segment images (5), detect lesions (6), and restore image super-resolution in computer vision tasks (7). Medical imaging has recently benefited from deep learning-based reconstruction methods (8)(9)(10)(11), such as using convolutional neural networks (CNNs) or generative adversarial networks (GANs) to denoise and reduce artifacts in images and improve the SNR of low-dose PET (LDPET) images. Deep learning has proven remarkably effective in predicting full-dose PET (FDPET) images from either LDPET or ultra-low-dose PET (ULDPET) images.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods

Hu¹,

Lv²,

Jian³

et al. 2023

Quant Imaging Med Surg

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Background: [18F] Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is an important tool for tumor assessment. Shortening scanning time and reducing the amount of radioactive tracer remain the most difficult challenges. Deep learning methods have provided powerful solutions, thus making it important to choose an appropriate neural network architecture.Methods: A total of 311 tumor patients who underwent 18 F-FDG PET/CT were retrospectively collected.The PET collection time was 3 min/bed. The first 15 and 30 s of each bed collection time were selected to simulate low-dose collection, and the pre-90s was used as the clinical standard protocol. Low-dose PET was used as input, convolutional neural network (CNN, 3D Unet as representative) and generative adversarial network (GAN, P2P as representative) were used to predict the full-dose images. The image visual scores, noise levels and quantitative parameters of tumor tissue were compared.Results: There was high consistency in image quality scores among all groups [Kappa =0.719, 95% confidence interval (CI): 0.697-0.741, P<0.001]. There were 264 cases (3D Unet-15s), 311 cases (3D Unet-30s), 89 cases (P2P-15s) and 247 cases (P2P-30s) with image quality score ≥3, respectively. There was significant difference in the score composition among all groups (χ 2 =1,325.46, P<0.001). Both deep learning models reduced the standard deviation (SD) of background, and increased the signal-to-noise ratio (SNR).When 8%PET images were used as input, P2P and 3D Unet had similar enhancement effect on SNR of tumor lesions, but 3D Unet could significantly improve the contrast-noise ratio (CNR) (P<0.05). There was no significant difference in SUVmean of tumor lesions compared with s-PET group (P>0.05). When 17%PET image was used as input, SNR, CNR and SUVmax of tumor lesion of 3D Unet group had no statistical difference with those of s-PET group (P>0.05). Conclusions: Both GAN and CNN can suppress image noise to varying degrees and improve image quality. However, when 3D Unet reduces the noise of tumor lesions, it can improve the CNR of tumor lesions. Moreover, quantitative parameters of tumor tissue are similar to those under the standard acquisition protocol, which can meet the needs of clinical diagnosis. ^ ORCID: 0000-0003-0376-982X.

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“…• In previous studies [8], [9], [11], [12] professional renginologists did the malignant tumor segmentation. The disadvantages of this approach are the need for more qualified specialists, the cost and time of manual segmentation, and the inconsistency of results.…”

Section: Introductionmentioning

confidence: 99%

Whole-body PET image denoising for reduced acquisition time

Kruzhilov¹,

Kudin²,

Vetoshkin³

et al. 2023

Preprint

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This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisition times. Our experiments consider 212 studies (56908 images), and evaluate the models using 2D (RMSE, SSIM) and 3D (SUVpeak and SUVmax error for the regions of interest) metrics. It was shown that, in contrast to previous studies, supervised models (ResNet, Unet, SwinIR) outperform unsupervised models (pix2pix GAN and CycleGAN with ResNet backbone and various auxiliary losses) in the reconstruction of 2D PET images. Moreover, a hybrid approach of supervised CycleGAN shows the best results in SUVmax estimation for denoised images, and the SUVmax estimation error for denoised images is comparable with the PET reproducibility error.

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Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT

Cited by 24 publications

References 36 publications

Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime

Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime

Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods

Whole-body PET image denoising for reduced acquisition time

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