Artificial Intelligence-Based Image Enhancement in PET Imaging

Liu, Juan; Malekzadeh, Masoud; Mirian, Niloufar; Song, Tzu-An; Liu, Chi; Dutta, Joyita

doi:10.1016/j.cpet.2021.06.005

Cited by 62 publications

(34 citation statements)

References 95 publications

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“…Deep learning (DL), a subdivision of artificial intelligence (AI), has many emerging applications in nuclear medicine [ 6 , 7 ]. DL is able to increase PET resolution, decrease noise, and thus enhance image quality [ 8 – 12 ]. It may allow for reducing injected activity, acquisition time, or a combination of both [ 10 , 13 – 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning (DL), a subdivision of artificial intelligence (AI), has many emerging applications in nuclear medicine [6,7]. DL is able to increase PET resolution, decrease noise, and thus enhance image quality [8][9][10][11][12]. It may allow This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence) for reducing injected activity, acquisition time, or a combination of both [10,[13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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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

Weyts

Lasnon

Ciappuccini

et al. 2022

Eur J Nucl Med Mol Imaging

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Purpose We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. Methods One hundred ninety-five patients referred for [18F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original “PET90” (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, “PET45AI and PET45”. Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SULmax and SULpeak), metabolic volumes (MV), and liver SULmean were measured, including both standard and EARL1 (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CVliv) were calculated. Results PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL1 SULpeak was not significantly different between both PET (P = 0.09). Lesion standard SULpeak, standard and EARL1 SULmax, LLR and CVliv were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SULmean were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SULmean (ICC ≥ 0.87). Conclusion AI allows [18F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted.

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Section: Introductionmentioning

confidence: 99%

“…Deep learning (DL), a subdivision of artificial intelligence (AI), has many emerging applications in nuclear medicine [6,7]. DL is able to increase PET resolution, decrease noise, and thus enhance image quality [8][9][10][11][12]. It may allow This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence) for reducing injected activity, acquisition time, or a combination of both [10,[13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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

Weyts

Lasnon

Ciappuccini

et al. 2022

Eur J Nucl Med Mol Imaging

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“…The quality of PET images is influenced by various factors, including imaging system hardware, non-collinearity of the emitted photon pairs, intercrystal scatter, and crystal penetration [ 20 , 21 ]. The development of denoising and deblurring methods for PET imaging remains an important research avenue to facilitate clinical decision-making and interpretation [ 22 , 23 ]. In the present study, 18 F-FDG PET images were obtained from 50 PET centers having nine different scanner models.…”

Section: Discussionmentioning

confidence: 99%

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

et al. 2022

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Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

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“…Recently,artificial intelligence (AI) using deep learning (DL) has been established to convert the low-count PET images to their standard-count image counterparts. 19 These methods usually employed a deep convolutional neural network (CNN) with a supervised training scheme using low-count and standard-count PET image pairs as training data. Such image pairs can be generated by performing rebinning on the standard-count list-mode data with a specific down-sampling rate.…”

Section: Introductionmentioning

confidence: 99%

Virtual high‐count PET image generation using a deep learning method

et al. 2022

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Purpose Recently, deep learning‐based methods have been established to denoise the low‐count positron emission tomography (PET) images and predict their standard‐count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U‐Net network to reduce the noise of standard‐count PET images to obtain the virtual‐high‐count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images. Methods The training datasets, including down‐sampled standard‐count PET images as the network input and high‐count images as the desired network output, were derived from 27 whole‐body PET datasets, which were acquired using 90‐min dynamic scan. The down‐sampled standard‐count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross‐validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard‐count and VHC PET images, with real‐high‐count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard‐count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard‐count and VHC images and conducted 5‐score ranking. A Wilcoxon signed‐rank test was used to compare differences in the grading of standard‐count and VHC images. Results The cross‐validation results showed that VHC PET images had improved quantitative metrics scores than the standard‐count PET images. The mean/max SUVs of 35 lesions in the standard‐count and true‐high‐count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true‐high‐count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard‐count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard‐count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard‐count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard‐count images. The Wilcoxon signed‐...

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Artificial Intelligence-Based Image Enhancement in PET Imaging

Cited by 62 publications

References 95 publications

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

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

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

Virtual high‐count PET image generation using a deep learning method

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