ArticlePhysics in medicine and biology2019
Higher SNR PET image prediction using a deep learning model and MRI image.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Visualizing Metabolism: Lipid-Based Raman Nanoreporters as an Enabling Platform for Metabolic Imaging and Precision Medicine.ACS nano medicine · 2026Review
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- DUAL PROMPTING FOR DIVERSE COUNT-LEVEL PET DENOISING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Cross noise level PET denoising with continuous adversarial domain generalization.Physics in medicine and biology · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Verification of image quality improvement of low-count bone scintigraphy using deep learning.Radiological physics and technology · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Application of intelligent pacifying strategy information system in reducing short-duration MRI sedation rate in children.Scientific reports · 2023Article
- Current Role of Delta Radiomics in Head and Neck Oncology.International journal of molecular sciences · 2023Review
- Optimization Model of Signal-to-Noise Ratio for a Typical Polarization Multispectral Imaging Remote Sensor.Sensors (Basel, Switzerland) · 2022Article
- Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.European journal of nuclear medicine and molecular imaging · 2022Review
- PET image enhancement using artificial intelligence for better characterization of epilepsy lesions.Frontiers in medicine · 2022Article
- Noise2Void: unsupervised denoising of PET images.Physics in medicine and biology · 2021Article
- Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.PET clinics · 2021Review
- Generation of synthetic PET images of synaptic density and amyloid fromMedical physics · 2021Article
- Anatomically aided PET image reconstruction using deep neural networks.Medical physics · 2021Article
- New PET technologies - embracing progress and pushing the limits.European journal of nuclear medicine and molecular imaging · 2021Article
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
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2 authors.
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Abstract
PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. Our proposed DNN model consists of three modified U-Nets (3U-net). The PET training input data and targets were reconstructed using filtered-backprojection (FBP) and maximum likelihood expectation maximization (MLEM), respectively. FBP reconstruction was used because of its computational efficiency so that the trained network not only removes noise, but also accelerates image reconstruction. Digital brain phantoms downloaded from BrainWeb were used to evaluate the proposed method. Poisson noise was added into sinogram data to simulate a 6 min brain PET scan. Attenuation effect was included and corrected before the image reconstruction. Extra Poisson noise was introduced to the training inputs to improve the network denoising capability. Three independent experiments were conducted to examine the reproducibility. A lesion was inserted into testing data to evaluate the impact of mismatched MRI information using the contrast-to-noise ratio (CNR). The negative impact on noise reduction was also studied when miscoregistration between PET and MRI images occurs. Compared with 1U-net trained with only PET images, training with PET/MRI decreased the mean squared error (MSE) by 31.3% and 34.0% for 1U-net and 3U-net, respectively. The MSE reduction is equivalent to an increase in the count level by 2.5 folds and 2.9 folds for 1U-net and 3U-net, respectively. Compared with the MLEM images, the lesion CNR was improved 2.7 folds and 1.4 folds for 1U-net and 3U-net, respectively. The results show that the proposed method could improve the PET SNR without having higher SNR PET images.
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