Applications of artificial intelligence in nuclear medicine image generation

Cheng, Zhibiao; Wen, Junhai; Huang, Gang; Yan, Jianhua

doi:10.21037/qims-20-1078

Cited by 39 publications

(13 citation statements)

References 189 publications

(193 reference statements)

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“…As a common generated network, CycleGAN and U-Net are typically used for medical images generation (41). Therefore, Li et al (14,20) compared the performance of U-Net and CycleGAN to transform brain MR/CT images to their counterpart modality.…”

Section: Discussionmentioning

confidence: 99%

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function

Li¹,

Huang²,

Zhang³

et al. 2022

Quant Imaging Med Surg

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Background: Magnetic resonance imaging (MRI) images synthesized from computed tomography (CT) data can provide more detailed information on pathological structures than that of CT data alone; thus, the synthesis of MRI has received increased attention especially in medical scenarios where only CT images are available. A novel convolutional neural network (CNN) combined with a contextual loss function was proposed for synthesis of T1-and T2-weighted images (T1WI and T2WI) from CT data.Methods: A total of 5,053 and 5,081 slices of T1WI and T2WI, respectively were selected for the dataset of CT and MRI image pairs. Affine registration, image denoising, and contrast enhancement were done on the aforementioned multi-modality medical image dataset comprising T1WI, T2WI, and CT images of the brain. A deep CNN was then proposed by modifying the ResNet structure to constitute the encoder and decoder of U-Net, called double ResNet-U-Net (DRUNet). Three different loss functions were utilized to optimize the parameters of the proposed models: mean squared error (MSE) loss, binary crossentropy (BCE) loss, and contextual loss. Statistical analysis of the independent-sample t-test was conducted by comparing DRUNets with different loss functions and different network layers. Results: DRUNet-101 with contextual loss yielded higher values of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Tenengrad function (i.e., 34.25±2.06, 0.97±0.03, and 17.03±2.75 for T1WI and 33.50±1.08, 0.98±0.05, and 19.76±3.54 for T2WI respectively). The results were statistically significant at P<0.001 with a narrow confidence interval of difference, indicating the superiority of DRUNet-101 with contextual loss. In addition, both image zooming and difference maps presented for the final synthetic MR images visually reflected the robustness of DRUNet-101 with contextual loss. The visualization of convolution filters and feature maps showed that the proposed model can generate synthetic MR images with high-frequency information. Conclusions: The results demonstrated that DRUNet-101 with contextual loss function provided better high-frequency information in synthetic MR images compared with the other two functions. The proposed DRUNet model has a distinct advantage over previous models in terms of PSNR, SSIM, and Tenengrad score.Overall, DRUNet-101 with contextual loss is recommended for synthesizing MR images from CT scans.

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Section: Discussionmentioning

confidence: 99%

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function

Li¹,

Huang²,

Zhang³

et al. 2022

Quant Imaging Med Surg

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“…It is challenging to store and pro-cess such enormous and complex datasets, which may be addressed by some automation forms us-ing more comprehensive approaches (e.g. segmen-tation) [81][82][83][84][85][86] or artificial intelligence [39,75,[87][88][89] Table 2 Characteristics and Challenges/Opportunities of total-body PET scanners.…”

Section: Opportunities and Challenges In Dynamic Total-body Pet Imagingmentioning

confidence: 99%

Quantitation of Dynamic Total-Body PET Imaging: Recent Developments and Future Perspectives

2023

Preprint

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Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania) and Biograph Vision Quadra (Siemens Healthineers), further stimulate valuable inspiration to map kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. In this contribution, the published dynamic total-body PET datasets are outlined and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model) and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments and future perspectives of these methodologies for a diverse-tissue environment.

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“…New digital PET reconstruction algorithms, such as maximum likelihood algorithms, better control the reconstruction quality, however at the expense of vendor-specific software requirements and computational power [ 31 , 32 ]. Hence, there is a need for AI techniques in this field [ 33 ].…”

Section: Technical Applicationsmentioning

confidence: 99%

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

Liberini¹,

Laudicella

Balma³

et al. 2022

Eur Radiol Exp

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In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

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Applications of artificial intelligence in nuclear medicine image generation

Cited by 39 publications

References 189 publications

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function

Quantitation of Dynamic Total-Body PET Imaging: Recent Developments and Future Perspectives

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

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