AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging

Purpose: Ovarian cancer is the fifth fatal cancer among women. Positron emission tomography (PET), which offers detailed metabolic data, can be effectively used for early cancer screening. However, proper attenuation correction is essential for interpreting the data obtained by this imaging modality. Computed tomography (CT) imaging is commonly performed alongside PET imaging for attenuation correction. This approach may introduce some issues in spatial alignment and registration of the images obtained by the two modalities. This study aims to perform PET image attenuation correction by using generative adversarial networks (GANs), without additional CT imaging. Material and methods: The PET/CT data from 55 ovarian cancer patients were used in this study. Three GAN architectures: Conditional GAN, Wasserstein GAN, and CycleGAN, were evaluated for attenuation correction. The statistical performance of each model was assessed by calculating the mean squared error (MSE) and mean absolute error (MAE). The radiological performance assessments of the models were performed by comparing the standardised uptake value and the Hounsfield unit values of the whole body and selected organs, in the synthetic and real PET and CT images. Results: Based on the results, CycleGAN demonstrated effective attenuation correction and pseudo-CT generation, with high accuracy. The MAE and MSE for all images were 2.15 ± 0.34 and 3.14 ± 0.56, respectively. For CT reconstruction, such values were found to be 4.17 ± 0.96 and 5.66 ± 1.01, respectively. Conclusions: The results showed the potential of deep learning in reducing radiation exposure and improving the quality of PET imaging. Further refinement and clinical validation are needed for full clinical applicability

Role of recently evolved miRNA regulation of sunflower HaWRKY6

MicroRNAs (miRNAs) are small 21-nucleotide RNAs that post-transcriptionally regulate gene expression. MiR396 controls leaf development by targeting GRF and bHLH transcription factors in Arabidopsis. WRKY transcription factors, unique to plants, have been identified as mediating varied stress responses. The sunflower (Helianthus annuus) HaWRKY6 is a particularly divergent WRKY gene exhibiting a putative target site for the miR396. A possible post-transcriptional regulation of HaWRKY6 by miR396 was investigated. Here, we used expression analyses, performed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and northern blots together with computational approaches to establish the regulatory interaction between HaWRKY6 and the identified sunflower miR396. Arabidopsis plants expressing a mi396-resistant version of HaWRKY6 confirmed the miRNA-dependency of the HaWRKY6 silencing. Sunflower plants exposed to high temperatures or salicylic acid presented opposite expression of HaWRKY6 and miR396. Experiments using the wildtype and miRNA-resistant versions of HaWRKY6 showed altered stress responses. Our results showed a role of the recently evolved miR396 regulation of HaWRKY6 during early responses to high temperature. Our study reveals how a miRNA that normally regulates development has been recruited for high-temperature protection in sunflower, a plant particularly well adapted to this type of stress