A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning

In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch between CT and PET due to patient body motion results in AC artifacts. In addition, artifact caused by metal, beam-hardening and count-starving in CT itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction of activity and attenuation (MLAA) was proposed to solve those issues by simultaneously reconstructing tracer activity ($\lambda$-MLAA) and attenuation map ($\mu$-MLAA) based on the PET raw data only. However, $\mu$-MLAA suffers from high noise and $\lambda$-MLAA suffers from large bias as compared to the reconstruction using the CT-based attenuation map ($\mu$-CT). Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map ($\mu$-CNN) from $\lambda$-MLAA and $\mu$-MLAA, in which an image-domain loss (IM-loss) function between the $\mu$-CNN and the ground truth $\mu$-CT was used. However, IM-loss does not directly measure the AC errors according to the PET attenuation physics, where the line-integral projection of the attenuation map ($\mu$) along the path of the two annihilation events, instead of the $\mu$ itself, is used for AC. Therefore, a network trained with the IM-loss may yield suboptimal performance in the $\mu$ generation. Here, we propose a novel line-integral projection loss (LIP-loss) function that incorporates the PET attenuation physics for $\mu$ generation. Eighty training and twenty testing datasets of whole-body 18F-FDG PET and paired ground truth $\mu$-CT were used. Quantitative evaluations showed that the model trained with the additional LIP-loss was able to significantly outperform the model trained solely based on the IM-loss function.Comment: Accepted at MICCAI 201

Fast-MC-PET: A Novel Deep Learning-aided Motion Correction and Reconstruction Framework for Accelerated PET

Patient motion during PET is inevitable. Its long acquisition time not only increases the motion and the associated artifacts but also the patient's discomfort, thus PET acceleration is desirable. However, accelerating PET acquisition will result in reconstructed images with low SNR, and the image quality will still be degraded by motion-induced artifacts. Most of the previous PET motion correction methods are motion type specific that require motion modeling, thus may fail when multiple types of motion present together. Also, those methods are customized for standard long acquisition and could not be directly applied to accelerated PET. To this end, modeling-free universal motion correction reconstruction for accelerated PET is still highly under-explored. In this work, we propose a novel deep learning-aided motion correction and reconstruction framework for accelerated PET, called Fast-MC-PET. Our framework consists of a universal motion correction (UMC) and a short-to-long acquisition reconstruction (SL-Reon) module. The UMC enables modeling-free motion correction by estimating quasi-continuous motion from ultra-short frame reconstructions and using this information for motion-compensated reconstruction. Then, the SL-Recon converts the accelerated UMC image with low counts to a high-quality image with high counts for our final reconstruction output. Our experimental results on human studies show that our Fast-MC-PET can enable 7-fold acceleration and use only 2 minutes acquisition to generate high-quality reconstruction images that outperform/match previous motion correction reconstruction methods using standard 15 minutes long acquisition data.Comment: Accepted at Information Processing in Medical Imaging (IPMI 2023

Effect of alkaline microwaving pretreatment on anaerobic digestion and biogas production of swine manure

Microwave assisted with alkaline (MW-A) condition was applied in the pretreatment of swine manure, and the effect of the pretreatment on anaerobic treatment and biogas production was evaluated in this study. The two main microwaving (MW) parameters, microwaving power and reaction time, were optimized for the pretreatment. Response surface methodology (RSM) was used to investigate the effect of alkaline microwaving process for manure pretreatment at various values of pH and energy input. Results showed that the manure disintegration degree was maximized of 63.91% at energy input of 54 J/g and pH of 12.0, and variance analysis indicated that pH value played a more important role in the pretreatment than in energy input. Anaerobic digestion results demonstrated that MW-A pretreatment not only significantly increased cumulative biogas production, but also shortened the duration for a stable biogas production rate. Therefore, the alkaline microwaving pretreatment could become an alternative process for effective treatment of swine manure

Research on self-learning control method for aircraft engine above idle state

The iterative learning control for aircraft engine above idle state is studied. An approach combining the proportional integral iterative learning with the traditional proportional integral derivative controller is proposed and then this hybrid iterative learning controller is constructed to control the speed of three typical engine models. In the simulation study, the proposed method is applied to the nonlinear component level engine model, state variable engine model, and linear parameter-varying engine model; the results show that the performance of the proposed hybrid iterative learning controller is much better than the traditional proportional integral derivative controller

Data-driven machine learning models for the quick and accurate prediction of thermal stability properties of OLED materials

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher-quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (Tg) and decomposition temperature (Td), but experimental determinations of these two important properties genernally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than one thousand samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of MAE, RMSE, and R2 were 17.15 K, 24.63 K, and 0.77 for Tg prediction and 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models were further tested by out-of-sample data, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based model. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://oledtppxmpugroup.com. We expect that this platform will become a useful tool for experimental investigation of Tg and Td, accelerating the design of OLED materials with desired properties

Metabolomics investigation on the volatile and non-volatile composition in enzymatic hydrolysates of Pacific oyster (Crassostrea gigas)

To investigate the differences of volatile and non-volatile metabolites between oyster enzymatic hydrolysates and boiling concentrates, molecular sensory analysis and untargeted metabolomics were employed. “Grassy,” “fruity,” “oily/fatty,” “fishy,” and “metallic” were identified as sensory attributes used to evaluate different processed oyster homogenates. Sixty-nine and 42 volatiles were identified by gas chromatography–ion mobility spectrometry and gas chromatography–mass spectrometry, respectively. Pentanal, 1-penten-3-ol, hexanal, (E)-2-pentenal, heptanal, (E)-2-hexenal, 4-octanone, (E)-4-heptenal, 3-octanone, octanal, nonanal, 1-octen-3-ol, benzaldehyde, (E)-2-nonenal, and (E, Z)-2,6-nonadienal were detected as the key odorants (OAV > 1) after enzymatic hydrolysis. Hexanal, (E)-4-heptenal, and (E)-2-pentenal were significantly associated with off-odor, and 177 differential metabolites were classified. Aspartate, glutamine, alanine, and arginine were the key precursors affecting the flavor profile. Linking sensory descriptors to volatile and nonvolatile components of different processed oyster homogenates will provide information for the process and quality improvement of oyster products

High-throughput sequencing-based identification of miRNAs and their target mRNAs in wheat variety Qing Mai 6 under salt stress condition

Soil salinization is one of the major abiotic stresses that adversely affect the yield and quality of crops such as wheat, a leading cereal crop worldwide. Excavating the salt-tolerant genes and exploring the salt tolerance mechanism can help breeding salt-tolerant wheat varieties. Thus, it is essential to identify salt-tolerant wheat germplasm resources. In this study, we carried out a salt stress experiment using Qing Mai 6 (QM6), a salt-tolerant wheat variety, and sequenced the miRNAs and mRNAs. The differentially expressed miRNAs and mRNAs in salt stress conditions were compared with the control. As results, a total of eight salt-tolerance-related miRNAs and their corresponding 11 target mRNAs were identified. Further analysis revealed that QM6 enhances salt tolerance through increasing the expression level of genes related to stress resistance, antioxidation, nutrient absorption, and lipid metabolism balance, and the expression of these genes was regulated by the identified miRNAs. The resulting data provides a theoretical basis for future research studies on miRNAs and novel genes related to salt tolerance in wheat in order to develop genetically improved salt-tolerant wheat varieties