Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography.

Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT reconstruction strategy that combines model-based iteration with score-based generative model was proposed. By incrementally adding noise to the training samples, prior knowledge can be learned from the complex probability distribution. The acquired prior is then utilized as constraint in model-based iteration. The information of missing views can be gradually compensated by cyclic iteration to achieve high-quality reconstruction. The performance of the proposed method was evaluated with the circular phantom and in vivo experimental data. Experimental results demonstrate the outstanding effectiveness of the proposed method in limited-view cases. Notably, the proposed method exhibits excellent performance in limited-view case of 70° compared with traditional method. It achieves a remarkable improvement of 203% in PSNR and 48% in SSIM for the circular phantom experimental data, and an enhancement of 81% in PSNR and 65% in SSIM for in vivo experimental data, respectively. The proposed method has capability of reconstructing PAT images in extremely limited-view cases, which will further expand the application in clinical scenarios.

Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration.

As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in low-quality image in photoacoustic tomography. Here, a novel model-based sparse reconstruction method for photoacoustic tomography via diffusion model was proposed. A score-based diffusion model is designed for learning the prior information of the data distribution. The learned prior information is utilized as a constraint for the data consistency term of an optimization problem based on the least-square method in the model-based iterative reconstruction, aiming to achieve the optimal solution. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction compared with conventional reconstruction methods and U-Net. In particular, under the extreme sparse projection (e.g., 32 projections), the proposed method achieves an improvement of ∼ 260 % in structural similarity and ∼ 30 % in peak signal-to-noise ratio for in vivo data, compared with the conventional delay-and-sum method. This method has the potential to reduce the acquisition time and cost of photoacoustic tomography, which will further expand the application range.

Multifactor effects on the N2O emissions and yield of potato fields based on the DNDC model.

Maintaining or increasing grain yields while also reducing the emissions of field agricultural greenhouse gases is an important objective. To explore the multifactor effects of nitrogen fertilizer on nitrous oxide (N2O) emissions and the yield of potato fields and to verify the applicability of the denitrification-decomposition (DNDC) model when used to project the N2O emission load and yield, this research chooses a potato field in Shenyang northeast China from 2017 to 2019 as the experiment site. The experiment includes four nitrogen levels observing the emission of N2O by static chamber/gas chromatograph techniques. The results of this study are as follows: (1) DNDC has a good performance regarding the projection of N2O emissions and yields. The model efficiency index EFs were 0.45 ~ 0.88 for N2O emissions and 0.91, 0.85, and 0.85 for yields from 2017 to 2019. (2) The annual precipitation, soil organic carbon, and soil bulk density had the most significant influence on the accumulated N2O emissions during the growth period of potatoes. The annual precipitation, annual average temperature, and CO2 mass concentration had the most significant influences on yield. (3) Under the premise of a normal water supply, sowing potatoes within 5 days after the 5-day sliding average temperature in this area exceeds 10℃ can ensure the temperature required for the normal growth of potatoes and achieve the purpose of maintaining and increasing yield. (4) The application of 94.5 kg·hm-2 nitrogen and 15 mm irrigation represented the best results for reducing N2O emissions while also maintaining the yield in potato fields.