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In this paper, a single-computed tomography (CT) image super-resolution (SR) reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse representation theory and dictionary learning of low- and high-resolution image patch pairs to improve the poor quality of low-resolution CT images obtained in clinical practice using low-dose CT technology. The proposed strategy is based on the idea that image patches can be well represented by sparse coding of elements from an overcomplete dictionary. To obtain similarity of the sparse representations, two dictionaries of low- and high-resolution image patches are jointly trained. Then, sparse representation coefficients extracted from the low-resolution input patches are used to reconstruct the high-resolution output. Sparse representation is used such that the trained dictionary pair can reduce computational costs. Combined with several appropriate iteration operations, the reconstructed high-resolution image can attain better image quality. The effectiveness of the proposed method is demonstrated using both clinical CT data and simulation image data. Image quality evaluation indexes (root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR)) indicate that the proposed method can effectively improve the resolution of a single CT image.

Lead halide perovskites have recently emerged as promising X/γ-ray scintillators. However, the small Stokes shift of exciton luminescence in perovskite scintillators creates problems for the light extraction efficiency and severely impedes their applications in hard X/γ-ray detection. Dopants have been used to shift the emission wavelength, but the radioluminescence lifetime has also been unwantedly extended. Herein, we demonstrate the intrinsic strain in 2D perovskite crystals as a general phenomenon, which could be utilized as self-wavelength shifting to reduce the self-absorption effect without sacrificing the radiation response speed. Furthermore, we successfully demonstrated the first imaging reconstruction by perovskites for application of positron emission tomography. The coincidence time resolution for the optimized perovskite single crystals (4 × 4 × 0.8 mm 3 ) reached 119 ± 3 ps. This work provides a new paradigm for suppressing the self-absorption effect in scintillators and may facilitate the application of perovskite scintillators in practical hard X/γ-ray detections. Efficient light extraction in perovskite X/γ-ray scintillators is hindered by the small Stokes shift of exciton luminescence. Here, Jin et al. exploit the intrinsic strain in 2D perovskite as “self-wavelength shifting” to reduce the self-absorption effect without sacrificing the device response speed.

Because radiation is harmful to patients, it is important to reduce X-ray exposure in the clinic. For CT, reconstructions from sparse views or limited angle tomography are being used more frequently for low dose imaging. However, insufficient sampling data causes severe streak artifacts in images reconstructed using conventional methods. To solve this issue, various methods have recently been developed. In this paper, we improve a statistical iterative algorithm based on the minimization of the image total variation (TV) for sparse or limited projection views during CT image reconstruction. Considering the statistical nature of the projection data, the TV is performed under a penalized weighted least-squares (PWLS-TV) criterion. During implementation of the proposed method, the image reconstructed using the filtered back-projection (FBP) method is used as the initial value of the first iteration. Next, the feature refinement (FR) step is performed after each PWLS-TV iteration to extract the fine features lost in the TV minimization, which we refer to as ‘PWLS-TV-FR’.

In this experiment, Jimai 22 was used as test material to investigate the impacts of acetochlor on the growth characteri*stics of dryland wheat and its residue in wheat grain and soil under field conditions. The results showed that acetochlor at a dosage of 750 g hm−2 could effectively control weeds in wheat fields and benefit the growth of wheat in dryland. The plant height, ear length, number of tillers, aboveground and underground dry weight were heavier compared to the control after the application of acetochlor. From the jointing period to the filling period, the residual amount of acetochlor in the 0–20 cm and 20–40 cm soil layers was gradually reduced in the overall change, and the residual amount of acetochlor in the 0–20 cm soil layer in each growth period was more than the residual that of in the 20–40 cm soil layer. At the time of harvest, the residual amount of acetochlor in wheat grains was lower than the maximum allowable residue limit (MRL) value of acetochlor on wheat recommended by the United States. In addition, the residue of acetochlor in the soil of wheat field was also very small, all of which were lower than 0.02 mg kg−1. It can be seen that the application of acetochlor is safe for wheat field soil and humans if the wheat is applied once after sowing, at the dosage is 750 g hm−2.

Stationary digital breast tomosynthesis (sDBT) with distributed X-ray sources based on carbon nanotube (CNT) field emission cathodes has been recently proposed as an approach that can prevent motion blur produced by traditional DBT systems. In this paper, we simulate a geometric calibration method based on a proposed multi-source CNT X-ray sDBT system. This method is a projection matrix-based approach with seven geometric parameters, all of which can be obtained from only one projection datum of the phantom. To our knowledge, this study reports the first application of this approach in a CNT-based multi-beam X-ray sDBT system. The simulation results showed that the extracted geometric parameters from the calculated projection matrix are extremely close to the input values and that the proposed method is effective and reliable for a square sDBT system. In addition, a traditional cone-beam computed tomography (CT) system was also simulated, and the uncalibrated and calibrated geometric parameters were used in image reconstruction based on the filtered back-projection (FBP) method. The results indicated that the images reconstructed with calibrated geometric parameters have fewer artifacts and are closer to the reference image. All the simulation tests showed that this geometric calibration method is optimized for sDBT systems but can also be applied to other application-specific CT imaging systems.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient’s clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.

Purpose This study aimed to implement high-end positron emission tomography (PET) equipment to assist conventional PET equipment in improving image quality via a distribution learning-based diffusion model. Methods A diffusion model was first trained on a dataset of high-quality (HQ) images acquired by a high-end PET device (uEXPLORER scanner), and the quality of the conventional PET images was later improved on the basis of this trained model built on null-space constraints. Data from 180 patients were used in this study. Among them, 137 patients who underwent total-body PET/computed tomography scans via a uEXPLORER scanner at the Sun Yat-sen University Cancer Center were retrospectively enrolled. The datasets of 50 of these patients were used to train the diffusion model. The remaining 87 cases and 43 PET images acquired from The Cancer Imaging Archive were used to quantitatively and qualitatively evaluate the proposed method. The nonlocal means (NLM) method, UNet and a generative adversarial network (GAN) were used as reference methods. Results The incorporation of HQ imaging priors derived from high-end devices into the diffusion model through network training can enable the sharing of information between scanners, thereby pushing the limits of conventional scanners and improving their imaging quality. The quantitative results showed that the diffusion model based on null-space constraints produced better and more stable results than those of the methods based on NLM, UNet and the GAN and is well suited for cross-center and cross-device imaging. Conclusion A diffusion model based on null-space constraints is a flexible framework that can effectively utilize the prior information provided by high-end scanners to improve the image quality of conventional scanners in cross-center and cross-device scenarios.

Many deep learning methods have been proposed to improve the quality of low‐dose PET images (LPET), which usually construct end‐to‐end networks with certain radiation dose inputs. However, these approaches have omitted the noise disparity in PET images, which may differ among manufacturers or populations. Therefore, we tend to exploit these noise differences among PET images to achieve adaptive restoration. We proposed a 3D noise level‐guided PET restoration network for LPET including (1) adaptive noise level‐aware subnetwork and (2) LPET restoration subnetwork. The first subnetwork aims to predict the noise level of the given LPET, while the second subnetwork treats the estimated noise level as a priori information to guide the restoration process from LPET to standard‐dose PET images. Experiments were performed on real human head and neck datasets while the peak signal‐to‐noise ratio and structural similarity index measure were used to evaluate LPET recovery performance. Moreover, we also compared the proposed network with several deep‐learning approaches. Experimental results demonstrate that our network with dual‐stage design can perform adaptive restoration for LPET, yielding better visual and quantitative results. In future work, we attempt to apply our method to other imaging tasks and adapt it for clinical practice. This flowchart illustrates a 3D noise level‐guided positron emission tomography (PET) restoration network for low‐dose PET (LPET) images to reduce radiation risk. The proposed method consists of an adaptive noise level‐aware subnetwork and a LPET restoration subnetwork, which generate high‐quality standard‐dose PET images from LPET images. Considering the noise differences that may occur among different populations and manufacturers, the proposed method could be applied on PET images with different noise intensities.

BackgroundThe total-body positron emission tomography (PET) scanner provides an unprecedented opportunity to scan the whole body simultaneously, thanks to its long axial field of view and ultrahigh temporal resolution. To fully utilize this potential in clinical settings, a dynamic scan would be necessary to obtain the desired kinetic information from scan data. However, in a long dynamic acquisition, patient movement can degrade image quality and quantification accuracy.MethodsIn this work, we demonstrated a motion correction framework and its importance in dynamic total-body FDG PET imaging. Dynamic FDG scans from 12 subjects acquired on a uEXPLORER PET/CT were included. In these subjects, 7 are healthy subjects and 5 are those with tumors in the thorax and abdomen. All scans were contaminated by motion to some degree, and for each the list-mode data were reconstructed into 1-min frames. The dynamic frames were aligned to a reference position by sequentially registering each frame to its previous neighboring frame. We parametrized the motion fields in-between frames as diffeomorphism, which can map the shape change of the object smoothly and continuously in time and space. Diffeomorphic representations of motion fields were derived by registering neighboring frames using large deformation diffeomorphic metric matching. When all pairwise registrations were completed, the motion field at each frame was obtained by concatenating the successive motion fields and transforming that frame into the reference position. The proposed correction method was labeled SyN-seq. The method that was performed similarly, but aligned each frame to a designated middle frame, was labeled as SyN-mid. Instead of SyN, the method that performed the sequential affine registration was labeled as Aff-seq. The original uncorrected images were labeled as NMC. Qualitative and quantitative analyses were performed to compare the performance of the proposed method with that of other correction methods and uncorrected images.ResultsThe results indicated that visual improvement was achieved after correction of the SUV images for the motion present period, especially in the brain and abdomen. For subjects with tumors, the average improvement in tumor SUVmean was 5.35 ± 4.92% (P = 0.047), with a maximum improvement of 12.89%. An overall quality improvement in quantitative Ki images was also observed after correction; however, such improvement was less obvious in K1 images. Sampled time–activity curves in the cerebral and kidney cortex were less affected by the motion after applying the proposed correction. Mutual information and dice coefficient relative to the reference also demonstrated that SyN-seq improved the alignment between frames over non-corrected images (P = 0.003 and P = 0.011). Moreover, the proposed correction successfully reduced the inter-subject variability in Ki quantifications (11.8% lower in sampled organs). Subjective assessment by experienced radiologists demonstrated consistent results for both SUV images and Ki images.ConclusionTo conclude, motion correction is important for image quality in dynamic total-body PET imaging. We demonstrated a correction framework that can effectively reduce the effect of random body movements on dynamic images and their associated quantification. The proposed correction framework can potentially benefit applications that require total-body assessment, such as imaging the brain-gut axis and systemic diseases.

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.