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Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT–PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health. [ABSTRACT FROM AUTHOR]

In this paper, weighted average consensus of second-order multi-agent systems (MASs) via sampled-data control is investigated. Specially, since the intelligence degree of each agent may be different in practical applications, the concept of intelligence level is introduced to characterize the intelligence degrees of agents in this paper. A novel distributed continuous-time control protocol with various intelligence levels is put forward based on sampled data in order to reduce the updating rates and computational burden of the controller. A necessary and sufficient condition is derived to achieve weighted average consensus under the proposed protocol. Moreover, according to the acquired result, the authors also give the choosing approaches of the feedback gains and sampling period. Finally, several numerical simulations are provided to illustrate the reliability of the theoretical results. [ABSTRACT FROM AUTHOR]

Purpose: Deep learning is an emerging reconstruction method for positron emission tomography (PET), which can tackle complex PET corrections in an integrated procedure. This paper optimizes the direct PET reconstruction from sinogram on a long axial field of view (LAFOV) PET. Methods: This paper proposes a novel deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network, where the perceptual loss is used with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra long axial FOV (LAFOV) PET/CT. The patients are randomly split into a training dataset of 60 patients, a validation dataset of 10 patients, and a test dataset of 10 patients. The 3D sinograms are converted into 2D sinogram slices and used as input to the network. In addition, the vendor reconstructed images are considered as ground truths. Finally, the proposed method is compared with DeepPET, a benchmark deep learning method for PET reconstruction. Results: Compared with DeepPET, the proposed network significantly reduces the root-mean-squared error (NRMSE) from 0.63 to 0.6 (p < 0.01) and increases the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) from 0.93 to 0.95 (p < 0.01) and from 82.02 to 82.36 (p < 0.01), respectively. The reconstruction time is approximately 10 s per patient, which is shortened by 23 times compared with the conventional method. The errors of mean standardized uptake values (SUVmean) for lesions between ground truth and the predicted result are reduced from 33.5 to 18.7% (p = 0.03). In addition, the error of max SUV is reduced from 32.7 to 21.8% (p = 0.02). Conclusion: The results demonstrate the feasibility of using deep learning to reconstruct images with acceptable image quality and short reconstruction time. It is shown that the proposed method can improve the quality of deep learning-based reconstructed images without additional CT images for attenuation and scattering corrections. This study demonstrated the feasibility of deep learning to rapidly reconstruct images without additional CT images for complex corrections from actual clinical measurements on LAFOV PET. Despite improving the current development, AI-based reconstruction does not work appropriately for untrained scenarios due to limited extrapolation capability and cannot completely replace conventional reconstruction currently. [ABSTRACT FROM AUTHOR]

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation. Deep learning-based methods have been proposed to substitute CT-based PET attenuation and scatter correction to achieve CT-free PET imaging. Here, the authors present a simple way to integrate domain knowledge in deep learning for CT-free PET imaging. [ABSTRACT FROM AUTHOR]

Objective: To retrospectively review the clinicopathological features and computed tomography (CT) and magnetic resonance imaging (MRI) findings of abdominal perivascular epithelioid cell tumor without visible fat (PEComawvf). Materials and methods: Sixteen patients with surgically and pathologically confirmed perivascular epithelioid cell tumor without visible fat were enrolled. Their clinicopathological data and imaging findings were retrospectively reviewed. The CT and MRI features, including location, size, shape, margin, density, calcification, cystic necrosis and enhancement pattern, were analyzed. Results: There were 4 males and 12 females (median age, 46 years; range, 21–65 years) in this study. All 16 patients were diagnostic asymptomatic unenhanced CT or MRI and revealed a well-defined (n = 13), oval (n = 10), mass with heterogeneous (n = 6) or homogeneous density/signal intensity (n = 7), calcification and hemorrhage was no found in any cases. On enhanced CT/MRI, markedly enhancement patterns (n = 14) were observed. The "peripheral enhancement" sign was observed in 13 cases. One in 16 cases recurrence was discovered during the follow-up period. Conclusions: Dynamic CT, MRI and pathology of PEComawvf had some characteristics of non-aggressive pattern of performance, and MRI would provide beneficial detection of microscopic fat. Enhanced imaging showed PEComawvf is characterized by a "peripheral enhancement" with a marked enhancement pattern. Knowing these characteristics could contribute to improving the understanding abdominal PEComawvf and related palliative care. [ABSTRACT FROM AUTHOR]

Purpose: To obtain the optimal simultaneous-multislice (SMS)—accelerated diffusion-weighted imaging (DWI) of the liver at 3.0 T MRI by systematically estimating the repeatability of apparent diffusion coefficient (ADC), signal-to-noise ratio (SNR) and image quality of different breathing schemes in comparison to standard DWI (STD) and other SMS sequences. Methods: In this institutional review board-approved prospective study, hepatic DWIs (b = 50, 300, 600 s/mm2) were performed in 23 volunteers on 3.0 T MRI using SMS and STD with breath-hold (BH-SMS, BH-STD), free-breathing (FB-SMS, FB-STD) and respiratory-triggered (RT-SMS, RT-STD). Reduction of scan time with SMS-acceleration was calculated. ADC and SNR were measured in nine anatomic locations and image quality was assessed on all SMS and STD sequences. An optimal SMS-DWI was decided by systematically comparing the ADC repeatability, SNR and image quality among above DWIs. Results: SMS-DWI reduced scan time significantly by comparison with corresponding STD-DWI (27 vs. 42 s for BH, 54 vs. 78 s for FB and 42 vs. 97 s for RT). In all DWIs, BH-SMS had the greatest intraobserver agreement (intraclass correlation coefficient (ICC): 0.920–0.944) and good interobserver agreement (ICC: 0.831–0.886) for ADC measurements, and had the best ADC repeatability (mean ADC absolute differences: 0.046–0.058 × 10−3mm2/s, limits of agreement (LOA): 0.010–0.013 × 10−3mm2/s) in nine locations. BH-SMS had the highest SNR in three representative sections except for RT-STD. There were no significant differences in image quality between BH-SMS and other DWI sequences (median BH-SMS: 4.75, other DWI: 4.5–5.0; P > 0.0.5). Conclusion: BH-SMS provides considerable scan time reduction with good image quality, sufficient SNR and highest ADC repeatability on 3.0 T MRI, which is thus recommended as the optimal hepatic DWI sequence for those subjects with adequate breath-holding capability. [ABSTRACT FROM AUTHOR]

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