Creeping and erupting dynamics in a pure-quartic soliton fiber laser.

Pure-quartic solitons (PQSs) are gradually becoming a hotspot in recent years due to their potential advantage to achieve high energy. Meanwhile, the fundamental research of PQSs is still in the fancy stage, and exploring soliton dynamics can promote the development of PQSs. Herein, we comprehensively and numerically investigate the impact of saturation power, small-signal gain, and output coupler on PQS dynamics in passively mode-locked fiber lasers. The result indicates that altering the above parameters makes PQSs exhibit pulsating or creeping dynamics similar to traditional solitons. Moreover, introducing an intra-cavity filter combined with intra-cavity large fourth-order dispersion makes PQSs go through stationary, pulsating to erupting. That is, the intra-cavity filter changes PQS dynamics. These findings provide new insights into PQS dynamics in fiber lasers.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint. MATERIALS AND METHODS: A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics. RESULTS: A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set. CONCLUSIONS: The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future. KEY POINTS: • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density.

OBJECTIVE: This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images. METHODS: A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31-94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1-L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix. RESULTS: The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979-1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779-0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891-0.989) for normal BMD versus osteopenia. CONCLUSIONS: The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.

Pure-high-even-order dispersion bound solitons complexes in ultra-fast fiber lasers.

Temporal solitons have been the focus of much research due to their fascinating physical properties. These solitons can form bound states, which are fundamentally crucial modes in fiber laser and present striking analogies with their matter molecules counterparts, which means they have potential applications in large-capacity transmission and all-optical information storage. Although traditionally, second-order dispersion has been the dominant dispersion for conventional solitons, recent experimental and theoretical research has shown that pure-high-even-order dispersion (PHEOD) solitons with energy-width scaling can arise from the interaction of arbitrary negative-even-order dispersion and Kerr nonlinearity. Despite these advancements, research on the bound states of PHEOD solitons is currently non-existent. In this study, we obtained PHEOD bound solitons in a fiber laser using an intra-cavity spectral pulse shaper for high-order dispersion management. Specifically, we experimentally demonstrate the existence of PHEOD solitons and PHEOD bound solitons with pure-quartic, -sextic, -octic, and -decic dispersion. Numerical simulations corroborate these experimental observations. Furthermore, vibrating phase PHEOD bound soliton pairs, sliding phase PHEOD bound soliton pairs, and hybrid phase PHEOD bound tri-soliton are discovered and characterized. These results broaden the fundamental understanding of solitons and show the universality of multi-soliton patterns.

Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage.

Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.

Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder.

Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT would enable automated lesion quantification to objectively assess treatment efficacy. Deep learning techniques, such as convolutional neural networks (CNNs) are state-of-the-art solutions for ROI segmentation, but require large amounts of annotated data during the training. However, collecting large datasets from emerging treatments such as LITT is impractical. In this paper, we propose a progressive brain lesion synthesis framework (PAVAE) to expand both the quantity and diversity of the training dataset. Concretely, our framework consists of two sequential networks: a mask synthesis network and a mask-guided lesion synthesis network. To better employ extrinsic information to provide additional supervision during network training, we design a condition embedding block (CEB) and a mask embedding block (MEB) to encode inherent conditions of masks to the feature space. Finally, a segmentation network is trained using raw and synthetic lesion images to evaluate the effectiveness of the proposed framework. Experimental results show that our method can achieve realistic synthetic results and boost the performance of down-stream segmentation tasks above traditional data augmentation techniques.

Automatic Grading Assessments for Knee MRI Cartilage Defects via Self-ensembling Semi-supervised Learning with Dual-Consistency.

Knee cartilage defects caused by osteoarthritis are major musculoskeletal disorders, leading to joint necrosis or even disability if not intervened at early stage. Deep learning has demonstrated its effectiveness in computer-aided diagnosis, but it is time-consuming to prepare a large set of well-annotated data by experienced radiologists for model training. In this paper, we propose a semi-supervised framework to effectively use unlabeled data for better evaluation of knee cartilage defect grading. Our framework is developed based on the widely-used mean-teacher classification model, by designing a novel dual-consistency strategy to boost the consistency between the teacher and student models. The main contributions are three-fold: (1) We define an attention loss function to make the network focus on the cartilage regions, which can both achieve accurate attention masks and boost classification performance simultaneously; (2) Besides enforcing the consistency of classification results, we further design a novel attention consistency mechanism to ensure the focusing of the student and teacher networks on the same defect regions; (3) We introduce an aggregation approach to ensemble the slice-level classification outcomes for deriving the final subject-level diagnosis. Experimental results show that our proposed method can significantly improve both classification and localization performances of knee cartilage defects. Our code is available on https://github.com/King-HAW/DC-MT.

Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis.

BACKGROUND: Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA. METHODS: The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate. DISCUSSION: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020203543.

Learning Hierarchical Attention for Weakly-Supervised Chest X-Ray Abnormality Localization and Diagnosis.

We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are not fully addressed, limiting its broader usage. While recent methods report high diagnostic accuracies, physicians have concerns trusting these algorithm results for diagnostic decision-making purposes because of a general lack of algorithm decision reasoning and interpretability. One potential way to address this problem is to further train these models to localize abnormalities in addition to just classifying them. However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications. In this work, we take a step towards addressing these issues by means of a new attention-driven weakly supervised algorithm comprising a hierarchical attention mining framework that unifies activation- and gradient-based visual attention in a holistic manner. Our key algorithmic innovations include the design of explicit ordinal attention constraints, enabling principled model training in a weakly-supervised fashion, while also facilitating the generation of visual-attention-driven model explanations by means of localization cues. On two large-scale chest X-ray datasets (NIH ChestX-ray14 and CheXpert), we demonstrate significant localization performance improvements over the current state of the art while also achieving competitive classification performance.

Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.

Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages. Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis. Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05). Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.

SLIR: Synthesis, localization, inpainting, and registration for image-guided thermal ablation of liver tumors.

Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, yet the contrast of tumors against normal soft tissues is often poor in CT scans, aggravating the accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay pre-procedural MR (pMR) and pre-procedural CT (pCT) images onto an intra-procedural CT (iCT) image to guide the thermal ablation of liver tumors. At the pre-procedural stage, the Cycle-GAN model with mutual information constraint is employed to generate the synthesized CT (sCT) image from the input pMR. Then, pMR-pCT image registration is carried out via traditional mono-modal sCT-pCT image registration. At the intra-procedural stage, the region of the probe and its artifacts are automatically localized and inpainted in the iCT image. Then, an unsupervised registration network (UR-Net) is used to efficiently align the pCT with the inpainted iCT (inpCT) image. The final transform from pMR to iCT is obtained by concatenating the two estimated transforms, i.e., (i) from pMR image space to pCT image space (via sCT) and (ii) from pCT image space to iCT image space (via inpCT). The proposed method has been evaluated over a real clinical dataset and compared with state-of-the-art methods. Experimental results confirm that the proposed method achieves high registration accuracy with fast computation speed.

Neuroimage-Based Consciousness Evaluation of Patients with Secondary Doubtful Hydrocephalus Before and After Lumbar Drainage.

Hydrocephalus is often treated with a cerebrospinal fluid shunt (CFS) for excessive amounts of cerebrospinal fluid in the brain. However, it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes, such as brain atrophy after brain damage and surgery. The non-trivial evaluation of the consciousness level, along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made. We studied 32 secondary mild hydrocephalus patients with different consciousness levels, who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage. We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages. Then, we built a regression model to regress the JFK Coma Recovery Scale-Revised (CRS-R) scores to quantify the level of consciousness. The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients. The regression model has high potential for the evaluation of consciousness in clinical practice.

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia.

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.