Optimization of the cavity length and pulse characterization based on germanene as a saturable absorber in an Er-doped fiber laser.

In this study, germanene-nanosheets (NSs) were synthesized by liquid-phase exfoliation, followed by an experimental investigation into the nonlinear saturable absorption characteristics and morphological structure of germanene. The germanene-NSs were employed as saturable absorbers, exhibiting saturation intensity and modulation depth values of 22.64M W/c m 2 and 4.48%, respectively. This demonstrated the feasibility of utilizing germanene-NSs passively mode-locked in an erbium-doped fiber laser (EDFL). By optimizing the cavity length, improvements in the output of EDFL characteristics were achieved, resulting in 883 fs pulses with a maximum average output power of 19.74 mW. The aforementioned experimental outcomes underscore the significant potential of germanene in the realms of ultrafast photonics and nonlinear optics.

Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery.

OBJECTIVES: To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS). METHODS: We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve. RESULTS: Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort. CONCLUSIONS: Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS. KEY POINTS: • Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%). • Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). • The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).

Soliton phenomena in normal and anomalous dispersion regions in Er-doped mode-locked fiber lasers based on Cr2Si2Te6 saturable absorbers.

Investigations of optical solitons have always been a hot topic due to their important scientific research value. In recent years, ultrafast lasers based on two-dimensional materials such as saturable absorbers (SAs) have become the focus of optical soliton research. In this work, various soliton operations are demonstrated in Er-doped fiber lasers (EDFLs) based on ${{\rm Cr}_2}{{\rm Si}_2}{{\rm Te}_6}$ SAs. First, a low-threshold passively mode-locked EDFL with traditional soliton output is constructed, and the pump threshold is as low as 10.1 mW. Second, by adjusting the net dispersion of the cavity, stable dissipative soliton operation can also be obtained. Traditional soliton mode-locked operation with controllable Kelly sidebands from first order to fourth order is realized by adjusting the pump power in a double-ended pumped structure, and the SNR is as high as 55 dB. All results prove that ${{\rm Cr}_2}{{\rm Si}_2}{{\rm Te}_6}$ used as SA material has great potential and wide application prospects in investigating optical soliton operations in mode-locked fiber lasers with both normal and anomalous dispersion.

Third-order harmonic mode-locked and Q-switched Er-doped fiber laser based on a Cr2Ge2Te6 saturable absorber.

This paper reports the generation of fundamental solitons and third-order solitons in an erbium-doped fiber laser (EDFL) by a Cr2Ge2Te6-polyvinyl alcohol (CGT-PVA) saturable absorber (SA). Stable fundamental solitons at 1559.09 nm at a repetition frequency of 5.1 MHz were detected, and third-order solitons with a maximum output power of 6.807 mW and narrowest monopulse duration of 615.2 fs were obtained under a repetition frequency of 15.3 MHz by changing pump power. To the best of our knowledge, it is the first time to achieve a Q-switched pulse with a minimum pulse duration of 2.2 µs and maximum single pulse energy of 12.11 nJ in EDFL based on CGT-PVA SA after reducing the cavity length. Its repetition rate monotonically increased from 18.8 kHz to 61.8 kHz with a tuning range of about 43 kHz. The experimental results sufficiently demonstrate that CGT has enormous potential as an ultrafast photonics device.

Semi-supervised classification of fundus images combined with CNN and GCN.

PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time. To alleviate these problems, in this paper, we propose a novel framework-graph attentional convolutional neural network (GACNN). METHODS AND MATERIALS: The network consists of convolutional neural network (CNN) and graph convolutional network (GCN). The global and spatial features of fundus images are extracted by using CNN and GCN, and attention mechanism is introduced to enhance the adaptability of GCN to topology map. We adopt semi-supervised method for classification, which greatly improves the generalization ability of the network. RESULTS: In order to verify the effectiveness of the network, we conducted comparative experiments and ablation experiments. We use confusion matrix, precision, recall, kappa score, and accuracy as evaluation indexes. With the increase of the labeling rates, the classification accuracy is higher. Particularly, when the labeling rate is set to 100%, the classification accuracy of GACNN reaches 93.35%. Compared with DenseNet121, the accuracy rate is improved by 6.24%. CONCLUSIONS: Semi-supervised classification based on attention mechanism can effectively improve the classification performance of the model, and attain preferable results in classification indexes such as accuracy and recall. GACNN provides a feasible classification scheme for fundus images, which effectively reduces the screening human resources.

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study.

Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning-based prediction models were then built from this and compared each other. The final radiomic signature was built using logistic regression in the primary cohort, and then tested in a validation cohort. Finally, we compared the efficacy of the radiomic signature to the clinical model and the radiomic-clinical nomogram. Among the 125 patients, 89 were classified as having PD-L1 positive expression. However, there was no significant difference in PD-L1 expression levels determined by clinical characteristics (P = 0.109-0.955). Upon selecting 9 radiomic features, we found that the logistic regression-based prediction model performed the best (AUC = 0.96, P < 0.001). In the external cohort, our radiomic signature showed an AUC of 0.85, which outperformed both the clinical model (AUC = 0.38, P < 0.001) and the radiomics-nomogram model (AUC = 0.61, P < 0.001). Our CT-based hand-crafted radiomic signature model can effectively predict PD-L1 expression levels, providing a noninvasive means of better understanding PD-L1 expression in patients with NSCLC.

Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.

Nonlinear optical absorption properties of zirconium selenide in generating dark soliton and dark-bright soliton pairs.

Our work reports the preparation of zirconium selenide (ZrSe2)-polyvinyl alcohol (PVA) film-type saturable absorber (SA) and its nonlinear absorption performance in obtaining dark soliton and dark-bright soliton pairs in an Er-doped fiber (EDF) laser for the first time, to the best of our knowledge. The saturation intensity and modulation depth of the ZrSe2-PVA SA were ∼12.72MW/cm2 and 2.3%, respectively. Due to the modulation of the SA, under a pump power of 525.2 mW, stable dark soliton operation with an average output power of 9.75 mW, and a pulse repetition frequency of 20.84 MHz, a pulse width of 3.85 ns was attained successfully. By adjusting the state of the polarization controllers, dark-bright soliton pairs were also observed. To the best of our knowledge, this was the first demonstration focusing on the nonlinear optical absorption applications of ZrSe2 in obtaining dark soliton and dark-bright soliton pairs. Our results show that ZrSe2 is a good two-dimensional SA material for acting as an ultrafast optical device due to its suitable nonlinear optical absorption properties.

Nonlinear optical characteristics of ZrSe2 and its application for designing multi-wavelength mode-locked operations.

In our work, a ZrSe2-polyvinyl alcohol film-type saturable absorber (SA) with a modulation depth of 4.99% and a saturable intensity of 12.42MW/cm2 was successfully prepared and employed in mode-locked Er-doped fiber laser. The fiber laser can generate stable multi-wavelength mode-locked operations with a threshold power of 224 mW and a maximum average output power of 3.272 mW at the repetition rate of 3.38 MHz for the first time, to the best of our knowledge. Our experimental results fully prove that ZrSe2 nanosheets were efficient SA candidates for demonstrating multi-wavelength mode-locked operation fiber lasers due to their tunable absorption peak and excellent saturable absorption properties.

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.

BACKGROUND: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). METHODS: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. CONCLUSIONS: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Securing Internet of Medical Things with Friendly-jamming schemes.

The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission.

Ferromagnetic insulator Cr2Ge2Te6 as a modulator for generating near-infrared bright-dark soliton pairs.

Novel two-dimensional (2D)-materials-based ultra-fast modulators exhibit significance in extending the fundamental investigations and practical applications of mode-locked fiber lasers. In our work, employing the liquid-phase exfoliation method, a ${{\rm Cr}_2}{{\rm Ge}_2}{{\rm Te}_6}$Cr2Ge2Te6 (CGT) optical modulator with a modulation depth and a saturable intensity of 1.64% and ${6.31}\,\,{{\rm MW/cm}^2}$6.31MW/cm2 was fabricated. Due to its suitable modulation properties and high nonlinear coefficient, a stable bright-dark soliton pair was successfully achieved within an Er-doped fiber laser. Under the pump power of 560 mW, the maximum average output power was 5.36 mW with a pulse repetition rate of 1.835 MHz. Our results fully present the capacity of CGT in designing bright-dark soliton operations and provide a meaningful reference for promoting the ultra-fast modulation applications of ferromagnetic insulators.

Prognostic value and molecular correlates of a CT image-based quantitative pleural contact index in early stage NSCLC.

PURPOSE: To evaluate the prognostic value and molecular basis of a CT-derived pleural contact index (PCI) in early stage non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: We retrospectively analysed seven NSCLC cohorts. A quantitative PCI was defined on CT as the length of tumour-pleura interface normalised by tumour diameter. We evaluated the prognostic value of PCI in a discovery cohort (n = 117) and tested in an external cohort (n = 88) of stage I NSCLC. Additionally, we identified the molecular correlates and built a gene expression-based surrogate of PCI using another cohort of 89 patients. To further evaluate the prognostic relevance, we used four datasets totalling 775 stage I patients with publically available gene expression data and linked survival information. RESULTS: At a cutoff of 0.8, PCI stratified patients for overall survival in both imaging cohorts (log-rank p = 0.0076, 0.0304). Extracellular matrix (ECM) remodelling was enriched among genes associated with PCI (p = 0.0003). The genomic surrogate of PCI remained an independent predictor of overall survival in the gene expression cohorts (hazard ratio: 1.46, p = 0.0007) adjusting for age, gender, and tumour stage. CONCLUSIONS: CT-derived pleural contact index is associated with ECM remodelling and may serve as a noninvasive prognostic marker in early stage NSCLC. KEY POINTS: • A quantitative pleural contact index (PCI) predicts survival in early stage NSCLC. • PCI is associated with extracellular matrix organisation and collagen catabolic process. • A multi-gene surrogate of PCI is an independent predictor of survival. • PCI can be used to noninvasively identify patients with poor prognosis.

Parotid gland radiation dose-xerostomia relationships based on actual delivered dose for nasopharyngeal carcinoma.

Xerostomia induced by radiotherapy is a common toxicity for head and neck carcinoma patients. In this study, the deformable image registration of planning computed tomography (CT) and weekly cone-beam CT (CBCT) was used to override the Hounsfield unit value of CBCT, and the modified CBCT was introduced to estimate the radiation dose delivered during the course of treatment. Herein, the beams from each patient's treatment plan were applied to the modified CBCT to construct the weekly delivered dose. Then, weekly doses were summed together to obtain the accumulated dose. A total of 42 parotid glands (PGs) of 21 nasopharyngeal carcinoma patients were analyzed. Doses delivered to the parotid glands significantly increased compared with the planning doses. V20 , V30 , V40 , Dmean , and D50 increased by 11.3%, 28.6%, 44.4%, 9.5%, and 8.4% respectively. Of the 21 patients included in the study, eight developed xerostomia and the remaining 13 did not. Both planning and delivered PG Dmean for all patients exceeded tolerance (26 Gy). Among the 21 patients, the planning dose and delivered dose of Dmean were 30.6 Gy and 33.6 Gy, respectively, for patients with xerostomia, and 26.3 Gy and 28.0 Gy, respectively, for patients without xerostomia. The D50 of the planning and delivered dose for patients was below tolerance (30 Gy). The results demonstrated that the p-value of V20 , V30 , D50 , and Dmean difference of the delivery dose between patients with xerostomia and patients without xerostomia was less than 0.05. However, for the planning dose, the significant dosimetric difference between the two groups only existed in D50 and Dmean . Xerostomia is closely related to V20 , V30 , D50 , and Dmean .

Investigation of dosimetric variations of liver radiotherapy using deformable registration of planning CT and cone-beam CT.

Many patients with technically unresectable or medically inoperable hepatocellular carcinoma (HCC) had hepatic anatomy variations as a result of interfraction deformation during fractionated radiotherapy. We conducted this retrospective study to investigate interfractional normal liver dosimetric consequences via reconstructing weekly dose in HCC patients. Twenty-three patients with HCC received conventional fractionated three-dimensional conformal radiation therapy (3DCRT) were enrolled in this retrospective investigation. Among them, seven patients had been diagnosed of radiation-induced liver disease (RILD) and the other 16 patients had good prognosis after treatment course. The cone-beam CT (CBCT) scans were acquired once weekly for each patient throughout the treatment, deformable image registration (DIR) of planning CT (pCT) and CBCT was performed to acquire modified CBCT (mCBCT), and the structural contours were propagated by the DIR. The same plan was applied to mCBCT to perform dose calculation. Weekly dose distribution was displayed on the pCT dose space and compared using dose difference, target coverage, and dose volume histograms. Statistical analysis was performed to identify the significant dosimetric variations. Among the 23 patients, the three weekly normal liver D50 increased by 0.2 Gy, 4.2 Gy, and 4.7 Gy, respectively, for patients with RILD, and 1.0 Gy, 2.7 Gy, and 3.1 Gy, respectively, for patients without RILD. Mean dose to the normal liver (Dmean) increased by 0.5 Gy, 2.6 Gy, and 4.0 Gy, respectively, for patients with RILD, and 0.4 Gy, 3.1 Gy, and 3.4 Gy, respectively, for patients without RILD. Regarding patients with RILD, the average values of the third weekly D50 and Dmean were both over hepatic radiation tolerance, while the values of patients without RILD were below. The dosimetric consequence showed that the liver dose between patients with and without RILD were different relative to the planned dose, and the RILD patients suffered from liver dose over hepatic radiation tolerance. Evaluation of routinely acquired CBCT images during radiation therapy provides biological information on the organs at risk, and dose estimation based on mCBCT could potentially form the basis for personalized response adaptive therapy.

Retracted: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model.

We have previously developed a retrospective 4D-MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases (5/7) in the liver, as well as a patient with non-small cell lung cancer (NSCLC), were enrolled in an IRB-approved prospective study. Both CT and MR simulations were performed for each patient for treatment planning. Multiple-slice, multiple-phase, cine-MRI images were acquired in the axial plane for 4D-MRI reconstruction. Single-slice 2D cine-MR images were acquired across the center of the tumor in axial, coronal, and sagittal planes. For a 4D MR image dataset, the DVFs in three orthogonal direction (inferior­superior (SI), anterior­posterior (AP), and medial­lateral (ML)) relative to a specific reference phase were calculated using an in-house DIR algorithm. The DVFs were preprocessed in three temporal and spatial dimensions using a polynomial fitting model, with the goal of correcting the potential registration errors introduced by three-dimensional DIR. Then PCA was used to decompose each fitted DVF into a linear combination of three principal motion bases whose spanned subspaces combined with their projections had been validated to be sufficient to represent the regular respiratory motion. By wrapping the reference MR image using the remodeled DVFs, 'synthetic' MR images with reduced motion artifacts were generated at selected phase. Tumor motion trajectories derived from cine-MRI, 4D CT, original 4D MRI, and 'synthetic' 4D MRI were analyzed in the SI, AP, and ML directions, respectively. Their correlation coefficient (CC) and difference (D) in motion amplitude were calculated for comparison. Of all the patients, the means and standard deviations (SDs) of CC comparing 'synthetic' 4D MRI and cine-MRI were 0.98 ± 0.01, 0.98 ± 0.01, and 0.99 ± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.59 ± 0.09 mm, 0.29± 0.10 mm, and 0.15 ± 0.05 mm in SI, AP and ML directions, respectively. The means and SDs of CC comparing 'synthetic' 4D MRI and 4D CT were 0.96 ± 0.01, 0.95± 0.01, and 0.95 ± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.76 ± 0.20 mm, 0.33 ± 0.14 mm, and 0.19± 0.07 mm in SI, AP, and ML directions, respectively. The means and SDs of CC comparing 'synthetic' 4D MRI and original 4D MRI were 0.98 ± 0.01, 0.98± 0.01, and 0.97± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.58 ± 0.10 mm, 0.30 ± 0.09mm, and 0.17 ± 0.04 mm in SI, AP, and ML directions, respectively. In this study we have proposed an approach employing PCA combined with a linear polynomial fitting model to capture the regular respiratory motion from a 4D MR image dataset. And its potential usefulness in reducing motion artifacts and improving image quality has been demonstrated by the preliminary results in oncological patients.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Background: Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. Methods: The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. Results: The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). Conclusions: The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients.

Accurate registration of 18F-FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from (18)F-FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information-based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

BACKGROUND: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy. PURPOSE: To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed. METHODS: In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U-shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter-window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively. RESULT: Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics. CONCLUSIONS: Accurately capturing the features at different levels is greatly beneficial for reconstructing high-quality scatter-free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image-guided radiation therapy.

Temperature guided network for 3D joint segmentation of the pancreas and tumors.

Accurate and automatic segmentation of pancreatic tumors and organs from medical images is important for clinical diagnoses and making treatment plans for patients with pancreatic cancer. Although deep learning methods have been widely adopted for this task, the segmentation accuracy, especially for pancreatic tumors, still needs to be further improved because (1) phenotypic differences, such as volumes, tend to make the models focus on pancreatic learning, resulting in insufficient tumor feature selection; (2) deep learning models may fall into local optima, leading to unsatisfactory segmentation results for tumors and pancreas. To alleviate the above issues, in this paper, we propose a 3D fully convolutional neural network with three temperature guided modules, namely, balance temperature loss, rigid temperature optimizer and soft temperature indictor, to realize joint segmentation of the pancreas and tumors. Specifically, balance temperature loss is designed to dynamically adjust the learning points between tumors and the pancreas to balance the selected features, and it is aimed at improving the accuracy of tumor segmentation without losing pancreas information. Rigid temperature optimizer is proposed to accept nonimproving moves probabilistically to adaptively avoid local optima. To further refine the segmentation results, we propose the soft temperature indictor to guide the network into a fine-tuning state automatically when the model tends to stability. Our experimental results are more accurate than the fourteen top-ranking methods in pancreas and tumors segmentation on the MSD pancreas dataset and six top-ranking methods in brain tumors segmentation. Ablation studies verify the effectiveness of the three temperature guided modules.