A landmark-supervised registration framework for multi-phase CT images with cross-distillation.

Objective.Multi-phase computed tomography (CT) has become a leading modality for identifying hepatic tumors. Nevertheless, the presence of misalignment in the images of different phases poses a challenge in accurately identifying and analyzing the patient's anatomy. Conventional registration methods typically concentrate on either intensity-based features or landmark-based features in isolation, so imposing limitations on the accuracy of the registration process.Method.We establish a nonrigid cycle-registration network that leverages semi-supervised learning techniques, wherein a point distance term based on Euclidean distance between registered landmark points is introduced into the loss function. Additionally, a cross-distillation strategy is proposed in network training to further improve registration performance which incorporates response-based knowledge concerning the distances between feature points.Results.We conducted experiments using multi-centered liver CT datasets to evaluate the performance of the proposed method. The results demonstrate that our method outperforms baseline methods in terms of target registration error. Additionally, Dice scores of the warped tumor masks were calculated. Our method consistently achieved the highest scores among all the comparing methods. Specifically, it achieved scores of 82.9% and 82.5% in the hepatocellular carcinoma and the intrahepatic cholangiocarcinoma dataset, respectively.Significance.The superior registration performance indicates its potential to serve as an important tool in hepatic tumor identification and analysis.

Phase Difference Network for Efficient Differentiation of Hepatic Tumors with Multi-Phase CT.

Liver cancer has been one of the top causes of cancer-related death. For developing an accurate treatment strategy and raising the survival rate, the differentiation of liver cancers is essential. Multiphase CT recently acts as the primary examination method for clinical diagnosis. Deep learning techniques based on multiphase CT have been proposed to distinguish hepatic cancers. However, due to the recurrent mechanism, RNN-based approaches require expensive calculations whereas CNN-based models fail to explicitly establish temporal correlations among phases. In this paper, we proposed a phase difference network, termed as Phase Difference Network (PDN), to identify two liver cancer, hepatocellular carcinoma and intrahepatic cholangiocarcinoma, from four-phase CT. Specifically, the phase difference was used as interphase temporal information in a differential attention module, which enhanced the feature representation. Additionally, utilizing a multihead self-attention module, a transformer-based classification module was employed to explore the long-term context and capture the temporal relation between phases. Clinical datasets are used in experiments to compare the performance of the proposed strategy versus conventional approaches. The results indicate that the proposed method outperforms the traditional deep learning based methods.

Effectiveness of the "Internet-Plus"-Based Life-Cycle Management on Pediatric Chronic Diseases.

Background: We aimed to assess the effect of life-cycle management on the satisfaction and health outcomes of children with chronic diseases and their parents, as well as the career benefits of healthcare workers. Methods: Participants were children with chronic diseases who received long-term treatment at the Three Gorges Hospital Affiliated to Chongqing University from January 2021 to November 2022. From the first admission, compare the children's disease onset, satisfaction and professional benefits of medical staff among "Medical and Nursing Integration" + "Internet plus"-based life cycle management group (n = 221, the experimental group), the routine management group (n = 53, the control group 1) and the "Medical and Nursing Integration" group (n = 67, control group 2). Results: The overall satisfaction of children in the experimental group (100 %) was higher than that in the control group 1 (98.11%) and control group 2 (98.51%). The times of second admission and third admission of patients in the experimental group were significantly lower than those in control group 1 were (both P<0.001) and control group 2 (both P<0.01). Nurses' sense of professional benefit, professional identity, and doctor's satisfaction with nurses in the experimental group were significantly higher than those in control group 1 (P<0.05, P<0.01, P<0.001) and control group 2 (all P<0.05). Conclusion: The application of "Medical and Nursing Integration" + "Internet plus"-based life cycle management in chronic disease nursing management can effectively improve the management on pediatric chronic diseases.

scENT for Revealing Gene Clusters From Single-Cell RNA-Seq Data.

Recently, the fast development of single-cell RNA-seq (scRNA-seq) techniques has enabled high-resolution transcriptomic statistical analysis of individual cells in heterogeneous tissues, which can help researchers to explore the relationship between genes and human diseases. The emerging scRNA-seq data results in new analysis methods aiming to identify cell-level clustering and annotations. However, there are few methods developed to gain insights into the gene-level clusters with biological significance. This study proposes a new deep learning-based framework, scENT (single cell gENe clusTer), to identify significant gene clusters from single-cell RNA-seq data. We started with clustering the scRNA-seq data into multiple optimal groups, followed by a gene set enrichment analysis to identify classes of over-represented genes. Considering high-dimensional data with extensive zeros and dropout issues, scENT integrates perturbation in the learning process of clustering scRNA-seq data to improve its robustness and performance. Experimental results show that scENT outperformed other benchmarking methods on simulation data. To validate the biological insights of scENT, we applied it to the public experimental scRNA-seq data profiled from patients with Alzheimer's disease and brain metastasis. scENT successfully identified novel functional gene clusters and associated functions, facilitating the discovery of prospective mechanisms and the understanding of related diseases.

A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning.

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity.

Delayed PET imaging using image synthesis network and nonrigid registration without additional CT scan.

PURPOSE: Attenuation correction is critical for positron emission tomography (PET) image reconstruction. The standard protocol for obtaining attenuation information in a clinical PET scanner is via coregistered computed tomography (CT) images. Therefore, for delayed PET imaging, the CT scan is repeated twice, which increases the radiation dose for the patient. In this paper, we propose a zero-extradose delayed PET imaging method that requires no additional CT scans. METHODS: A deep learning-based synthesis network is designed to convert PET data into pseudo-CT images for delayed scans. Then, nonrigid registration is performed between this pseudo CT image and the CT image of the first scan, warping the CT image of the first scan to an estimated CT image for the delayed scan. Finally, the PET image attenuation correction in the delayed scan is obtained from this estimated CT image. Experiments with clinical datasets are implemented to assess the effectiveness of the proposed method with the well-recognized Generative Adversarial Networks (GAN) method. The average peak signal-to-noise ratio (PSNR) and the mean absolute percent error (MAPE) are used for comparison. We also use scoring from three experienced radiologists as subjective measurement means based on the diagnostic consistency of the PET images reconstructed from GAN and the proposed method with respect to the ground truth images. RESULTS: The experiments show that the average PSNR is 47.04 dB (the proposed method) vs. 44.41 dB (the traditional GAN method) for the reconstructed delayed PET images in our evaluation dataset. The average MAPEs are 1.59% for the proposed method and 3.32% for the traditional GAN method across five organ regions of interest (ROIs). The scores for the GAN and the proposed method rated by three experienced radiologists are 8.08±0.60 and 9.02±0.52, indicating that the proposed method yields more consistent PET images with the ground truth. CONCLUSIONS: This work proposes a novel method for CT-less delayed PET imaging based on image synthesis network and nonrigid image registration. The PET image reconstructed using the proposed method yields delayed PET images with high image quality without artifacts and is quantitatively more accurate than the traditional GAN method.

Experimental Study on Alleviating Sports Injury through Data Screening of Functional Motor Biological Images.

In order to better reduce sports injury, a method based on functional motion biological image data is proposed. Through performing functional motion screening test on wushu athletes, including 7 items of test, each athlete is given a score according to the test standard. This paper summarizes the mistakes and deficiencies of common movement patterns of athletes and makes different intervention plans to improve the effect of sports injury screening. The results show that, at P > 0.001, there was a significant difference, and the experimental group FMS total score (15.02 ± 3.7) was lower than the control group FMS total score (18.51 ± 1.45). The recognition rate of the system is higher than that of the system based on single feature, and the recognition performance is better than that of the standard SVM and KNN recognition methods. It is proved that the design of the system is feasible, reliable, and effective.

Application of magnetic resonance imaging in evaluating ankle motion injury / Aplicação de imagens de ressonância magnética na avaliação de lesões por movimento do tornozelo / Aplicación de la resonancia magnética en la evaluación de la lesión por movimiento del tobillo

ABSTRACT Introduction Discuss the application of magnetic resonance imaging in evaluating ankle motion injury. Objective Verify the influencing factors of magnetic resource imaging (MRI) diagnosis based on the linear regression algorithm model. Methods The experimental group was diagnosed by MRI, while the control group was diagnosed by plain X-ray. After that, the mathematical model of the linear regression algorithm was constructed. Results It could be concluded that the MRI detection rate was 85.71%, and the X-ray plain film detection rate was 77.14%. The linear regression model analysis showed that the P-value of cartilage injury, tendon fracture, bone contusion, and soft tissue swelling was greater than 0.05. Conclusions MRI has more advantages in the application of ankle joint diagnosis. And ligament injury and joint effusion are the influencing factors of MRI diagnosis, which can highly indicate the authenticity of the injury in the ankle joint. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Introdução Discutir a aplicação da ressonância magnética na avaliação da lesão motora do tornozelo. Objetivo Verificar os fatores que influenciam o diagnóstico de imagens de recursos magnéticos (RM) com base no modelo de algoritmo de regressão linear. Métodos O grupo experimental foi diagnosticado por ressonância magnética, enquanto o grupo controle foi diagnosticado por radiografia simples. Em seguida, foi construído o modelo matemático do algoritmo de regressão linear. Resultados Concluiu-se que a taxa de detecção da ressonância magnética foi de 85,71% e a taxa de detecção da placa de raios X simples foi de 77,14%. A análise do modelo de regressão linear mostrou que o valor P para lesão da cartilagem, fratura do tendão, contusão óssea e edema do tecido mole foi maior que 0,05. Conclusões a ressonância magnética apresenta mais vantagens na aplicação do diagnóstico da articulação do tornozelo. E a lesão ligamentar e derrame articular são os fatores que influenciam o diagnóstico de ressonância magnética, o que pode indicar amplamente a autenticidade da lesão articular do tornozelo. Nível de evidência II; Estudos terapêuticos: investigação dos resultados do tratamento.

RESUMEN Introducción Discutir la aplicación de la resonancia magnética en la evaluación de la lesión por movimiento del tobillo. Objetivo Verificar los factores que influyen en el diagnóstico de imágenes de recursos magnéticos (IRM) basado en el modelo de algoritmo de regresión lineal. Métodos El grupo experimental fue diagnosticado por resonancia magnética, mientras que el grupo control fue diagnosticado por radiografía simple. Después de eso, se construyó el modelo matemático del algoritmo de regresión lineal. Resultados Se pudo concluir que la tasa de detección de resonancia magnética fue del 85,71% y la tasa de detección de la placa simple de rayos X fue del 77,14%. El análisis del modelo de regresión lineal mostró que el valor P de la lesión del cartílago, la fractura del tendón, la contusión ósea y la hinchazón de los tejidos blandos fue superior a 0,05. Conclusiones la RM tiene más ventajas en la aplicación del diagnóstico de la articulación del tobillo. Y la lesión de ligamentos y el derrame articular son los factores que influyen en el diagnóstico de resonancia magnética, que pueden indicar en gran medida la autenticidad de la lesión en la articulación del tobillo. Nivel de evidencia II; Estudios terapéuticos: investigación de los resultados del tratamiento.

A novel supervised learning method to generate CT images for attenuation correction in delayed pet scans.

BACKGROUND AND OBJECTIVES: Attenuation correction is important for PET image reconstruction. In clinical PET/CT scans, the attenuation information is usually obtained by CT. However, additional CT scans for delayed PET imaging may increase the risk of cancer. In this paper, we propose a novel CT generation method for attenuation correction in delayed PET imaging that requires no additional CT scans. METHODS: As only PET raw data is available for the delayed PET scan, routine image registration methods are difficult to use directly. To solve this problem, a reconstruction network is developed to produce pseudo PET images from raw data first. Then a second network is used to generate the CT image through mapping PET/CT images from the first scan to the delayed scan. The inputs of the second network are the two pseudo PET images from the first and delayed scans, and the CT image from the first scan. The labels are taken from the ground truth CT image in the delayed scan. The loss function contains an image similarity term and a regularization term, which reflect the anatomy matching accuracy and the smoothness of the non-rigid deformation field, respectively. RESULTS: We evaluated the proposed method with simulated and clinical PET/CT datasets. Standard Uptake Value was computed and compared with the gold standard (with coregistered CT for attenuation correction). The results show that the proposed supervised learning method can generate PET images with high quality and quantitative accuracy. For the test cases in our study, the average MAE and RMSE of the proposed supervised learning method were 4.61 and 22.75 respectively, and the average PSNR between the reconstructed PET image and the ground truth PET image was 62.13 dB. CONCLUSIONS: The proposed method is able to generate accurate CT images for attenuation correction in delayed PET scans. Experiments indicate that the proposed method outperforms traditional methods with respect to quantitative PET image accuracy.

Non-rigid point cloud registration based lung motion estimation using tangent-plane distance.

Accurate estimation of motion field in respiration-correlated 4DCT images, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported treatment planning. However, the lung motion estimation often suffers from the sliding motion. In this paper, a novel lung motion method based on the non-rigid registration of point clouds is proposed, and the tangent-plane distance is used to represent the distance term, which describes the difference between two point clouds. Local affine transformation model is used to express the non-rigid deformation of the lung motion. The final objective function is expressed in the Frobenius norm formation, and matrix optimization scheme is carried out to find out the optimal transformation parameters that minimize the objective function. A key advantage of our proposed method is that it alleviates the requirement that the source point cloud and the reference point cloud should be in one-to-one corresponding relationship, and the requirement is difficult to be satisfied in practical application. Furthermore, the proposed method takes the sliding motion of the lung into consideration and improves the registration accuracy by reducing the constraint of the motion along the tangent direction. Non-rigid registration experiments are carried out to validate the performance of the proposed method using popi-model data. The results demonstrate that the proposed method outperforms the traditional method with about 20% accuracy increase.

DFT investigation on the metabolic mechanisms of theophylline by cytochrome P450 monooxygenase.

Theophylline, one of the most commonly used bronchodilators and respiratory stimulators for the treatment of acute and chronic asthmatic conditions, can cause permanent neurological damage through chronic or excessive ingestion. In this work, DFT calculation was performed to identify the metabolic mechanisms of theophylline by cytochrome P450 (CYP450) monooxygenase. Two main metabolic pathways were investigated, namely, N1- (path A) and N3- (path B) demethylations, which proceeded through N-methyl hydroxylation followed by the decomposition of the generated carbinolamine species. N-methyl hydroxylation involved a hydrogen atom transfer (HAT) mechanism, which can be generalized as the N-demethylation mechanism of xanthine derivatives. The energy gap between the low-spin double state (LS) and the high-spin quartet state (HS) was low (<1 kcal mol-1), indicating a two-state reactivity (TSR) mechanism. The generated carbinolamine species preferred to decompose through the adjacent heteroatom (O6 for path A and O2 for path B) mediated mechanism. Path B was kinetically more feasible than path A attributed to its relatively lower activation energy. 1-Methylxanthine therefore was the energetically favorable metabolite of theophylline. The observations obtained in the work were in agreement with the experimental observation, which can offer important implications for further pharmacological and clinic studies.