The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment.

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

The LMIT: Light-mediated minimally-invasive theranostics in oncology.

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform.

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods.

Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review.

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Vascular elasticity measurement of the great saphenous vein based on optical coherence elastography.

Vascular elasticity is important in physiological and clinical problems. The mechanical properties of the great saphenous vein (GSV) deserve attention. This research aims to measure the radial elasticity of ex vivo GSV using the optical coherence elasticity (OCE). The finite element model of the phantom is established, the displacement field is calculated, the radial mechanical characteristics of the simulation body are obtained. Furthermore, we performed OCE on seven isolated GSVs. The strain field is obtained by combining the relationship between strain and displacement to obtain the radial elastic modulus of GSVs. In the phantom experiment, the strain of the experimental region of interest is mainly between 0.1 and 0.4, while the simulation result is between 0.06 and 0.40. The radial elastic modulus of GSVs ranged from 3.83 kPa to 7.74 kPa. This study verifies the feasibility of the OCE method for measuring the radial elastic modulus of blood vessels.

Wavelet Analysis of Cerebral Oxygenation Signal Measured by Near-Infrared Spectroscopy in Moyamoya Disease.

BACKGROUND: Spontaneous low-frequency oscillations (LFOs) have been widely studied in cerebrovascular disease, but little is known about their role in moyamoya disease (MMD). The objective of this study was to assess the value of spontaneous LFOs in MMD based on wavelet analysis of near-infrared spectroscopy signals. METHODS: Sixty-four consecutive idiopathic adult patients were prospectively enrolled. The regional tissue oxygenation index (TOI) obtained from continuous near-infrared spectroscopy signals. Five frequency intervals of spontaneous LFOs (I, 0.0095-0.02 Hz; II, 0.02-0.06 Hz; III, 0.06-0.15 Hz; IV, 0.15-0.40 Hz; and V, 0.40-2.00 Hz) were extracted based on wavelet analysis. The data were compared between the patients and healthy control groups. Clinical features, cognitive function, and disease progression of MMD were analyzed using TOI and frequency interval data. RESULTS: Compared with the healthy control group, patients with MMD had a higher cerebral TOI in both hemispheres. Based on wavelet analysis, the spontaneous LFO of TOI was found to be significantly lower for patients with MMD in frequency intervals II to IV than that for the controls. The spontaneous LFO of TOI is also related to the Suzuki stages in intervals II to IV, stroke in interval III, and cognitive impairment in intervals III to Ⅳ. CONCLUSIONS: There were significant differences in spontaneous LFO between patients with MMD and healthy controls. The change in spontaneous LFO in MMD is related to Suzuki stage, cerebral infarction, and cognitive impairment. This might be an effective method for evaluating the severity and monitoring the progression of MMD.

Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.

Optical coherence tomography (OCT) has considerable application potential in noninvasive diagnosis and disease monitoring. Skin diseases, such as basal cell carcinoma (BCC), are destructive; hence, quantitative segmentation of the skin is very important for early diagnosis and treatment. Deep neural networks have been widely used in the boundary recognition and segmentation of diseased areas in medical images. Research on OCT skin segmentation and laser-induced skin damage segmentation based on deep neural networks is still in its infancy. Here, a segmentation and quantitative analysis pipeline of laser skin injury and skin stratification based on a deep neural network model is proposed. Based on the stratification of mouse skins, a laser injury model of mouse skins induced by lasers was constructed, and the multilayer structure and injury areas were accurately segmented by using a deep neural network method. First, the intact area of mouse skin and the damaged areas of different laser radiation doses are collected by the OCT system, and then the labels are manually labeled by experienced histologists. A variety of deep neural network models are used to realize the segmentation of skin layers and damaged areas on the skin dataset. In particular, the U-Net model based on a dual attention mechanism is used to realize the segmentation of the laser-damage structure, and the results are compared and analyzed. The segmentation results showed that the Dice coefficient of the mouse dermis layer and injury area reached more than 0.90, and the Dice coefficient of the fat layer and muscle layer reached more than 0.80. In the evaluation results, the average surface distance (ASSD) and Hausdorff distance (HD) indicated that the segmentation results are excellent, with a high overlap rate with the manually labeled area and a short edge distance. The results of this study have important application value for the quantitative analysis of laser-induced skin injury and the exploration of laser biological effects and have potential application value for the early noninvasive detection of diseases and the monitoring of postoperative recovery in the future.

Reporter Genes for Brain Imaging Using MRI, SPECT and PET.

The use of molecular imaging technologies for brain imaging can not only play an important supporting role in disease diagnosis and treatment but can also be used to deeply study brain functions. Recently, with the support of reporter gene technology, optical imaging has achieved a breakthrough in brain function studies at the molecular level. Reporter gene technology based on traditional clinical imaging modalities is also expanding. By benefiting from the deeper imaging depths and wider imaging ranges now possible, these methods have led to breakthroughs in preclinical and clinical research. This article focuses on the applications of magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) reporter gene technologies for use in brain imaging. The tracking of cell therapies and gene therapies is the most successful and widely used application of these techniques. Meanwhile, breakthroughs have been achieved in the research and development of reporter genes and their imaging probe pairs with respect to brain function research. This paper introduces the imaging principles and classifications of the reporter gene technologies of these imaging modalities, lists the relevant brain imaging applications, reviews their characteristics, and discusses the opportunities and challenges faced by clinical imaging modalities based on reporter gene technology. The conclusion is provided in the last section.

Molecular Contrast Optical Coherence Tomography and Its Applications in Medicine.

The growing need to understand the molecular mechanisms of diseases has prompted the revolution in molecular imaging techniques along with nanomedicine development. Conventional optical coherence tomography (OCT) is a low-cost in vivo imaging modality that provides unique high spatial and temporal resolution anatomic images but little molecular information. However, given the widespread adoption of OCT in research and clinical practice, its robust molecular imaging extensions are strongly desired to combine with anatomical images. A range of relevant approaches has been reported already. In this article, we review the recent advances of molecular contrast OCT imaging techniques, the corresponding contrast agents, especially the nanoparticle-based ones, and their applications. We also summarize the properties, design criteria, merit, and demerit of those contrast agents. In the end, the prospects and challenges for further research and development in this field are outlined.

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods.

Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated hemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. For 20-min signals, the 10-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-min signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. The method proposed in this article has potential for detecting and screening moyamoya with high proficiency. Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases.

Current research and future prospects of IVOCT imaging-based detection of the vascular lumen and vulnerable plaque.

Intravascular optical coherence tomography (IVOCT) is an imaging method that has developed rapidly in recent years and is useful in coronary atherosclerosis diagnosis. It is widely used in the assessment of vulnerable plaque. This review summarizes the main research methods used in recent years for blood vessel lumen boundary detection and segmentation and vulnerable plaque segmentation and classification. This article aims to comprehensively and systematically introduce the research progress on internal tissues of blood vessels based on IVOCT images. The characteristics and advantages of various methods have been summarized to provide theoretical ideas and methods for the reference of relevant researchers and scholars.

A Brief History and Future Prospects of CEST MRI in Clinical Non-Brain Tumor Imaging.

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging tool which allows the specific detection of metabolites that contain exchangeable amide, amine, and hydroxyl protons. Decades of development have progressed CEST imaging from an initial concept to a clinical imaging tool that is used to assess tumor metabolism. The first translation efforts involved brain imaging, but this has now progressed to imaging other body tissues. In this review, we summarize studies using CEST MRI to image a range of tumor types, including breast cancer, pelvic tumors, digestive tumors, and lung cancer. Approximately two thirds of the published studies involved breast or pelvic tumors which are sites that are less affected by body motion. Most studies conclude that CEST shows good potential for the differentiation of malignant from benign lesions with a number of reports now extending to compare different histological classifications along with the effects of anti-cancer treatments. Despite CEST being a unique 'label-free' approach with a higher sensitivity than MR spectroscopy, there are still some obstacles for implementing its clinical use. Future research is now focused on overcoming these challenges. Vigorous ongoing development and further clinical trials are expected to see CEST technology become more widely implemented as a mainstream imaging technology.

Application of Carbon Ion and Its Sensitizing Agent in Cancer Therapy: A Systematic Review.

Carbon ion radiation therapy (CIRT) is the most advanced radiation therapy (RT) available and offers new opportunities to improve cancer treatment and research. CIRT has a unique physical and biological advantage that allow them to kill tumor cells more accurately and intensively. So far, CIRT has been used in almost all types of malignant tumors, and showed good feasibility, safety and acceptable toxicity, indicating that CIRT has a wide range of development and application prospects. In addition, in order to improve the biological effect of CIRT, scientists are also trying to investigate related sensitizing agents to enhance the killing ability of tumor cells, which has attracted extensive attention. In this review, we tried to systematically review the rationale, advantages and problems, the clinical applications and the sensitizing agents of the CIRT. At the same time, the prospects of the CIRT in were prospected. We hope that this review will help researchers interested in CIRT, sensitizing agents, and radiotherapy to understand their magic more systematically and faster, and provide data reference and support for bioanalysis, clinical medicine, radiotherapy, heavy ion therapy, and nanoparticle diagnostics.

Retrograde branched extension limb assembling stent of pararenal abdominal aortic aneurysm: A longitudinal hemodynamic analysis for stent graft migration.

PURPOSE: Pararenal abdominal aortic aneurysms (PRAAAs) are a life-threatening disease, and hemodynamic analysis may provide greater insight into the effectiveness and long-term outcomes of endovascular aneurysm repair (EVAR). However, the lack of patient-specific boundary conditions on the periphery compromises the accuracy. Windkessel (WK) boundary conditions coupled to hemodynamic follow-up models of a PRAAA patient, aims to provide insights into the link between hemodynamics and poor prognosis. METHOD: One PRAAA patient underwent EVAR and reintervention after one branch of stent-graft (SG) had migrated. Totally five computational follow-up models were studied. Patient-specific flow data acquired via ultrasound were used to define the boundary conditions in the ascending aorta and the following three branches. Coupled zero-dimensional WK models representing the distal vasculature were used to define the outlet boundary conditions under the abdomen. RESULTS: Flow divisions of the main SG branches were 40.7% and 24.7%, respectively. Time-averaged wall shear stress and oscillatory shear index (OSI) increased at the junction connected the SG branch and the stent leading to the right common iliac artery (RCIA) where the stent migrated. The OSI and relative residence time (RRT) value in superior mesenteric artery increased notably after the migration, the RRT continuously increased following the reintervention. CONCLUSION: Unbalanced flow, resulting in locally high-speed flow, high WSS and OSI might significantly affect stent stability. Results suggest that diameters and interconnection design of stents in complex cases should take the flow division into consideration and computational simulations might be considered as a tool for intervention protocol design.

The Simulation of the Recharging Method Based on Solar Radiation for an Implantable Biosensor.

A method of recharging implantable biosensors based on solar radiation is proposed. Firstly, the models of the proposed method are developed. Secondly, the recharging processes based on solar radiation are simulated using Monte Carlo (MC) method and the energy distributions of sunlight within the different layers of human skin have been achieved and discussed. Finally, the simulation results are verified experimentally, which indicates that the proposed method will contribute to achieve a low-cost, convenient and safe method for recharging implantable biosensors.

Simulation of the recharging method of implantable biosensors based on a wearable incoherent light source.

Recharging implantable electronics from the outside of the human body is very important for applications such as implantable biosensors and other implantable electronics. In this paper, a recharging method for implantable biosensors based on a wearable incoherent light source has been proposed and simulated. Firstly, we develop a model of the incoherent light source and a multi-layer model of skin tissue. Secondly, the recharging processes of the proposed method have been simulated and tested experimentally, whereby some important conclusions have been reached. Our results indicate that the proposed method will offer a convenient, safe and low-cost recharging method for implantable biosensors, which should promote the application of implantable electronics.

Multi-subject brain decoding with multi-task feature selection.

In the neural science society, multi-subject brain decoding is of great interest. However, due to the variability of activation patterns across brains, it is difficult to build an effective decoder using fMRI samples pooled from different subjects. In this paper, a hierarchical model is proposed to extract robust features for decoding. With feature selection for each subject treated as a separate task, a novel multi-task feature selection method is introduced. This method utilizes both complementary information among subjects and local correlation between brain areas within a subject. Finally, using fMRI samples pooled from all subjects, a linear support vector machine (SVM) classifier is trained to predict 2-D stimuli-related images or 3-D stimuli-related images. The experimental results demonstrated the effectiveness of the proposed method.

Wavelet analysis of cerebral oxygenation oscillations in the screening of Moyamoya disease.

Near-infrared spectroscopy (NIRS) was used to investigate the cerebral oxygenation of Moyamoya and healthy subjects. Continuous recordings of NIRS signals for 20 min (20 min signals) were obtained from 17 healthy subjects (age: 37.4 ± 11.3) and 17 Moyamoya subjects (age: 40.1 ± 11.2). Spectral analysis based on wavelet transformation identified five frequency intervals (I, 0.0095 Hz to 0.02 Hz; II, 0.02 Hz to 0.06 Hz; III, 0.06 Hz to 0.15 Hz; IV, 0.15 Hz to 0.40 Hz; and V, 0.40 Hz to 2.00 Hz) in the 20 min signals and three frequency intervals (III, 0.06 Hz to 0.15 Hz; IV, 0.15 Hz to 0.40 Hz; and V, 0.40 Hz to 2.00 Hz) in the 3 min signals (the first 3 min signals were continuously extracted from the 20 min signals). Significant differences (p < 0.05) were found in frequency intervals III and V. The former exhibited the myogenic activity of smooth muscle inside the blood vessels in both 20 min and 3 min signal analyses; the latter showed hemodynamic oscillation caused by heart pumping. This finding agrees with the vascular pathological changes in Moyamoya disease. As a potential screening method for Moyamoya disease, the simple threshold method exhibited 73.5% accuracy.

Generic pixel-wise speckle detection in Fourier-domain optical coherence tomography images.

We present a generic phase-domain processing method for detecting speckles in Fourier-domain optical coherence tomography (OCT) images. The physics behind the interferometry is revisited and analytically along with simulation results it indicates that the speckle formation comes with phase distortion to the complex OCT signal. The first and the second derivatives of phase along the imaging depth are then calculated for speckle identification. The phase-domain processing method was applied to images acquired by both spectral-domain OCT and swept-source OCT systems, and the experimental results show that this method enables pixel-wise speckle identification.