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Abstract Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non‐invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning‐based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics‐informed and self‐supervised Deep learning entitled “DIMOND” is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self‐supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.

During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.

Summary: Background: Effective monitoring and management are crucial during long-term home noninvasive positive pressure ventilation (NPPV) in patients with hypercapnic chronic obstructive pulmonary disease (COPD). This study investigated the benefit of Internet of Things (IOT)-based management of home NPPV. Methods: This multicenter, prospective, parallel-group, randomized controlled non-inferiority trial enrolled patients requiring long-term home NPPV for hypercapnic COPD. Patients were randomly assigned (1:1), via a computer-generated randomization sequence, to standard home management or IOT management based on telemonitoring of clinical and ventilator parameters over 12 months. The intervention was unblinded, but outcome assessment was blinded to management assignment. The primary outcome was the between-group comparison of the change in health-related quality of life, based on severe respiratory insufficiency questionnaire scores with a non-inferiority margin of −5. This study is registered with Chinese Clinical Trials Registry (No. ChiCTR1800019536). Findings: Overall, 148 patients (age: 72.7 ± 6.8 years; male: 85.8%; forced expiratory volume in 1 s: 0.7 ± 0.3 L; PaCO2: 66.4 ± 12.0 mmHg), recruited from 11 Chinese hospitals between January 24, 2019, and June 28, 2021, were randomly allocated to the intervention group (n = 73) or the control group (n = 75). At 12 months, the mean severe respiratory insufficiency questionnaire score was 56.5 in the intervention group and 50.0 in the control group (adjusted between-group difference: 6.26 [95% CI, 3.71–8.80]; P

Fixed-pattern noise seriously affects the clinical application of optical coherence tomography (OCT), especially, in the imaging of tumorous tissue. We propose a Hough transform-based fixed-pattern noise reduction (HTFPNR) method to reduce the fixed-pattern noise for optimizing imaging of tumorous tissue with OCT system. Using by the HTFPNR method, we detect and map the outline of fixed-pattern noise in the OCT images, and finally efficiently reduce the fixed-pattern noise by the longitudinal and horizontal intelligent processing procedure. We adopt the image-to-noise ratio with full information (INRfi) and the noise reduction ratio (NRR) to evaluate the outcome of fixed-pattern noise reduction ratio, respectively. The INRfi of OCT image's noise reduction of ex vivo brainstem tumor is approximate 21.92 dB. Six groups of OCT images, including three types of fixed-pattern noises, have been validated via experimental evaluation of the ex vivo gastric tumor. In the different types of fixed-pattern noise, the mean INRfis are 25.24, 23.04, and 19.35 dB, respectively. This result demonstrates that it is highly efficient and useful in fixed-pattern noise reduction. The fluctuating range of the NRR is 0.84-0.88 for three types of added noise in the OCT images. This result demonstrates that the HTFPNR method can as possible as save useful information by comparing to previous research. This proposed HTFPNR method can be used into the fixed-pattern noise reduction of OCT images in other soft biological tissue in the future.

High-intensity focused ultrasound (HIFU) therapy represents an image-guided and non-invasive surgical approach to treat uterine fibroid. During the HIFU operation, it is challenging to obtain the real-time and accurate lesion contour automatically in ultrasound (US) video. The current intraoperative image processing is completed manually or semi-automatic. In this Letter, the authors propose a morphological active contour without an edge-based model to obtain accurate real-time and non-rigid US lesion contour. Firstly, a targeted image pre-processing procedure is applied to reduce the influence of inadequate image quality. Then, an improved morphological contour detection method with a customised morphological kernel is harnessed to solve the low signal-to-noise ratio of HIFU US images and obtain an accurate non-rigid lesion contour. A more reasonable lesion tracking procedure is proposed to improve tracking accuracy especially in the case of large displacement and incomplete lesion area. The entire framework is accelerated by the GPU to achieve a high frame rate. Finally, a non-rigid, real-time and accurate lesion contouring for intraoperative US video is provided to the doctor. The proposed procedure could reach a speed of more than 30 frames per second in general computer and a Dice similarity coefficient of 90.67% and Intersection over Union of 90.14%.

The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder–decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives.

Trunk anatomy education is critical in the training of the surgeon. Most trunk anatomy education systems use a personal or synthetic anatomical model. It remains difficult to obtain appropriate population anatomy information and create individualized anatomy customization based on a quantity of diverse data. Furthermore, the naked-eye virtual reality modality provides advantages in immersive learning and meta-information transmission. In this study, we present a novel VR torso anatomy teaching environment with naked-eye three-dimensional visualization and free-hand interaction. This article allows us for comprehensive modeling, visualization, and interaction of torso anatomy for human anatomical variation education. In particular, we derive the genuine shape of Chinese male and female torso organs based on the statistical shape model generated from 129 healthy Chinese individuals. The following visual information is given with interaction: 1) Anatomy for human torso organs from CT imaging: geometric modeling, visualization, and interaction of male and female torso organs, including skin, muscle, torso skeleton, heart, lungs, liver, spleen, and kidneys; 2) morphological differences of trunk organs from meta-information: motion deformation modeling, visualization, and interaction of male and female deformable torso model; 3) instant personalized customization: real-time interaction and visualization for personalized customization with shape mode coefficients and soft body simulation.

Objectives. To evaluate the abdominal visceral fat area (VFA), we developed novel ultrasonographic (US) methods for estimating. Methods. 100 male volunteers were recruited, and their VFA was calculated by two novel US methods, the triangle method and the ellipse method. The VFA calculated by these methods was compared with the VFA calculated by CT. Results. Both the VFA calculated by the triangle method (r=0.766, p

To provide shock-wave exposure with high accuracy and widen the application area of shock-wave therapy, a miniaturized shock-wave device for endoscopic surgery was developed. The shock-wave device applies an electrohydraulic mechanism for generating shock waves in light of device miniaturization and suitability for standard clinical practice. The outer diameter of the shock-wave generator is 11 mm, so it can be inserted in the body through a trocar used in endoscopic surgery or a natural orifice in the case of natural-orifice translumenal endoscopic surgery (NOTES). The distance between the shock-wave focal point and the front of the device is 10 mm. The focused shock wave was observed at the focal point by visualization with the schlieren imaging technique. The pressure at the focal point was measured, and the measurement revealed that the device can produce a peak pressure of 2.32±0.81 MPa at 2-kV discharge voltage, 3.69±1.06 MPa at 3 kV, 5.67±2.44 MPa at 4 kV, and 7.27±2.33 MPa at 5 kV.

An ultrasound (US) navigation system using global and local information is presented for transcatheter aortic stent deployment. The system avoids the use of contrast agents and radiation required in traditional fluoroscopically-guided procedures and helps surgeons precisely visualize the surgical site. To obtain a global 3D (three-dimensional) navigation map, we use magnetic resonance (MR) to provide a 3D context to enhance 2D (two-dimensional) US images through image registration. The US images are further processed to obtain the trajectory of interventional catheter. A high-resolution aortic model is constructed by using trajectory and segmented intravascular ultrasound (IVUS) images. The constructed model reflects morphological characteristics of the aorta to provide local navigation information. Our navigation system was validated using in vitro phantom of heart and aorta. The mean target registration error is 2.70 mm and the average tracking error of the multi-feature particle filter is 0.87 mm. These results confirm that key parts of our navigation system are effective. In the catheter intervention experiment, the vessel reconstruction error of local navigation is reduced by 80% compared to global navigation. Moreover, the targeting error of the navigation combining global and local information is reduced compared to global navigation alone (1.72 mm versus 2.87 mm). Thus, the US navigation system which integrates the large view of global navigation and high accuracy of local navigation can facilitate transcatheter stent deployment.