Basic principles of magnetic resonance imaging for beginner oral and maxillofacial radiologists

Compact bunker shielding assessment for 1.5 T MR-Linac

This study evaluated the effect of the 1.5 T magnetic field of the magnetic resonance-guided linear accelerator (MR-Linac) on the radiation leakage doses penetrating the bunker radiation shielding wall. The evaluated 1.5 T MR-Linac Unity system has a bunker of the minimum recommended size. Unlike a conventional Linac, both primary beam transmission and secondary beam leakage were considered independently in the design and defined at the machine boundary away from the isocenter. Moreover, additional shielding was designed considering the numerous ducts between the treatment room and other rooms. The Linac shielding was evaluated by measuring the leakage doses at several locations. The intrinsic vibration and magnetic field were inspected at the proposed isocenter of the system. For verification, leakage doses were measured before and after applying the magnetic field. The intrinsic vibration and magnetic field readings were below the permitted limit. The leakage dose (0.05–12.2 µSv/week) also complied with internationally stipulated limits. The special shielding achieved a five-fold reduction in leakage dose. Applying the magnetic field increased the leakage dose by 0.12 to 4.56 µSv/week in several measurement points, although these values fall within experimental uncertainty. Thus, the effect of the magnetic field on the leakage dose could not be ascertained.

Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery

In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients to locate the anesthesia point of patients during scapular fracture surgery treated with the regional nerve block. 100 patients with scapular fracture who were hospitalized for emergency treatment in the hospital were recruited. Patients in the algorithm group used ultrasound-guided regional nerve block puncture, and patients in the control group used traditional body surface anatomy for anesthesia positioning. The ultrasound images of the scapula of the contrast group were used for the identification of the deep learning model and analysis of anesthesia acupuncture sites. The ultrasound images of the scapula anatomy of the patients in the contrast group were extracted, and the convolutional neural network model was employed for training and test. Moreover, the model performance was evaluated. It was found that the adoption of deep learning greatly improved the accuracy of the image. It took an average of 7.5 ± 2.07 minutes from the time the puncture needle touched the skin to the completion of the injection in the algorithm group (treated with artificial intelligence ultrasound positioning). The operation time of the control group (anatomical positioning) averaged 10.2 ± 2.62 min. Moreover, there was a significant difference between the two groups (p < 0.05). The method adopted in the contrast group had high positioning accuracy and good anesthesia effect, and the patients had reduced postoperative complications of patients (all P < 0.005). The deep learning model can effectively improve the accuracy of ultrasound images and measure and assist the treatment of future clinical cases of scapular fractures. While improving medical efficiency, it can also accurately identify patient fractures, which has great adoption potential in improving the effect of surgical anesthesia.

Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning

To analyze the brain CT imaging data of children with cerebral palsy (CP), deep learning-based electronic computed tomography (CT) imaging information characteristics were used, thereby providing help for the rehabilitation analysis of children with CP and comorbid epilepsy. The brain CT imaging data of 73 children with CP were collected, who were outpatients or inpatients in our hospital. The images were randomly divided into two groups. One group was the artificial intelligence image group, and hybrid segmentation network (HSN) model was employed to analyze brain images to help the treatment. The other group was the control group, and original images were used to help diagnosis and treatment. The deep learning-based HSN was used to segment the CT image of the head of patients and was compared with other CNN methods. It was found that HSN had the highest Dice score (DSC) among all models. After treatment, six cases in the artificial intelligence image group returned to normal (20.7%), and the artificial intelligence image group was significantly higher than the control group (X² = 335191, P < 0.001). The cerebral hemodynamic changes were obviously different in the two groups of children before and after treatment. The VP of the cerebral artery in the child was (139.68 ± 15.66) cm/s after treatment, which was significantly faster than (131.84 ± 15.93) cm/s before treatment, P < 0.05. To sum up, the deep learning model can effectively segment the CP area, which can measure and assist the diagnosis of future clinical cases of children with CP. It can also improve medical efficiency and accurately identify the patient's focus area, which had great application potential in helping to identify the rehabilitation training results of children with CP.