Clinical Research of Orthopedic Trauma Based on Computer 3D Image Analysis

In order to improve the clinical research effect of orthopedic trauma, this paper applies computer 3D image analysis technology to the clinical research of orthopedic trauma and proposes the BOS technology based on FFT phase extraction. The background image in this technique is a “cosine blob” background image. Moreover, this technology uses the FFT phase extraction method to process this background image to extract the image point displacement. The BOS technology based on FFT phase extraction does not need to select a diagnostic window. Finally, this paper combines computer 3D image analysis technology to build an intelligent system. According to the experimental research results, the clinical analysis system of orthopedic trauma based on computer 3D image analysis proposed in this paper can play an important role in the clinical diagnosis and treatment of orthopedic trauma and improve the diagnosis and treatment effect of orthopedic trauma.

The Method of Pose Judgment of Potential Motion Damage in 3D Image Analysis

Objective. This work aimed to study the posture judgment method of 3D image analysis of potential motion damage. Methods. The motion damage collection was implemented by the 3D image analysis method, and 3D image data were adopted to identify the motion damage data. Moreover, 3D image acquisition technology was adopted to analyze the model of potential motion damage and analyze the simulation judgment result of potential motion damage. Specifically, it included simulation parameters, motion damage posture collection effect, damage detection speed at the collection point, damage accuracy, and damage degree. Results. (1) The analysis of the damage monitoring speed at multiple collection points of the athletes in the sports environment confirmed that the range of changes in different time periods was different, and the changes showed a fast to slow to fast trend. (2) The 3D image analysis had high accuracy in analyzing the posture of potential motion damage, which rationalized the evolution of injuries. (3) The degree of motion damage under a 3D image changed from rising to gradual, which was in line with the theoretical results (all p < 0.05). Conclusion. 3D image analysis can collect a high degree of small-sample-size data, then perform specific analysis, judgment, and summary, and finally, obtain objective and reasonable data. It greatly reduced the risk of potential motion damage for athletes and also improved the efficiency of injury recognition. Moreover, it reduced the chances of blind prevention and error prevention by athletes, thereby avoiding waste of resources. The simulation test confirmed the advantages of 3D image data collection in the sports environment, and it was solved that the current athletes cannot accurately and timely judge the potential motion damage. It also met the instability needs of the movement personnel of the acquisition system in the changing sports environment and provided a reliable guarantee for the safety and health of the sport personnel.

The efficacy of transbronchial indocyanine green instillation for fluorescent-guided wedge resection

Central image: Virtual wedge resection of right S2aiiα+S2aiiβ and the same intraoperative fluorescent image. OBJECTIVES The purpose of this study was to investigate the feasibility of lung wedge resection by combining 3-dimensional (3D) image analysis with transbronchial indocyanine green (ICG) instillation, in order to delineate the intended area for resection. METHODS From December 2017 to July 2020, 28 patients undergoing wedge resection (17 primary lung cancers, 11 metastatic lung tumours) were enrolled, and fluorescence-guided wedge resection was attempted. Virtual sublobar resections were created preoperatively for each patient using a 3D Image Analyzer. Surgical margins were measured in each sublobar resection simulation in order to select the most optimal surgical resection area. After transbronchial instillation of ICG, near-infrared thoracoscopic visualization allowed matching of the intended area for resection to the virtual sublobar resection area. To investigate the effectiveness of ICG instillation, the clarity of the ICG-florescent border was evaluated, and the distance from the true tumour to the surgical margins was compared to that of simulation. RESULTS Mean tumour diameter was 12.4 ± 4.3 mm. The entire targeted tumour was included in resected specimens of all patients (100% success rate). The shortest distances to the surgical margin via 3D simulation and by actual measurement of the specimen were11.4 ± 5.4 and 12.2 ± 4.1 mm, respectively (P = 0.285) and were well correlated (R2 = 0.437). While all specimens had negative malignant cells at the surgical margins, one loco-regional recurrence was observed secondary to the dissemination of neuroendocrine carcinoma. CONCLUSIONS ICG-guided lung wedge resection after transbronchial ICG instillation and preoperative 3D image analysis allow for adequate negative surgical margins, providing decreased risk of local recurrence.

Does intra-articular injection of adipose-derived stem cells improve cartilage mass? A case report on a 3D MRI quantitative evaluation of cartilage in knee osteoarthritis using SYNAPSE VINCENT: a case report

Background: Mesenchymal stem cells (MSC) are currently in focus because of the possibility of cartilage regeneration through several ways, including MSC sheets. However, there is no published report that visualizes cartilage in three dimensions. Here, we report a case of improved cartilage volume. We purified and cultured adipose-derived mesenchymal stem cells (ASC) and then performed ASC therapy by directly injecting these cells into the articular cartilage. Cartilage was quantitatively evaluated before and after injection using a three-dimensional (3D) image analysis software based on the MRI imagery.Case presentation: The patient, a 55-year-old woman, experienced pain in both knees and was diagnosed with osteoarthritis of the knee. We performed ASC therapy in both knees at our hospital and quantitatively evaluated cartilage before and after the treatment using the 3D image analysis software “SYNAPSE VINCENT”.Conclusions: For the quantitative analysis of cartilage, SYNAPSE VINCENT visualizes the state of cartilage in a high-definition 3D image, which is excellent for understanding the state of the disease and explaining it to the patient. Though there is room for debate about the reproducibility of errors, etc., SYNAPSE VINCENT would be useful as a clinical tool for regenerative medicine.