Taichi Kin

Background: Facial information acquired via three-dimensional reconstruction of head computed tomography (CT) data may be considered personal information, which can be problematic for neuroimaging studies. However, no study has verified the relationship between slice thickness and face reproducibility. This study determined the relationship and match rate between image slice thickness and face detection accuracy of face-recognition software in facial reconstructed models. Methods: Head CT data of 60 cases comprising entire faces obtained under conditions of non-contrast and 1-mm slice thickness were resampled to obtain 2-10-mm slice-thickness data. Facial models, reconstructed by image thresholding, were acquired from the data. We performed face detection tests per slice thickness on the models and calculated the face detection rate. The reconstructed facial models created from 1-mm slice-thickness data and other slice thicknesses were used as training and test data, respectively. Match confidence scores were obtained via three programs, match rates were calculated per slice thickness, and generalized estimating equations were used to evaluate the match rate trend. Results: In general, the face detection rates for the 1-10-mm slice thicknesses were 100, 100, 98.3, 98.3, 95.0, 91.7, 86.7, 78.3, 68.3, and 61.7 %, respectively. The match rates for the 2-10-mm slice thicknesses were 100, 98.3, 98.3, 95.0, 85.0, 71.7, 53.3, 28.3, and 16.7 %, respectively. Conclusions: The reconstructed models tended to have higher match rates as the slice thickness decreased. Thus, thin-slice head CT imaging data may increase the possibility of the information becoming personally identifiable health information.

Head CT, which includes the facial region, can visualize faces using 3D reconstruction, raising concern that individuals may be identified. We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as "original images" and the others as "reference images." Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were < 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.

Microscopic suturing in neurosurgery is a challenging medical technique that requires time to master. Since skilled surgeons are too busy to spend much time with novice surgeons, novice surgeons need to train alone in monotonous tasks to acquire skills. To address this problem, this study proposes a system that incorporates gamification elements, such as scoring and displaying real-time feedback, to improve motivation for training. This system detects the technical factors necessary for microscopic suturing from video capture, and calculates a score using these factors. According to neurosurgeons, suturing has three important factors: speed, accuracy, and carefulness. These technical factors are detected by tracking instruments and gauze using machine learning and image processing. The experiment was conducted with ten novices using this system. The results of this experiment showed that the system is easy to use and contributed to increased motivation, according to the User Experience Questionnaire (UEQ) and System Usability Scale (SUS).

Head CT, which includes the facial region, can visualize faces using 3D reconstruction, raising concern that individuals may be identified. We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as "original images" and the others as "reference images." Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were < 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.

Rebleeding from a ruptured intracranial aneurysm has poor outcomes. Although numerous factors are associated with rebleeding, studies on computational fluid dynamics (CFD) on hemodynamic parameters associated with early rebleeding are scarce. In particular, no report of rebleeding in ultra-early phase exists. We aimed to elucidate the specific hemodynamic parameters associated with ultra-early rebleeding using CFD. In this study, the rebleeding group included patients with aneurysmal subarachnoid hemorrhage (aSAH) that rebled within 6 h from the onset. The control group included patients without rebleeding, observed for >10 h following the initial rupture. Clinical images after initial rupture and before rebleeding were used to build 3D vessel models for hemodynamic analysis focusing on the following parameters: time-averaged wall shear stress (WSS), normalized WSS, low shear area, oscillatory shear index, relative residence time, pressure loss coefficient, and aneurysmal inflow rate coefficient (AIRC). Five and 15 patients in the rebleeding and control groups, respectively, met the inclusion criteria. The World Federation of Neurosurgical Surgeons grade was significantly higher in the rebleeding group (p = 0.0088). Hemodynamic analysis showed significantly higher AIRC in the rebleeding group (p = 0.042). The other parameters were not significantly different between groups. There were no significant differences or correlations between SAH severity and AIRC. AIRC was identified as a hemodynamic parameter associated with ultra-early rebleeding of ruptured intracranial aneurysms. Thus, AIRC calculation may enable the prediction of ultra-early rebleeding.

BACKGROUND: Present neurosurgical simulators are not portable. OBJECTIVE: To maximize portability of a virtual surgical simulator by providing online learning and to validate a unique psychometric method (“audiovisual capture”) to provide tactile information without force feedback probes. METHODS: An online interactive neurosurgical simulator of a posterior petrosectomy was developed. The difference in the hardness of compact vs cancellous bone was presented with audiovisual effects as inclinations of the drilling speed and sound based on engineering perspectives. Three training methods (the developed simulator, lectures and review of slides, and dissection of a 3-dimensional printed temporal bone model [D3DPM]) were evaluated by 10 neurosurgical residents. They all first attended a lecture and were randomly allocated to 2 groups by the training D3DPM (A: simulator; B: review of slides, no simulator). In D3DPM, objective measures (required time, quality of completion, injury scores of important structures, and the number of instructions provided) were compared between groups. Finally, the residents answered questionnaires. RESULTS: The objective measures were not significantly different between groups despite a younger tendency in group A (graduate year −2.4 years, 95% confidence interval −5.3 to 0.5, P = .081). The mean perceived hardness of cancellous bone on the simulator was 70% of that of compact bone, matching the intended profile. The simulator was superior to lectures and review of slides in feedback and repeated practices and to D3DPM in adaptability to multiple learning environments. CONCLUSION: A novel online interactive neurosurgical simulator was developed, and satisfactory validity was shown. Audiovisual capture successfully transmitted the tactile information.

BACKGROUND AND PURPOSE: The hemodynamics associated with cerebral AVMs have a significant impact on their clinical presentation. This study aimed to evaluate the hemodynamic features of AVMs using 3D phase-contrast MR imaging with dual velocity-encodings. MATERIALS AND METHODS: Thirty-two patients with supratentorial AVMs who had not received any previous treatment and had undergone 3D phase-contrast MR imaging were included in this study. The nidus diameter and volume were measured for classification of AVMs (small, medium, or large). Flow parameters measured included apparent AVM inflow, AVM inflow index, apparent AVM outflow, AVM outflow index, and the apparent AVM inflow-to-outflow ratio. Correlation coefficients between the nidus volume and each flow were calculated. The flow parameters between small and other AVMs as well as between nonhemorrhagic and hemorrhagic AVMs were compared. RESULTS: Patients were divided into hemorrhagic (n = 8) and nonhemorrhagic (n = 24) groups. The correlation coefficient between the nidus volume and the apparent AVM inflow and outflow was .83. The apparent AVM inflow and outflow in small AVMs were significantly smaller than in medium AVMs (P < .001 for both groups). The apparent AVM inflow-to-outflow ratio was significantly larger in the hemorrhagic AVMs than in the nonhemorrhagic AVMs (P = .02). CONCLUSIONS: The apparent AVM inflow-to-outflow ratio was the only significant parameter that differed between nonhemorrhagic and hemorrhagic AVMs, suggesting that a poor drainage system may increase AVM pressure, potentially causing cerebral hemorrhage.