A new method of intracranial aneurysm modeling for stereolithography apparatus 3D printer: the "Wall-carving technique" using digital imaging and communications in medicine data

Patient-specific cerebral 3D vessel model reconstruction using deep learning

Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (MRA) images often requires some manual maneuvers. This study aimed to establish the deep learning (DL)-based method for vessel model reconstruction. Time of flight MRA of 40 patients with internal carotid artery aneurysms was prepared, and three-dimensional vessel models were constructed using the threshold and region-growing method. Using those datasets, supervised deep learning using 2D U-net was performed to reconstruct 3D vessel models. The accuracy of the DL-based vessel segmentations was assessed using 20 MRA images outside the training dataset. The dice coefficient was used as the indicator of the model accuracy, and the blood flow simulation was performed using the DL-based vessel model. The created DL model could successfully reconstruct a three-dimensional model in all 60 cases. The dice coefficient in the test dataset was 0.859. Of note, the DL-generated model proved its efficacy even for large aneurysms (> 10 mm in their diameter). The reconstructed model was feasible in performing blood flow simulation to assist clinical decision-making. Our DL-based method could successfully reconstruct a three-dimensional vessel model with moderate accuracy. Future studies are warranted to exhibit that DL-based technology can promote medical image processing.

Three-Dimensional (3D) Microcatheter Shaping Using Touch Screen Devices for Cerebral Aneurysm Coil Embolization

OBJECTIVE

In cerebral aneurysm coil embolization, proper microcatheter shaping is crucial to reduce complications and achieve sufficient embolization. Shaping a microcatheter in 3 dimensions (3D) is often required but can be challenging. We assessed the usefulness of a novel shaping on screen (SOS) method that displays real-size 3D rotational angiography (RA) images on a touch screen device during cerebral aneurysm embolization to facilitate 3D microcatheter shaping.

METHODS

In this study, 18 patients with cerebral aneurysm treated with this technique were included. Real-size 3D-RA images obtained during the embolization procedure were displayed on the touch screen device, which allowed for real-time manipulation. The shape of the microcatheter was adjusted to conform to the curvature of the vessel by swiping the touch screen device and bending the mandrel accordingly. We assessed the clinical and angiographic results, along with the accuracy and stability of the microcatheter.

RESULTS

No procedure-related complications were observed. The mean packing density was 41% ± 12%. In all but 1 case, microcatheters were inserted into the aneurysms without guidewire assistance. After coiling, all microcatheter forms were stable.

CONCLUSIONS

Three-dimensional (3D) microcatheter shaping using touch screen devices during cerebral aneurysm coil embolization may be simple and safe and can achieve high packing density of aneurysms.

Questionnaire Survey of Neurointerventional Simulation Training in the Japanese Society for Neuroendovascular Therapy

Objective

Simulation training has focused on education and practical training. However, the adoption rate of neurointerventional simulation training in Japan is unknown. Therefore, we sent a questionnaire survey form to consulting specialists from the Japanese Society for Neuroendovascular Therapy (JSNET) to clarify the actual simulation training situation and compare the differences between university hospitals and general hospitals in Japan.

Methods

The questionnaire survey was conducted in 243 neurosurgical training facilities that had JSNET consulting specialists between May 31, 2021 and July 31, 2021. The questionnaire survey forms were distributed by Google Forms.

Results

A total of 162 facilities responded to the survey (response rate: 66.7%; 35.2% from university hospitals and 64.8% from general hospitals). The adoption rate for simulation training was 53.7%, and it was significantly higher in the university hospitals than in the general hospitals (64.9% vs. 47.6%, p = 0.035). On the simulation effectiveness survey, more than 80% of respondents answered that the simulation training was a useful tool for upskill training. The open-ended question on interventional simulation training showed that there are limiting factors such as financial constraints. Additionally, respondents expressed a desire for a standard neurointerventional simulation training and education program.

Conclusion

The adoption rate for simulation training was 53.7% in the training facilities of JSNET, and it was higher in the university hospitals than in the general hospitals. Most of the respondents answered that simulation training is an effective tool to improve neurointerventional skills. They also requested the establishment of simulation training programs and simulation tools.