Background: Stereotactic surgery, specifically Deep Brain Stimulation (DBS), has been a well-established intervention for treating neurosurgical disorders, including essential tremor and Parkinson's disorders. Intra- operative MRI-guided DBS implantation has become more a prevalent practice, particularly after stereotactic guidance devices for MRI became available. However, we and others have discovered little to no literature contributions regarding the design and validation of tools to facilitate intraoperative CT-scans for DBS implantation. Objective and Methods: Our goal is to design and validate a guidance device and software to enable CT- guided DBS; specifically, we validated a skull-mounted guidance device integrated for DBS combined with its 3D Slicer software, and tested a hypothesis that the device provides a tool insertion error (Target Point Error) of less than 3 mm. The measurements were done using a skull phantom, with seven clinically relevant targets distributed at variable depths from two different entry points located in the Frontal and Parietal bones. Further analysis was carried out to understand the reason for increased TPE values at certain targets. Results: We found out that our device could produce a TPE of 2.09 ± 0.9 mm for the Frontal bone entry point [p < 0.0001] and 2.52 ± 0.6 mm [p < 0.0016] for the Parietal bone entry point. Additionally, multivariate analysis suggests that depth is the main contributor to larger TPE values when compared to entry points. Implication: These results conclude that our iCT-guided device is capable of replacing DBS tools while enjoying the shorter imaging cycles of CT-scanners. The device proposed may also increase opportunities for patients to receive image-guided DBS since CT-scanners are more accessible to the public than MRI is.
Purpose: The objective of this paper is to present heatmaps from the likelihood of clinically significant prostate cancer with a deep learning model. This will give radiologists more information on the location of prostate lesions. Methods: 3D Slicer module was developed using a machine learning model to predict pixel-by-pixel PI-RADS scores. The working hypothesis is that the machine learning algorithm will be capable of producing heatmaps with hotspots within a typical size of a lesion with PI-RAD score of 4. Discussion and conclusion: The study provided insight into the future of MRI assessment using Deep Learning models.
Navigated bronchoscopy for the lung biopsy using an electro-magnetic (EM) sensor is often inaccurate due to patient breathing movement during procedures. The objective of this study is to evaluate whether registration of neural network- generated depth images can localize the bronchoscope in navigated bronchoscopy negating the need for EM sensor and error caused by breathing motion. [Methods] Dual CNN-generated depth images followed chained ICP registration were validated in the study. Accuracy was measured by the error between the location after registration and the location of the standard electromagnetic sensor. Difference in accuracy between regions that the neural networks had trained on (seen regions) and regions the networks had never encountered (unseen regions) was validated. [Results] The data collected points to the success of the bronchoscopic localization. Overall mean error of accuracy was 8.75 mm and the overall standard deviation was 4.76mm. For the seen region, the mean error was 6.10mm and the standard deviation was 2.65mm. For the unseen region, the mean error was 11.6mm and the standard deviation was 4.87mm. The results of the two-sample t-test shows that there is a statistically significant difference between the unseen and the seen region. [Conclusion] The results for registration demonstrate that this technique has potential to be implemented in navigational bronchoscopy. The technique produced less error than the electromagnetic sensor in practice, especially accounting for the estimated practical error due to experimental setup.
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