Corrigendum: Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

[This corrects the article DOI: 10.3389/fneur.2022.755492.].

Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

Background: Computed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up. Methods: We deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculated based on a pre-generated calibration table. Multiple cohorts including 1,124 patients were used for performance validation under three clinical scenarios. Results: Cranial Automatic Planbox Imaging Towards AmeLiorating neuroscience (CAPITAL)-CT was invented. RPN model had an error distance of 4.46 ± 0.02 pixels with a success rate of 98.7% in the training set and 100% with 2.23 ± 0.10 pixels in the testing set. V-Net-derived segmentation maintained a clinically tolerable distance error, within 3 mm on average, and all lines presented with a tolerable angle error, within 3° on average in all boundaries. Real-time, accurate, and repeatable automatic scanning was accomplished with and a lower radiation exposure dose (all P < 0.001). Conclusions: CAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.

Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care.

BACKGROUND: Interstitial lung disease requires frequent re-examination, which directly causes excessive cumulative radiation exposure. To date, AI has not been applied to CT for enhancing clinical care; thus, we hypothesize AI may empower CT with intelligence to realize automatic and accurate pulmonary scanning, thus dramatically decrease medical radiation exposure without compromising patient care. METHODS: Facial boundary detection was realized by recognizing adjacent jaw position through training and testing a region proposal network (RPN) on 76,882 human faces using a preinstalled 2-dimensional camera; the lung-fields was then segmented by V-Net on another training set with 314 subjects and calculated the moving distance of the scanning couch based on a pre-generated calibration table. A multi-cohort study, including 1,186 patients was used for validation and radiation dose quantification under three clinical scenarios. FINDINGS: A U-HAPPY (United imaging Human Automatic Planbox for PulmonarY) scanning CT was designed. Error distance of RPN was 4·46±0·02 pixels with a success rate of 98·7% in training set and 2·23±0·10 pixels with 100% success rate in testing set. Average Dice's coefficient was 0·99 in training set and 0·96 in testing set. A calibration table with 1,344,000 matches was generated to support the linkage between camera and scanner. This real-time automation makes an accurate plan-box to cover exact location and area needed to scan, thus reducing amounts of radiation exposures significantly (all, P<0·001). INTERPRETATION: U-HAPPY CT designed for pulmonary imaging acquisition standardization is promising for reducing patient risk and optimizing public health expenditures. FUNDING: The National Natural Science Foundation of China.

Hemodynamic changes in a rat parietal cortex after endothelin-1-induced middle cerebral artery occlusion monitored by optical coherence tomography.

A blockage of the middle cerebral artery (MCA) on the cortical branch will seriously affect the blood supply of the cerebral cortex. Real-time monitoring of MCA hemodynamic parameters is critical for therapy and rehabilitation. Optical coherence tomography (OCT) is a powerful imaging modality that can produce not only structural images but also functional information on the tissue. We use OCT to detect hemodynamic changes after MCA branch occlusion. We injected a selected dose of endothelin-1 (ET-1) at a depth of 1 mm near the MCA and let the blood vessels follow a process first of occlusion and then of slow reperfusion as realistically as possible to simulate local cerebral ischemia. During this period, we used optical microangiography and Doppler OCT to obtain multiple hemodynamic MCA parameters. The change trend of these parameters from before to after ET-1 injection clearly reflects the dynamic regularity of the MCA. These results show the mechanism of the cerebral ischemia-reperfusion process after a transient middle cerebral artery occlusion and confirm that OCT can be used to monitor hemodynamic parameters.

In vivo assessment of wall strain in embryonic chick heart by spectral domain optical coherence tomography.

The ability to measure in vivo wall strain in embryonic hearts is important for fully understanding the mechanisms of cardiac development. Optical coherence tomography (OCT) is a powerful tool for the three-dimensional imaging of complex myocardial activities in early-stage embryonic hearts with high spatial and temporal resolutions. We describe a method to analyze periodic deformations of myocardial walls and evaluate in vivo myocardial wall strains with a high-speed spectral domain OCT system. We perform four-dimensional scanning on the outflow tract (OFT) of chick embryonic hearts and determine a special cross-section in which the OFT can be approximated as an annulus by analyzing Doppler blood-flow velocities. For each image acquired at the special cross-section, the annular myocardial wall is segmented with a semiautomatic boundary-detection algorithm, and the fluctuation myocardial wall thickness is calculated from the area and mean circumference of the myocardial wall. The experimental results shown with the embryonic chick hearts demonstrate that the proposed method is a useful tool for studying the biomechanical characteristics of embryonic hearts.