Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10−6 mm2/s to 480 × 10−6 mm2/s with a 20 × 10−6 mm2/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3–4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT.
SummaryThe normal tube voltage in computed tomography colonography (CTC) is 120 kV. Some reports indicate that the use of a low tube voltage (lower than 120 kV) technique plays a significant role in reduction of radiation dose. However, to determine whether a lower tube voltage can reduce radiation dose without compromising diagnostic accuracy, an evaluation of images that are obtained while maintaining the volume CT dose index (CTDIvol) is required. This study investigated the effect of reduced tube voltage in CTC, without modifying radiation dose (i.e. constant CTDIvol), on image quality. Evaluation of image quality involved the shape of the noise power spectrum, surface profiling with volume rendering (VR), and receiver operating characteristic (ROC) analysis. The shape of the noise power spectrum obtained with a tube voltage of 80 kV and 100 kV was not similar to the one produced with a tube voltage of 120 kV. Moreover, a higher standard deviation was observed on volume-rendered images that were generated using the reduced tube voltages. In addition, ROC analysis revealed a statistically significant drop in diagnostic accuracy with reduced tube voltage, revealing that the modification of tube voltage affects volumerendered images. The results of this study suggest that reduction of tube voltage in CTC, so as to reduce radiation dose, affects image quality and diagnostic accuracy.
SummaryThis study aimed to reduce contrast medium dose without reducing the diagnostic capability of computed tomography (CT) angiography of the head. We evaluated the advanced statistical iterative reconstruction (ASiR) settings to adjust to low tube voltage CT. A syringe phantom was constructed using dilute contrast medium and was imaged at tube voltages of 80 120 kV. The iodine volumes, CT values, and image noise were measured in these images. The noise-power spectrum and modulation transfer function were measured from quality assurance phantom images that had been obtained using the tube voltage selected after considering the image noise results as described above and reconstructed using different ASiR rate settings and convolution kernels. Our results suggested that imaging at 100 kV could reduce the contrast medium dose by 14%, compared with imaging at 120 kV, and that the resulting image quality could equal that of conventional imaging by performing reconstruction at a 40% ASiR rate and detail kernel.
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