The management of the radiation dose is very important in interventional radiology (IVR), especially in percutaneous coronary intervention (PCI). Therefore, we measured entrance surface doses at the interventional reference point of 27 cardiac intervention procedures in 22 cardiac catheterization laboratories around Hiroshima, and compared these doses. Recently, for cardiac interventional radiology, the X-ray machines using flat-panel detectors (FPD) instead of image intensifiers (I.I.) is increasing; 13 systems used FPD and 14 systems used I.I. For fluoroscopy rate, the difference between laboratories was 9 times. For cineangiography rate, the difference between laboratories was 7 times. In addition, between both devices, the I.I. group is bigger than the FPD group. When comparing by the same condition, for the dose at the interventional reference point, no significant difference was detected between the FPD group and the I.I. group. This study shows that FPD is not available for reducing the radiation dose simply. Therefore, it is necessary that we think of the balance with image quality and radiation dose. The optimization of the devices and cardiac intervention procedures becomes very important.
Evaluation of the coronary arterial intima is important in the follow-up of coronary arterial lesions complicated by Kawasaki disease (KD). We evaluated the lesions in four cases of KD using optical coherence tomography (OCT), which can show vessel wall thickness on magnetic resonance imaging (MRI) of the coronary vessel wall using the black blood method (BB). All cases showed intimal thickening on OCT at areas of thickened vessel wall using BB. Although vessel wall thickening was detected as iso-intensity with BB, OCT showed various histological features of the intima at these lesions. We revealed that intimal thickening is evaluable using BB compared with OCT findings. MRI of coronary vessel walls with BB may be useful in screening for intimal thickening of coronary arterial lesions complicated with KD.
Purpose: In synthetic q-space learning (synQSL), which uses deep learning to infer the diffusional kurtosis (K), a bias that depends on the noise level added to the synthetic training data occurs. The purpose of this study was to evaluate K inference using synQSL and bias correction. Methods: Using the synthetic test data and the real image data, K was inferred by synQSL, and bias correction was performed. Then, those results were compared with K inferred by fitting by the least-squares fitting (LSF) method. At this time, the noise level of the training data was set to 3 types, the noise level of the synthesis test data was set to 5 types, and the number of excitation (NEX) of the real image data was set to 4 types. Robustness of inference was evaluated by the outlier rate, which is the ratio of K outliers to the whole brain. We also evaluated the root mean square error (RMSE) of the inferred K. Results: The outlier rate inferred by synQSL without correction was significantly lower in the test data of each noise level than that by the LSF method and was further reduced by correction. In addition, the RMSE of NEX 1 with NEX 4 as the correct answer based on the real image data had the smallest correction result of K by synQSL. Conclusion: Inferring K using synQSL and bias correction is a robust and small error method compared to that using the LSF method.
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