ObjectiveTo investigate the accuracy of multi-slice spiral CT (MSCT) scan and image reconstruction technology for measuring morphological parameters of costal cartilages and to evaluate the volume of costal cartilages. MethodsBetween March and August 2013, 75 patients with congenital microtia and scheduled for auricle reconstruction were included in the study. Of 75 patients, there were 49 males and 26 females with a mean age of 8 years and 5 months (range, 5 years and 7 months to 32 years and 7 months) and a mean weight of 29.5 kg (range, 21-82 kg). A Philips Brilliance 64 MSCT machine was used to scan 1st-12th costal cartilages with the parameters based on the age and weight of the patients. All the data were transported to the workstation for reconstructing the image of the costal cartilages with the technique of maximum intensity projection (MIP) and volume rendering technique (VRT). Then the morphologies of costal cartilages were observed through the images on VRT; the width of the costal cartilaginous ends close to ribs (W) and the length of the total cartilage (L) were measured and compared with their counterparts (W' and L') after the costal cartilages were harvested during the processes of auricle reconstructions to analyze consistency between these two sets of data. ResultsThe morphologies of ribs and costal cartilages shown on VRT image got fine sharpness, verisimilitude, and stereoscopic impressions. A total of 192 costal cartilages were examined. The results showed that the widths of the costal cartilaginous ends close to ribs (W) was (9.69±1.67) mm, and W' was (9.73±1.64) mm, showing no significant difference between W and W' (t=-1.800, P=0.073), and interclass correlation coefficient (ICC) test showed Cronbach's α=0.993. The length of the total cartilage (L) was (83.03±23.86) mm, and L' was (81.83±16.43) mm, showing no significant difference between L and L' (t=1.367, P=0.173), and ICC test showed Cronbach's α=0.904. Linear-regression analysis showed L=1.28×L'-21.93 (R2=0.780, F=673.427, P=0.000). The results suggested there was a good consistency between these two sets of data. ConclusionScanning costal cartilages with appropriate parameters and reconstructing the cartilaginous image with MIP is an effective method to measure the width and length of costal cartilage and to estimate costal cartilage volume, which can provide accurate reference for plastic surgery together with reading the morphology from the image on VRT.
Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients’ cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.
Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.