基于模型的迭代重建算法与自适应迭代重建算法对肺炎患儿胸部低剂量CT图像质量的优化

Optimization of low-dose chest CT image quality with model-based iterative reconstruction and adaptive statistical iterative reconstruction in pneumonia children

目的评价基于模型的迭代重建(MBIR)算法、自适应迭代重建(ASiR)算法及滤波反投影(FBP)算法对肺炎患儿胸部低剂量CT图像质量的优化。方法选取肺炎患儿41例,根据年龄设定噪声指数(NI)。将原始数据重建为5mm的MBIR图像(序列A),5mm的30%ASiR与70%滤波反射投影(FBP)混合图像(序列B),5mm的FBP(序列C)图像,由2名医师主观评价图像质量,并计算优化信噪比(SNR)及辐射剂量。结果序列A图像的主观噪声、细微结构的显示方面明显优于序列B及序列C图像,但结构边缘略为模糊;序列A图像的客观噪声值较序列B及序列C图像噪声降低,SNR较序列B及序列C升高,辐射剂量约为(0.24±0.05)mSv。结论序列A的图像质量明显优于序列B及序列C的图像。

Objective To evaluate the optimization of low-radiation dose chest CT image quality in pneumonia children using adaptive statistical iterative reconstruction （ASiR）, filtered back-projection （FBP） and full model-based iterative reconstruction （MBIR） algorithm. Methods Forty-one children with pneumonia underwent low-dose chest CT scans. Agedependent noise index （NI） was used for acquisition. Images were retrospectively reconstructed using three methods： MBIR （group A）, 30% of ASiR and 70% of conventional filtered back-projection （FBP, group B）, and FBP （group C）. The quality of the images was evaluated by two radiologists. Optimized signal-to-noise ratios （SNR） and effective dose were calculated. Results Subjective noise in group A were significantly higher than that in group B and group C, especially the tiny structures which edges were slightly blurred. In terms of objective noise, MBIR decreased the image noise, and increased the SNR compared with group C and group B. Effective dose was （0.24±0.05）mSv. Conclusion Compared with group B and group C, image quality are significantly improved and noise are significantly decreased in MBIR.