Department of Diagnostic Radiology, San Gerardo Hospital, Italy.
School of Medicine, University of Milano-Bicocca, Italy.
Neuroradiol J. 2021 Oct;34(5):462-469. doi: 10.1177/19714009211008751. Epub 2021 Apr 19.
To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach.
We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics.
A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all <0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; =0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; <0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher (=0.003).
Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.
评估基于模型的重建算法在急诊非增强 CT 评估急性创伤性脑损伤中的应用价值，与标准混合迭代重建方法进行比较。
我们回顾性评估了 350 例因脑外伤在急诊行 256 排非增强 CT 扫描的患者。使用混合和基于模型的迭代算法重建图像。两名放射科医生在不了解临床数据的情况下记录急性发现的存在、性质、数量和位置。使用 4 分制进行主观图像质量评分。通过计算信噪比和对比噪声比来确定客观图像质量。使用 k 统计量评估两位读者之间的一致性。
与混合迭代重建相比，基于模型的迭代重建进行主观图像质量分析时，急性创伤相关病变的检出率更高（硬膜外血肿 116 例比 68 例，硬膜下血肿 162 例比 98 例，蛛网膜下腔出血 118 例比 78 例，脑实质血肿 94 例比 64 例，挫裂伤 36 例比 28 例，弥漫性轴索损伤 75 例比 31 例；均<0.001）。评估所有损伤的观察者间一致性为中度至极好（硬膜外血肿 k=0.79，硬膜下血肿 k=0.82，蛛网膜下腔出血 k=0.91，脑实质血肿 k=0.98，挫裂伤 k=0.88，弥漫性轴索损伤 k=0.70）。定量分析显示，基于模型的迭代重建图像中丘脑的平均标准差低于混合迭代重建（2.12±0.92 比 3.52±1.10；=0.030），而对比噪声比和信噪比显著更高（对比噪声比 3.06±0.55 比 1.55±0.68，信噪比 14.51±1.78 比 8.62±1.88；<0.0001）。基于模型的迭代重建的中位数主观图像质量评分明显更高（=0.003）。
基于模型的迭代重建在更薄的切片上提供更高的图像质量，与混合迭代重建相比，可识别出更多的急性创伤性病变，同时噪声显著降低。
