Paruccini Nicoletta, Villa Raffaele, Pasquali Claudia, Spadavecchia Chiara, Baglivi Antonia, Crespi Andrea
Medical Physics Department, ASST Monza, Italy.
Medical Physics Department, ASST Monza, Italy; Scuola di Specializzazione in Fisica Medica, University of Milan, Italy.
Phys Med. 2017 Sep;41:58-70. doi: 10.1016/j.ejmp.2017.05.066. Epub 2017 Jun 3.
Iterative reconstruction algorithms have been introduced in clinical practice to obtain dose reduction without compromising the diagnostic performance.
To investigate the commercial Model Based IMR algorithm by means of patient dose and image quality, with standard Fourier and alternative metrics.
A Catphan phantom, a commercial density phantom and a cylindrical water filled phantom were scanned both varying CTDI and reconstruction thickness. Images were then reconstructed with Filtered Back Projection and both statistical (iDose) and Model Based (IMR) Iterative reconstruction algorithms. Spatial resolution was evaluated with Modulation Transfer Function and Target Transfer Function. Noise reduction was investigated with Standard Deviation. Furthermore, its behaviour was analysed with 3D and 2D Noise Power Spectrum. Blur and Low Contrast Detectability were investigated. Patient dose indexes were collected and analysed.
All results, related to image quality, have been compared to FBP standard reconstructions. Model Based IMR significantly improves Modulation Transfer Function with an increase between 12% and 64%. Target Transfer Function curves confirm this trend for high density objects, while Blur presents a sharpness reduction for low density details. Model Based IMR underlines a noise reduction between 44% and 66% and a variation in noise power spectrum behaviour. Low Contrast Detectability curves underline an averaged improvement of 35-45%; these results are compatible with an achievable reduction of 50% of CTDI. A dose reduction between 25% and 35% is confirmed by median values of CTDI.
IMR produces an improvement in image quality and dose reduction.
迭代重建算法已被引入临床实践，以在不影响诊断性能的情况下实现剂量降低。
通过患者剂量和图像质量，使用标准傅里叶和替代指标来研究基于模型的商业迭代模型重建（IMR）算法。
对一个Catphan体模、一个商业密度体模和一个圆柱形水填充体模进行扫描，改变CTDI和重建厚度。然后使用滤波反投影以及统计（iDose）和基于模型的（IMR）迭代重建算法对图像进行重建。通过调制传递函数和目标传递函数评估空间分辨率。用标准差研究降噪情况。此外，用三维和二维噪声功率谱分析其行为。研究模糊和低对比度可探测性。收集并分析患者剂量指标。
所有与图像质量相关的结果均与滤波反投影标准重建结果进行了比较。基于模型的IMR显著提高了调制传递函数，提高幅度在12%至64%之间。目标传递函数曲线证实了高密度物体的这一趋势，而对于低密度细节，模糊显示出锐度降低。基于模型的IMR强调降噪在44%至66%之间，且噪声功率谱行为有所变化。低对比度可探测性曲线强调平均提高了35 - 45%；这些结果与可实现的CTDI降低50%相符。CTDI的中位数证实剂量降低了25%至35%。
IMR提高了图像质量并降低了剂量。
