Pula Michal, Kucharczyk Emilia, Zdanowicz-Ratajczyk Agata, Dorochowicz Mateusz, Guzinski Maciej
Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw University Hospital, Wrocław, Poland.
Wroclaw Medical University, Wrocław, Poland.
Neuroradiol J. 2025 May 23:19714009251345108. doi: 10.1177/19714009251345108.
This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image quality and reduce radiation dose compared to the established standard of Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in unenhanced head computed tomography (CT). A retrospective analysis of 163 consecutive unenhanced head CTs was conducted. Image quality assessment was computed on the objective parameters of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), derived from 5 regions of interest (ROI). The evaluation of DLIR dose reduction abilities was based on the analysis of the PACS derived parameters of dose length product and computed tomography dose index volume (CTDIvol). Following the application of rigorous criteria, the study comprised 35 patients. Significant image quality improvement was achieved with the implementation of DLIR, as evidenced by up to a 145% and 160% increase in SNR in supra- and infratentorial regions, respectively. CNR measurements further confirmed the superiority of DLIR over ASIR-V, with an increase of 171.5% in the supratentorial region and a 59.3% increase in the infratentorial region. Despite the signal improvement and noise reduction DLIR facilitated radiation dose reduction of up to 44% in CTDIvol. Implementation of DLIR in head CT scans enables significant image quality improvement and dose reduction abilities compared to standard ASIR-V. However, the dose reduction feature was proven insufficient to counteract the lack of gantry angulation in wide-detector scanners.
本研究聚焦于一项客观评估，即在未增强头部计算机断层扫描（CT）中，一种新型重建算法——深度学习图像重建（DLIR）相较于既定标准自适应统计迭代重建-V（ASIR-V），在改善图像质量和降低辐射剂量方面的能力。对163例连续的未增强头部CT进行了回顾性分析。基于从5个感兴趣区域（ROI）得出的信噪比（SNR）和对比噪声比（CNR）等客观参数进行图像质量评估。对DLIR剂量降低能力的评估基于对PACS导出的剂量长度乘积和计算机断层扫描剂量指数容积（CTDIvol）参数的分析。遵循严格标准后，该研究纳入了35例患者。实施DLIR后实现了显著的图像质量改善，幕上和幕下区域的SNR分别提高了高达145%和160%，这证明了这一点。CNR测量进一步证实了DLIR优于ASIR-V，幕上区域增加了171.5%，幕下区域增加了59.3%。尽管DLIR实现了信号改善和噪声降低，但其在CTDIvol方面仍使辐射剂量降低了高达44%。与标准ASIR-V相比，在头部CT扫描中实施DLIR能够显著改善图像质量并降低剂量。然而，剂量降低功能被证明不足以抵消宽探测器扫描仪中机架角度缺乏的问题。
