Department of Radiology, 14903Charité, Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany.
Acta Radiol. 2023 Jan;64(1):42-50. doi: 10.1177/02841851211070119. Epub 2022 Jan 5.
Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability.
To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection.
Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion.
A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRST showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRST; < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRST showing the closest results ( < 0.001).
Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.
计算机断层扫描（CT）是检测肝脏病变（如转移瘤）的标准成像程序，这些病变通常较小且对比度低，因此难以检测。图像重建技术的进步已显示出降低图像噪声和提高低对比度检测能力的潜力。
研究一种新的、专门的基于模型的迭代重建（MBIR）技术，以提高低对比度肝脏病变的检测能力。
回顾性地从原始数据中使用低对比度衰减算法（FIRST-LCD）对报告为对比度差的局灶性肝脏病变的患者图像进行重建。比较了所有三种 FIRST-LCD 预设与既定的混合迭代重建方法（AIDR-3D）的背景和肝脏噪声的肝病变对比度、信噪比和对比噪声比。两位有经验的放射科医生对每个病变的可视度进行了额外的评分。
共纳入 57 次检查中的 82 个病变进行分析。所有三种 FIRST-LCD 算法均显著提高了肝病变对比度，其中 FIRST 提高幅度最大（AIDR-3D 中为 40.47 HU，FIRST 中为 45.84 HU，P<0.001）。对比噪声指标也有显著改善。病变的视觉分析显示，与 AIDR-3D 相比，所有 FIRST 方法的病变可见度均降低，FIRST 结果最接近（P<0.001）。
客观的图像指标显示 MBIR 方法在提高低对比度肝脏病变的检测能力方面有一定的潜力；然而，主观图像质量可能会被认为较差。需要进一步改进以提高图像质量和病变检测能力。
