肝脏病变插入临床光子计数检测器CT投影数据。

IF 3.4 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Hao Gong, Shravani Kharat, Jarod Wellinghoff, Ahmed Omar El Sadaney O Rabie, Joel G Fletcher, Shaojie Chang, Lifeng Yu, Shuai Leng, Cynthia H McCollough
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引用次数: 0

摘要

目的:为了促进临床光子计数CT (PCD-CT)病变可检出性的任务驱动图像质量评估,需要有已知病理和精确注释的患者图像数据。标准病例收集和参考标准的建立需要耗费大量的时间和资源。为了缓解这一挑战,我们的目标是开发一个投影域病变插入框架,通过将真实的放射病理特征数字插入到患者的PCD-CT图像中,有效地创建真实的患者病例。& # xD;方法。该框架使用人工智能辅助(AI)半自动注释从真实病变图像生成数字病变模型。对PCD-CT系统中商用束硬化校正的x射线能量进行了估计,并用于计算这些病变模型在不同能量阈值下的多能正演投影。病变投影随后被添加到PCD-CT检查的患者投影中。使用CT制造商的离线重建软件,对修改后的投影进行重建,形成真实的病变患者图像。通过目视检查、CT数准确性、结构相似指数(SSIM)和放射学特征分析,对幻影扫描和肝脏病变患者的图像质量进行定性和定量验证。统计学检验采用Wilcoxon符号秩检验。& # xD;主要结果。原始和重新插入的模拟组织和造影剂棒与肝脏病变的CT数无统计学差异(p>0.05)(平均值±标准差):模拟组织和造影剂棒0.4±2.3 HU,模拟组织和造影剂棒-1.8±6.4 HU。原始病变和插入病变在原始和再插入位置的形态学特征相似:SSIM平均值±标准差0.95±0.02。相应的放射学特征呈现高度相似的特征簇,差异无统计学意义(p>0.05)。& # xD;意义。所提出的框架可以使用存档的患者数据和病变图像生成具有真实肝脏病变的患者PCD-CT检查。它将促进对PCD-CT系统的系统评价和先进的具有目标病理特征的重建和后处理算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insertion of hepatic lesions into clinical photon-counting-detector CT projection data.

Objective.To facilitate task-driven image quality assessment of lesion detectability in clinical photon-counting-detector CT (PCD-CT), it is desired to have patient image data with known pathology and precise annotation. Standard patient case collection and reference standard establishment are time- and resource-intensive. To mitigate this challenge, we aimed to develop a projection-domain lesion insertion framework that efficiently creates realistic patient cases by digitally inserting real radiopathologic features into patient PCD-CT images.Approach.This framework used an artificial-intelligence-assisted semi-automatic annotation to generate digital lesion models from real lesion images. The x-ray energy for commercial beam-hardening correction in PCD-CT system was estimated and used for calculating multi-energy forward projections of these lesion models at different energy thresholds. Lesion projections were subsequently added to patient projections from PCD-CT exams. The modified projections were reconstructed to form realistic lesion-present patient images, using the CT manufacturer's offline reconstruction software. Image quality was qualitatively and quantitatively validated in phantom scans and patient cases with liver lesions, using visual inspection, CT number accuracy, structural similarity index (SSIM), and radiomic feature analysis. Statistical tests were performed using Wilcoxon signed rank test.Main results.No statistically significant discrepancy (p> 0.05) of CT numbers was observed between original and re-inserted tissue- and contrast-media-mimicking rods and hepatic lesions (mean ± standard deviation): rods 0.4 ± 2.3 HU, lesions -1.8 ± 6.4 HU. The original and inserted lesions showed similar morphological features at original and re-inserted locations: mean ± standard deviation of SSIM 0.95 ± 0.02. Additionally, the corresponding radiomic features presented highly similar feature clusters with no statistically significant differences (p> 0.05).Significance.The proposed framework can generate patient PCD-CT exams with realistic liver lesions using archived patient data and lesion images. It will facilitate systematic evaluation of PCD-CT systems and advanced reconstruction and post-processing algorithms with target pathological features.

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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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