BAYESIAN IMAGING FOR IMPROVED NODULE DETECTION

用于改进结节检测的贝叶斯成像

• 批准号: 2101621
• 负责人: CAREY E FLOYD
• 金额: $ 20.16万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-06-01 至 1999-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2101621
• 关键词: digital imaging; human subject; image processing; lung neoplasms; mathematical model; method development; neoplasm /cancer radiodiagnosis; statistics /biometry

The long term goal of this proposal is to improve the early detection of lung cancer by improving the detectability and discrimination of low contrast nodules in digital chest radiographs. Nodule detection is improved by a Bayesian estimation algorithm which increases signal-to- noise (SNR)(and thus detectability) for low contrast nodules. SNR is increased by simultaneous contrast enhancement and noise reduction. Contrast is enhanced by compensating for scattered photons. The appearance of the contrast-enhanced image is natural to a radiologist since it is an extension of the appearance commonly provided by anti-scatter grids. Noise is reduced by including prior information regarding region smoothness through a Gibbs prior distribution which applies a penalty to the variation between neighboring pixels. While this penalty is strong for small variations (to suppress Poisson noise), it is weak for larger variations (to avoid affecting resolution for anatomical structure). The scatter reduced and noise reduced images allow better visualization and decrease the false positive nodule identification since the structured background is easier to interpret. In preliminary work with anatomical phantoms, SNR was increased by a factor of two. This is encouraging when compared to the improvement factor of 1.6 provided by an aggressive anti-scatter grid. Radiologists subjectively rated the images as superior. A preliminary ROC study indicates that the Bayesian processing both increases sensitivity and simultaneously decreases false positive rates. The utility of three types of prior information will be investigated: 1 )the Gibbs prior on the image 2)a line-site model in which region boundaries are estimated and variation is suppressed within but not across the boundaries (maintaining resolution for anatomical structures), and 3)a segmentation model in which region boundaries are assigned through Bayesian classification. The technique will be applied to images acquired both with and without anti-scatter grids. Parameters controlling scatter compensation and noise reduction will be optimized to maximize SNR for nodule detection. Detectability will be evaluated using human observer ROC studies. This represents the first scatter compensation algorithm for chest radiography which increases SNR. The improved early detection of low contrast nodules in chest radiographs will significantly improve the outcome probability for patients with early developing lung cancer.

该提议的长期目标是改善 通过改善低肺癌的可检测性和辨别于低的肺癌 数字胸部X光片中的对比度结节。结节检测是 通过贝叶斯估计算法提高了信号到信号的算法 低对比度结节的噪声（SNR）（因此可检测性）。 SNR是 通过同时增强和降噪的同时增强。 通过补偿散射光子来增强对比度。外观 对比增强图像对放射科医生是自然的，因为它是 抗分散网格通常提供的外观的扩展。噪音 通过包括有关区域平滑度的先前信息来减少 通过Gibbs先验分配，该分布适用于 相邻像素之间的变化。虽然这种罚款很强烈 微小的变化（以抑制泊松噪声），较大的变化很弱 变化（避免影响解剖结构的分辨率）。这 散射减少和减少噪声的图像可以更好地可视化，并且 由于结构化，减少假阳性结节识别 背景更容易解释。 在解剖体幻象的初步工作中，SNR增加了 两个因子。与改进因素相比，这是令人鼓舞的 1.6由侵略性抗散发网提供。放射科医生 主观上将图像评为优越。初步的ROC研究 表明贝叶斯加工既提高了灵敏度，又 同时降低假阳性率。 将研究三种类型的先验信息的实用性：1 ）图像上的Gibbs先验2）在哪个区域 界限是估计的，并且在内部抑制了变化 边界（保持解剖结构的分辨率）和3）a 分割模型在哪些区域边界通过 贝叶斯分类。该技术将应用于获取的图像 带有和不带抗碎片的网格。控制散射的参数 赔偿和降低降低将被优化以最大化SNR 结节检测。将使用人类观察者ROC评估可检测性 研究。 这代表了胸部的第一个散点补偿算法 X射线照相会增加SNR。改善了低点的早期检测 胸部X光片中的对比结节将显着改善 早期发育肺癌患者的结果概率。