Quantification of breast density using dual energy mammography

使用双能乳房X线照相术定量乳房密度

基本信息

批准号：
8469288
负责人：
SABEE MOLLOI
金额：
$ 29.84万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-05-24 至 2014-04-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Among American women breast cancer is the most common cancer and the 2nd leading cause of death from cancer. Breast density, which is defined as the ratio of fibroglandular tissue to the total fibroglandular and adipose tissue, is an important risk factor in the development of breast cancer. A mammographically dense breast refers to a breast which attenuates a greater proportion of x-ray photons due to its significant volume fraction of fibroglandular tissue relative to adipose tissue. There are currently no accepted standards to reliably measure breast density. Subjective visual assessment alone is inadequate to quantify breast density. Other more quantitative attempts have suffered from a number of limitations such as providing only an area as opposed to volumetric measure of breast density or assuming a constant breast thickness. Previous studies have shown that the relative risk for developing breast cancer increases by 2% for every 1% increase in mammographic density, indicating the need for an accurate method to measure breast density. The goal of this proposal is to develop a method to objectively and quantitatively measure the density of a woman's breast using dual energy mammography. Dual energy imaging exploits the differential energy dependence in x-ray photon attenuation between fibroglandular and adipose tissue. Image pixels are decomposed into separate thickness measurements for each of the two primary breast tissues. The specific aims are: (1) Development of an analytical simulation model to investigate the feasibility of accurate breast density measurements using dual energy mammography. (2) Investigation of the hypothesis that breast density can be accurately measured in breast phantoms using dual energy mammography. (3) Validation of the dual energy technique for breast density quantification with magnetic resonance imaging (MRI) and cone-beam computed tomography in postmortem breast tissue. (4) To test the hypothesis that breast density measured in human volunteers with dual energy mammography and breast density measured with MRI have a correlation coefficient higher than 0.95. The proposed method provides a volumetric measure of breast density on a pixel by pixel basis and it has the potential to be incorporated into routine screening mammography. These advantages should translate into a more accurate assessment of breast cancer risk. The long-range plan for this project is to develop a clinically feasible method to measure the density of a woman's breast with dual energy x-ray mammography.

描述（由申请人提供）：在美国女性乳腺癌中，是最常见的癌症，也是癌症死亡的第二大死亡原因。乳腺密度被定义为纤维结构组织与总纤维岩和脂肪组织的比率，是乳腺癌发展的重要危险因素。乳房密集的乳房是指乳房，由于其相对于脂肪组织的纤维球组织的显着体积分数，乳房会减弱X射线光子的比例更大。目前尚无可靠测量乳房密度的公认标准。仅主观视觉评估就不足以量化乳房密度。其他更定量的尝试也遭受了许多局限性，例如仅提供一个区域，而不是乳房密度的体积度量或假设持续的乳房厚度。先前的研究表明，乳腺癌的相对风险每增加1％的乳房X线摄影密度增加2％，这表明需要一种准确的方法来测量乳腺密度。该提案的目的是开发一种使用双能量X线摄影的方法来客观和定量测量女性乳房的密度。双能成像利用纤维兰科和脂肪组织之间X射线光子衰减的差异能量依赖性。图像像素被分解为两个主要乳腺组织中每一个的单独厚度测量。具体目的是：（1）开发分析模拟模型，以研究使用双能乳房X线摄影的精确乳房密度测量的可行性。（2）研究乳腺密度可以在乳房幻像中精确测量的假设。（3）用磁共振成像（MRI）和锥形束计算机层表术在死后乳腺组织中验证双能技术用于乳房密度定量。（4）测试以下假设：在双能乳房X线摄影和用MRI测量的人类志愿者中测量的乳房密度的相关系数高于0.95。所提出的方法通过像素在像素上提供了乳房密度的体积度量，并且有可能将其纳入常规筛选乳房X线摄影。这些优点应转化为对乳腺癌风险的更准确评估。该项目的远程计划是开发一种临床上可行的方法，以双重能量X射线乳房摄影测量女性乳房的密度。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantification of breast density with dual energy mammography: an experimental feasibility study.

双能乳房X线照相术定量乳房密度：实验可行性研究。

DOI：
10.1118/1.3284975
发表时间：
2010
期刊：
Medical physics
影响因子：
3.8
作者：
Ducote,JustinL;Molloi,Sabee
通讯作者：
Molloi,Sabee

Breast-density measurement using photon-counting spectral mammography.

使用光子计数光谱乳房X线照相术进行乳房密度测量。

DOI：
10.1002/mp.12279
发表时间：
2017
期刊：
Medical physics
影响因子：
3.8
作者：
Johansson,Henrik;vonTiedemann,Miriam;Erhard,Klaus;Heese,Harald;Ding,Huanjun;Molloi,Sabee;Fredenberg,Erik
通讯作者：
Fredenberg,Erik

Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application.