A university-industry partnership for improving the imaging performance of digita

描述(申请人提供)：这项校企合作基金的目标是提高临床数字乳房断层合成术(DBT)的成像性能。我们的目标有两个：1.通过提高非晶态硒(a-Se)全场数字乳腺摄影(FFDM)探测器的探测量子效率(DQE)和优化X射线传输方案来提高DBT的固有信噪比(SNR)。2.建立图像质量评价方法，量化不同DBT实现方式对病变显着性改善的影响，并在原型DBT系统上进行验证。我们的目标将通过以下具体目标来实现：(1)开发一种用于DBT的新型探测器，具有改进的低剂量和时间性能。目标1将导致一种新的a-se平板成像仪的开发和临床应用，该成像仪可以使DBT的每个投影面的探测器的DQE增加一倍，并且对于用于对比增强DBT成像应用的高能束，DQE增加30%。(2)建立特定于任务的SNR框架，以指导用于乳腺病变检测的DBT图像采集的优化。目标2将导致开发基于频域任务的图像评估框架，该框架量化探测器性能、辐射传输方案和重建滤光片对使用重建图像的乳腺病变检测任务的影响。(3)开发了具有真实感三维数字乳腺体模和计算机观察者的DBT仿真平台，以量化乳腺病变的显着性并验证优化结果。目标3与目标2相辅相成，形成一种平衡的评价方法。它将调查线性系统假设可以在多大程度上使用，并提供线性系统理论不适用的图像评估方法，即非平稳背景和系统响应，以及非线性重建算法。(4)在一个原型DBT系统上验证了针对不同病变检测任务的优化策略。AIM 4将实施成像系统硬件改进，并使用在AIMS 2和3中开发的框架来指导开发新的实用策略，以在临床原型DBT系统中提高乳腺病变的显着性。数字乳腺断层合成自十多年前提出以来，受到了越来越多的关注，但如何在克服固有系统限制的同时，最大限度地利用其3D成像能力，还需要更多的知识。我们的目标是利用大学与行业合作的能力为这一知识库做出贡献。这将支持系统硬件和优化方法的改进在临床上的快速转化。 公共卫生相关性：这项大学和产业合作基金的目标是开发工程方法，以提高数字乳房断层合成术(DBT)的临床成像性能，用于乳腺癌的早期检测。这些改进将通过成像理论进行量化，并通过临床可行性测试进行验证。所获得的知识将用于指导未来多机构临床试验的设计。

DESCRIPTION (provided by applicant): The goal of this university-industry collaborative grant is to improve the imaging performance of clinical digital breast tomosynthesis (DBT). Our objectives are twofold: 1. Increase the inherent signal to noise ratio (SNR) of DBT by improving the detective quantum efficiency (DQE) of amorphous selenium (a-Se) full-field digital mammography (FFDM) detectors, and optimizing the x-ray delivery scheme. 2. Establish image quality evaluation methods that quantify the improvement in lesion conspicuity with different DBT implementations, with the results verified on a prototype DBT system. Our objectives will be accomplished through the following specific aims: (1) Develop a new detector with improved low dose and temporal performance for DBT. Aim 1 will lead to the development and clinical translation of a new a-Se flat-panel imager that can double the DQE of the detector for each projection view of DBT, and increase the DQE by 30% for the higher energy beams used in contrast enhanced DBT imaging applications. (2) Develop a task-specific SNR framework to guide the optimization of DBT image acquisition for the detection of breast lesions. Aim 2 will lead to the development of a frequency domain task-based image evaluation framework that quantifies the impact of detector performance, radiation delivery scheme and reconstruction filters on the detection tasks of breast lesions using reconstructed images. (3) Develop a DBT simulation platform with realistic 3D digital breast phantom and a computerized observer to quantify breast lesion conspicuity and verify optimization results. Aim 3 complements Aim 2 to form a balanced evaluation methodology. It will investigate to what extent linear system assumptions can be used, and provide an image evaluation method where linear system theory does not apply, i.e. non-stationary background and system response, and non-linear reconstruction algorithms. (4) Verify optimization strategies for different lesion detection tasks on a prototype DBT system. Aim 4 will implement the imaging system hardware improvements and use the framework developed in Aims 2 and 3 to guide the development of new, practical strategies to improve breast lesion conspicuity in a clinical prototype DBT system. Digital breast tomosynthesis has been receiving increasing attention since it was proposed more than a decade ago, yet more knowledge is needed in how to best utilize its 3D imaging capability while overcoming inherent system limitations. We aim to take advantage of the capabilities of university-industry collaboration to contribute to this knowledge base. This will support rapid clinical translation of improvements in both system hardware and optimization methodologies. PUBLIC HEALTH RELEVANCE: The goal of this university-industry collaborative grant is to develop engineering methods to improve the clinical imaging performance of digital breast tomosynthesis (DBT) for the early detection of breast cancer. The improvements will be quantified by imaging theory and verified by clinical feasibility testing. The knowledge gained will be used to guide the design of future multi-institutional clinical trials.