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

描述（由申请人提供）：该大学-行业合作资助的目标是提高临床数字乳腺断层合成摄影（DBT）的成像性能。我们的目标有两个：1。通过提高非晶硒（a-Se）全视野数字乳腺摄影（FFDM）探测器的探测量子效率（DQE）和优化X射线传输方案，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实现对病变显著性的改善，并在原型DBT系统上验证结果。我们的目标将通过以下具体目标来实现：（1）开发一种新的探测器，具有改进的低剂量和时间性能的DBT。目标1将导致一种新的a-Se平板成像仪的开发和临床转化，该成像仪可以使DBT的每个投影视图的探测器的DQE加倍，并使对比度增强DBT成像应用中使用的更高能量射束的DQE增加30%。(2)开发特定于任务的SNR框架，以指导DBT图像采集的优化，用于检测乳腺病变。目标2将导致开发基于频域任务的图像评价框架，该框架量化探测器性能、辐射输送方案和重建滤波器对使用重建图像的乳腺病变检测任务的影响。(3)开发一个DBT模拟平台，该平台具有逼真的3D数字乳腺体模和计算机化观察器，以量化乳腺病变的显著性并验证优化结果。目标3是对目标2的补充，以形成一种平衡的评价方法。它将研究线性系统假设可以在多大程度上使用，并提供一种图像评估方法，其中线性系统理论不适用，即非平稳背景和系统响应，以及非线性重建算法。(4)验证原型DBT系统上不同病变检测任务的优化策略。目标4将实施成像系统硬件改进，并使用目标2和3中开发的框架指导开发新的实用策略，以提高临床原型DBT系统中的乳腺病变显著性。自十多年前提出以来，数字乳腺断层合成摄影一直受到越来越多的关注，但在如何最好地利用其3D成像能力，同时克服固有的系统限制方面，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来为这个知识库做出贡献。这将支持系统硬件和优化方法的快速临床转化。 公共卫生相关性：这项大学-行业合作资助的目标是开发工程方法，以提高数字乳腺断层合成摄影（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.