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

描述（由申请人提供）：这项大学-工业合作资助的目标是提高临床数字乳腺断层合成（DBT）的成像性能。我们的目标是双重的：通过提高非晶硒（a-Se）全场数字乳房x线摄影（FFDM）探测器的探测量子效率（DQE）和优化x射线传递方案，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实现对病灶显著性的改善，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)开发一种具有改进的低剂量和时间性能的新型DBT检测器。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，该成像仪可以将探测器的DQE提高一倍，用于DBT的每个投影视图，并将DQE提高30%，用于对比度增强DBT成像应用的高能量束。(2)建立特定任务的信噪比框架，指导乳腺病变检测中DBT图像采集的优化。目标2将导致基于频域任务的图像评估框架的发展，该框架量化检测器性能，辐射传递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)开发具有逼真三维数字乳房假体和计算机化观测器的DBT仿真平台，量化乳房病变显著性并验证优化结果。目标3与目标2相辅相成，形成平衡的评价方法。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)在原型DBT系统上验证不同病灶检测任务的优化策略。目标4将实现成像系统硬件改进，并使用目标2和3中开发的框架来指导开发新的实用策略，以改善临床原型DBT系统中乳腺病变的显著性。自十多年前提出以来，数字乳房断层合成技术一直受到越来越多的关注，但如何在克服固有系统限制的同时最好地利用其3D成像能力还需要更多的知识。我们的目标是利用大学与工业合作的能力，为这个知识库做出贡献。这将支持系统硬件和优化方法改进的快速临床翻译。

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.