Digital Tomosynthesis Mammography: Computer-Aided Analysis of Masses

描述(申请人提供)：数字断层合成乳房摄影(DTM)是一种新的方式，有望提高乳癌检测和诊断的敏感性，特别是对于致密的乳房。这项研究的主要目标是：(1)开发一个DTM乳腺肿块的计算机辅助检测(CAD)系统；(2)开发一个用于DTM良恶性肿块分类的计算机辅助诊断(CADx)系统；(3)评估CAD(无论是CAD还是CADx)对放射科医生解释DTMS的影响。以往的CAD系统都是针对常规投影式乳房X线片(PM)开发的，所提出的CAD系统利用DTM的三维(3D)信息来改进肿块的检测和表征。建议项目的创新包括：(1)开发新的计算机视觉技术来利用DTMS中的3D体积信息，(2)评估CAD性能对重建算法的依赖，以及(3)DTMS、投影视图乳房X光(PV)(在断层合成成像期间从多个角度拍摄的非重建乳房X光图像)和PM中计算机化的肿块检测和表征的比较。我们假设在DTM上对肿块的检测和定性将比在常规PM上的相应任务更准确，并且CAD系统可以提高放射科医生的准确性。将设计带有测试对象的3D乳房模型，并使用原型DTM系统进行成像。研究重建算法及其参数和图像获取技术对DTM图像质量的影响。将根据体模和患者研究选择适当的重建技术。在患者知情同意的情况下，将收集一个数据库，其中包括恶性和良性肿块的DTM和相应的PM，以及一组正常病例。将开发用于质量检测和分类的CAD系统。将比较两种方法：一种使用重建的DTM切片，另一种使用PV作为CAD系统的输入。对于DTMS，将设计新的3D预处理、图像分割、特征提取和特征分类技术。对于PV，我们以前为常规PM开发的技术将适用于这些低剂量图像，并将开发使用神经网络或支持向量机等技术的信息融合方法来合并多PV信息。为了验证我们的假设，我们将比较这两种方法和相应的常规PM的CAD系统的性能，并进行观察者ROC研究，以评估CAD系统对放射科医生性能的影响。CAD将是一个重要的工具，有助于加速DTM在临床实践中的实施。带有CAD的DTM有望帮助充分利用这一新模式的潜力，以改进乳腺癌的检测。

DESCRIPTION (provided by applicant): Digital tomosynthesis mammography (DTM) is a new modality that holds the promise of improving mammographic sensitivity of breast cancer detection and diagnosis, especially for dense breasts. The main goals of the proposed research are (1) to develop a computer-aided detection (CADd) system for breast masses in DTM, (2) to develop a computer-aided diagnosis (CADx) system for classification of malignant and benign masses in DTM, and (3) to evaluate the effects of CAD (either CADd or CADx) on radiologists' interpretation of DTMs. Previous CAD systems are developed for regular projection mammograms (PMs).The proposed CAD system makes use of the 3-dimensional (3D) information in DTM to improve mass detection and characterization. The innovations in the proposed project include: (1) development of new computer-vision techniques to exploit the 3D volumetric information in DTMs, (2) evaluation of the dependence of CAD performance on reconstruction algorithms, and (3) comparison of computerized mass detection and characterization in DTMs, projection view mammograms (PVs) (the non-reconstructed mammograms taken at multiple angles during tomosynthesis imaging), and PMs. We hypothesize that detection and characterization of masses on DTMs will be more accurate than corresponding tasks on regular PMs, and that the CAD systems can improve radiologists' accuracy. 3D breast phantoms with test objects will be designed and imaged with a prototype DTM system. The dependence of DTM image quality on reconstruction algorithms and their parameters, and on image acquisition techniques will be studied. The appropriate reconstruction techniques will be selected based on phantom and patient studies. A database of DTMs and corresponding PMs with malignant and benign masses and a set of normal cases will be collected with patient informed consent. CAD systems for detection and classification of masses will be developed. Two approaches will be compared: one uses the reconstructed DTM slices and the other uses the PVs as input to the CAD systems. For the DTMs, new techniques for 3D preprocessing, image segmentation, feature extraction, and feature classification will be designed. For the PVs, our previous techniques developed for regular PMs will be adapted to these low- dose images, and information fusion methods using techniques such as neural networks or support vector machines will be developed to merge the multiple-PV information. To test our hypotheses, we will compare the CAD system performances from these two approaches and that from the corresponding regular PMs, and conduct observer ROC studies to evaluate effects of the CAD systems on radiologists' performance. CAD will be an important tool that can help accelerate the implementation of DTM in clinical practice. DTM with CAD is expected to help fully utilize the potential of this new modality to improve breast cancer detection.