Advanced breast tomosynthesis reconstruction for improved cancer diagnosis

先进的乳房断层合成重建可改善癌症诊断

• 批准号: 10323267
• 负责人: HEANG-PING CHAN
• 金额: $ 47.67万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-10 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10323267
• 关键词: Address; Advanced Development; Algorithms; Biological Models; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Breast Magnetic Resonance Imaging; Breast Microcalcification; Clinical; Coupled; Data; Data Analyses; Data Collection; Data Set; Detection; Development; Diagnosis; Digital Breast Tomosynthesis; Digital Mammography; Dose; Early Diagnosis; Evaluation; Future; Geometry; Goals; Human; Image; Image Analysis; Imaging Techniques; Incidence; Knowledge; Lesion; Magnetic Resonance Imaging; Mammography; Manuscripts; Measurement; Methods; Modality; Modeling; Morphologic artifacts; Noise; Performance; Physics; Positron-Emission Tomography; Preparation; Publications; Research; Resolution; Roentgen Rays; Scanning; Screening for cancer; Signal Transduction; Source; Spiculate; System; Systems Development; Techniques; Technology; Time; Tissues; Translations; attenuation; base; breast cancer diagnosis; breast lesion; cancer diagnosis; computer aided detection; contrast enhanced; cost; cost effective; design; detection platform; detector; digital; digital models; human subject; image reconstruction; imaging system; improved; machine vision; malignant breast neoplasm; model development; models and simulation; public health relevance; radiation risk; radiologist; reconstruction; screening; screening guidelines; single photon emission computed tomography; soft tissue; statistics; tomography; tomosynthesis

Digital Breast Tomosynthesis (DBT) has been shown to significantly improve the detection and characterization of soft-tissue lesions and reduce false positive recalls in breast cancer screening. However, DBT is still at its early stage of clinical use and continued improvement of the system design and reconstruction methods are crucial to fully exploit its potential. Noise and resolution are major factors in optimization of an imaging system. The noise in DBT is much higher than that in digital mammograms (DMs) because the multiple low-dose projections increase the total detector noise. The oblique incidence to the breast and the detector at large-angle projections further aggravates the noise problem and reduces spatial resolution. Synthetic mammograms cannot resolve these problems because they are generated from the DBT. It is known from CT that iterative reconstruction (IR) with properly designed regularizer can significantly reduce noise. However, IR for CT generally does not consider spatial blur and noise correlation/aliasing. Modeling these factors has recently started in CBCT that uses flat panel detectors. Model-based IR (MBIR) technology has not been developed for DBT. DBT is a limited-angle tomography, which, coupled with the very different target signals that are signs of breast cancer (microcalcifications, spiculated/ill-defined masses and distortions) than those in CT or CBCT, makes it much more challenging to develop MBIR for DBT. The goal of the proposed project is to develop MBIR for DBT by accurate physics and statistics modeling of the imaging system to improve the image quality of DBT. We will develop accelerated reconstruction algorithms for these models to facilitate both research and eventual translation to clinical use of such methods. Our specific aims are: (SA1) prepare three data sets for development of the MBIR method (simulated DBT projection data, DBT projections of physical phantoms, and human subject DBT projections), and study the impacts of various image degrading factors on the reconstructed DBT; (SA2) develop MBIR by optimizing the design of the objective function and the iterative algorithm using the three types of data obtained in (SA1) and a four-tier approach; and (SA3) validate the developed MBIR method by comparison with current reconstruction techniques in terms of the detection accuracy of target signals by radiologists (ROC study) and by computer-aided detection (CAD) systems in human subject DBT images. This project brings together two research teams with complementary expertise, one in imaging physics, image analysis and lesion detection in DBT, the other in statistical iterative reconstruction for CT/SPECT/ PET/MRI, to tackle this limited-angle reconstruction problem. If successful, DBT reconstructed with the new MBIR method is expected to improve the efficacy of early breast cancer detection and diagnosis and reduce dose. Reducing dose and noise will also facilitate the optimization of overall DBT system design, and development of advanced DBT techniques such as dual-energy contrast-enhanced DBT or dynamic contrast-enhanced DBT, which may be cost-effective alternatives to breast MRI for cancer diagnosis.

数字乳腺断层合成摄影（DBT）已被证明可以显著改善检测， 乳腺癌筛查中的软组织病变的表征和减少假阳性召回。然而，在这方面， DBT仍处于临床使用的早期阶段，系统设计仍在不断改进， 重建方法对于充分发挥其潜力至关重要。噪声和分辨率是影响 成像系统的优化。DBT中的噪声远高于数字乳腺X线照片（DM）中的噪声 因为多个低剂量投影增加了总的探测器噪声。斜入射到 大角度投影下的乳房和检测器进一步消除了噪声问题， 分辨率合成乳房X线照片不能解决这些问题，因为它们是由 DBT。从CT可知，具有适当设计的正则化器的迭代重建（IR）可以显著地 减少噪音。然而，用于CT的IR通常不考虑空间模糊和噪声相关/混叠。 最近，在使用平板探测器的CBCT中开始对这些因素进行建模。基于模型的信息检索（MBIR） DBT技术尚未开发。DBT是一种有限角度断层扫描，与 作为乳腺癌体征的非常不同的靶信号（微钙化、毛刺/边界不清的肿块 和失真），使得开发用于DBT的MBIR更具挑战性。 该项目的目标是通过精确的物理和统计方法为DBT开发MBIR 成像系统的建模，以提高DBT的图像质量。我们将加速发展 这些模型的重建算法，以促进研究和最终转化为临床使用 这样的方法。我们的具体目标是：（SA 1）为MBIR方法的开发准备三个数据集 （模拟DBT投影数据、物理幻象的DBT投影和人类受试者DBT投影）， 并研究了各种图像退化因素对重建DBT的影响;（SA 2）通过以下方法开发MBIR： 使用三种类型的数据优化目标函数和迭代算法的设计 在（SA 1）和四层方法中获得;和（SA 3）通过比较验证开发的MBIR方法 就放射科医师对目标信号的检测精度而言 (ROC研究）和计算机辅助检测（CAD）系统在人类受试者DBT图像。 该项目汇集了两个具有互补专业知识的研究团队，一个在成像物理学， DBT中的图像分析和病变检测，CT/SPECT的统计迭代重建， PET/MRI，来解决这个有限角度的重建问题。如果成功，则使用新的 MBIR方法有望提高乳腺癌早期检测和诊断的疗效， 次给药结束减少剂量和噪声也将促进整个DBT系统设计的优化， 先进的DBT技术的发展，如双能量对比度增强DBT或动态 对比增强DBT，这可能是乳腺MRI用于癌症诊断的成本效益替代方案。

项目成果

Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.

• DOI: 10.1002/mp.14678
• 发表时间: 2021-06
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Samala RK;Chan HP;Hadjiiski L;Helvie MA
• 通讯作者: Helvie MA

Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.

• DOI: 10.1109/tmi.2021.3066896
• 发表时间: 2021-07
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Gao M;Fessler JA;Chan HP
• 通讯作者: Chan HP

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.

医学中的人工智能：通过质量保证、质量控制和验收测试降低风险并最大化收益。

• DOI: 10.1093/bjrai/ubae003
• 发表时间: 2024
• 期刊: BJR artificial intelligence
• 作者: Mahmood,Usman;Shukla-Dave,Amita;Chan,Heang-Ping;Drukker,Karen;Samala,RaviK;Chen,Quan;Vergara,Daniel;Greenspan,Hayit;Petrick,Nicholas;Sahiner,Berkman;Huo,Zhimin;Summers,RonaldM;Cha,KennyH;Tourassi,Georgia;Deserno,ThomasM;G
• 通讯作者: G

Synthesizing mammogram from digital breast tomosynthesis.

从数字乳房断层合成合成乳房X线照片。

• DOI: 10.1088/1361-6560/aafcda
• 发表时间: 2019
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Wei,Jun;Chan,Heang-Ping;Helvie,MarkA;Roubidoux,MarilynA;Neal,ColleenH;Lu,Yao;Hadjiiski,LubomirM;Zhou,Chuan
• 通讯作者: Zhou,Chuan

Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.

• DOI: 10.1088/1361-6560/ad0eb4
• 发表时间: 2023-12-13
• 期刊: Physics in medicine and biology
• 影响因子: 3.5