MRI: Acquisition of OCT Imaging System and Deep Learning Workstation for Interdisciplinary Healthcare Research and Education

MRI：采购 OCT 成像系统和深度学习工作站，用于跨学科医疗保健研究和教育

• 批准号: 1920345
• 负责人: Hassan Shahidi Salehi
• 金额: $ 11.04万
• 依托单位: Chico State Enterprises
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920345&HistoricalAwards=false
• 关键词: MRI; Acquisition; OCT; Imaging; System

This proposal requests acquisition of a swept-source optical coherence tomography (OCT) imaging system and a deep learning workstation (DLW) at California State University, Chico (CSU, Chico). The proposed OCT imaging system and DLW will enable studies on the application of OCT imaging in dentistry to detect early carious lesions, micro-fractures, pulpal inflammation, early dysplastic changes in oral malignancies and signatures of other dental diseases. Acquisition of the OCT imaging system and DLW will significantly enhance current research programs and enable new research directions at CSU, Chico. These instruments will also support the established collaborative research with faculty at the Stony Brook University School of Dental Medicine. Approximately 500 students from the Departments of Electrical and Computer Engineering, Computer Science, and Mechanical and Mechatronic Engineering and Sustainable Manufacturing will be among the major users of the OCT system and DLW and will be available for use by the broader CSU, Chico research community. CSU, Chico is a minority-serving institution with a large proportion of students from underrepresented and underserved groups, including veterans, and Hispanics. This cutting-edge OCT system and DLW will provide these students with hands-on experience. The OCT system and DLW will be used to (1) increase involvement of undergraduate students in research, (2) promote active learning and skills development, (3) train upper-division undergraduate and graduate students on state-of-the-art imaging and algorithm development techniques, and (4) stimulate collaborations with faculty and students across CSU, Chico and from other institutions of higher learning. The proposed swept-source optical coherence tomography (OCT) imaging system and deep learning workstation (DLW) will stimulate interdisciplinary research projects in healthcare and industrial nondestructive testing at CSU, Chico. OCT is a noninvasive optical imaging modality based on low-coherence interferometry that utilizes non-ionizing near-infrared laser to obtain images with 1-10 micrometer resolution. Currently, the major biomedical application of OCT is in ophthalmology. Many other applications of OCT are under investigation as researchers take advantage of the ability to rapidly acquire images noninvasively. Machine learning and deep learning techniques can be used to supplement the OCT images to more accurately identify diseased and damaged tissue. The following research projects at CSU, Chico will utilize the OCT imaging system and DLW: (1) the development of a deep learning model, namely convolutional neural networks (CNN), and quantitative analysis of OCT images for early dental caries detection; (2) the investigation of various deep learning optimization methods and their performances with OCT images to minimize back-propagating errors; (3) the development of signal and image processing algorithms to extract meaningful features from OCT data for image classification; (4) the design of compact and low-cost fiber optic based probe for noninvasive OCT imaging of occlusal caries; and (5) the use of deep learning to analyze and model non-speech sounds, such as music or industrial noise, as well as automatic analysis and classification of musical timbre. The OCT imaging system and DLW will help catalyze interdisciplinary efforts between engineering, science, and agriculture faculty, and develop an undergraduate and graduate research and teaching laboratory at CSU, Chico.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该提案要求在加州州立大学奇科分校（CSU，奇科）购买扫频光学相干断层扫描 (OCT) 成像系统和深度学习工作站 (DLW)。拟议的 OCT 成像系统和 DLW 将使 OCT 成像在牙科中的应用研究成为可能，以检测早期龋损、微骨折、牙髓炎症、口腔恶性肿瘤的早期发育不良变化以及其他牙科疾病的特征。收购 OCT 成像系统和 DLW 将显着增强当前的研究项目，并为奇科科罗拉多州立大学带来新的研究方向。这些仪器还将支持与石溪大学牙科医学院教师之间已建立的合作研究。来自电气和计算机工程、计算机科学、机械和机电工程以及可持续制造系的大约 500 名学生将成为 OCT 系统和 DLW 的主要用户，并将可供更广泛的科罗拉多州立大学奇科研究社区使用。 奇科科罗拉多州立大学是一所为少数族裔服务的机构，其中很大一部分学生来自代表性不足和服务不足的群体，包括退伍军人和西班牙裔。这种尖端的 OCT 系统和 DLW 将为这些学生提供实践经验。 OCT 系统和 DLW 将用于 (1) 增加本科生对研究的参与，(2) 促进主动学习和技能发展，(3) 对高年级本科生和研究生进行最先进的成像培训和算法开发技术，(4) 促进与科罗拉多州立大学、奇科和其他高等教育机构的教师和学生的合作。 拟议的扫频光学相干断层扫描 (OCT) 成像系统和深度学习工作站 (DLW) 将刺激奇科科罗拉多州立大学在医疗保健和工业无损检测方面的跨学科研究项目。 OCT 是一种基于低相干干涉测量的无创光学成像方式，利用非电离近红外激光获取分辨率为 1-10 微米的图像。目前，OCT的主要生物医学应用是眼科。随着研究人员利用快速非侵入性获取图像的能力，OCT 的许多其他应用正在研究中。机器学习和深度学习技术可用于补充 OCT 图像，以更准确地识别患病和受损的组织。科罗拉多州立大学奇科分校的以下研究项目将利用 OCT 成像系统和 DLW：（1）开发深度学习模型，即卷积神经网络（CNN），并对用于早期龋齿检测的 OCT 图像进行定量分析； (2) 研究各种深度学习优化方法及其在 OCT 图像上的性能，以最大限度地减少反向传播误差； (3) 开发信号和图像处理算法，从 OCT 数据中提取有意义的特征进行图像分类； (4)用于咬合面龋齿无创OCT成像的紧凑且低成本光纤探头的设计； （5）利用深度学习对非语音声音（例如音乐或工业噪音）进行分析和建模，以及音乐音色的自动分析和分类。 OCT 成像系统和 DLW 将有助于促进工程、科学和农业教师之间的跨学科努力，并在奇科科罗拉多州立大学建立本科生和研究生研究和教学实验室。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

Optimization methods for deep neural networks classifying OCT images to detect dental caries

深度神经网络 OCT 图像分类检测龋齿的优化方法

• DOI: 10.1117/12.2545421
• 发表时间: 2020
• 期刊: Lasers in Dentistry XXVI
• 作者: Salehi, Hassan S.;Barchini, Majd;Mahdian, Mina
• 通讯作者: Mahdian, Mina

Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging

• DOI: 10.1117/12.2581318
• 发表时间: 2021-01-01
• 期刊: MEDICAL IMAGING 2021: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING
• 作者: Salehi, Hassan S.;Barchini, Majd;Mahdian, Mina
• 通讯作者: Mahdian, Mina