Conference on Modern Challenges in Imaging in the Footsteps of Allan Cormack

该奖项将支持2019年8月5日至9日在塔夫茨大学举行的名为《追随艾伦·科马克的脚步的现代成像挑战》会议的参赛者。塔夫茨教授艾伦·科马克为X射线计算机断层成像提供了数学基础，他因这项工作于1979年获得了诺贝尔生理学或医学奖。这次国际会议将通过聚集数学、工程、科学和医学领域的顶尖国际研究人员来交流当前的研究挑战并激发新的探索路线，以纪念他的成就并扩大他的遗产。图像重建在人类生活的许多方面都发挥着至关重要的作用，如医疗保健、国家安全、无损检测和地球物理勘探-所有这些都将在这次会议上代表。图像重建涉及到基于物体外部的一些测量或观察数据来确定物体的特征。测量是使用X射线、压力波、声波、电流等数据获得的。从这些数据中恢复对象称为逆问题，使用解析重建公式或迭代方法求解。这次会议旨在提供不同领域之间的交叉施肥，并为高级和初级研究人员提供互动的机会。这将在几个层面上得到促进：在学科领域之间；在杰出的和年轻的研究人员之间；以及非常重视研究生和知名研究人员之间的讨论。研究生和该领域的新手将被鼓励参加一个展示他们研究成果的海报会议。不同的教职员工和学生群体将通过广泛的广告招聘。结果计划在反问题领域的主要期刊之一的特刊上传播，并在可能的情况下，在会议网站https://math.tufts.edu/faculty/equinto/Cormack2019/.A上张贴演讲，会议的大部分内容将涉及以下主题：X射线、光学、光声、多能量、康普顿和多光谱层析成像。会议还将讨论动态断层扫描和有限数据问题。会议主题包括层析重建背后的数学和算法，包括机器学习、数据多样性、稀疏采样、正则化、词典学习和相关方法。此外，还将讲授层析成像的理论基础，包括积分几何和微局部重建。最后，会议将讨论医疗、安全和工业领域的应用。这些主题高度相关，这次会议将成为研究人员在不同专业之间分享他们的见解和结果的途径。来自纯数学、应用数学、计算数学和计算机科学以及科学、工业和医学的研究人员将参加会议。这将促进来自不同领域的参与者之间的新合作，以瞄准跨学科协同的机会，以催化新的研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award will support participants in the conference "Modern Challenges in Imaging in the Footsteps of Allan Cormack" at Tufts University, August 5-9, 2019. Tufts professor Allan Cormack provided the mathematical foundations of X-ray computed tomography, winning the Nobel Prize in Physiology or Medicine in 1979 for this work. This international conference will honor his achievements and expand on his legacy by gathering top international researchers in mathematics, engineering, science, and medicine to communicate current research challenges and to inspire new lines of inquiry. Image reconstruction plays a crucial role in many aspects of human life such as healthcare, national security, non-destructive testing, and geophysical exploration - all of which will be represented at this conference. Image reconstruction involves the determination of features of an object based on some measured or observed data from the exterior of the object. The measurements are acquired using data from X-rays, pressure waves, sound waves, electric current, etc. The recovery of the object from this data is known as an inverse problem, which is solved using either analytic reconstruction formulas or iterative methods. The conference is structured to provide cross-fertilization between fields and to provide opportunities for senior and junior researchers to interact. This will be promoted at several levels: between subject areas; between prominent and younger researchers; and with a very strong emphasis on discussions between graduate students and established researchers. Graduate students and those new to the field will be encouraged to participate in a poster session in which they will present their research. Diverse groups of faculty and students will be recruited through broad advertising. Results are planned to be disseminated in a special issue of one of the premier journals in the field of inverse problems and, when possible, talks will be posted on the conference website https://math.tufts.edu/faculty/equinto/Cormack2019/.A large portion of the conference will involve the following topics: X-ray, optical, photo-acoustic, multi-energy, Compton, and multi-spectral tomography. The conference will also include dynamic tomography and limited data problems. Conference themes include the mathematics and algorithms behind tomographic reconstruction, including machine learning, data diversity, sparse sampling, regularization, dictionary learning, and related approaches. Along with this, lectures will be given on the theoretical underpinnings of tomography including integral geometry and microlocal reconstruction. Finally, the conference will deal with applications including those in medical, security, and industrial domains. These topics are highly interrelated and the conference will serve as an avenue for researchers to share their insights and results among the different specializations. Researchers from pure, applied, and computational mathematics and computer science, as well as from science, industry, and medicine will participate in the conference. This will facilitate new collaborations among participants from the different areas to target opportunities for synergy across disciplines to catalyze new research.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.