The classic imaging geometry for computed tomography is for collection of un-truncated projections and reconstruction of a global image, with the Fourier transform as the theoretical foundation that is intrinsically non-local. Recently, interior tomography research has led to theoretically exact relationships between localities in the projection and image spaces and practically promising reconstruction algorithms. Initially, interior tomography was developed for x-ray computed tomography. Then, it has been elevated as a general imaging principle. Finally, a novel framework known as omni-tomography is being developed for grand fusion of multiple imaging modalities, allowing tomographic synchrony of diversified features.
Gauge invariability guarantees the same form of the Maxwell equations in different coordinate systems, and is instrumental for electromagnetic cloaking to hide a region of interest (ROI) perfectly. On the other hand, interior tomography is to reconstruct an ROI exactly. In this article, the recent results in these two disconnected areas are brought together to justify the general interior tomography principle. Several opportunities are suggested for tomographic research.
Interior tomography is a typical strategy for radiation dose reduction in computed tomography, where only a certain region-of-interest (ROI) is scanned. However, given the truncated projection data, ROI reconstruction by conventional analytical algorithms may suffer from severe cupping artifacts. In this paper, we proposed a new extraction-based deep learning method for the reconstruction of interior tomography. Our approach works in dual domains where a sinogram-domain network (SDNet) estimates the contribution of the exterior region to the truncated projection and an image-domain network (ID…
X-ray diffraction tomography (XDT) resolves spatially-variant XRD profiles within macroscopic objects, and provides improved material contrast compared to the conventional transmission-based computed tomography (CT). However, due to the small diffraction cross-section, XDT suffers from long imaging acquisition time, which could take tens of hours for a full scan using a table-top X-ray tube. In medical and industrial imaging applications, oftentimes only the XRD measurement within a region-of-interest (ROI) is required, which, together with the demand to reduce imaging time and radiation dose …
Differential phase contrast interior tomography allows for reconstruction of a refractive index distribution over a region of interest (ROI) for visualization and analysis of internal structures inside a large biological specimen. In this imaging mode, x-ray beams target the ROI with a narrow beam aperture, offering more imaging flexibility at less ionizing radiation. Inspired by recently developed compressive sensing theory, in numerical analysis framework, we prove that exact interior reconstruction can be achieved on an ROI via the total variation minimization from truncated differential pr…
While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary example. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and technologies of micro-focus X-ray source, photon-counting detector (PCD), and …
Purpose: The goal is to develop a new architecture for computed tomography (CT) which is at an ultra-low-dose for developing countries, especially in rural areas. Methods: The proposed scheme is inspired by the recently developed compressive sensing and interior tomography techniques, where the data acquisition system targets a region of interest (ROI) to acquire limited and truncated data. The source and detector are translated in opposite directions for either ROI reconstruction with one or more localized linear scans or global reconstruction by combining multiple ROI reconstructions. In oth…
Omni-tomography is enabled by interior tomography that has been developed over the past five years. By omni-tomography, we envision that the next stage of biomedical imaging will be the grand fusion of many tomographic modalities into a single gantry (all in one) for simultaneous data acquisition of numerous complementary features (all at once). This integration has great synergistic potential for development of systems biology, personalized and preventive medicine, because many physiological processes are dynamic and complicated, and must be observed promptly, comprehensively, sensitively, sp…
Biological soft tissues encountered in clinical and pre-clinical imaging mainly consist of light element atoms, and their composition is nearly uniform with little density variation. Thus, x-ray attenuation imaging suffers from low image contrast resolution. By contrast, x-ray phase shift of soft tissues is about a thousand times greater than x-ray absorption over the diagnostic energy range, thereby a significantly higher sensitivity can be achieved in terms of phase shift. In this paper, we propose a novel Fourier transform based iterative method to perform x-ray tomographic imaging of the r…
Fast imaging methods are needed to promote widespread clinical adoption of Ultrasound Tomography (UST), and more widely available UST hardware could support the experimental validation of new measurement configurations. In this work, an open-source 256-element transducer ring array was developed (morganjroberts.github.io/open-UST) and manufactured using rapid prototyping, for only £2k. Novel manufacturing techniques were used, resulting in a 1.17$^{\circ}$ mean beam axis skew angle, a 104 $μ$m mean element position error, and a $\pm$13.6 $μ$m deviation in matching layer thickness. The nominal …
Photon counting detection is a promising approach toward effectively reducing the radiation dose in x-ray computed tomography (CT). Full CT reconstruction from a fraction of the detected photons required by scintillation-based detectors has been demonstrated. Current efforts in photon-counting CT have focused mainly on reconstruction techniques. In medical and industrial x-ray computed tomography (CT) applications, truncated projection from the region-of-interest (ROI) is another effective way of dose reduction, as information from the ROI is usually sufficient for diagnostic purpose. Projecti…
Standardized Images and Evaluation Metrics for Tomography
The Cyprus Institute（塞浦路斯研究所）；Frederick University（弗雷德里克大学）；Frederick Research Center（弗雷德里克研究中心）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；The Cyprus Academy of Sciences, Letters and Arts（塞浦路斯科学、文学与艺术学院）
Omni-tomography/Multi-tomography -- Integrating Multiple Modalities for Simultaneous Imaging
Virginia Tech（弗吉尼亚理工大学）；Wake Forest University（维克森林大学）；University of Minnesota（明尼苏达大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Baylor University Medical Center（贝勒大学医学中心）；Texas Instruments Inc.（德州仪器公司）；University of Chicago（芝加哥大学）
College of Computer Science, Sichuan University（四川大学计算机学院）；Laboratory of Image Science and Technology, Southeast University（东南大学图像科学与技术实验室）；Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所北京市射线成像技术与装备工程研究中心）；School of Electrical Engineering Information, Sichuan University（四川大学电气信息学院）；Department of Radiology, West China Hospital of Sichuan University（四川大学华西医院放射科）
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography. Traditional compressed sensing focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in th…
Benchmarking Deep Learning-Based Reconstruction Methods for Photoacoustic Computed Tomography with Clinically Relevant Synthetic Datasets
Department of Bioengineering, University of Illinois Urbana-Champaign（生物工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign（电气与计算机工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Biomedical Engineering, The University of Texas at Austin（生物医学工程系，德克萨斯大学奥斯汀分校）；Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（奥登计算工程与科学研究所，德克萨斯大学奥斯汀分校）
