Image Reconstruction and the Solution of Inverse Problems in Medical Imaging

Author(s):  
Harrison H. Barrett
2021 ◽  
Vol 69 ◽  
pp. 101967
Author(s):  
Chang Min Hyun ◽  
Seong Hyeon Baek ◽  
Mingyu Lee ◽  
Sung Min Lee ◽  
Jin Keun Seo

2017 ◽  
pp. 491-535
Author(s):  
Shailendra Tiwari ◽  
Rajeev Srivastava

Image reconstruction from projection is the field that lays the foundation for Medical Imaging or Medical Image Processing. The rapid and proceeding progress in medical image reconstruction, and the related developments in analysis methods and computer-aided diagnosis, has promoted medical imaging into one of the most important sub-fields in scientific imaging. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features and physiological functioning, free from overlying structures. In this chapter, the authors share their opinions on the research and development in the field of Medical Image Reconstruction Techniques, Computed Tomography (CT), challenges and the impact of future technology developments in CT, Computed Tomography Metrology in industrial research & development, technology, and clinical performance of different CT-scanner generations used for cardiac imaging, such as Electron Beam CT (EBCT), single-slice CT, and Multi-Detector row CT (MDCT) with 4, 16, and 64 simultaneously acquired slices. The authors identify the limitations of current CT-scanners, indicate potential of improvement and discuss alternative system concepts such as CT with area detectors and Dual Source CT (DSCT), recent technology with a focus on generation and detection of X-rays, as well as image reconstruction are discussed. Furthermore, the chapter includes aspects of applications, dose exposure in computed tomography, and a brief overview on special CT developments. Since this chapter gives a review of the major accomplishments and future directions in this field, with emphasis on developments over the past 50 years, the interested reader is referred to recent literature on computed tomography including a detailed discussion of CT technology in the references section.


2019 ◽  
Vol 62 (3) ◽  
pp. 417-444
Author(s):  
A. Chambolle ◽  
M. Holler ◽  
T. Pock

AbstractA variational model for learning convolutional image atoms from corrupted and/or incomplete data is introduced and analyzed both in function space and numerically. Building on lifting and relaxation strategies, the proposed approach is convex and allows for simultaneous image reconstruction and atom learning in a general, inverse problems context. Further, motivated by an improved numerical performance, also a semi-convex variant is included in the analysis and the experiments of the paper. For both settings, fundamental analytical properties allowing in particular to ensure well-posedness and stability results for inverse problems are proven in a continuous setting. Exploiting convexity, globally optimal solutions are further computed numerically for applications with incomplete, noisy and blurry data and numerical results are shown.


2021 ◽  
Vol 1 ◽  
Author(s):  
Shanshan Wang ◽  
Guohua Cao ◽  
Yan Wang ◽  
Shu Liao ◽  
Qian Wang ◽  
...  

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.


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