Abstract
In recent years, more and more detector systems utilizing Compton scattering effect to detect gamma rays have been developed due to their potential for improved image sensitivity. However, the standard non-electron-tracking Compton based detectors can restrict the origin of the photon only to a cone. Therefore, one key challenge in the data analysis process is the reconstruction of the source distribution, from the measured data. Several different techniques have been developed, ranging from basic back-projections, iterative maximum likelihood and Bayesian techniques, as well as stochastic and analytic methods. This paper reviews the existing image reconstruction techniques developed for Compton imaging, their basic approach, their application areas, their restrictions and difficulties.
Keywords: Compton scattering, image reconstruction techniques, inverse problem, medical imaging, nuclear medicine, regularization methods.
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
24
4
1
1