… Biomedical image reconstruction translates signals acquired by a wide range of sensors into
images that … Each biomedical imaging modality leverages signals in different bands of the …

… This tutorial covers biomedical image reconstruction, from the foundational concepts of
system modeling and direct reconstruction to modern sparsity and learning-based approaches. …

… in biomedical applications, in particular for breast imaging. … of forming an accurate image
because of the increased … -D) multiple scattering effects in biomedical imaging applications, we …

… , and iterative and model-based image reconstruction techniques. The final chapter discusses
the … in biomedical imaging. The text also includes a thorough introduction to general image …

… color-CT image reconstruction algorithm … to reconstruct each channel in the local spectral
imaging chain, where the reconstructed global gray-scale image from the conventional imaging …

… As it is quite reasonable for biomedical applications, we will assume that the domain of the
function is contained in a finite region of the plane. This special problem corresponds to X-ray …

… image reconstruction are suboptimal, and iterative algorithms based on appropriate models
can improve image … the use of iterative algorithms for model-based MR image reconstruction. …

… , segmentation, image synthesis, and image reconstruction. … -learning-based MR image
reconstruction. We review the … to image data, and specifically examine accelerated imaging and …

… In the case of image reconstruction, the input is a sensor dataset and the output is an image
… Besides the actual image reconstruction, deep learning also offers opportunities to optimize …

… 1.3 Image Reconstruction In this section, we illustrate the common image reconstruction
strategy by considering a point source. Let us consider an empty two-dimensional (2D) plane …