Attenuation Map

Attenuation maps represent the varying absorption of radiation (e.g., X-rays, ultrasound) across a scanned object, providing crucial information for image reconstruction and analysis in diverse fields like medical imaging and geophysics. Current research focuses on improving the accuracy and efficiency of attenuation map generation, particularly in low-dose or sparse-view scenarios, employing deep learning models such as convolutional neural networks (CNNs), implicit neural representations (INRs), and diffusion models. These advancements are significant for reducing radiation exposure in medical imaging, enhancing image quality in various modalities (CT, PET, SPECT, DSA, ultrasound), and improving the accuracy of geophysical data processing.