A Deep-learning Method for Detruncation of Attenuation Maps

In hybrid imaging, such as with SPECT/CT, the use of CT-derived attenuation maps has the potential to improve image quality. However, the benefits of attenuation correction can be reduced when the patient CT (e.g. obese) is truncated. We investigate the use of Deep Learning to complete truncated regions within cone-beam CT-derived attenuation maps for attenuation correction in cardiac perfusion SPECT. Our technique is based on inpainting, which attempts to reconstruct missing parts of an image using a special type of Convolutional Neural Networks called a context encoder to learn the size and shape of the patient's body. For training, we used 1,169 non-truncated low-dose conebeam CTs acquired with a SPECT/CT clinical imaging system from an existing cardiac perfusion study under an IRB approved protocol. Using our method, we were able to construct contours for the truncated images and fill them in with appropriate voxel values. Our method can be advantageous over other de-truncation methods due to being image-based and not requiring specialized reconstruction methods. We also show that utilizing the detruncated CTs for attenuation correction is beneficial in improving the photon counts in cardiac perfusion studies.