2 research outputs found
Fast PET reconstruction using Multi-scale Fully Convolutional Neural Networks
Reconstruction of PET images is an ill-posed inverse problem and often
requires iterative algorithms to achieve good image quality for reliable
clinical use in practice, at huge computational costs. In this paper, we
consider the PET reconstruction a dense prediction problem where the large
scale contextual information is essential, and propose a novel architecture of
multi-scale fully convolutional neural networks (msfCNN) for fast PET image
reconstruction. The proposed msfCNN gains large receptive fields with both
memory and computational efficiency, by using a downscaling-upscaling structure
and dilated convolutions. Instead of pooling and deconvolution, we propose to
use the periodic shuffling operation from sub-pixel convolution and its inverse
to scale the size of feature maps without losing resolution. Residual
connections were added to improve training. We trained the proposed msfCNN
model with simulated data, and applied it to clinical PET data acquired on a
Siemens mMR scanner. The results from real oncological and neurodegenerative
cases show that the proposed msfCNN-based reconstruction outperforms the
iterative approaches in terms of computational time while achieving comparable
image quality for quantification. The proposed msfCNN model can be applied to
other dense prediction tasks, and fast msfCNN-based PET reconstruction could
facilitate the potential use of molecular imaging in interventional/surgical
procedures, where cancer surgery can particularly benefit
FastPET: Near Real-Time PET Reconstruction from Histo-Images Using a Neural Network
Direct reconstruction of positron emission tomography (PET) data using deep
neural networks is a growing field of research. Initial results are promising,
but often the networks are complex, memory utilization inefficient, produce
relatively small 2D image slices (e.g., 128x128), and low count rate
reconstructions are of varying quality. This paper proposes FastPET, a novel
direct reconstruction convolutional neural network that is architecturally
simple, memory space efficient, works for non-trivial 3D image volumes and is
capable of processing a wide spectrum of PET data including low-dose and
multi-tracer applications. FastPET uniquely operates on a histo-image (i.e.,
image-space) representation of the raw data enabling it to reconstruct 3D image
volumes 67x faster than Ordered subsets Expectation Maximization (OSEM). We
detail the FastPET method trained on whole-body and low-dose whole-body data
sets and explore qualitative and quantitative aspects of reconstructed images
from clinical and phantom studies. Additionally, we explore the application of
FastPET on a neurology data set containing multiple different tracers. The
results show that not only are the reconstructions very fast, but the images
are high quality and lower noise than iterative reconstructions.Comment: Submitted to Transactions on Radiation and Plasma Medical Science