TY - CHAP
T1 - Self-Supervised Deep Learned 3D Filtered Backprojection for Image Reconstruction Objectives with a Poisson Likelihood
AU - Dassanayake, Movindu
AU - Schnabel, Julia
AU - Reader, Andrew
PY - 2024/9/25
Y1 - 2024/9/25
N2 - Filtered backprojection (FBP) is a widely known direct one-step PET reconstruction method that has been surpassed by alternative iterative methods with superior physics and noise modelling. However, iterative image reconstruction for fully 3D sinogram data can be computationally demanding. With the recent arrival of long axial field of view PET scanners, direct (non- iterative) deep learning (DL) based approaches have gained appeal due to their speed advantage when dealing with large datasets. Most data-driven DL based reconstruction methods to date have primarily used supervised learning for optimisation; if these methods were to be used on patients who are outliers of the training set, erroneous reconstructions could occur. In contrast, with this work we propose a model that uses self-supervised DL to model the filtering process within FBP to execute 3D reconstructions, while using a Poisson noise model and a more general forward model than just the Radon transform. Preliminary experiments of bias-variance trade-off show comparable results with our method against OSEM when reconstructing noisy data. The methodology will need expanding to utilise fully 3D data, explore generalisation of the learned filters, investigate the degree of self-supervised fine tuning, and explore other maximum a posteriori objective functions. These optimisations could result in overall speed benefits for training and final image reconstruction. Then, the next steps would be to use real-patient data to compare the reconstruction quality and speed of our method to that of vendor supplied reconstruction methods and state-of-the-art data-driven methods.
AB - Filtered backprojection (FBP) is a widely known direct one-step PET reconstruction method that has been surpassed by alternative iterative methods with superior physics and noise modelling. However, iterative image reconstruction for fully 3D sinogram data can be computationally demanding. With the recent arrival of long axial field of view PET scanners, direct (non- iterative) deep learning (DL) based approaches have gained appeal due to their speed advantage when dealing with large datasets. Most data-driven DL based reconstruction methods to date have primarily used supervised learning for optimisation; if these methods were to be used on patients who are outliers of the training set, erroneous reconstructions could occur. In contrast, with this work we propose a model that uses self-supervised DL to model the filtering process within FBP to execute 3D reconstructions, while using a Poisson noise model and a more general forward model than just the Radon transform. Preliminary experiments of bias-variance trade-off show comparable results with our method against OSEM when reconstructing noisy data. The methodology will need expanding to utilise fully 3D data, explore generalisation of the learned filters, investigate the degree of self-supervised fine tuning, and explore other maximum a posteriori objective functions. These optimisations could result in overall speed benefits for training and final image reconstruction. Then, the next steps would be to use real-patient data to compare the reconstruction quality and speed of our method to that of vendor supplied reconstruction methods and state-of-the-art data-driven methods.
U2 - 10.1109/NSS/MIC/RTSD57108.2024.10655874
DO - 10.1109/NSS/MIC/RTSD57108.2024.10655874
M3 - Conference paper
SN - 979-8-3503-8816-9
T3 - IEEE Symposium on Nuclear Science (NSS/MIC)
SP - 1
EP - 1
BT - 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD)
PB - IEEE
CY - Tampa, FL, USA
ER -