Bayesian reconstruction of emission tomography images using edge-preserving smoothing priors

University of Technology, Sydney. Dept. of Applied Physics.It is common in modem medical imaging practice to correlate scans of a patient from different imaging modalities to improve accuracy of the clinical diagnosis. In nuclear medicine, it is becoming possible to enhance image quality and improve quantitative accuracy of single photon emission computed tomography (SPECT) by making use of image data provided by anatomical modalities, such as magnetic resonance imaging (MRI), in the reconstruction of SPECT images. This thesis explores and improves one such reconstruction method, the minimum cross-entropy (MXE) algorithm. MXE is an iterative reconstruction algorithm which permits the incorporation of a priori information, such as anatomical edges obtained from MRI scans of the same subject. Like most Bayesian reconstruction algorithms, MXE suppresses noise and preserves edges in the reconstructed images, thereby improving edge resolution, signal to noise ratio, and accuracy of reconstruction. The use of an anatomical prior, however, only preserves anatomical edges in the reconstructed images. Furthermore, when anatomical edges in MRI scans of the same subject do not match the functional/physiological edges in the current estimate of the radionuclide distribution, it may result in blurring of the functional edges. This problem is overcome by incorporating functional edge information from the current estimate of the radionuclide distribution as a component of the MXE prior. The main challenge of this thesis is to determine the balance between anatomical and functional priors that optimises the quality of reconstruction. A number of phantom studies were performed to investigate the performance of the MXE algorithm incorporating both anatomical information and a proposed additional prior that preserves high contrast edges in the emission data that may not coincide with anatomical edges. MXE reconstructions compared favourably with conventional maximum likelihood-expectation maximisation (ML-EM) reconstructions. MXE reconstructions not only produced images with lower noise levels and sharper edges but also generated higher recovery coefficient values when compared to the “noise- equivalent” ML-EM reconstructions. MXE reconstruction requires more iterations for a “noise-equivalent” ML-EM reconstruction, however ordered subset implementation provided acceleration that was found to cause no measurable degradation to the reconstructed images. The phantom studies provided insight to the parameter values that should be used for optimal reconstruction. The MXE algorithm was further assessed in a retrospective clinical study of patients with focal epilepsy where subtle changes in cerebral perfusion between seizures provided a useful model for evaluation that could be verified in ictal studies. Again, MXE compared favourably to ML-EM and also to filtered back projection (EBP). Detection of inter-ictal perfusion abnormalities was evaluated using receiver operating characteristic (ROC) analysis where MXE appeared superior to ML-EM and FBP, although the difference in areas under the ROC curves was not statistically significant. Wilcoxon’s matched-pairs signed-ranked tests from region of interest analysis, however, did indicate significant differences in favour of MXE. This study is the first demonstration that MXE reconstruction with priors can influence clinical interpretation. Evidence is provided to support the use of the MXE algorithm as a useful reconstruction technique that easily incorporates prior information from multiple sources

Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization

Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible. Based on inverse problem methods, our contribution introduces the recently developed total generalized variation (TGV) norm to regularize PET image deconvolution. Moreover, we stabilize this procedure with additional image constraints such as positivity and photometry invariance. A criterion for updating and adjusting automatically the regularization parameter in case of Poisson noise is also presented. Experiments are conducted on both synthetic data and real patient images.Comment: First published in the Proceedings of the 23rd European Signal Processing Conference (EUSIPCO-2015) in 2015, published by EURASI

K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.Comment: 9 pages, 2 figure

Level Set Method for Positron Emission Tomography

In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications

4-D Tomographic Inference: Application to SPECT and MR-driven PET

Emission tomographic imaging is framed in the Bayesian and information theoretic framework. The first part of the thesis is inspired by the new possibilities offered by PET-MR systems, formulating models and algorithms for 4-D tomography and for the integration of information from multiple imaging modalities. The second part of the thesis extends the models described in the first part, focusing on the imaging hardware. Three key aspects for the design of new imaging systems are investigated: criteria and efficient algorithms for the optimisation and real-time adaptation of the parameters of the imaging hardware; learning the characteristics of the imaging hardware; exploiting the rich information provided by depthof- interaction (DOI) and energy resolving devices. The document concludes with the description of the NiftyRec software toolkit, developed to enable 4-D multi-modal tomographic inference