Iterative PET Image Reconstruction Using Convolutional Neural Network Representation (1710.03344v1)

Abstract: PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool. We formulate the objective function as a constraint optimization problem and solve it using the alternating direction method of multipliers (ADMM) algorithm. Both simulation data and hybrid real data are used to evaluate the proposed method. Quantification results show that our proposed iterative neural network method can outperform the neural network denoising and conventional penalized maximum likelihood methods.

• The paper presents a novel framework embedding a deep residual CNN within the iterative PET reconstruction process to leverage inter-patient data for improved image representation.
• Evaluation shows the iterative CNN model outperforms traditional methods by enhancing contrast recovery and significantly reducing noise while preserving fine structural details.
• This research has significant implications for improving PET imaging quality in medical diagnostics and offers potential for future applications in other imaging modalities using similar iterative deep learning approaches.

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

The exploration of convolutional neural networks (CNNs) for the enhancement of Positron Emission Tomography (PET) image quality in the context of iterative reconstruction represents a significant technical advance in medical imaging. This paper presents a novel framework that leverages the power of CNNs to address the inherent challenges associated with PET imaging, specifically focusing on the issues of low image resolution and high noise levels.

Technical Contributions and Methodology

The methodology introduced in this paper centers on embedding a deep residual CNN within the iterative reconstruction process. This integration diverges from previous approaches that predominantly utilized neural networks as a post-processing step after image reconstruction. By employing a CNN to represent the unknown PET image within the iterative process, this method capitalizes on inter-patient information, effectively circumventing the limitations of requiring patient-specific prior information. The neural network is pre-trained on inter-patient dynamic datasets, aligning low-count input with high-count reconstructed outputs. This training paradigm enhances the model's capacity to generalize across varying patient data, thus broadening its applicability in clinical contexts.

The mathematical formulation is constructed as a constrained optimization problem, resolved using the Alternating Direction Method of Multipliers (ADMM). The authors detail the adaptation of these mathematical principles to the CNN architecture, resulting in a robust optimization framework that iteratively refines the PET image reconstruction process.

Results and Evaluation

Evaluation of the proposed iterative CNN framework was conducted using both simulated and hybrid real datasets, with quantitative assessments focusing on contrast recovery (CR) and standard deviation (STD). The results notably indicate that the iterative CNN model surpasses traditional denoising techniques and penalized reconstruction methods in performance metrics, showcasing improved contrast recovery and reduced noise. Specifically, the results displayed in the paper show that the proposed method retains fine structural details that are often subdued in other approaches. The two-fold reduction in STD compared to Gaussian post-filtering methods underscores its efficacy in enhancing image quality without sacrificing detail.

Implications and Future Directions

The implications of this research are extensive for the field of medical imaging, particularly in improving PET imaging applications ranging from oncology and neurology to cardiology. By incorporating dynamic inter-patient datasets in training, this paper demonstrates a scalable approach to leveraging pre-existing data for better image quality in medical diagnostics.

Looking forward, the flexibility of the proposed method opens potential avenues for further enhancements. Future research could explore the integration of Generative Adversarial Networks (GANs) or the application to other imaging modalities such as CT or MRI, potentially widening the applicability of this iterative construction framework across different medical imaging contexts. Additionally, the optimization of network architectures to further reduce the computational cost and enhance feature preservation remains an encouraging avenue for investigation.

In summary, this paper successfully bridges advanced deep learning techniques with iterative PET image reconstruction, evidencing marked improvements in image quality that may drive future developments in computational medical imaging methods.