In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multi-dimensional data. The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture. The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer's software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speed-up increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.

No abstract

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

No abstract

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a temporal regularization in the reconstruction process of the PET dynamic series led to substantial quantitative improvements and motion artifact reduction. Future work will include the integration of a linear FDG kinetic model, in order to directly reconstruct parametric images.

The best contrast is obtained at the 4-hour acquisition time. Large discrepancies were found between the three tested methods of volume segmentation.

Positron emission tomography (PET) image reconstruction needs to be corrected for scatter in order to produce quantitatively accurate images. Scatter correction is traditionally achieved by incorporating an estimated scatter sinogram into theforward model during image reconstruction. Existing scatter estimated methods compromise between accuracy and computing time. Nowadays scatter estimation is routinely performed using single scatter simulation (SSS), which does not accurately model multiple scatter and scatter from outside the field-of-view (FoV), leading to reduced qualitative and quantitative PET reconstructed image accuracy. On the other side, Monte-Carlo (MC) methods provide a high precision, but are computationally expensive and time-consuming, even with recent progress in MC acceleration. In this work we explore the potential of deep learning (DL) for accurate scatter correction in PET imaging, accounting for all scatter coincidences. We propose a network based on a U-Net convolutional neural network (CNN) architecture with 5 convolutionnal layers. The network takes as input the emission and computed tomography-derived attenuation factor (AF) sinograms and returns the estimated scatter sinogram. The network training was performed using MC simulated PET datasets. Multiple anthropomorphic extended cardiac-torso (XCAT) phantoms of two different regions (lung and pelvis) were created, considering three different body sizes and different levels of statistics. In addition, two patient datasets were used to assess the performance of the method in clinical practice. Our experiments showed that the accuracy of our method, namely deep learning-based scatter estimation (DLSE), was independent of the anatomical region (lungs or pelvis). They also showed that the DLSE-corrected images were similar to that reconstructed from scatter-free data and more quantitatively accurate than SSS- corrected images.

This study demonstrates the feasibility of incorporating the Lucy-Richardson deconvolution associated with a wavelet-based denoising in the reconstruction process to better correct for PVE. Future work includes further evaluations of the proposed method on clinical datasets and the use of improved PSF models.