Image reconstructions from super‐sampled data sets with resolution modeling in PET imaging. Issue 12 (1st December 2014)

• Record Type: Journal Article
• Authors: Li, Yusheng; Matej, Samuel; Metzler, Scott D.
• Is Part Of: Medical physics. Volume 41:Issue 12(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-12-01
• Subjects: data acquisition -- image reconstruction -- image resolution -- medical image processing -- phantoms -- positron emission tomography; Positron emission tomography (PET) -- Spatial resolution -- Magnetic resonance imaging -- Reconstruction; Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Scintigraphy -- Measuring half‐life of a radioactive substance; image reconstruction -- super‐sampling -- super resolution -- resolution modeling -- positron emission tomography (PET) -- PET imaging; Image reconstruction -- Data sets -- Positron emission tomography -- Medical image reconstruction -- Spatial resolution -- Medical image quality -- Coupled channel methods -- Biomedical modeling -- Data acquisition; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4901552
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Spatial resolution in positron emission tomography (PET) is still a limiting factor in many imaging applications. To improve the spatial resolution for an existing scanner with fixed crystal sizes, mechanical movements such as scanner wobbling and object shifting have been considered for PET systems. Multiple acquisitions from different positions can provide complementary information and increased spatial sampling. The objective of this paper is to explore an efficient and useful reconstruction framework to reconstruct super‐resolution images from super‐sampled low‐resolution data sets. Methods: The authors introduce a super‐sampling data acquisition model based on the physical processes with tomographic, downsampling, and shifting matrices as its building blocks. Based on the model, we extend the MLEM and Landweber algorithms to reconstruct images from super‐sampled data sets. The authors also derive a backprojection‐filtration‐like (BPF‐like) method for the super‐sampling reconstruction. Furthermore, they explore variant methods for super‐sampling reconstructions: the separate super‐sampling resolution‐modeling reconstruction and the reconstruction without downsampling to further improve image quality at the cost of more computation. The authors use simulated reconstruction of a resolution phantom to evaluate the three types of algorithms with different super‐samplings at different count levels. Results: Contrast recovery coefficient (CRC) versus background variability, as an image‐quality metric, is calculated at each iteration for all reconstructions. The authors observe that all three algorithms can significantly and consistently achieve increased CRCs at fixed background variability and reduce background artifacts with super‐sampled data sets at the same count levels. For the same super‐sampled data sets, the MLEM method achieves better image quality than the Landweber method, which in turn achieves better image quality than the BPF‐like method. The authors also demonstrate that the reconstructions from super‐sampled data sets using a fine system matrix yield improved image quality compared to the reconstructions using a coarse system matrix. Super‐sampling reconstructions with different count levels showed that the more spatial‐resolution improvement can be obtained with higher count at a larger iteration number. Conclusions: The authors developed a super‐sampling reconstruction framework that can reconstruct super‐resolution images using the super‐sampling data sets simultaneously with known acquisition motion. The super‐sampling PET acquisition using the proposed algorithms provides an effective and economic way to improve image quality for PET imaging, which has an important implication in preclinical and clinical region‐of‐interest PET imaging applications.