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      List-mode reconstruction for the Biograph mCT with physics modeling and event-by-event motion correction

      , , , , , ,
      Physics in Medicine and Biology
      IOP Publishing

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          Fully 3-D PET reconstruction with system matrix derived from point source measurements.

          The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties.
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            Performance evaluation of the ECAT HRRT: an LSO-LYSO double layer high resolution, high sensitivity scanner.

            The ECAT high resolution research tomograph (HRRT) is a dedicated brain and small animal PET scanner, with design features that enable high image spatial resolution combined with high sensitivity. The HRRT is the first commercially available scanner that utilizes a double layer of LSO/LYSO crystals to achieve photon detection with depth-of-interaction information. In this study, the performance of the commercial LSO/LYSO HRRT was characterized, using the NEMA protocol as a guideline. Besides measurement of spatial resolution, energy resolution, sensitivity, scatter fraction, count rate performance, correction for attenuation and scatter, hot spot recovery and image quality, a clinical evaluation was performed by means of a HR+/HRRT human brain comparison study. Point source resolution varied across the field of view from approximately 2.3 to 3.2 mm (FWHM) in the transaxial direction and from 2.5 to 3.4 mm in the axial direction. Absolute line-source sensitivity ranged from 2.5 to 3.3% and the NEMA-2001 scatter fraction equalled 45%. Maximum NECR was 45 kcps and 148 kcps according to the NEMA-2001 and 1994 protocols, respectively. Attenuation and scatter correction led to a volume uniformity of 6.3% and a system uniformity of 3.1%. Reconstructed values deviated up to 15 and 8% in regions with high and low densities, respectively, which can possibly be assigned to inaccuracies in scatter estimation. Hot spot recovery ranged from 60 to 94% for spheres with diameters of 1 to 2.2 cm. A high quantitative agreement was met between HR+ and HRRT clinical data. In conclusion, the ECAT HRRT has excellent resolution and sensitivity properties, which is a crucial advantage in many research studies.
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              New, faster, image-based scatter correction for 3D PET

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                Author and article information

                Journal
                Physics in Medicine and Biology
                Phys. Med. Biol.
                IOP Publishing
                0031-9155
                1361-6560
                August 21 2013
                August 21 2013
                : 58
                : 16
                : 5567-5591
                Article
                10.1088/0031-9155/58/16/5567
                23892635
                d3a866c2-6640-443e-a1a0-f5af901bcf2f
                © 2013

                http://iopscience.iop.org/info/page/text-and-data-mining

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