Abstract
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Objectives Quantification in medical imaging improves confidence in diagnosis, and Standard Uptake Values (SUVs) have proven a useful tool for monitoring disease progression and treatment response. As SUVs are dependent on non-biological factors such as scanner hardware and the type of acquisition and reconstruction used, it is recommended that image optimisation and quality control (QC) checks are performed as part of a quality assurance (QA) programme to ensure accurate and reproducible SUVs. Qualitative image quality and Quantitative SUV checks are typically performed with the NEMA-IEC Image Quality Phantom and scanning this is often a requirement for entry into multi-centre PET-CT research trials to enable harmonisation between scanners. However, the six independent spheres and background of the NEMA-IEC image quality phantom can be time consuming to fill, there is no qualitative image quality feature and slices can’t be summed for spheres when contrast recovery curves are generated for noisy data. This can be a problem when only a small residue of activity is available for phantom work with expensive SPECT tracers. In addition, a phantom that would allow insertion of a long lived check source to assess constancy would be an advantage, particularly for SUVs in SPECT-CT systems. This has led to testing a new phantom (Esser SUV Phantom) designed for PET and SPECT to allow a more comprehensive and practical range of testing to be performed. Briefly, the phantom consists of a fillable: - Background chamber o Uniformity section (25 x 20 cm) o Hot rods o External tube allowing insertion of a calibrated C-Vial o A rear port available to hold a line source. - Reservoir chamber allowing simultaneous filling of all 9 cylinders (4, 5, 6, 8, 10, 12, 15, 20, 25mm diameter). The objective of this work was to assess whether the new SUV phantom could be used to optimise image quality, calibrate the scanner and validate the resulting SUVs. Its routine use in SPECT sensitivity QC checks with insertion of 57Co source was also assessed.
Methods The phantom was filled on three occasions with 99mTc (8:1 and 2.5:1 contrast) and 177Lu (2.5:1) with known activity concentrations. Acquisitions were performed as per clinical protocol on Siemens Symbia T16 and T SPECT-CT systems and all reconstructions were performed using Hermes SUV-SPECT®. The phantom data was used to: - Optimise the image quality by qualitatively assessing the hot rods by experienced nuclear medicine physicists - Calibrate the scanner using the Hermes SUV-SPECT® reconstruction (calibration mode) with the volume of interest placed in the uniformity section - Validate SUVs in the fillable cylinders In addition, a 57Co resin vial source was placed in the external sleeve of the inactive phantom and acquisitions performed over several weeks.
Results Visibility of the rods for 99mTc and 177Lu were sensitive to changes in the reconstruction technique allowing optimisation to be performed. Calibration factors obtained using the uniformity section of the SUV phantom were within 1% of those obtained using a standard uniformity phantom. Fillable cylinders were of adequate size and range to be visible and be used to generate contrast recovery curves and noise was reduced by summing multiple slices (essential for 177Lu residue available). A 100MBq 57Co resin vial source was used for the constancy check of sensitivity.
Conclusions The new SUV phantom was practical to use (20min to fill), and provided a comprehensive dataset allowing image optimisation, sensitivity calibration and SUV validation as part of a QA program without the need for further phantom work. Constancy checks with the 57Co source in the external sleeve of the inactive phantom was also straightforward to perform. The new SUV phantom has been introduced in collaboration with Hermes into the departmental QA programme. This will enable greater confidence in the accuracy and reproducibility of SPECT-SUV®s used clinically, standardisation between systems and is considered essential for radionuclide dosimetry calculations. $$graphic_1E10405B-F9A0-41BD-BA59-43AB38E8518F$$