A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET Images.

Textural analysis might give new insights into the quantitative characterization of metabolically active tumors. More than thirty textural parameters have been investigated in former F18-FDG studies already. The purpose of the paper is to declare basic requirements as a selection strategy to identify the most appropriate heterogeneity parameters to measure textural features. Our predefined requirements were: a reliable heterogeneity parameter has to be volume independent, reproducible, and suitable for expressing quantitatively the degree of heterogeneity. Based on this criteria, we compared various suggested measures of homogeneity. A homogeneous cylindrical phantom was measured on three different PET/CT scanners using the commonly used protocol. In addition, a custom-made inhomogeneous tumor insert placed into the NEMA image quality phantom was imaged with a set of acquisition times and several different reconstruction protocols. PET data of 65 patients with proven lung lesions were retrospectively analyzed as well. Four heterogeneity parameters out of 27 were found as the most attractive ones to characterize the textural properties of metabolically active tumors in FDG PET images. These four parameters included Entropy, Contrast, Correlation, and Coefficient of Variation. These parameters were independent of delineated tumor volume (bigger than 25-30 ml), provided reproducible values (relative standard deviation< 10%), and showed high sensitivity to changes in heterogeneity. Phantom measurements are a viable way to test the reliability of heterogeneity parameters that would be of interest to nuclear imaging clinicians.

A Promising Future: Comparable Imaging Capability of MRI-Compatible Silicon Photomultiplier and Conventional Photosensor Preclinical PET Systems.

UNLABELLED: We recently completed construction of a small-animal PET system-the MiniPET-3-that uses state-of-the-art silicon photomultiplier (SiPM) photosensors, making possible dual-modality imaging with MRI. In this article, we compare the MiniPET-3 with the MiniPET-2, a system with the same crystal geometry but conventional photomultiplier tubes (PMTs). METHODS: The standard measurements proposed by the National Electrical Manufacturers Association NU 4 protocols were performed on both systems. These measurements included spatial resolution, system sensitivity, energy resolution, counting rate performance, scatter fraction, spillover ratio for air and water, recovery coefficient, and image uniformity. The energy windows were set to 350-650 keV on the MiniPET-2 and 360-662 keV on the MiniPET-3. RESULTS: Spatial resolution was approximately 17% better on average for the MiniPET-3 than the MiniPET-2. The systems performed similarly in terms of peak absolute sensitivity (∼1.37%), spillover ratio for air (∼0.15), spillover ratio for water (∼0.25), and recovery coefficient (∼0.33, 0.59, 0.81, 0.89, and 0.94). Uniformity was 5.59% for the MiniPET-2 and 6.49% for the MiniPET-3. Minor differences were found in scatter fraction. With the ratlike phantom, the peak noise-equivalent counting rate was 14 kcps on the MiniPET-2 but 24 kcps on the MiniPET-3. However, with the mouselike phantom, these values were 55 and 91 kcps, respectively. The optimal coincidence time window was 6 ns for the MiniPET-2 and 8 ns for the MiniPET-3. CONCLUSION: Images obtained with the SiPM-based MiniPET-3 small-animal PET system are similar in quality to those obtained with the conventional PMT-based MiniPET-2.

Cold wall effect eliminating method to determine the contrast recovery coefficient for small animal PET scanners using the NEMA NU-4 image quality phantom.

The contrast recovery coefficients (CRC) were evaluated for five different small animal PET scanners: GE Explore Vista, Genisys4, MiniPET-2, nanoScan PC and Siemens Inveon. The NEMA NU-4 2008 performance test with the suggested image quality phantom (NU4IQ) does not allow the determination of the CRC values for the hot regions in the phantom. This drawback of NU4IQ phantom motivated us to develop a new method for this purpose. The method includes special acquisition and reconstruction protocols using the original phantom, and results in an artificially merged image enabling the evaluation of CRC values. An advantageous feature of this method is that it stops the cold wall effect from distorting the CRC calculation. Our suggested protocol results in a set of CRC values contributing to the characterization of small animal PET scanners. GATE simulations were also performed to validate the new method and verify the evaluated CRC values. We also demonstrated that the numerical values of this parameter depend on the actual object contrast of the hot region(s) and this mainly comes from the spillover effect. This effect was also studied while analysing the background activity level around the hot rods. We revealed that the calculated background mean values depended on the target contrast in a scanner specific manner. Performing the artificially merged imaging procedure and additional simulations using the micro hollow sphere (MHS) phantom geometry, we also proved that the inactive wall around the hot spheres can have a remarkable impact on the calculated CRC. In conclusion, we have shown that the proposed artificial merging procedure and the commonly used NU4IQ phantom prescribed by the NEMA NU-4 can easily deliver reliable CRC data otherwise unavailable for the NU4IQ phantom in the conventional protocol or the MHS phantom.