Development and validation of a direct-comparison method for cardiac (123)I-metaiodobenzylguanidine washout rates derived from late 3-hour and 4-hour imaging.

PURPOSE: The washout rate (WR) has been used in (123)I-metaiodobenzylguanidine (MIBG) imaging to evaluate cardiac sympathetic innervation. However, WR varies depending on the time between the early and late MIBG scans. Late scans are performed at either 3 or 4 hours after injection of MIBG. The aim of this study was to directly compare the WR at 3 hours (WR3h) with the WR at 4 hours (WR4h). METHODS: We hypothesized that the cardiac count would reduce linearly between the 3-hour and 4-hour scans. A linear regression model for cardiac counts at two time-points was generated. We enrolled a total of 96 patients who underwent planar (123)I-MIBG scintigraphy early (15 min) and during the late phase at both 3 and 4 hours. Patients were randomly divided into two groups: a model-creation group (group 1) and a clinical validation group (group 2). Cardiac counts at 15 minutes (countearly), 3 hours (count3h) and 4 hours (count4h) were measured. Cardiac count4h was mathematically estimated using the linear regression model from countearly and count3h. RESULTS: In group 1, the actual cardiac count4h/countearly was highly significantly correlated with count3h/countearly (r = 0.979). In group 2, the average estimated count4h was 92.8 ± 31.9, and there was no significant difference between this value and the actual count4h (91.9 ± 31.9). Bland-Altman analysis revealed a small bias of -0.9 with 95 % limits of agreement of -6.2 and +4.3. WR4h calculated using the estimated cardiac count4h was comparable to the actual WR4h (24.3 ± 9.6 % vs. 25.1 ± 9.7 %, p = ns). Bland-Altman analysis and the intraclass correlation coefficient showed that there was excellent agreement between the estimated and actual WR4h. CONCLUSION: The linear regression model that we used accurately estimated cardiac count4h using countearly and count3h. Moreover, WR4h that was mathematically calculated using the estimated count4h was comparable to the actual WR4h.

[Basic evaluation of sampling step angle and spatial resolution in continuous rotating acquisition with SPECT].

In the data sampling in single photon emission computed tomography (SPECT), the continuous rotating acquisition method has high clinical utility. There have been various reports about the optimum sampling step angle for continuous rotating acquisition. Objective evaluation was performed visually and by measuring spatial resolution with a column phantom to find the optimum sampling step angle for continuous rotating acquisition. In locations far from the rotation center, a large sampling step angle produced artificial images with tangential elongation. The spatial resolution was 11.58 ± 0.19 mm full width half maximum (FWHM) as measured at a sampling step angle of 3 degrees and at 10 cm away from the rotation center. Increasing the sampling step angle to more than 3 degrees resulted in an increase of FWHM in the tangential direction. The optimum sampling step angle for continuous rotating acquisition in SPECT needs to be below that calculated from the sampling theorem.

[Comparison of OS-EM reconstruction algorithms among different processors using a digital phantom dedicated for SPECT data evaluation].

OBJECTIVE: In the OS-EM method, reconfigured images may be different among reconstruction image processors because the programs are highly arbitrary. The purpose of this study was to evaluate the difference in OS-EM algorithms among four different image processors using a digital phantom dedicated for SPECT data evaluation. The image processors used were GMS-5500A/PI (Toshiba), GENIE Xeleris (GE), e.soft (Siemens), and Odyssey FX (Shimadzu). METHODS: Multiple images were reconstructed with OS-EM fluctuating the number of subsets and iterations. A region of interest (ROI) was placed on each image. The average counts, contrast, root mean square uncertainty (%RMSU), and normalized mean squared error (NMSE) of each ROI were calculated and compared with those of different image processors. RESULTS: There was no significant difference in contrast among the algorithms. However, the average counts and (%RMSU were significantly different among algorithms as the number of updates increased. In addition, the minimums of NMSE were also different among algorithms. CONCLUSION: In the OS-EM method, careful evaluation is necessary when using multiple image processors in research studies on the standardization of nuclear medicine imaging or in clinical applications.