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In this study, we developed a simple and robust semi-automatic method to measure the right ventricle to left ventricle (RV-to-LV) transit time (TT) in mice using 2-[/sup 18/F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET). The accuracy of the method was first evaluated using a 4-D digital dynamic mouse phantom. The RV-to-LV TTs of twenty-nine mouse studies were measured using the new method and compared to those obtained from the conventional ROI-drawing method. The results showed that the new method correctly separated different structures (e.g., RV, lung, and LV) in the PET images and generated corresponding time activity curve (TAC) of each structure. The RV-to-LV TTs obtained from the new method and ROI method were not statistically different (p=0.20; r=0.76). We expect that this fast and robust method is applicable to the pathophysiology of cardiovascular diseases using small animal models such as rats and mice.

The peripherally born metabolite of FDOPA, 3-O-Methyl-FDOPA (3OMFD), crosses the blood-brain barrier, thus complicating positron emission tomography-FDOPA (PET-FDOPA) data analysis. In previous reports the distribution volume (DV) of 3OMFD was constrained to unity. We have recently shown that the forward transport rate-constant of FDOPA ( KS1) and the cerebellum-to-plasma ratio ( Cb/ Cp), a measure for the DV of 3OMFD, are functions of plasma large neutral amino acid (LNAA) concentration. Given large interstudy and intersubject differences in plasma LNAA levels, variations in the DV of 3OMFD are significant. In this report, the authors propose a constraint on the DV of 3OMFD that accounts for these variations. Dynamic PET-FDOPA scans were performed on 12 squirrel monkeys and 12 vervet monkeys. Two sets of constraints were employed on the compartmental model—M1 or M2. In M1, the striatal DV of 3OMFD was constrained to unity; in M2, the striatal DV of 3OMFD was constrained to an estimate derived from the cerebellum. Striatal and cerebellar time-activity curves were fitted using FDOPA and 3OMFD plasma input functions. The estimate of KS1 and that of the compartmental FDOPA uptake-constant ( Ki), both obtained using M2, were adjusted to values corresponding to average LNAA levels. Finally, Ki was compared with the graphical uptake-constant ( PKi). With the use of constraint M2, intersubject variability of squirrel monkey kS3 and Ki was reduced by 45% and 53%, respectively; and for vervet monkeys, by 54% and 44%, respectively. Intersubject variability of Kl and Ki was further reduced after correction for variations in intersubject plasma LNAA levels (for squirrel monkeys, by 67% and 41%; for vervet monkeys, by 40% and 36%, respectively). Ki correlation to PKi was enhanced to identity. Finally, average cerebellar kC2 estimates were more than 2.5-fold higher than striatal kS2 estimates ( P < 0.0001). In modeling of PET-FDOPA data, it cannot be assumed that the DV of 3OMFD is unity. The cerebellar-derived constraint furnishes a reliable estimate for the DV of 3OMFD. Invoking the constraint and correcting for variations in plasma LNAA significantly reduced interstudy and intersubject variations in parameter estimates.

Image-derived input functions (EDIF) are desirable for quantifying biological functions in mouse microPET studies. Due to difficulties in taking many blood samples from each mouse, conventional IDIF validation method of comparing blood samples with IDIF in a single animal is not applicable. A new approach that requires statistical testing on data of multiple animals has been conceived and investigated for IDIF validation. In this study, we evaluate and compare the power of 5 common statistical tests-Chi-square, sign test, signed-ranks, runs test, serial correlation-with the new approach for their ability to detect errors in IDIF. Computer simulation was used to simulate mouse FDG kinetics (60 min) and error-containing IDIF of various conditions. Deviations of IDIF from blood samples were examined with the 5 statistical tests. Results show that sign test, runs test and serial correlation do not have comparable power as the other two tests. The signed-ranks test generally had high statistical power, but was unable to detect errors that are random among animals or studies. Chi-square test could detect error in IDIF that was variable from study to study, but required accurate knowledge of noise variance. Combining signed-ranks test with Chi-square test is overall most sensitive for validation of IDIF.

Purpose Image-derived input functions are desirable for quantifying biological functions in dynamic mouse micro positron emission tomography (PET) studies, but the input function so derived needs to be validated. Conventional validation using serial blood samples is difficult in mice. We introduced the theoretical basis and used computer simulations to show the capability of a new approach that requires only a small number of blood samples per mouse but uses multiple animals. Procedures 2-Deoxy-2-[ 18 F]fluoro-D-glucose (FDG) kinetics (60 minutes) were simulated for 10 to 20 animals with three to six blood samples available per animal. Various amounts/types of noise/errors in the blood measurements were assumed, and different amounts/types of errors were added to the true input function to simulate image-derived input function. Deviations between blood samples and the derived input function were examined by statistical techniques to evaluate the capability of the approach for detecting the simulated errors in the derived input function. Results For a total of 60 blood samples and a 10% measurement noise, a 5% contaminating error in image-derived input function can be detected with a statistical power of ∼0.9 and with a 95% confidence. The power of the approach is directly related to the error magnitude in the image-derived input function, and is related to the total number of blood samples taken, but is inversely related to the measurement noise of the blood samples. Conclusion The new validation approach is expected to be useful for validating input functions derived with image-based methods in dynamic mouse microPET studies.