Methods for assessing skeletal health which can replace the gold standard bone biopsy are sorely needed in the clinical CKD-MBD setting, e.g. effective and necessary bone treatment may be withheld if a patient has suspect adynamic bone. Access to non-invasive diagnostic methods to confirm or negate this would be an important clinical gain. This study implements both dynamic and static 18F-NaF PET/CT imaging using multiple kinetic analysis methods: non-linear regression, Patlak multi- and single-point analysis, as well as determines a representative CKD-MBD SPIF for use with static PET data, for comparison of the various techniques. All presented methods are suitable for skeletal plasma clearance evaluation, where the simplified Patlak and WB PET methods are suitable for implementation in clinical practice, with static WB 18F-NaF PET/CT opening up possibilities for easy clinical assessment of skeletal health in CKD-MBD.
To some extent CKD-MBD is present in all patients suffering from severe kidney failure in need of dialysis treatment. Therefore, the present study includes chronic dialysis patients as representatives of the CKD-MBD population. The participants in the present study were mostly males (70%) and diabetic nephropathy was the most common cause of kidney failure (29%). Previous studies using 18F-NaF PET/CT for dynamic bone examination have mostly studied female osteoporotic patients and excluded patients with CKD [16, 12]. However, a recently published study of a CKD-MBD population also included 50% males with diabetic nephropathy as the most common cause of kidney failure (34%) [17].
Input functions
Ideally, an AIF should be derived from the arteria supplying the bone region of interest by direct arterial sampling. However, an image-derived AIF is known to be a good approximation of an AIF [10]. In the past years, research in osteoporosis has frequently used 18F-NaF PET/CT quantitative analysis, where the most common region to obtain the IDAIF has been a TAC derived from the abdominal aorta feeding the nearby bone regions. However, use of the abdominal aorta for the AIF has some limitations: the AUC is underestimated when using the abdominal aorta TAC compared with direct arterial sampling [14] and the abdominal aorta is not always close to the bone-of-interest in a whole-body scan. In addition, the aorta is often calcified and twisted in the elderly CKD-MBD population [18], which results in VOIs placed in the aorta being more sensitive to local variable flow patterns and thus local variations in radioactive concentration. In comparison, a VOI placed in the LV allows the definition of large VOIs which will be much less sensitive to partial-volume-effects as well as local flow and radioactive concentration variations. Thus, the LV may provide the most universal activity pool for use with extended WB anatomical regions-of-interest studies. In the present study IDAIFs are obtained from LV TACs, but the results from AO TACs have also been measured for comparison with previous results. It was observed that LV VOIs performed better than VOIs placed in the AO.
The present study evaluates several different input functions (Fig. 5). All IDAIFs were converted to plasma activity using the average plasma to whole blood ratio found by venous blood sampling as described by Cook et al. [10]. In this present study, we find the plasma/whole blood ratio to be slightly lower, where some of the difference may be explained by renal anemia in the CKD-MBD population. As we did not have arterial plasma samples, we were unable to correct for changes in the plasma/whole blood ratio over time as done by Cook et al. [10].
The recovery coefficient β was calculated to correct the IDAIF for PVE and spillover between activity in the lumen and background structures (β-corrected IDAIF). We found the variation of this β-coefficient to be slightly dependent on image quality but even more sensitive to blood sampling errors. Hence, optimal blood sampling is particularly important to obtain a reliable β-correction. The mean AUC of a β-corrected IDAIF has previously been shown to be comparable with the AUC from direct arterial sampling[14] and as such, the β-corrected IDAIF was chosen as our reference input function for comparison with results from other input functions.
The mean β-coefficient was lower (0.69 ± 0.15) when the IDAIFs were derived from the LV as compared with the AO (1.06 ± 0.44). These findings suggest that β-correction is required with LV derived input functions, but not for AO input functions. The higher AO β-coefficient results from a low background activity and is found to be in the same range as previously published values for correction of AO (0.97 ± 0.54) [14].
The effect of applying the various corrections to the input curves has been compared. The mean AUC and mean peak-value were significantly lower for uncorrected IDAIF compared with β-corrected IDAIF (Table 2). Consequently, using an IDAIF without β-correction will result in higher Ki-results (0.0480 ml min− 1 ml− 1) compared to results from a β-corrected IDAIF (Ki = 0.0415 ml min− 1 ml− 1) as shown in Table 6. However, the results were not significantly different (p = 0.06).
β-corrected IDAIF combined with blood sample adjustment results in a slightly lower mean AUC than β-corrected IDAIFs alone. This difference is statistically significant when the 30–90 minute blood samples are used. Nevertheless, the difference seems to be in an acceptable range (Table 2). Likewise, Ki-results are higher when β-corrected IDAIFs adjusted by venous blood samples are used, compared to β-corrected IDAIF, as the input function. Again, a statistically significant difference is observed for the 30–90 minute blood samples with an acceptably low (1.5%) difference. The reason for this is possibly due to steady-state NaF distribution not being reached by 60 minutes, and thus we can expect slight differences between plasma curves fitted to data at 60 minutes and 90 minutes.
To enable future sp-Patlak analysis of multiple bone regions from a single static WB scan, a SPIF combining population residual 2 (having lowest SD) and venous blood samples for an AIF was derived. Once more, the AUC is found to be slightly, but insignificantly, lower than that of the β-corrected IDAIF AUC, resulting in higher Ki-results (Table 6).
Table 4 compares differences in mean AUC and mean peak-values when the first and second exponentials are obtained from image-derived or population residual curves. AUCs show no statistically significant differences. This suggests it is feasible for a fully dynamic IDAIF to be replaced by a generalized SPIF for estimation of dynamic information in bone clearance studies, which is necessary for investigation of extended, multiple bone regions.
V0 is observed to vary with the choice of input function and analysis model as shown in Table 6. Using the Hawkins compartmental model with β-corrected IDAIF gives a mean V0 value of 0.51. Correspondingly a value of 0.39 is found for mp-Patlak analysis with the same input function, or 0.43 when the AO TAC, instead of the LV TAC, is applied for the β-corrected IDAIF. This AO value is comparable to the population value previously reported by Siddique et al [11] in a population of 10 women with osteoporosis. Additionally, when SPIFs are used for the mp-Patlak analysis, mean V0-values tend to be comparable or even higher. For sp-Patlak, Siddique et al have previously published a V0 of 0.46 when using a SPIF [19].
The value of V0 is known to be skeletal site, treatment and analysis model specific [11, 19, 20]. However, Ki estimates have been shown to be relatively independent of the choice of V0. A 20% difference in V0 resulted in only a 5% change in Ki [11], making the sp-Patlak analysis robust for clinical use, despite variability in the population V0-value.
Skeletal plasma clearance
Greatest variability in the bone TACs occurs at initial uptake when the measured activity is low (Fig. 3), making it very difficult to accurately determine the exact time of arrival of tracer to the bone. As a consequence, attempts to correct for time delay failed to improve our data. Other published studies have used an average TAC over all vertebra to be investigated and longer time-bins for each frame, which will improve counting statistics but will lower the time resolution and as such may be counterproductive[11, 12]. However, this problem did not affect the Patlak analysis, as the data was sampled at a later time between 14–60 minutes.
Hawkins model: Non-linear regression analysis
The mean Ki-value was 0.042 ± 0.01 ml min− 1 ml− 1 applying β-corrected IDAIFs in the Hawkins two-tissue compartment model (Table 6).
The first quantitative 18F-NaF study evaluating kinetics in renal osteodystrophy, reported a mean Ki-value of 0.071 ± 0.03 ml min− 1 ml− 1 [5]. A reason for such a high value may, in part, be that 72% of the population studied had untreated secondary hyperparathyroidism. Correspondingly, a new study by Aaltonen et al reported a mean value of 0.067 in dialysis patients with high turnover bone disease and 0.038 in dialysis patients with low turnover bone disease [17]. In comparison, our value of 0.042 ml min− 1 ml− 1 lies within the lower cut-off limit defined in the Aaltonen study and above the value reported for two patients with hyperparathyroidism as found by Schiepers et al. (0.034 ml min− 1 ml− 1). Additionally, the latest study looking at Ki related to Paget disease, has published a much higher mean value of 0.114 ml min− 1 ml− 1 [21].
Patlak: multiple-point graphical analysis
This study finds a statistically significant correlation between the Ki-values obtained using non-linear regression analysis and mp-Patlak analysis (R: 0.92, p-value: 0.001) (Fig. 7a), similar to previously published Ki-values in a healthy female population and in a postmenopausal female population with osteoporosis [16, 22].
The mean Ki-value is 0.034 ± 0.01 ml min− 1 ml− 1 using β-corrected IDAIFs as input to the mp-Patlak analysis. This is similar to results published for a chronic dialysis population (L1 − 4) with a mean value of 0.039 ml min− 1 ml− 1 and, as expected, higher than the mean value of 0.028 ml min− 1 ml− 1 found for a hemodialysis population with suspected adynamic bone disease (L1 − 4) [17, 23].
The mean Ki-value is significantly lower when analyzed using the mp-Patlak method than with the Hawkins two-tissue compartmental model (Table 6). The mean average difference between Ki-values for the two methods is 19% using β-corrected IDAIFs. This difference was reported to be 28.6% by Installé J. et al and 13% by Puri et al. [16, 24].
Previously, it has been suggested that mp-Patlak results are lower than those derived from the Hawkins two-tissue compartmental model due to efflux of tracer from the bone during the scan. If such efflux is present, it may be corrected by the method described by Siddique et al [19]. Since additional investigation showed the Patlak data including later time-point data from the WB scans fit very well to a straight line with regression coefficients close to 1, and taken together with k4-values ≤ 0.011, we found it unnecessary to correct for efflux in this study.
The mp-Patlak analysis has been reported to be superior to the Hawkins two-tissue compartmental model for research purposes; mp-Patlak analysis is computationally simpler and a lower number of participants are required to show a statistically significant result due to small precision error combined with a large treatment response [15, 22]
In addition, as the Ki-values using the various input functions (Table 6) are not significantly different, our data strongly suggests that dynamic analysis using image derived input functions without blood sampling is feasible for clinical analysis, under the prerequisite that the scanner is accurately cross-calibrated.
Accordingly, we find the mp-Patlak results in the present study to be very robust with no inter-observer significant difference (Table 7).
Patlak: single-point graphical analysis
Using the sp-Patlak analysis with a SPIF as described above for the four vertebrae Th7-Th10 imaged in static WB scans, we found the Ki-value to be 0.0395 ± 0.011 ml min− 1 ml− 1. Unsurprisingly, the Ki-result in the present study is higher than the Ki-result from a previous published study in patients with suspected adynamic bone disease (0.028 ± 0.012 ml min− 1 ml− 1). For comparison, the Ki-result in a study of patients with osteoporosis was in the lower range with a value of 0.025 ± 0.007 ml min− 1 ml− 1 [22, 23].
Comparing Ki-results from the sp-Patlak analysis with Ki-results from the mp-Patlak analyses using the same input functions, resulted in 14% higher Ki-results (p < 0.001). Despite this, the correlation between the methods is very good (R2 = 0.942, P < 0.001). This however, emphasizes the importance of using the same analysis method when comparing results of tracer kinetic parameters.