Practical method for radioactivity distribution analysis in small-animal PET cancer studies
Introduction
Complex clinical decisions on treatment are often guided by positron emission tomography (PET) imaging principally using 18FDG. PET is often used with other imaging techniques (by combining with CT or MRI) to obtain complementary information. Imaging with 18FDG or other agents often requires quantitative measurements associated with the imaging data.
The factors that affect PET quantitation are resolution, photon attenuation and scattering, random coincidence rate, detector normalization, dead time and noise. It is very difficult to account for these factors for quantitative analysis of 3D reconstructed radioactivity in tumors or mouse organs.
In most PET studies the standardized uptake value (SUV) method is used to quantify tumor radioactivity. As the most widely used semi-quantitative parameter for tumor diagnosis, SUV determination involves measuring activity at a target site, with correction for injected dose, plasma glucose level, uptake period, body weight and, more important, correction for reconstruction method (Di Chiro and Brooks, 1988; Keyes, 1996; Thie et al., 2000; Huang, 2000; Truong et al., 2004; Kok et al., 2005; Popperl et al., 2006). To eliminate the need for body-weight correction, SUV has been calculated on the basis of body weight: tissue concentration (MBq/g)/injected dose (MBq)/body weight (g) (see other SUV determinations in de Boer et al., 2002). However, the SUV method does not correct for any inaccuracy in the measured dose, which may occur with injected dose extravasations, or with an elevated uptake elsewhere in the body. The accuracy of the SUV and the accuracy of relative change during treatment are not well documented and it might be a problem for diagnostic purposes in multicenter studies (Boellaard et al., 2004). Recent studies even find that SUV readings vary on different PET systems (Takahashi et al., 2007), and regions of interest (ROI) can influence quantitative FDG-PET study results (Evilevich et al., 2007).
Thus, the microPET-R4 rodent scanner and ASIPro reconstruction software (both from CTI Concorde Microsystems Inc., Knoxville, TN) were used for semi-quantitative radioactivity analysis in two recent studies: to delineate the stages involved in the development of arthritis (Wipke et al., 2004) and breast cancer metastasis (Liang et al., 2005). In the first study quantitative analysis of ROI was performed over the selected mouse tissues and averaging the radioactive concentration over the contained voxels. In the second study a pixel ROI was outlined in the regions of increased FDG uptake, and after correction for radioactivity decay, the maximum SUV was semi-quantitatively calculated according to the method of Truong et al. (2004).
A more sophisticated method to determine the maximum radioactivity concentration within a tumor or an organ was described in Zhang et al. (2006). In this method, mean pixel values within the multiple ROI volumes were converted to μCi/mL/min by using a calibration constant (Wu et al., 2005). Recently, an analytic semi-automated approach to calculate body distribution of PET tracers using co-registration a digital mouse phantom with small-animal images was proposed in Kesner et al. (2006). The main goal in Di Domenico et al. (2003) was to quantify the activity measured in an ROI within a reconstructed image of a small-animal and compare results with the ones derived from standard biodistribution methods: sacrificing the animal and putting each organ of interest in a calibrated gamma counter. Note that all PET devices are calibrated periodically for detector sensitivity using a calibrated source (generally a syringe filled with a known amount of radioisotope). From this scan, the number of counts per radioactivity detected by each detector pair is recorded. These numbers are used for subsequent scans to normalize the number of counts for detector efficiencies and to determine the amount of activity within the scanned object, but it not possible with high resolution to determine activity in small-animal tumors or organs.
We present a new practical method (Slavine and Antich, 2007) to determine radioactivity distribution in ROI from reconstructed PET images with a source of known activity and size in the field of view (FOV) in an example using osteolytic and osteoblastic bone metastasis from prostate cancer xenografts. Our method is different, more precise and yet simpler than that described above. It is based on a 3D reconstruction method capable of delivering high-resolution images, which has the possibility of saving time, effort and the necessity to sacrifice animals. The 3D high-resolution reconstruction and radioactivity analysis can help to analyze the size and aggressiveness of the tumor, determine its growth in time and the effectiveness of the treatment.
Section snippets
Biological objectives and experimental methods
The prostate cancer (CaP) is the second leading cause of cancer-related death among men (Gao et al., 1997; Ghosh and Heston, 2004). Prostate-specific antigen testing has been widely adopted for screening prostate cancer, which have a propensity to metastasize to bone (Wu et al., 1998; Hall et al., 2005; Foss et al., 2005; Guise et al., 2006). Prostate cancer may cause osteolysis or abnormal new bone formation (Roodman, 2004). As shown by histologic examination (Roudier et al., 2003) the same
Calibrated radioactivity source
In this practical method for radioactivity distribution analysis we used a 22Na radioactive source (half-life is 2.6003 years, average positron energy is 0.215 MeV, positron decay ratio of 89.8%, see National Nuclear Data Center, 2004) produced by Amersham Buchler GmbH & Co KG (Braunschweig, Germany) and certified by Lloyd's Register Quality Assurance (LRQA, Houston, TX). The active material is absorbed in a 1-mm diameter ion-exchange bead, and sealed by ultrasonic welding between 0.5 mm
Iterative 3D image reconstruction method
Prior to performing the 3D image reconstruction, we calculated corrections for the ratios of geometrical efficiencies for different detector positions and took into account the radioisotope's decay at the time of imaging.
Our reconstruction algorithm consists of two main steps: 3D reconstruction of the imaged mouse together with a calibrated source under the same conditions used to determine the characteristics of calibrated source (size and activity), geometry and volume of each ROI, followed
Radioactivity distribution value calculation
In our PET imaging system two high sensitivity detectors simultaneously record data in list-mode format. The data for each of the eight (45° angle step) positions were combined for all rotation angles and used for reconstruction. The total FOV of 110×130×120 mm3 in our 3D image reconstruction was subdivided into voxels of 0.5 mm3 in size. About 2.5 million events were reconstructed into a 220×260×240 array. Resolution was modeled with a Gaussian function. The resolution parameter σ was 1.2 mm
Results
Fig. 5a and b demonstrates the transverse slices of the right femur tumor through the 3D reconstruction and evidently confirm the particular characteristics of osteolytic and osteoblastic cancer disease-damaged femur is visible for the PC-3 tumor (Fig. 5a) and abnormal new bone formation for the C4-2 tumor (Fig. 5b). The reconstructed PET results (Fig. 5, Fig. 6 and Table 2) strongly indicated a significantly elevated 18FDG uptake in the right tumor-bearing femur compared to the left control in
Conclusion
In Table 2 and Fig. 6 we compare our quantitative results for mice femurs and other organs calculated by using the iterative MLSM algorithm and RDV calculation method. The results of this example study of osteolytic and osteoblastic bone metastasis from prostate cancer xenografts have provided quantitative data, show the tomographic capabilities of our small-animal PET imaging system and clearly show perfect practice ability of this method for radioactivity distribution calculations in tumors
Acknowledgments
We are very grateful to Drs. Xiankai Sun, R.P. Mason, G. Arbique, J. Anderson, Jer-Tsong Hsieh, V.D. Kodibagkar, M.A. Lewis, R. McColl, O.Oz, R. Parkey, Zhengwang Zhang, A. Zinchenko and our student Mai Lin for their advice and the valuable discussions. We would like to thank Mr. S. Seliounine and Dr. E. Tsyganov for experimental assistance with small-animal PET measurements.
This work was supported in part by the NIH/National Cancer Institute U24 CA126608-SAIRP.
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