The present report describes the procedure of performing mouse in vivo PET imaging with the BCAM molds and analyzing data with the automated SaaS tool. Step-by-step instructions on mouse prepping, imaging acquisition, and data processing are provided. This automation workflow also yielded data that was consistent with manual analysis. A few key technical considerations are highlighted below.
Although a Tumor is not one of the organs defined by the OPM, a critical step in this protocol is the proper implantation of subcutaneous tumors to ultimately ensure optimal alignment of all the mouse organs within the body cavity and BCAM. The OPM can only produce accurate data when a mouse's spine is relatively straight and the internal organs are neutrally positioned. Excessively large or misplaced tumors could impact the mouse's posture and press into its body cavity, thereby shifting the internal organs. This shift, in turn, will skew the resulting quantitative analysis from the OPM algorithm. Others have reported the impact of an unstraight spine on the alignment of internal organs using various imaging modalities and segmentation algorithms13,14. Therefore, it is crucial to use the template to guide tumor implantation and monitor the tumor growth closely to ensure the tumors are both properly positioned and in the ideal size range.
Another area of possible deviation is the selection of a properly sized BCAM. BCAMs are created in 2 g integrals, with a range of 18-26 g. However, since mice with different body compositions (e.g., more muscle versus more fat) may have the same body weight but different physical sizes, it is important to choose the most fitting BCAM while using their body weight as a general reference. Level up or down by trial and error, and keep in mind that proper alignment is critical. Choosing a BCAM that is too large will cause the mouse's internal organs to 'sink' closer to the bottom of the bed, which will particularly impact the accuracy of analysis for the dorsal organs such as the kidneys and spine. In contrast, placing a mouse in BCAM that is too small could press on their body cavities, skew organ positioning, and potentially cause difficulty in their breathing. Using tape to ensure limbs are secured on paw platforms will help with setup and positioning.
Although this reported workflow enables standardized and automated PET image analysis, it is worthwhile to point out that the current OPM is constructed based on the immunocompetent C57BL/6 mouse strain and healthy mice. Other strains and health conditions of mice may present different anatomic features that require additional adjustment, optimization, and validation of the algorithm. For example, immune-deficient strains such as NSG or NCG have smaller spleens. Similarly, mice bearing orthotopic tumor implants may have different sizes, shapes, and locations of internal organs depending on their disease status. Another limitation of this workflow is the overall throughput during the acquisition phase of an experiment. This is mainly due to the additional time required to set up a mouse properly in the BCAM. Training and practice could increase the operator's efficiency in proper placement. The addition of a second docking station can also increase overall throughput, where one dock can serve for BCAM setup, and the other can serve for removing a mouse to a recovery cage. Nonetheless, even with the increased time that is required for BCAM setup prior to scanning, the investment in time pays off by saving time on the data analysis end. For example, in the present study, it took less than 10 min for the OPM to analyze ten images (with 4 ROIs), whereas it took 3-4 h for a highly experienced analyst to manually segment and analyze the same set of images and ROIs. An inexperienced analyst would require even more time for manual analysis. The SaaS platform allows for the analysis of PET scans in large batches, where full in vivo biodistribution analysis can be completed within minutes following data upload. This is an extremely helpful feature when handling large amounts of data from multiple groups and/or across several timepoints, as is discussed below.
Manual segmentation of organs for a single subject can be time-consuming, and it usually requires significant upfront training of the operator. Even with adequate training, manual analyses inevitably face inter-operator variabilities, which can skew quantitative results15,16. In contrast, automated segmentation and data analysis can accurately and efficiently determine a radiotracer's overall biodistribution and eliminate variabilities that are rooted in human operations. Such benefits of automation have been seen both in clinical image analysis and in the preclinical space. For example, Sluis et al. demonstrated that a single scan required 4 h to segment the organs of interest compared to 30 min by AI cloud-based methods17. Another study from Nazari et al. also reported that algorithm-based analysis of the liver and kidneys demonstrated accurate results within 7.0% when compared to two human medical physicists18. The data in Figure 4 also clearly showcased the reliability of the SaaS-based PET imaging analysis workflow, and that automated analysis yielded consistent results when compared to manual analysis.
In summary, this article illustrates the workflow of using BCAM molds to facilitate standardized preclinical PET/CT image acquisition as well as SaaS-enabled automated PET data analysis. It is demonstrated that this platform technology is relatively straightforward to employ, and the quality of automation-generated data is consistent with manual analysis but boasts significant time-saving benefits. Therefore, this workflow can save researchers hundreds of hours in data analysis time and help standardize and reduce inter-operator variability. This procedure can aid in the development of a multitude of drug compounds across modalities and indications. Particularly, large molecule modalities such as therapeutic antibodies, bi-specific antibody immune cell engagers, antibody-drug-conjugates, and even nanoparticles can be readily labeled with radiometals (e.g., Zr-89, Cu-64) to enable PET imaging-mediated biodistribution assessment19,20. Similarly, this platform technology can help determine the dosimetry of novel radio therapeutics in mice21. The application of this procedure will be a great value-add to drug development.